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Abstract Additive Manufacturing, also known as 3D printing, can potentially improve the after-sales service supply chain of the Royal Netherlands Army. This thesis aims to explore this potential and to develop a method to identify the most promising spare parts for this technique.

Impact of on-location, temporary fix Additive Manufacturing of spare parts on service supply chains at the RNLA Bachelor Thesis

Nina Rooijakkers June 20th, 2017


1BEP0: Bachelor Thesis As part of the research project: ‘Sustainability Impact of New Technology on After-sales service Supply Chains’ Royal Netherlands Army

Impact of on-location, temporary fix Additive Manufacturing of spare parts on service supply chains at the RNLA Author N. (Nina) Rooijakkers – 0888620 n.rooijakkers@student.tue.nl

Operations, Planning, Accounting & Control Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology

Internal supervisors Dr. ir. R.J.I. (Rob) Basten ir. B. (Bram) Westerweel

June 20th, 2017

External supervisors J. (Jelmar) den Boer S.J.C.M. (Stephan) Wildenberg

Internal Use

1) The author declares that the text and work presented in this thesis is original and that no sources other than those mentioned in the text and its references have been used in creating this thesis. 2) The copyright of this thesis lies with the owner. The author is responsible for its contents. TU/e cannot be held responsible for any claims with regard to implementing results of the thesis.


Executive summary In this thesis, the potential of additive manufacturing (AM, or 3D-printing) at the Royal Netherlands Army (RNLA) is investigated. Printing spare parts of military systems on location, near the mission area, is expected to contribute to the reduction of downtime and the improvement of the logistical footprint. Due to the technological limitations of AM at this moment in time, this study focusses on the advantages of printing temporary solutions. These are printed parts that can be used to temporarily repair a vehicle, to bridge the lead time of an original part. With this setting, we accept printed parts that are reliability-wise (but not functionally) inferior to the original.

Research question and methodology To summarize the above, we recognize the possibilities for on-site, temporary AM. However, there are thousands of spare parts at the RNLA and not all of them can and should be printed. Therefore, we attempt to develop a method to identify the spare parts with the highest potential. In other words, we answer the following question: Main research question How can promising temporary spare parts for on-location additive manufacturing be identified for the Royal Netherlands Army? An existing framework provides the foundation for answering this research question (Knofius, Van der Heijden, & Zijm, 2016). The framework is adapted to the context of this thesis, so that promising spare parts for AM can be identified. The resulting model is shown in Figure 1.

Figure 1 – Hierarchical model of weighted spare part attributes

The three goals ‘reduce costs’, ‘secure supply’ and ‘reduce downtime’ represent the potential benefits to the service supply chain that can be achieved through AM. For example, being able to print on-demand improves the ability to respond to demand variability and it reduces the need for carrying inventory, because a part can be obtained much faster than the lead time. I


On the basis of these attributes, all spare parts of a dataset are given potential scores between 0 and 1. The parts that are at the top of this list can then be checked for technological feasibility. Ideally, this feasibility check is done prime to the application of the model, but this was found impossible at the RNLA because the data required for this is not in the system.

Results The developed model was applied to three systems: the Boxer, the Fennek and the MercedesBenz (MB). The dataset originally contained 12,715 spare parts, but was reduced to 2791 parts through a filtering and cleaning process. The results are shown in Figure 2.

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Data entry Figure 2 - Results of the Fennek (green), Boxer (purple) and Mercedes-Benz 290GD (blue)

The results show that the Fennek and Boxer have more potential for AM than the MB. This is mostly due to the assumption that it is easier to obtain MB parts at alternative suppliers in case of an emergency, than to acquire Fennek or Boxer parts. Aside from that, the three systems show similar patterns in the distribution of their potential scores, which can be explained by the distributions of their attribute data. After the computation of potential scores, the top 50 spare parts are assessed with regards to their technological feasibility, as well as the top 20 Boxer parts and the top 15 MB parts. About 10% of this assessed assortment is feasible for AM. Finally, to get an idea of the potential savings that can be achieved by printing temporary spare parts, a case study is performed. The results are encouraging: though the absolute numbers are small, significant savings (>10%) are achieved in terms of percentages.

Recommendations The developed method can be used to identify promising business cases, which is recommended for further research. Out of the three analysed systems, the Boxer is considered the most promising, not because of the attributes that were used but because the RNLA has more rights concerning drawings and designs for this vehicle. Ideally, all RNLA systems need to be analysed to find out where the biggest potential lies. Other recommendations are the improvement of availability and interpretability of data, which caused some problems when conducting this research. Most importantly, it is recommended that the RNLA continues to research and invest in AM so that it can be successfully embedded in the service supply chains. II


Table of Contents Executive summary.................................................................................................................... I Table of Contents .....................................................................................................................III Preface...................................................................................................................................... VI Introduction ....................................................................................................................... 1 Context and motivation............................................................................................... 1 Outline ......................................................................................................................... 1 Research description .......................................................................................................... 2 Problem definition ...................................................................................................... 2 Scope ........................................................................................................................... 3 Research questions and methodology ........................................................................ 4 Intermediate level maintenance service supply chains ...................................................... 5 Characteristics of maintenance levels ......................................................................... 5 Mission Preparations .................................................................................................. 6 Flow descriptions ........................................................................................................ 7 Advantages and disadvantages of additive manufacturing ............................................... 11 Advantages of AM on service supply chains .............................................................. 11 Advantages in the context of the RNLA .................................................................... 12 Challenges & obstacles .............................................................................................. 13 Framework........................................................................................................................ 15 Framework description ............................................................................................. 15 Adaptations to the framework .................................................................................. 16 Presenting the final method ...................................................................................... 18 Application of the method ................................................................................................20 Boxer information and data ......................................................................................20 Fennek information and data ................................................................................... 22 Mercedes-Benz 290GD information and data .......................................................... 23 Results ....................................................................................................................... 24 Sensitivity analysis ........................................................................................................... 27 Case study .........................................................................................................................28 Case selection ............................................................................................................28 Model application ..................................................................................................... 29 Conclusion and recommendations ...................................................................................30 Conclusions ...............................................................................................................30 Recommendations .................................................................................................... 31 Limitations and future research................................................................................ 32 References ................................................................................................................................ 33 Appendix A) Levels of maintenance .................................................................................... 35 Appendix B) Service supply chain ....................................................................................... 36 Appendix C) Additive manufacturing ................................................................................. 37 C.1 General description ................................................................................................... 37 C.2 History of AM ............................................................................................................ 37 C.3 Generic Additive Manufacturing process .................................................................38 C.4 AM processes, technologies and materials ............................................................... 39 Appendix D) Analytical Hierarchy Process ........................................................................ 40 III


D.1 Hierarchy.................................................................................................................. 40 D.2 Pairwise comparisons .............................................................................................. 40 D.3 Synthesise judgments................................................................................................ 42 D.4 Evaluate consistency ................................................................................................. 42 Appendix E) Framework adaptations ................................................................................. 43 E.1 Adaptations due to context ....................................................................................... 44 E.2 Adaptations due to data availability.......................................................................... 47 Appendix F) Applied AHP................................................................................................... 49 Appendix G) Data cleaning .................................................................................................. 51 G.1 Boxer data cleaning ................................................................................................... 51 G.2 Fennek data cleaning ................................................................................................ 54 G.3 Mercedes-Benz 290GD data cleaning ....................................................................... 56 Appendix H) Results ............................................................................................................ 58

List of Tables Table 5.1 - Attributes of Knofius et al., 2016............................................................................ 16 Table 5.2 - Spare part attributes assortment ........................................................................... 19 Table 5.3 - Attribute values ...................................................................................................... 19 Table A.1 - Maintenance levels ................................................................................................ 35 Table B.1 - Legend equipment service supply chain ................................................................ 36 Table C.1 - Specifications Mark Two printer ........................................................................... 39 Table D.1 - Preference scores .................................................................................................. 40 Table D.2 - Example AHP table ............................................................................................... 41 Table E.1 - Weighted attributes in different frameworks ........................................................ 43 Table E.2 - Spare part attributes assortment, before considering data................................... 47 Table F.1 - Respondents AHP process ..................................................................................... 49 Table F.2 - Pair-wise comparison Company Goals .................................................................. 49 Table F.3 - Pair-wise comparison Reduce Cost .......................................................................50 Table F.4 - Pair-wise comparison Reduce Downtime .............................................................50 Table F.5 - Total attribute weights ...........................................................................................50 Table G.1 - Spare part data Boxer, raw .................................................................................... 51 Table G.2 - Spare part data Boxer, cleaned ............................................................................. 52 Table G.3 - Spare part data Fennek, raw ................................................................................. 54 Table G.4 - Spare part data Fennek, cleaned ........................................................................... 55 Table G.5 - Spare part data MB 290GD, raw ........................................................................... 56 Table G.6 - Spare part data MB 290GD, cleaned .................................................................... 57 Table H.1 - Top 50 spare parts ................................................................................................. 58 Table H.2 - Top 20 spare parts, Boxer .................................................................................... 60 Table H.3 - Top 15 spare parts, MB 290GD............................................................................. 61 Table H.4 - Percentages Go/No-Go ......................................................................................... 61

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List of Figures Figure 3.1 - Maintenance levels ................................................................................................. 5 Figure 3.2 - Supply in the Netherlands......................................................................................8 Figure 3.3 - Strategic movement ...............................................................................................8 Figure 3.4 - Deployed supply chain ........................................................................................... 9 Figure 3.5 - After-sales service supply chain ........................................................................... 10 Figure 4.1 - Advantages of AM in the RNLA............................................................................ 12 Figure 5.1 - Framework steps of Knofius et al. ........................................................................ 15 Figure 5.2 - Steps of the framework, adapted .......................................................................... 17 Figure 5.3 - Results of the AHP ............................................................................................... 19 Figure 6.1 - Boxer (armoured fighting vehicle) .......................................................................20 Figure 6.2 - Boxer data SAP ..................................................................................................... 21 Figure 6.3 - Fennek (light armoured reconnaissance vehicle) ................................................ 22 Figure 6.4 - Fennek data SAP .................................................................................................. 22 Figure 6.5 - Mercedes-Benz 290GD ........................................................................................ 23 Figure 6.6 - MB 290GD data SAP ............................................................................................ 23 Figure 6.7 - Scatter plot of potential scores per system ........................................................... 24 Figure 6.8 - T-piece, pipe ......................................................................................................... 26 Figure 6.9 - Abgasrohr ............................................................................................................. 26 Figure 7.1 - Sensitivity analysis ................................................................................................ 27 Figure 8.1 - Fusion 360 simulation of damper support (Hoek, 2017) .....................................28 Figure 8.2 - Prototype sump plug, fully functional (Hoek, 2017)............................................28 Figure 8.3 - Optimal ordering policy costs .............................................................................. 29 Figure B.1 - Equipment service supply chain .......................................................................... 36 Figure C.1 - Layer thickness in AM .......................................................................................... 37 Figure C.2 - Historical moments AM ....................................................................................... 37 Figure C.3 - CAD-design (left) and STL-design (right) ...........................................................38 Figure D.1 - Hierarchy ............................................................................................................ 40 Figure E.1 - Time-related attributes ........................................................................................ 46 Figure G.1 - Boxer data, raw .................................................................................................... 51 Figure G.2 - Boxer data, cleaned.............................................................................................. 52 Figure G.3 - Fennek data, raw ................................................................................................. 54 Figure G.4 - Fennek data, cleaned ........................................................................................... 55 Figure G.5 - MB 290GD data, raw ........................................................................................... 56 Figure G.6 - MB 290GD data, cleaned..................................................................................... 57

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Preface This thesis, or Final Bachelor’s Project, was conducted as part of the Bachelor studies Industrial Engineering at Eindhoven University of Technology (TU/e). It was performed at the Logistical Expertise Centre of the Royal Netherlands Army (RNLA) in Soesterberg, between February and July of 2017, within the time frame of ±280 hours. It is part of the ‘Sustainability Impact of New Technology on After-sales service Supply Chains’ (SINTAS) research project. Acknowledgement In the past months, I was able to visit seven different locations of the RNLA and what stood out is that the people I spoke to or needed information from were always very much willing to help. I have learned a lot about spare parts management and the service supply chain, but also about additive manufacturing and the military world – I have even learned the NATO alphabet. My thanks go out to the following people that supported me in my thesis. First of all, I would like to thank my supervisors Jelmar den Boer and Stephan Wildenberg at the RNLA, both of whom were a tremendous help throughout the project. They provided me with the necessary contacts and they helped me become familiar with the unique organizational environment and processes. Their valuable feedback and input is truly appreciated, especially because they were very kind, flexible and always quick to respond. The same goes for my supervisor Bram Westerweel at the TU/e, whose advise, support and feedback have shaped this thesis, and who has helped me perform a case study using his model. I would also like to thank my second university supervisor and work package leader in the SINTAS project, Rob Basten, for enabling me to perform my thesis as part of SINTAS, providing feedback on my research proposal and completing my assessment committee. I am thankful to Nils Knofius for allowing me to use his framework and helping me when necessary. Also thanks to Gino Balistreri and Marvin Hoek for sharing their theses with me. Furthermore, I would like to thank Michiel Aalbregt of the Systems & Analysis department for providing me with all necessary data and for answering my questions regarding this data, and Thys Metz of the Defence Materiel Organisation (DMO) for assessing the technological feasibility of Additive Manufacturing for more than eighty spare parts. Finally, my thanks go out to the people throughout the organization that provided input for the Analytical Hierarchy Process, and the people at DMO for providing me with detailed spare part information. Nina Rooijakkers Eindhoven, June 2017 VI


Introduction Context and motivation Every year, thousands of military personnel are deployed all over the world to execute missions on behalf of the Royal Netherlands Army (RNLA). These men and women depend on their knowledge and training, but also on equipment and vehicles to successfully complete these missions. The logistical processes underlying these military operations can be challenging. The Logistical Expertise Centre continuously strives to improve these processes by collecting, developing and sharing experiences and innovations (Rijksoverheid, n.d.-c). One of these innovations is Additive Manufacturing (AM), popularly known as 3D printing. This technique can be used to manufacture spare parts needed to maintain the RNLA’s systems (vehicles). Research regarding this innovation is done as part of the ‘Sustainability Impact of New Technology on After-sales service Supply Chains’ (SINTAS) project. The RNLA has identified two major challenges or objectives concerning its logistical operations: the improvement of the system readiness and the reduction of the logistical footprint. This research, which was conducted at the RNLA’s Logistical Expertise Centre, explores how the innovative technology of AM can help the RNLA to achieve these two goals. The readiness of the RNLA’s equipment and vehicles is of the utmost importance, especially in combat missions. In these missions, defective equipment implies a lesser availability of force to exert on the enemy, which potentially leads to (more) dangerous situations in the field. With regards to the latter challenge, the Expertise Centre has the objective to “reduce footprint”, as stated in the Army Knowledge and Innovation Plan (LAKIP). The aim is to “reduce usage, reduce the logistical organisation in the deployment area and reduce the ecological footprint to achieve a more sustainable supply chain” (Vink, 2016).

Outline This report is divided into nine chapters. The problem description, its corresponding research questions and the research approach are given in Chapter 2. Chapter 3 serves to give a better understanding of the context and processes relevant to this topic. In Chapter 4, a literature study is conducted to identify the most important characteristics of AM, both positive and negative. The main methodology for finding and ranking appropriate spare parts is presented in Chapter 5 and applied in Chapter 6. In this way, Chapters 3, 4, 5 and 6 respectively answer the four sub questions that are described in Chapter 2. A sensitivity analysis is performed in Chapter 7, followed by a case study in Chapter 8. Finally, the conclusion and recommendations are given in Chapter 9.

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Research description Problem definition Within the discipline of Engineering, researchers are starting to see AM as a more and more suitable technique to manufacture spare parts. Though currently the applications of AM for spare parts are sometimes technologically limited (i.e. not all spare parts can be printed), increasingly better 3D printers are being developed that can print stronger, more reliable and cheaper parts of more and more different sizes and material types. Industrial Engineers are recognizing the potential impact of AM on the configuration of spare parts supply chains. For example, AM could significantly reduce shipping costs and shorten delivery lead times (Liu, Huang, Mokasdar, Zhou, & Hou, 2014). Furthermore, as parts can be produced on demand with AM technology, the need of maintaining safety inventory is reduced. In other words, opportunities exist for the RNLA to improve its mission readiness and logistical footprint by using AM to print spare parts. Several questions concerning this topic are yet to be explored and resolved. This research aims to answer one question in particular: how can promising spare parts for AM be identified? The focus of this research lies on printing spare parts on-location (on missions), where corrective and preventive maintenance is performed on systems that are actively used in the operations nearby. AM in these unpredictable and less controlled circumstances is believed to be relatively more advantageous than printing spare parts in the Dutch maintenance workshops, where the type of maintenance is much more predictable and can be anticipated more easily. This is further explained in Sections 3.1 and 4.2. Furthermore, this thesis investigates the opportunities of temporary fix spare parts, which we define as additively manufactured parts that function identically to the original, but are reliability-wise inferior. This means a printed part could temporarily bridge the period until the intended replaceable becomes available (Knofius et al., 2016). This choice is made to cope with the fact that the current technology of AM is flawed and therefore, 3D printing spare parts with the exact same reliability as the original part is expected to be very difficult. The variable conditions of mission areas complicate this even further. The assumption of temporary parts allows for the RNLA to use a generic printer and to deviate from the material that was initially used, increasing the practical relevance of the solution. Consequently, the pool of eligible spare parts is expanded relative to the pool of promising spare parts that only includes exact copies.

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The remarks and assumptions made in this section can be summarized into the following Problem Statement: Problem Statement There exists a need to investigate the effects of additive manufacturing of spare parts on the service supply chains of the Royal Netherlands Army, by means of researching which spare parts are most appropriate for on-site, temporary fix additive manufacturing.

Scope The following demarcations are applied in this thesis (some have already been explained in Section 2.1). For more information as to why the thesis is scoped this way, please refer to the Project Plan. 

The research focusses on Intermediate Level Maintenance (ILM) The RNLA distinguishes three levels of maintenance: Depot, Intermediate and Organic. Section 3.1 gives the characteristics of each level and argues why the second type is considered the most appropriate for AM

The research focusses on ‘temporary fix’ opportunities As explained in Section 2.1, this choice increases the practical relevance of this research and increases the pool of eligible spare parts

Only decisions on a tactical level are investigated, i.e. which sub-population of temporary spare parts are suitable for on-location AM

Judicial issues, such as intellectual property or warranty problems, are not considered

The spare part assortment that is investigated is limited to that of the Boxer, the Fennek and the Mercedes Benz 290GD

Two very relevant sources for this thesis are: “Selecting parts for additive manufacturing in service logistics” (Knofius et al., 2016) and “Potential of additive manufacturing in the aftersales service supply chains of ground based military systems” (Balistreri, 2015). Both of these papers were, like this thesis, written as part of the SINTAS project. Knofius’ research mostly differs from this thesis as it focusses on the development of a generic method, not specified towards the military industry. Balistreri’s research is performed in the context of the RNLA, however it contrasts with this thesis as it concerns identical spare parts and printing in the Netherlands maintenance workshop, as opposed to temporary fix and on-location solutions.

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Research questions and methodology With the demarcations taken into account, the main research question of this thesis is formulated as follows: Main research question How can promising temporary spare parts for on-location additive manufacturing be identified for the Royal Netherlands Army? This question is analysed and answered using four sub questions. Note that the application of the solution in practice is not a part of the Bachelor thesis. In sub question one, the current relevant processes regarding ILM service supply chains are described. This is done by using the information that can be found in official (instructive) RNLA documents and by interviewing RNLA employees. Secondly, the potential of AM with regards to these processes is investigated. The advantages and disadvantages of this technique are explored by means of a literature study and are put into the context of the RNLA. Papers and articles that combine the subjects of spare parts management with additive manufacturing are especially useful for this research, as well as literature that has been written as part of the SINTAS project. In the third sub question, the main methodology for identifying and ranking eligible spare parts is presented. Knofius’ research paper provides the foundation for setting up this method (Knofius et al., 2016). Also, interviews with different experts at the RNLA are conducted to answer (a part of) this sub question. The method is then applied to the three RNLA systems. The appropriate data is gathered and a ranking of appropriate spare parts for AM is created. Finally, a case study is performed. The four sub questions of this thesis are as follows: Sub questions 1. How can the current relevant processes regarding Intermediate Level Maintenance service supply chains of the Royal Netherlands Army be described? 2. What are the advantages and disadvantages of additively manufacturing temporary spare parts on-site, regarding these service supply chains? 3. How should the existing framework of Knofius et al. (2016) be adapted and used to assess the potential of on-site additive manufacturing for temporary spare parts? 4. Regarding the Boxer, the Fennek and the Mercedes-Benz 290GD, which temporary spare parts would be most promising for on-site additive manufacturing? 4


Intermediate level maintenance service supply chains To answer the core sub questions we require a general understanding of the (logistical) processes and procedures of the Royal Netherlands Army (RNLA) and to analyse the current way of working thoroughly. This is obtained by answering sub question 1 Sub question 1 How can the current relevant processes regarding Intermediate Level Maintenance service supply chains of the Royal Netherlands Army be described? To answer this question, first the differentiation between levels of maintenance is elaborated upon. Then, for the intermediate level, the preparatory procedures for the mission are described. Finally, the personnel, goods and information flows relevant to this research are mapped.

Characteristics of maintenance levels The RNLA distinguishes three levels of maintenance: Organic, Intermediate and Depot Level Maintenance (OLM, ILM and DLM respectively). As shown in Figure 3.1, the type of maintenance performed on these levels is increasingly complex, time-intensive and less frequent. These properties were adopted from Table A.1 provided in Appendix A, which was issued by the RNLA.

Figure 3.1 - Maintenance levels

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Figure 3.1 lists some specific properties of the three maintenance levels, such as the tool specialisation and mobility of each type. Furthermore, it illustrates which staff members are involved on each maintenance level. In principle, OLM is performed by operational users, ILM is performed by the recovery platoon and DLM is performed by the technical department in the Netherlands. However, in practice one sometimes deviates from this structure, as illustrated by the dotted lines in the figure. Moreover, often one type of maintenance leads to another, i.e. when the operational unit diagnoses the battle damage (OLM), they could encounter problems that are too complex for them to solve, so they transfer it to the ILM level. The preventive and predictable nature of DLM makes it less suitable for AM. OLM, the least predictable type of maintenance, is also not a suitable candidate for AM. This is due to the fact that the units performing OLM may not have the required technical expertise to operate the 3D printer, and the circumstances of this maintenance type can be dangerous (in combat missions). Furthermore, a 3D printer on board of a vehicle or container can limit the flexibility of a unit with regards to rapid changes in the combat situation. Therefore, the most relevant maintenance type for AM is ILM. A 3D printer can be placed at the recovery platoon, which includes experts who are able to operate the printer. There, it can print spare parts to repair relatively complex system defects, as opposed to obtaining these parts from stocks or suppliers. Chapter 4 elaborates on the benefits and opportunities of this.

Mission Preparations The information provided in this section has been adapted from the Joint Operational Logistics Instruction (DDOPS, 2014). 3.2.1

Politics and strategy

A mission begins with a mission request from the Minister of Defence to the Chief of Defence (CHOD). This triggers the decision making process on a political level, i.e. aspects such as mission type, size and strategy are determined. Then, the appropriate Combat (C), Combat Support (CS) and Combat Service Support (CSS) units for the mission are assigned. The latter, CSS, includes logistical units. 3.2.2

Force composition list

In cooperation with the logistical department of Direct Operations (DOPS/J4), the units compose lists of systems they expect to need on the mission. Such a list is called a Force Composition List (FCL). For example, the FCL could include ten Boxer vehicles, twenty Mercedes Benzes, eighty radios, etc. Most systems include a standard package of spare parts with small parts such as bolts or nuts. Apart from that, the FCL does not yet include any spare parts. 6


3.2.3

Inventory and replenishment policies on the ILM level

CSS includes, among others, the logistical units Supply & Transport and Recovery. The recovery unit receives the FCL and consequently, the staff of this unit composes a list of expected requirements for the mission, including both personnel capacity as well as spare parts. This is often done in cooperation with other experts (e.g. the assortment management department) within the RNLA. For the spare parts, inventory and replenishment policies need to be determined. However, there is not one fixed way in which this occurs at the RNLA. For some missions, it is done manually. This often happens for very small missions (only a few people involved) but it can also occur due to pragmatic issues, such as capacity shortage or lack of knowledge. In other cases, the process is automatized by a forecasting algorithm. The assortment management department uses a programme called Slim4, which can forecast the expected needs of spare parts on the basis of past data. The inventory levels, ordering cycles and quantities can be determined by this programme. However, some calculated policies of Slim4 are overruled because they involve parts for which no risks may be taken; they must be there on the mission, no matter the cost. Unfortunately, many spare parts cannot be forecasted well with the algorithms of Slim4. This is partially due to the fact that the RNLA has many slow moving spare parts, which are known to be very unpredictable in their usage. Furthermore, the variability of the environments in which these systems operate cause an unpredictable type of wear; e.g. tires wear differently in sandy conditions than in rocky conditions. Note that these problems of sporadic and variable demand fit well with the proposed solution of AM, as is further explained in Chapter 4. In conclusion, there is more than one way to answer the question on how inventory and replenishment policies are determined at the RNLA. Some specific properties of the RNLA cause this need for custom solutions: the types and sizes of missions vary, as well as the environments of operations, the criticality of spare parts and the availability of knowledge.

Flow descriptions The goods, personnel and information flows described in this section are a simplified representation of reality, they are neither complete nor without exception. The focus of these flows is on logistical and ILM aspects and they are primarily based on the material service chain provided in Appendix B, as well as a thesis report on this subject (Langevelde, 2016). First, the general logistical chain of personnel and regular goods are described (Section 3.3.1). Then, the spare parts flow – the heart of this sub question – is described (Section 3.3.2). Finally, the information flow that supports these processes is explained (Section 3.3.3). 7


3.3.1

General supply chain

Before engaging on a mission, all necessary goods need to be collected from the depots in the Netherlands. This material is obtained from the suppliers, or Original Equipment Manufacturers (OEMs), and is stocked in Den Helder, Steenwijk, Lettele and Woensdrecht. The material is collected at the Point of Embarkation (POE). This is shown in Figure 3.2. The goods and personnel for the mission are then transported to the Point of Debarkation (POD), usually by airplane or ship. This is called the strategic movement (Figure 3.3).

Figure 3.3 - Strategic movement Figure 3.2 - Supply in the Netherlands

The units (personnel) then gather at the National Support Element (NSE), which is a safe location where preparations for the mission take place. The Deployed Central Stock (DCS) is usually located at the NSE. From this large stock centre, goods are distributed to units in the mission area that require them. Also, return goods are bundled here before being sent back to the Netherlands. The final location within friendly territory is the Main Operating Base (MOB), a permanently manned location used to support deployed forces. This is where the 3D printer is located according to the assumptions of this thesis. Specialised mechanics (members of the recovery platoon) are here to perform ILM. A Base Stock (BS) is kept at the MOB. Then, units diverge and move to the Forward Operating Bases (FOBs), located in the field. These units can, but do not always include members with some maintenance knowledge. From there, actions that are part of the mission are performed by these units. Figure 3.4 illustrates this description.

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Figure 3.4 - Deployed supply chain

When an action is completed, the unit moves back to the FOB and there, maintenance is performed on the equipment (OLM). As a result of performing these standard maintenance procedures, the units often discover more complicated equipment defects which they cannot resolve themselves, because they do not have the necessary equipment, spare parts and knowledge. In Section 3.3.2, it is described what steps are taken when this happens. 3.3.2

Spare parts flow

If the unit, located at the FOB, does not have the required spare parts to repair a certain system, the recovery platoon, located at the MOB, is asked to see if they have the parts. More specifically, a part of the recovery platoon called the AS-32-group is responsible for this. If the recovery platoon has the required items, they are sent to FOB. If they do not, the manager of the DCS, located at the NSE is asked the same question. If the parts are not at the DCS, the message is forwarded to the depot managers in the Netherlands. If the items are not available there either, it is checked if the required articles can be obtained at any other Defence unit or, ultimately, at the OEM. Supplier lead times of 6-9 months are no exception, so this last option is not preferred. Once the items are available in the Netherlands, they are transported to the unit that requested them at the FOB, via the regular route shown in blue in Figure 3.5. This figure schematically captures the most relevant parts of the supply chain of the RNLA. 9


3.3.3

Information flow

The process described above is supported by the Enterprise Resource Planning system SAP Material Logistics & Finance (SAP M&F). When the unit at the FOB requires certain spare parts, the Maintenance, Diagnosis and Storage group (ODB) sends out a request to order on via SAP M&F. If possible, the parts are then provided by the AS-32-group, the DCS manager or the managers at the different Dutch depots (in that order). If the parts are not available at either of these locations, the assortment manager at the depot alerts DOPS/J4. They check if the articles are available anywhere else within Defence and they order the required items at the OEM if this is not the case. The black circles and lines in Figure 3.5 visualise this process.

Figure 3.5 - After-sales service supply chain

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Advantages and disadvantages of additive manufacturing Additive Manufacturing is the term used to describe the technologies that add material layer by layer to create 3D objects. This is done with the help of 3D modelling software (Computer Aided Design or CAD). General information on AM can be found in Appendix C. In this chapter, the opportunities and challenges of this technology are explored and put into the context of the RNLA. The question to be answered is formulated as follows: Sub question 2 What are the advantages and disadvantages of additively manufacturing temporary spare parts on-site, regarding ILM service supply chains? This question is answered by means of a literature study and with the processes of Chapter 3 in mind. We differentiate between two aspects of AM: printing on demand and on location. Regarding the former, AM is seen as an alternative method of obtaining spare parts fast, i.e. when a demand takes place (and the part is not on stock), one can quickly manufacture the necessary part as opposed to having to order it from the OEM. Printing on location refers to the decentralized approach of AM in this thesis: 3D printers are placed on-site so that no transportation is necessary once the spare part has been printed. This chapter is divided into three sections. Section 4.1 explores the general advantages of printing on demand and on location respectively (i.e. not specified towards any industry in particular). Section 4.2 puts these advantages in the specific context of the RNLA and this thesis. Section 4.3 comments upon the challenges and hurdles that are yet to overcome.

Advantages of AM on service supply chains The subject of AM and its effects on service supply chains has become an increasingly popular research topic during the last few years. Several research papers have been written on the benefits of AM methods in service supply chains. This section summarizes these findings. The following list shows the general advantages of AM over conventional production methods (Holmström, Partanen, Tuomi, & Walter, 2013; Lindemann, Jahnke, Moi, & Koch, 2012). 

Potential for simpler supply chains; shorter lead times, lower inventories

Small or custom production batches are more feasible and economical

Faster deployment of changes, less time-to-market for products

No production tools necessary, less assembly

Design customization and product optimization

Possibility to reduce waste 11


Furthermore, additional benefits can be found for decentralized AM; printing on location near the major market (in RNLA’s case, the mission area), as opposed to centralized AM (Khajavi, Partanen, & Holmström, 2014). 

Less transportation; shorter lead times, sustainability improvements

Reduced need for inventory management

Reduced need for logistics information systems

Lower downtime, higher flexibility

Higher robustness to supply chain disruptions

As can be derived from these lists, AM has the potential to improve the current situation in terms of both efficiency as well as cost. The technology can simultaneously improve service and reduce inventory, waste and transportation. The next section elaborates on how these advantages apply in the context of the RNLA.

Advantages in the context of the RNLA The advantages listed in Section 4.1 apply to the Defence industry as well. The Problem definition states two prime objectives of the RNLA: to improve system readiness and to reduce the logistical footprint. However, from this section onwards, more specific terminology is used for these objectives, as this smoothens the transfer to a framework in Chapter 5. The improvement of system readiness is split up into two goals: reduce downtime and secure supply (Knofius et al., 2016). The second objective, reducing the logistical footprint, will be simplified to the term reduce costs. This simplification is rough but necessary in order to enable the transfer to Knofius’ model. An undesired implication of this is that the aspect of sustainability is lost. This is reflected upon in the Chapter 9. In this section, it is explained how the benefits of the previous section relate to these objectives. These relations are shown schematically in Figure 4.1. Note that the + or – signs indicate whether an increase in concept ‘x’ increases or decreases concept ‘y’. E.g.: a higher ‘lead time’’ causes a higher ‘inventory’. The signs do not indicate whether or not this effect is desirable.

Figure 4.1 - Advantages of AM in the RNLA

12


In the military world, OEM lead times can be over a year (Basten, 2009). A reason for this is that it is inefficient for OEMs to interrupt their current production processes to produce a small batch for the RNLA. The lead time of a printed spare part is drastically lower, as the process of AM does not take weeks or months, but hours or days. Note that we use the term lead time to describe the time until which a spare part becomes available, regardless of whether this is a temporary, 3D printed part or a part ordered from the OEM. By being able to print on demand, the RNLA gains independence from the OEMs and can, therefore, shorten the lead time to quickly repair a system. Even though the temporary spare part might fail quicker than an original part would, printing on demand is beneficial for the reduction of downtime, because it allows for the system to be repaired fast when it fails. The military often has to use its equipment for decades and for this reason, many parts are obsolete (Louis, Seymour, & Joyce, 2014). By obsolescence, it is meant that a system is still used, but the spare parts required to keep it operational are no longer being produced. If the RNLA can produce its own parts using AM, this problem can be countered. This benefits the objective secure supply. For many spare parts of the RNLA, demand can be sporadic (Basten, 2015). As described in Section 3.3.3, the demand for spare parts is hard to predict due to the variability of the environment in which the systems operate. These slow-moving, unpredictable parts drive up inventory costs the most. Printing parts on demand can provide a solution here: it can reduce downtime and inventory cost. Printing on location reduces transportation costs and further increases the flexibility during unexpected changes in demand, which again benefits the reduction of downtime.

Challenges & obstacles Currently, the applications of AM are still limited and it is mostly suitable for relatively simple, mechanical spare parts. Though the technology of AM is developing very quickly, there are still some challenges to overcome, especially when aiming for a decentralized structure (with printers placed on location in the mission areas). The main obstacles to a radical change in spare parts supply chain operations are as follows (Lindemann et al., 2012; Louis et al., 2014): 

AM machine acquisition price, high material costs

Quality of parts is in need of improvement, rework often necessary

High calibration effort

Available software is a limiting factor

Parts testing and qualification 13




Information and communications security



Training and development of necessary skill sets



Intellectual Property issues

Swift advancements in precision, speed, affordability, materials range, etc. are being made by the AM industry (Khajavi et al., 2014). Therefore, some of these challenges, such as acquisition price, material cost or quality, are expected to cease to exist over time. Other hurdles, such as training and Intellectual Property issues, are not likely to disappear by themselves over time; they need to be overcome by the RNLA, which requires (monetary) investments. Given the advantages AM could bring, as listed in Section 4.2, the RNLA could benefit from taking an active approach in overcoming these challenges.

14


Framework So far, the relevant context and processes of AM and ILM service supply chains at the RNLA have been described and explored. This information, as well as additional research, is used in this chapter to develop a method to assess the potential of spare parts regarding AM. There already exists a framework on which such a method can be based. In the research paper “Selecting parts for additive manufacturing in service logistics (Knofius et al., 2016)”, a procedure is described to identify appropriate parts for AM. This paper was, just like this thesis, written as part of the SINTAS project. However, it was not specified towards the RNLA. The framework developed by Knofius et al. is a simple but effective top-down approach to identify promising spare parts using information that is often available in information systems. Therefore, this framework provides the foundation for the method of this thesis. Sub question 3 How should the existing framework of Knofius et al. (2016) be adapted and used to assess the potential of on-site additive manufacturing for temporary spare parts? In Section 5.1 we describe the steps of Knofius’ framework. These steps require adaptations due to the context of this thesis and the availability of data at the RNLA. An extensive explanation of the thought process of these adaptations has been omitted from the main text but can be found in Appendix E. A summarized version of this explanation is found in Section 5.2 The resulting framework is presented in Section 5.3.

Framework description The framework developed by Knofius et al. consists of five stages, as illustrated in Figure 5.1. Go/No-Go attributes

Weighted attributes

Weights

Values

Weighted average score

Figure 5.1 - Framework steps of Knofius et al.

The first step is to determine which spare parts should be taken into account, i.e. to select a spare part assortment by means of Go/No-Go attributes. Examples of these attributes are ‘size’ or ‘material type’ that prevent the part from being technologically feasible for AM. Secondly, the selected spare part assortment is scored on the basis of certain attributes. Table 5.1 shows several spare part attributes and their improvement potential (Knofius et al., 2016). Note that these attributes relate to one or more of three company goals: secure supply, reduce downtime and reduce costs. These goals have previously been introduced in Section 4.2. 15


Table 5.1 - Attributes of Knofius et al., 2016

The attributes in this table should not be misunderstood as an exclusive list. Also, it is likely that part of the data needed to evaluate these precise attributes is difficult to retrieve, in which case one should try to find similar indicators in the available data. After the attributes have been selected, their weights should be determined. Knofius et al. and Balistreri both suggest using the Analytic Hierarchy Process (AHP), a technique developed by Thomas L Saaty in the 1970s to analyse complex decisions by means of the judgment of experts (Saaty, 2008). The (mathematical) steps of this process are explained in Appendix D. The fourth step is to give scores (ranging from 0 to 1) to the attributes of each spare part, to indicate the extent to which they are ‘promising for AM’. For each attribute, an appropriate scoring method (e.g. linear, binary, two-point scale, logarithmic) is selected. Furthermore, one could choose to exclude some data outliers to protect against data pollution. Once the attributes have been given weights and scores, the weighted average score (a value from 0 to 1) for each spare part can be determined, as shown in Equation 5.1. Equation 5.1 – Weighted average score

The Weighted Average Score (WAS) for spare part đ?‘– is computed as follows: đ?‘›

đ?‘Šđ??´đ?‘†đ?‘– =

∑đ?‘˜=1(đ?‘Ľđ?‘–đ?‘˜ ∙ đ?‘¤đ?‘˜ ) ∑đ?‘›đ?‘˜=1 đ?‘Ľđ?‘–đ?‘˜

đ?‘¤đ?‘˜ = weight of attribute đ?‘˜,

∀đ?‘˜ ∈ [1, đ?‘›]

đ?‘Ľđ?‘–đ?‘˜ = value of attribute đ?‘˜, spare part đ?‘–,

∀đ?‘– ∈ [1, đ?‘š], ∀đ?‘˜ ∈ [1, đ?‘›]

Adaptations to the framework This section summarizes the framework adaptations that are made due to the context of this thesis and data availability. Appendix E provides an extensive version of this section. 5.2.1

Go/No-Go attributes

Knofius et al. (2016) use the Go/No-Go criteria ‘material type’ and ‘size’ to determine the subset of the spare parts assortment that is further evaluated. The attribute size is indeed included in this thesis as well: an spare part should not be too large to be 3D printed. However, 16


the material type does not need to be an exclusive constraint as this research investigates the potential of temporary spare parts, so it is discarded. The criterion complexity is added to the Go/No-Gos, because parts that are too complex to print should not be taken into account. Neither of these two Go/No-Go attributes are in the system; they have to be assessed manually. To reduce the time necessary for this, the order of the framework steps as described in Figure 5.1 is changed, as shown in Figure 5.2. In this way, only the most promising attributes need to be assessed on the basis of their Go/No-Go attributes (technological feasibility). Weighted attributes

Weights

Values

Weighted average score

Go/No-Go attributes

Figure 5.2 - Steps of the framework, adapted

5.2.2

Weighted attributes

Table 5.1 shows the eight weighted attributes that are used in the existing framework. Half of them are considered appropriate in this context as well, the other half are discarded. Furthermore, three new attributes are introduced in our methodology. 5.2.2.1

Retained attributes

A long lead time, especially in combination with a high demand variability, increases the need for a large inventory, resulting in increased costs. It also results in a longer downtime when a part is not available. As found in Chapter 4, AM can provide a solution here; the operational flexibility towards failures of parts with long lead times and high variability is improved through on-demand printing. Unfortunately, the demand variability is hard to measure and, therefore, it is replaced with the attribute demand rate, where a low demand rate indicates a high variability (Knofius et al., 2016). The third attribute that can be copied from Knofius’ framework are the ordering or purchasing costs of attributes. The higher these costs are, the more likely it is that AM can offer a cheaper production alternative. Finally, the ‘remaining usage period’ is included in this thesis. It is economically more attractive to invest in printing a spare part (e.g. acquire the CAD designs), if the system this part belongs to will be used for a long time to come. The time until which the system is used is called the End Life of Type (ELOT) at the RNLA, so this attribute is renamed time to ELOT. 5.2.2.2 Discarded attributes The ‘agreed response time’ is a service level agreement between a company and another party. The RNLA has no such agreements with logistical providers, so it is irrelevant. Another suggested attribute is the number of supply options. Knofius et al. state that if there are only a few supply options, AM may offer a chance to reduce order costs because an additional supply 17


option improves the negotiation position (Knofius et al., 2016). In the context of the RNLA this does not seem very relevant, because there is often only one OEM for the systems. Thirdly, the attribute ‘supply risk’ is suggested, where the potential for AM is considered to be higher for parts that might soon become obsolete (i.e. the part can no longer be supplied). Though we agree with this reasoning, the attribute is discarded due to the unavailability of data Finally, Knofius et al. argue that for higher safety stock costs, the potential for AM is higher. The logic behind this is that AM (often) reduces the lead time and, consequently, reduces the need for keeping a high stock. The main cause of these high inventory costs are expensive items with long lead times and a variable demand. The attributes “lead time”, “demand variability” and “purchasing costs” therefore already account for part of this suggested parameter. Also, as explained in Section 3.2.3, there is not one methodological way in which the RNLA determines its stock levels. Furthermore, RNLA does not measure its inventory costs like ordinary companies do (it works with budgets). In conclusion, this attribute is not expected to be a reliable indicator of the AM potential of spare parts and its most important aspects are already covered with other attributes. Therefore, it is discarded. 5.2.2.3 New attributes Three new attributes are introduced to complete the set of weighted attributes. The first is criticality: if a spare part is critical to the mission, i.e. it has a direct effect on the functional usage of the system, it is important that this part becomes available as soon as possible so that downtime is reduced. The (German) phrase used to describe this functional usage is “fahren, funken, schießen, navigieren” (drive, send radio signals, shoot, navigate). Secondly, the supplier base is deemed important. If the relationship with the OEM is well-established, then it might be easier to obtain the designs needed for AM, so the potential is higher. Finally, the emergency supply options are introduced. If it is easy to acquire a spare part in the host nation of a mission in case of an emergency, it is less interesting to have AM as an alternative method to obtain the part.

Presenting the final method Table 5.2 gives an overview of each attribute and the value levels corresponding to different improvement potentials. For three of the weighted attributes (criticality, supplier base, emergency supply options), a binary scoring method seems appropriate as these can only take the values ‘yes’ or ‘no’. For the other weighted attributes, a linear scoring procedure is applied, as these attributes can take many different values. An overview of the scoring methods can be found in Table 5.3. Figure 5.3 shows the weights of the attributes, which were determined by means of the AHP process. Details on this process, involving seven respondents with different functions at the RNLA or other companies, are given in Appendix F. 18


Table 5.2 - Spare part attributes assortment

Attribute Go/No-Go attributes

Value

Size

Max. 320 mm x 132 mm x 154 mm*

Complexity

TBD by expert

Attribute Weighted spare part attributes

Improvement potential Reduce costs

Reduce downtime

Lead time

Long

Long

Demand rate

Low

Low

Purchasing costs

High

Time to ELOT

Long

Emergency supply options

Low

Mission criticality Supplier base

Secure supply

Yes Good

Read: If spare part attribute ‘x’ belongs to value level ‘y’ then this indicates improvement potential ‘z’. *Specifications of the RNLA’s current printer, see Appendix C. Table 5.3 - Attribute values

Attribute

Scoring type

=0 if

=1 if

Complexity

Binary

Too complex

Technologically feasible

Size

Binary

> 320 x 132 x 154 mm

≤ 320 x 132 x 154 mm

Lead time

Linear

Minimum

Maximum

Demand rate

Linear

Maximum

Minimum

Purchasing cost

Linear

Minimum

Maximum

Time to ELOT

Linear

Minimum

Maximum

Emergency supply options

Binary

No (vehicle is used only by the military)

Yes (vehicle is used by civilians too)

Mission criticality

Binary

Non-critical

Critical

Supplier base

Binary

Distant

Close

Figure 5.3 - Results of the AHP

19


Application of the method The method that has been developed in Chapter 5 can be applied to a large amount of data rather quickly. Four out of the weighted attributes can be retrieved directly from SAP: lead time, purchasing cost, demand rate and criticality. Although there could be some faulty or polluted data in SAP, very little effort is required to filter and prepare this data for the method. The three other weighted attributes (emergency supply options, time to ELOT and supplier base) are not in the system, which limits the intended top-down approach of the method. However, these attributes only differ per system and not per spare parts, which means a fast assessment of much data is still possible. The RNLA operates many systems. To be able to carefully examine the data from SAP, while still having a diverse dataset, we have chosen to include three of the RNLA’s systems in the analysis: the Boxer, the Fennek and the Mercedes-Benz 290GD. The final research question is formulated as follows: Sub question 4 Regarding the Boxer, the Fennek and the Mercedes-Benz 290GD, which temporary spare parts would be most promising for on-site AM? The Boxer, the Fennek and the Mercedes-Benz 290GD are described in Sections 6.1, 6.2 and 6.3 respectively. The data that was retrieved from SAP is analysed using IBM’s statistical programme SPSS. The results of the method’s application are given in Section 6.4.

Boxer information and data The Boxer (Figure 6.1) is an armoured fighting vehicle that has been developed cooperatively

by

Germany

and

the

Netherlands. It has gradually replaced the different editions of the YPR and the M-477 systems since 2014. The RNLA owns 200 Boxer vehicles of five different versions and the systems are used by the units in, or directly behind, the front lines (Rijksoverheid, n.d.-a).

Figure 6.1 - Boxer (armoured fighting vehicle)

The Boxer is produced by the firms Rheinmetall and Krauss Maffei Wegmann (KMW). The Dutch Boxers are assembled by Rheinmetall Man Military Vehicles the Netherlands, located in Ede. The RNLA expects to use this system for thirty years. As the introduction of this vehicle takes place between 2013-2018, the ELOT is reached in 2048 (Heetkamp, 2016). 20


The Boxer data that was retrieved from SAP (on 19-04-2017) needs to be prepared for usage. This process is explained in Appendix G. There are 315 data entries of spare parts with all relevant information available and without pollution or faulty data. The histograms provide more insight regarding the values of these spare parts. Note that a logarithmic scale is often used on the Y-axis of the graphs.

Figure 6.2a - Lead time (days) Boxer spare parts

Figure 6.2b - Demand rate (yearly) Boxer spare parts

Figure 6.2 - Boxer data SAP

Figure 6.2c - Purchasing cost (â‚Ź) Boxer spare parts

Figure 6.2d - Criticality Boxer spare parts

The lead time (in days) of the spare parts is given in Figure 6.2a. Lead times range from roughly one to four months, with a large peak at 90 days. From the demand rate graph (Figure 6.2b) it is clear that most spare parts are only used a few times per year. Only about a third of the spare parts is used more than 10 times a year. This makes a large part of the assortment of spare parts interesting for AM. Figure 6.2c shows the purchasing costs of spare parts (in euros). There prices are spread quite nicely, although there are relatively many cheap parts, which will get low potential scores. Out of the 315 spare parts, 254 are non-critical (Figure 6.2d). 21


Fennek information and data The Fennek (Figure 6.3) is a light armoured reconnaissance vehicle that was introduced at the RNLA around 2005 to replace the YPR infantry vehicles. It is a fast, light and discrete vehicle that is used in the front lines of a mission. The RNLA owns 7 types of Fenneks, which amount to a total of 365 vehicles (Rijksoverheid, n.d.-b).

Figure 6.3 - Fennek (light armoured reconnaissance vehicle)

The Fennek is produced by KMW (just like the Boxer) and Dutch Defence Vehicle Systems. According to the system plan, the vehicles are expected to be used until 2030, which is when the ELOT is reached (Maas Geesteranus & Zwang, 2014). The Fennek data from SAP was cleaned and filtered in Appendix G. The system has 868 clean data entries. The lead times of the Fennek (Figure 6.4a) are generally higher than those of the Boxer. Therefore, downtimes of the Fennek are likely to be longer and inventories higher. Like the Boxer, the majority of the Fennek spare parts have a low demand rate (Figure 6.4b) and are thus highly variable. The purchasing costs (Figure 6.4c) of the Fennek are distributed similarly to those of the Boxer. Again, there exists some spread in these costs, which causes differentiation in the potential scores. Contrarily to the Boxer, the Fennek has more critical than non-critical spare parts (Figure 6.4d); there are about 2.5 as many critical items as there are non-critical items.

Figure 6.4a - Lead time (days) Fennek spare parts

Figure 6.4b - Demand rate Fennek spare parts

Figure 6.4 - Fennek data SAP

22


Figure 6.4c - Purchasing cost (â‚Ź) Fennek spare parts

Figure 6.4d - Criticality Fennek spare parts

Mercedes-Benz 290GD information and data The Mercedes-Benz (MB) 290GD is shown in Figure 6.5. It is a militarized civilian vehicle that was introduced at the RNLA in 1979. Its ELOT was initially set to 2013, but it has recently been extended to 2023 (DMO, 2017). Note that it is not unlikely that the ELOT will be extended again in the future.

Figure 6.5 - Mercedes-Benz 290GD

The supplier of this four-wheel drive vehicle is the firm DaimlerChrysler AG in Stuttgart, Germany. There are five types of MB 290GD’s, intended for different purposes: transportation of goods, personnel or wounded personnel, and command vehicles and vehicles for air defence. Appendix G shows the filtering process for the MB 290GD. Information about the remaining 1608 data entries is given in the graphs below.

Figure 6.6a - Lead time (days) MB 290GD spare parts

Figure 6.6b - Demand rate MB 290GD spare parts

Figure 6.6 - MB 290GD data SAP 23


Figure 6.6c - Purchasing cost (€) MB 290GD spare parts

Figure 6.6d - Criticality MB 290GD spare parts

The lead times of the MB 290GD (Figure 6.6a) are higher than those of the Boxer and the Fennek. Most of the spare parts are delivered within 200 days, but about a hundred parts have lead times even higher than that. Like the Boxer and the Fennek, the majority of the MercedesBenz’s spare parts have a very low demand rate (Figure 6.6b) and are thus highly variable. The purchasing costs (Figure 6.6c) of the MB 290GD are distributed similarly to those of the Boxer and the Fennek. About a quarter of the spare parts analysed are more expensive than €100 and about 10% are cheaper than €1. The Mercedes-Benz has more critical than non-critical spare parts (Figure 6.6d); the precise numbers are 1121 critical versus 488 non-critical parts.

Results The green, purple and blue dots in Figure 6.7 are the scatter plots of potential scores for each of the three systems. 1 0,9

Potential score

0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 0

200

400

600

800

1000

1200

1400

1600

Data entry Boxer

Fennek

MB 290GD

MB 290GD (adapted)

Figure 6.7 - Scatter plot of potential scores per system

24


6.4.1

Interpretation of results

Out of the 2791 analysed parts, 2684 scores were unique (96.17%), which is a good outcome. Two different scoring procedures were used for the seven attributes: linear and binary. The impact of the linear attributes is subtle but the binary attributes can have a larger impact, which is reflected upon in the sensitivity analysis (Chapter 7). It is important to be aware of these effects to correctly interpret the results. Two out of three binary values indeed have a such a big impact in our case. The first is ‘emergency supply options’: if it is relatively easy to obtain a part for a system, all spare parts are valued ‘0’, otherwise, they are valued ‘1’, because the potential for AM is higher when there are no alternative supply options. This attribute has a weight of 35%, which means that scoring a ‘1’ for this attribute sets the lower limit of the potential score to 0.35. This attribute is the sole reason why the MB has scored much lower than the Fennek and the Boxer. Though the reasoning behind the parameter is sound, this scoring method may be too heavy. Also, it is conceivable that there are countries where the MB parts are just as difficult to obtain as the Fennek/Boxer parts. The grey dots show the results of the MB in this scenario. The second binary value is ‘criticality’. We can clearly recognize the impact of this attribute in the graphs as well: the sudden decrease around the potential value 0.55 are the results of this attribute going from 1 to 0 (Figure 6.7). We see that for the Fennek and the MB, most items are critical and therefore there are more items to the left than to the right of this decrease, which means that the majority of parts is suitable for AM. The opposite applies to the Boxer. The final binary value is ‘supplier base’. This attribute only has a weight of 1.3% so it has limited impact. All three systems score a ‘1’ for this attribute, so it causes no differentiation. Though the amount of entries differs for each system, the patterns of the three data plots are very much alike. This makes sense, because the data distributions of each of the systems’ attributes are also fairly similar, as shown in Figure 6.2, Figure 6.4 and Figure 6.6. The top 50 of the ranked parts (before the MB adaptation) includes 48 Fennek spare parts and only 2 Boxer spare parts. However, this does not imply that the Fennek system is intrinsically better fit for AM than the Boxer. A more likely reason for this is that there are 868 Fennek entries versus 315 Boxer entries. When including the MB in the top-scoring parts (by adapting the ‘emergency supply options’ as described above), a similar phenomenon is observed: the majority of top-ranked items become MB parts because there are 1608 MB data entries. 6.4.2

Go/No-Go scoring

The final step of the framework is to evaluate the Go/No-Go attributes. This is done for the top 50 spare parts that have been identified. As explained in Section 6.4.1, these are mostly 25


Fennek parts for reasons that might not be completely fair to the other systems. Therefore, we also evaluate the top 20 Boxer parts and the top 15 MB parts. The purpose of this is to find out how the evaluation of Go/No-Go attributes affects the efficiency of the top-down method, and to get an idea of the percentage of spare parts that is lost due to technological limitations. Also, it is a useful step towards the identification of business cases for future research. The current data from SAP is not sufficient for the assessment of technological feasibility of spare parts. More detailed data is collected. Note that this is a time-intensive process; it can take several minutes per spare part to find the required information (usually a drawing) and to judge its feasibility. The results are given in Appendix H. Based on the 65 evaluated items, we can expect that up to 90% of the assortment is lost due to infeasibility. 6.4.3

High potential cases

We take a closer look at two spare parts that scored well and we evaluate if this result seems practically relevant. The overall highest scoring spare part is called a “T-piece, pipe” (Figure 6.8), belonging to the winch of the Fennek. It cannot be printed with Defence’s current printer, but it can be additively manufactured using a metal printer. It is a critical item that scores very high because of its extremely long lead time (417 days), low demand rate (6/year) and high cost (€981,73). The part seems to be quite simple and so it might be possible to produce it cheaper using AM. The highest scoring case of the Boxer is an article called “abgasrohr”, shown in Figure 6.9. According to the 3D print expert, this part is complicated but not impossible to print. It is critical, has a long lead time of 92 days, a high cost of €548,61 and a low demand rate (1/year). Furthermore, the RNLA owns the designs of the Boxer parts, which limits the setup costs for AM. This item could be an interesting business case for future research.

Figure 6.8 - T-piece, pipe

Figure 6.9 - Abgasrohr

26


Sensitivity analysis The attribute weights that were determined through the AHP method might incur inaccuracies due to subjectivity of the decision makers. In this chapter, a standard procedure for a sensitivity analysis is performed to assess the robustness of the model towards weight changes. The analysis is done by varying one attribute weight at a time. Then, to keep the sum of all weights at 100%, all other weights are changed while maintaining the same proportion to the total as they did before. This is done in steps of 3%, varying from attribute weights of 0% to 50%. With each change in weights a new ranking is computed. Then, the correlations of each of these new rankings with the original ranking are measured with Spearman’s rho, where a value >0.5 is considered significant (Kornbrot, 2005). The results are shown in Figure 7.1. 1,00

Spearman's rho

0,95 Purchasing cost

0,90

Time to ELOT Lead time

0,85

Supplier base

0,80

Demand rate Criticality

0,75

Emergency supply options 0,70 0%

10%

20%

30%

40%

50%

Spare part attribute weights Figure 7.1 - Sensitivity analysis

Note that the value of Spearman’s rho is equal to 1 for the original weights of the model. All attributes appear to be insensitive to weight changes; even with 20-25% changes in the assigned attribute weights, the ranking correlations are above 0.7, as shown in the graph. This means that the model is well-protected against inaccuracies of the AHP process. The supplier base is the most robust parameter, which makes sense because all spare parts of all three systems were given the same value of ‘1’ for this parameter. If the model would be performed on systems without a good supplier base, these outcomes are expected to change quite radically. This is exemplified by the other binary scored attributes: criticality and emergency supply options. Because of this type of scoring method, the changes in the graph are sudden and steep. On the contrary, the linearly scored attributes result in predictable and gradually changing lines.

27


Case study In Section 6.4, the output of the main methodology of this thesis is shown. The result is a ranking of promising parts for AM, based on logistical and economical parameters. However, this does not mean that the top-ranked parts should, from this moment onwards, always be 3D printed. On the contrary, in many scenarios, it might still be better to order the necessary part via the regular supply chain or to expedite it, instead of printing a temporary solution. As part of the SINTAS project, Bram Westerweel developed a model to support this operational decision. In this Chapter, this model is applied to a selected case. In order to apply the model, some more detailed information is required regarding the spare part that is put under investigation. This information can be obtained through further research, but unfortunately we lack the time and expertise to do this. Therefore, we exploit the efforts of a student at the Netherlands Defence Academy who has recently conducted two case studies on the MB 290GD and has gathered the necessary data (Hoek, 2017).

Case selection Hoek researched two mission critical parts of the MB 290GD: a shock absorber support (NL: “schokbrekersteun”) and a sump plug (NL: “carterplug”) of the swivel housing (NL: “fuseelichaam”). Figure 8.1 shows how simulations are being run on the damper support. Figure 8.2 shows a fully functional, 3D printed prototype of the sump plug.

Figure 8.1 - Fusion 360 simulation of damper support (Hoek, 2017)

Figure 8.2 - Prototype sump plug, fully functional (Hoek, 2017)

The support is known to fail due to rust, which causes downtime for the system. It ranked 4th out of the 1608 Mercedes-Benz parts that were analysed using in the model of this thesis. Unfortunately, the result of Hoek’s analysis was that this part is not (yet) appropriate for additive manufacturing, mostly because it took almost 21 days to print (Hoek, 2017). The other part, the sump plug, is a technologically feasible product but it ranked much lower (698 th in the MB list). The sump plug closes off the swivel housing but it is often damaged when this housing is smeared. Without a sump plug, the system cannot be used (so it is critical). Its low purchasing costs of €2,27 limit the benefits, but it can be printed very cheaply as well, which takes 5 hours and 16 minutes. 28


Model application In order to apply the model, the model parameters need to be filled out. Some values can be retrieved from SAP or from the results of Hoek’s thesis, but others need to be estimated. The parameters are as follows: Parameters of Westerweel’s model -

Emergency shipment costs Production costs of printing Purchasing costs of a regular part Failure rate of a printed part Failure rate of a regular part

-

Amount of systems on a mission Penalty cost per time unit Inventory holding costs per time unit Time between the planned replenishment

The emergency shipments (expediting the product, e.g. by helicopter) will never be an interesting option, because of the low purchasing and printing costs of €2,27 and €0,31 respectively. Therefore, the option of emergency supplies is removed from the model. Note that the printing costs only include raw material costs, so it should actually be higher. According to SAP, the yearly demand rate averaged 3 parts over the last few years. Furthermore, there are 26 systems in Mali. However, computing the optimal ordering policy for 26 systems takes a lot of time, so we assume 10 systems and compensate for this in the failure rate (which is set to 1/365 days). We set the failure rate of the printed part at 1/50 days. This has not been established in practice, however, we expect the model outcome to be insensitive to this parameter due to the short replenishment interval of 7 days. The penalty and holding costs are set to €10 and €0,01 respectively, which translates to a service rate of 99.90%. This choice is made because we do not want to accept downtime caused by a critical spare part with such a low purchasing and printing cost. Figure 8.3 shows the costs of optimal ordering policies with and without temporary AM. The policy with AM depends on the printing costs, which are presumably around €1,- (€0,31 for materials). In that case, cost savings of almost 50% can be achieved by using a printer.

Figure 8.3 - Optimal ordering policy costs

29


Conclusion and recommendations In this final chapter, we return to the motivation of the thesis and we evaluate to what extent the objective has been reached. We answer the research question, draw conclusions and reflect on the results in Section 9.1. Then, we suggest recommendations (Section 9.2) and point out opportunities for further research (Section 9.3).

Conclusions The drivers behind this research are the RNLA’s objectives to improve their system readiness and to reduce the logistical footprint. The most important conclusion of Chapter 4 is that AM has the potential to contribute to these objectives, even though there are still some challenges to overcome. This especially applies if the printers are placed on the site of the mission (at the recovery platoon); i.e. if a system fails on a mission and the necessary spare part is not on stock, it can be printed on-demand to prevent a long downtime. Printing in such conditions may affect the quality and reliability of the printed parts, and the RNLA does not have the equipment to print a large variety of materials with high precision. Therefore, a temporary solution is assumed, where the temporary part is used to bridge the lead time of a regular part. Though AM has potential in reducing downtime, reducing costs and securing supply, not all parts can and should be printed in all situations. The RNLA’s systems contain thousands of spare parts and the AM potential among them differs. Therefore, it is useful to have a way of identifying the most promising parts. In this thesis, a method was developed to achieve this. An existing framework by Knofius et al. (2016) provided the foundation for this method. The result is a list of spare parts, ranked from most to least interesting for AM, from a logistical point of view. The parameters of the model have been established through a literature study and the input of RNLA employees, and its robustness towards inaccuracies has been evaluated through a sensitivity analysis. Based on this, we have reason to believe that the model is suitable to identify interesting business cases for AM. However, only by experimenting further with these top-ranked parts (simulating, testing reliability, etc.) can we validate the model. The model was applied to three military systems: the Boxer, the Fennek and the MercedesBenz 290GD. The first two systems ranked much higher than the last, because of a rather heavy parameter assumption concerning ‘emergency supply options’, as explained in Section 6.4.1. As long as the reasoning behind the parameter applies (it is easier to obtain MB 290GD parts on the market than Fennek or Boxer parts), this is acceptable, else, the parameter should be adapted. Furthermore, an important parameter, ‘time to obsolescence’, was left out due to data unavailability. Ideally, this attribute should be added because it supplements the model.

30


Most of the top-ranked parts are Fennek spare parts, because there were more Fennek than Boxer parts. Regardless of this outcome, the Boxer is considered to be more promising for reasons that were not included in the model: the RNLA has more rights concerning drawings and designs for this vehicle. Furthermore, when evaluating Go/No-Go attributes, we found that 10-20% of the promising articles can be printed with existing technology. This is more than enough for the RNLA to start experimenting with AM. We expect that this technology will develop over time and so the opportunities will improve.

Recommendations The most important recommendation has already been implied in previous sections: to proceed with AM research at the RNLA and to investigate how to embed it in the organisation. The application of AM can make the service supply chain more efficient and effective. It is therefore useful to monitor the developments of the technology, to invest in knowledge and to collaborate with suppliers on AM. The recommendations in the remainder of this section reflect the problems that were encountered when conducting this thesis. A common problem at any type of organisation is the availability and interpretation of data. This complicated the development of an efficient, top-down method to identify promising parts. It is impossible to collect a dataset of all spare parts of one specific system at once: when selecting an article group, you can only see the articles that are used exclusively by this system. This is very impractical for our purposes. Also, seemingly general information about spare parts, such as material and size, is not (reliably) available in the system. Furthermore, the dataset from SAP is polluted with faulty or outdated information (e.g. ‘mover type’). The same goes for the system plan of the MB. Updating this kind of information is advisable. The understanding and interpretation of information also caused some problems. The data columns in SAP are not always straightforward but there is no documentation to support this. Additionally, many terms and abbreviations are used and not all of them are documented, which makes it difficult to get acquainted quickly with the information that is needed. The lack of documentation is especially a problem if a term is ambiguous, such as ‘criticality’, or if it is not easily understandable for an outsider, such as ‘End Life of Type’ or ‘A, B and C-systems’. Section 3.2.3 describes the different ways in which inventory and replenishment policies are determined. As mentioned in that section, there are some pragmatic problems that cause inefficiencies in these policies, such as lack of knowledge or staff. It is recommended that the RNLA reconsiders their strategy with regards to these policies. An important problem that has been identified has to do with the obsolescence of spare parts. Though the contract between the OEM of a system and the RNLA states until when the OEM 31


is responsible for supplying spare parts, the RNLA personnel on missions is not notified when a part is about to be discontinued. This means that, for example, the commander of a recovery platoon does not find out that a spare part can no longer be ordered until (s)he tries to order it, resulting in high system downtimes. The RNLA should monitor the time to obsolescence more effectively, so that the risk of missing a ‘lifetime buy’ opportunity is decreased.

Limitations and future research As explained in the conclusion, a limitation to this research is validation; we cannot see if the model works in practice, unless it is implemented. Further research is advised by means of business cases, to evaluate the printability and the expected performance of the most promising parts. Only then can we establish whether or not the benefits outweigh the costs. Only a small set of three systems has been evaluated in this research, though the potential of other systems might be higher. Therefore, applying the method to a larger dataset is advisable. Also, it is important to use the most recent data available, to achieve accurate results. This applies especially in the case of the Boxer, a very new system, of which the data might not yet reflect the wear and tear of the spare parts. The demand (failure) rate is expected to increase over time and so a repetition of the method with newer data is advised. Furthermore, there is always room for improvement regarding the set of attributes that were used. It might be worthwhile to consider design improvements for future research efforts (Knofius et al., 2016). The demarcations that were applied in this thesis are listed in Section 2.2. It is recommended that future research is done regarding these limitations. Especially judicial issues such as intellectual property and warranty problems are expected to be a challenge for AM at the RNLA. As described in Section 4.3, the RNLA needs to take on an active approach in overcoming these issues. Other demarcations involve the strategic decisions regarding the embedding of AM within the organization (e.g. what printers to invest in, where to located hem, how to train staff to operate the machines, etc.). Further research is advisable here. An important note is that this thesis has disregarded the topic of sustainability. The objective of the RNLA to reduce the logistical footprint, as stated in the Army Knowledge and Innovation Plan, was simplified to the goal to reduce costs (Vink, 2016). This leaves out the ecological aspects of this footprint, as noted in Section 4.2. Also, when examining the research that has been done so far in the context of the SINTAS project, the topic of sustainability seems to be missing. Therefore, further research is recommended to be done regarding the sustainability impact of AM.

32


References Balistreri, G. (2015). Potential of Additive Manufacturing in the After-Sales Service Supply Chains of Ground Based Military Systems. University of Twente. Basten, R. J. I. (2009). Designing logistics support systems: Level of repair analysis and spare parts inventories.

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https://doi.org/10.3990/1.9789036529679 Basten, R. J. I. (2015). 3D printen van reserveonderdelen: een kans voor een goedkopere en duurzamere service. Scope, 12–15. Cotteleer, M., Holdowsky, J., & Mahto, M. (2014). The 3D opportunity primer: The basics of additive

manufacturing.

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http://dupress.com/articles/the-3d-opportunity-primer-the-basics-of-additivemanufacturing/ DDOPS. (2014). CDS-400 Joint Operational Logistic Instruction. DMO. (2017). Nota Beperkt ILM MB 290GD tot ELOT. Gibson, I., Rosen, D., & Stucker, B. (2015). Additive Manufacturing Technologies (2nd ed.). Springer. https://doi.org/10.1007/978-1-4939-2113-3 Heetkamp, K. W. B. (2016). Systeemplan SP045000 Boxer. Hoek, M. (2017). 3D Printing - Een onderzoek naar de toegevoegde waarde binnen de Koninklijke Landmacht. Koninklijke Militaire Academie. Holmström, J., Partanen, J., Tuomi, J., & Walter, M. (2013). Rapid manufacturing in the spare parts supply chain: Alternative approachees to capacity deployment. Journal of Manufacturing

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https://doi.org/http://dx.doi.org/10.1108/MRR-09-2015-0216 Khajavi, S. H., Partanen, J., & Holmström, J. (2014). Additive manufacturing in the spare parts supply

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https://doi.org/10.1016/j.compind.2013.07.008 Knofius, N., Van der Heijden, M. C., & Zijm, W. H. M. (2016). Selecting parts for additive manufacturing in service logistics. Journal of Manufacturing Technology Management, 27(7), 915–931. https://doi.org/10.1108/JMTM-02-2016-0025 Kornbrot, D. (2005). Pearson product moment correlation. In B. Everitt & D. Howell (Eds.), Encyclopedia of Statistics in Behavioral Science (pp. 398–400). West Sussex: Wiley. Langevelde, J. M. van. (2016). Onderzoek naar de invloed van 3D printen op de supply chain van

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Defensie. Hogeschool Windesheim. Lindemann, C., Jahnke, U., Moi, M., & Koch, R. (2012). Analyzing product lifecycle costs for a better understanding of cost drivers in additive manufacturing. International Solid Freeform Fabrication Symposium, 23, 177–188. https://doi.org/10.1007/s13398-014-0173-7.2 Liu, P., Huang, S. H., Mokasdar, A., Zhou, H., & Hou, L. (2014). The impact of additive manufacturing in the aircraft spare parts supply chain: supply chain operation reference (scor) model based analysis. Production Planning & Control, 25(13–14), 1169–1181. https://doi.org/10.1080/09537287.2013.808835 Louis, M. J., Seymour, T., & Joyce, J. (2014). 3D opportunity in the Department of Defense. Deloitte

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manufacturing-defense-3d-printing/ Maas Geesteranus, K. S., & Zwang, P. M. (2014). Systeemplan SP005981 Fennek. Rijksoverheid.

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34


Appendix A)

Levels of maintenance

Table A.1 below gives extensive description of the three maintenance levels within the RNLA. The colours indicate the type of personnel that is (mostly) involved, where blue = civilian and green = military. Table A.1 - Maintenance levels

Matlogco, DMI, LCW, Industry/OEM Royal Netherlands Army, NLD Maintenance Concept

35


Appendix B)

Service supply chain

Figure B.1 below is a schematic representation of the equipment service supply chain (‘materieeldienstketen’), issued by the RNLA. The legend corresponding to this image is given in Table B.1.

Figure B.1 - Equipment service supply chain

Table B.1 - Legend equipment service supply chain

Legend Infantry

Mechanized Infantry Crew or Patrol Platoon Company

AOO BG BRIG BSA DMI LCW Matlogco MCP MSP

Area of Operations Battle Group Brigade Brigade Support Area Naval Maintenance and Sustainment Agency Logistical Centre Woensdrecht Material logistics command Material Control Point Material Support Point

Battalion

NSE

National Support Element

Netherlands

OEM

Original Equipment Manufacturer

Maintenance

• ••• I II NLD

36


Appendix C)

Additive manufacturing

This appendix provides general information about Additive Manufacturing (AM).

C.1

General description

AM is the term used to describe the technologies that add material layer by layer to create 3D objects. This is done with the help of 3D modelling software (Computer Aided Design or CAD). Each part layer that is printed by the 3D printer, is a thin cross-section of the part derived from this CAD data. The thinner the layers are, the closer the printed part is to the original. The images below in Figure C.1 illustrate two printed hemispheres with different layer thicknesses. The image on the right, with thinner layers, resembles the original design much more closely than the image on the left.

Figure C.1 - Layer thickness in AM

C.2

History of AM

The development of AM began in the 1980s, when Charles Hull, co-founder of a company called 3D systems, invented stereolithography (3D printing). Figure C.2 marks some important moments with regards to the technology during the decades that followed.

Figure C.2 - Historical moments AM

Today, swift advancements in precision, speed, affordability, materials range, etc. are being made by the AM industry (Khajavi et al., 2014); the technology is still actively being developed. 37


C.3

Generic Additive Manufacturing process

AM is the movement from a CAD description to an physical resultant part. There are several stages to this process. The process of AM can be summarized in eight steps, as listed below (Gibson, Rosen, & Stucker, 2015): 1) Computer-Aided Design (CAD) The external geometry of a part is described by a software model. The output is a 3D solid or surface representation. Reverse engineering can be used to create this representation. An example of a CAD is shown in the left image of Figure C.3. 2) Conversion to stereolithography-file format (STL) Nearly every CAD system can output this format, and nearly every AM machine accepts it. An example of an STL design is shown in the right image of Figure C.3. 3) Transfer to AM machine and STL file manipulation The file is transferred to the machine, which might slightly manipulate it so that it is the correct size, position and orientation for building. 4) Machine setup The AM machine should be set up in terms of build parameters like material constraints, energy source, layer thickness, timings, etc. 5) Building the part This step is mainly automated. Superficial monitoring should take place to ensure that no errors take place (running out of material, power or software glitches, etc.). 6) Removal All parts are removed from the machine once the build is completed. 7) Post-processing Additional cleaning up and manual manipulation to the part if necessary. 8) Application Parts are ready for use.

Figure C.3 - CAD-design (left) and STL-design (right)

38


C.4

AM processes, technologies and materials

There are seven different process categories for additive manufacturing: 1) Vat photopolymerisation Processes that utilize a liquid photopolymer that is contained in a vat and processed by selectively delivering energy to cure specific regions of a part cross-section. 2) Powder bed fusion Processes that utilize a container filled with powder that is processed selectively using an energy source, most commonly a scanning laser or electron beam. 3) Material extrusion Processes that deposit a material by extruding it through a nozzle, typically while scanning the nozzle in a pattern that produces a part cross-section. 4) Material jetting Ink-jet printing processes. 5) Binder jetting Processes where a binder is printed into a powder bed in order to form part crosssections. 6) Sheet lamination Processes that deposit a layer of material at a time, where the material is in sheet form. 7) Directed energy deposition Processes that simultaneously deposit a material (usually powder or wire) and provide energy to process that material through a single deposition device. There are many different types of AM technologies, but all of them use either one of these seven processes to print parts. For example, stereolithography uses vat polymerisation, where plaster-based 3D printing uses binder jetting. Each technology has its advantages and disadvantages in speed, complexity, cost, strength, size, etc. Also, some AM machines have limitations to the types of material (polymers, metals, ceramics and composites) that can be printed (Cotteleer, Holdowsky, & Mahto, 2014). These technological features of different AM machines are not further investigated in this research. The specifications of the RNLA’s current printer, the Markforged ‘Mark Two’ printer, are shown in Table C.1. Table C.1 - Specifications Mark Two printer

Build volume

320 mm x 132 mm x 154 mm

Plastic materials

Onyx, Nylon

Fibre materials

Carbon Fibre, Fiberglass, HSHT Fiberglass, Kevlar

Z layer resolution

100 micron 39


Appendix D)

Analytical Hierarchy Process

This appendix describes the Analytical Hierarchy Process (AHP) that has been applied in this report. The four steps of this process are as follows: Analytical Hierarchy Process 1)

Decompose the decision-making problem into a hierarchy

2)

Make pairwise comparisons and establish priorities among the elements in the hierarchy

3)

Synthesise judgments

4) Evaluate and check the consistency of judgments The following sections walk through each of these steps.

D.1

Hierarchy

The first step of the AHP is to decompose the decision-making problem into a hierarchy. An example of this is shown in Figure D.1. The objective of the AHP is to derive the weights of the criteria on each level with respect to the level above.

D.2

Pairwise comparisons

The next step is to make pairwise comparisons between the criteria. Experts should judge the importance of each criterion with respect to the each of the other criteria. The scale of 1 to 9 that is used for this is shown in Table D.1. Table D.1 - Preference scores

Score 1 2 3 4 5

Figure D.1 - Hierarchy

6 7 8 9

Degree of preference Equal importance Moderate importance of one factor over another Strong or essential importance Very strong importance Extreme importance

40


The experts fill out tables to express their judgments. An example is given in Table D.2. Table D.2 - Example AHP table

Contribution to the goal Criterion 1

Criterion 2

Criterion 3

Criterion 1

1**

9*

4***

Criterion 2

1/9

1

1/3

Criterion 3

1/4***

3

1

*Read: criterion 1 is considered extremely more important than criteria 2 **Note that the numbers in the diagonal are always 1 (each criterion is equally as important as itself) ***The numbers to the left of the diagonal (the lower triangular matrix) are the inverse of the numbers to the right of the diagonal (the upper triangular matrix).

From this point onwards, the experts are not involved in the AHP anymore, it is merely a mathematical process. Therefore, from now on a mathematical notation is used. The pairwise comparison table is represented as a matrix as follows: đ??ś11 đ?‘ƒđ?‘Žđ?‘–đ?‘&#x;đ?‘¤đ?‘–đ?‘ đ?‘’ đ?‘?đ?‘œđ?‘šđ?‘?đ?‘Žđ?‘&#x;đ?‘–đ?‘ đ?‘œđ?‘› đ?‘šđ?‘Žđ?‘Ąđ?‘&#x;đ?‘–đ?‘Ľ = [đ??ś21 đ??ś31

đ??ś12 đ??ś22 đ??ś32

đ??ś13 đ??ś23 ] đ??ś33

To generate the weights of the criteria, the matrix needs to be normalized. This is done in three steps: 1) Sum the values in each column of the pairwise matrix đ?‘›

đ??śđ?‘–đ?‘— = ∑ đ??śđ?‘–đ?‘— đ?‘–=1

2) Divide each element in the matrix by its column total to generate a normalized pairwise matrix đ??śđ?‘–đ?‘— đ?‘‹đ?‘–đ?‘— = đ?‘› ∑đ?‘–=1 đ??śđ?‘–đ?‘—

−→

đ?‘‹11 đ?‘‹ đ?‘ đ?‘œđ?‘&#x;đ?‘šđ?‘Žđ?‘™đ?‘–đ?‘ đ?‘’đ?‘‘ đ?‘?đ?‘œđ?‘šđ?‘?đ?‘Žđ?‘&#x;đ?‘–đ?‘ đ?‘œđ?‘› đ?‘šđ?‘Žđ?‘Ąđ?‘&#x;đ?‘–đ?‘Ľ = [ 21 đ?‘‹31

đ?‘‹12 đ?‘‹22 đ?‘‹32

đ?‘‹13 đ?‘‹23 ] đ?‘‹33

3) Divide the sum of the normalized row of the matrix by the number of criteria used (n) to generate the weighted matrix đ?‘Šđ?‘–đ?‘— =

∑đ?‘›đ?‘—=1 đ?‘‹đ?‘–đ?‘— đ?‘›

−→

đ?‘Š11 đ?‘Šđ?‘’đ?‘–đ?‘”â„Žđ?‘Ąđ?‘’đ?‘‘ đ?‘šđ?‘Žđ?‘Ąđ?‘&#x;đ?‘–đ?‘Ľ = [đ?‘Š12 ] đ?‘Š13

41


D.3

Synthesise judgments

There might be inconsistency between the judgments of different experts. For example, an operations director could value a criterion named ‘cost’ over ‘performance’ where a sales manager would do the opposite. Therefore, before proceeding to the next step, one should synthesise judgments by taking the averages of the weights given by these experts.

D.4

Evaluate consistency

The final step of the AHP is to compute the consistency ratio (CR), in order to make sure that the original preference ratings were consistent. The following steps are taken for this: 1) Calculate Cw by multiplying the pairwise matrix by the weights vector đ??ś11 đ??ś [ 21 đ??ś31

đ??ś12 đ??ś22 đ??ś32

đ??ś13 đ??ś23 ] ∗ đ??ś33

đ??śđ?‘¤11 đ?‘Š11 đ?‘Š = [ 12 ] [đ??śđ?‘¤12 ] đ?‘Š13 đ??śđ?‘¤13

2) Compute the Consistency Vector (Cv) đ??śđ?‘Łđ?‘–đ?‘— =

đ??śđ?‘¤đ?‘–đ?‘— đ?‘Šđ?‘–đ?‘—

đ??śđ?‘Ł11 đ??śđ?‘œđ?‘›đ?‘ đ?‘–đ?‘ đ?‘Ąđ?‘’đ?‘›đ?‘?đ?‘Ś đ?‘‰đ?‘’đ?‘?đ?‘Ąđ?‘œđ?‘&#x; = [đ??śđ?‘Ł12 ] đ??śđ?‘Ł13

−→

3) Calculate Îť đ?‘›

1 đ?œ† = ∑ đ??śđ?‘Łđ?‘–đ?‘— đ?‘› đ?‘–=1

4) Calculate the Consistency Index (CI) đ??śđ??ź =

đ?œ†âˆ’đ?‘› đ?‘›âˆ’1

5) Calculate the Consistency Ratio (CR). The Random Index (RI) can be found in the table đ??śđ?‘… =

đ??śđ??ź đ?‘…đ??ź

n RI

1 0

2 0

3 4 5 0.58 0.90 1.12

6 7 8 1.24 1.32 1.41

9 1.45

10 1.49

A CR of 0.1 or below is considered acceptable. If the value exceeds 0.1, the judgments need to be re-examined.

42


Appendix E)

Framework adaptations

This appendix gives an extended version of the adaptations that are described in Section 5.2. As explained in the beginning of Chapter 5, the main methodology of this thesis is based on the existing framework of Knofius et al. (2016). However, we have not yet mentioned that a preliminary version of this framework was later adapted by Balistreri and has been used to analyse roughly 12000 parts for the Army Maintenance and Logistics Command (MatLogCo) (Balistreri, 2015). Many of his findings are relevant for this research, as they concern the same context. However, Balistreri’s research differs from this research in that he focussed on DLM and identical parts, whereas this research concerns ILM and temporary solutions. Therefore, the conclusions of Section 5.2 with regards to the establishment of a final model, were drawn using the preliminary version of Knofius’ model (2015), the adapted version by Balistreri (2015) and the final, published framework of Knofius et al. (2016). The weighted attributes that were used in these three models are shown in Table E.1. Table E.1 - Weighted attributes in different frameworks

Knofius, 2015

Balistreri, 2015

Knofius et al., 2016

Average days in inventory

Average days in inventory on hand

Customer order lead time

Resupply lead time

Resupply lead time

Demand rate

Average annual usage

Demand rate

Design ownership

Design ownership

Holding cost

(no data)

Installed base accessibility

(irrelevant)

Item in initial lifecycle phase

Item in initial lifecycle phase

Remaining usage period

Manufacturing cost

(no data)

Purchasing cost

Purchasing cost

Manufacturing/order costs

End Life of Type

Supply risk

Safety stock costs

Agreed response time Number of supply options The blue rows in the table indicate the attributes on which the different research papers agree. To summarize, the four main reasons this thesis requires adaptations with regards to the previous papers are: 1) specific characteristics of the Defence industry, 2) the use of temporary spare parts, 3) printing on-location and 4) the availability of data. The adaptations in Section E.1 are made for reasons 1, 2 and 3. The data availability (4) is considered in Section E.2. 43


E.1

Adaptations due to context

Let us now elaborate on which attributes should be retained, discarded, changed or added to make the existing framework fit to this research, regardless of the data that is available. The three main company goals: reduce costs, reduce downtime and secure supply, are referred to throughout the remainder of this section.

Attributes to retain All papers agree that the lead time of a spare part is an appropriate attribute to measure the potential of the part for AM, so it is included in this research as well. A long resupply lead time increases the need for a large safety stock. Also, it could result in a higher system downtime when a spare part is not available. As found in Chapter 4, AM can shorten this lead time. Especially the spare parts which currently have a very long lead time can potentially benefit from this technique in terms of reducing cost and reducing downtime. A high demand variability can lead to inefficiencies in the manufacturing process due to high tooling and setup costs (Knofius et al., 2016). It also causes the need to keep a high inventory. AM has the potential to shorten the lead time and therefore increase the operational flexibility towards this variability, thus reducing downtime and reducing costs. The preliminary version of Knofius’ paper differentiated between manufacturing and purchasing cost. The published version of Knofius et al. does not separate the two attributes but simply introduces the manufacturing/order costs. Balistreri noted that as an asset owner, the manufacturing costs are unknown and so the purchasing costs can be used to analyse the spare part pool (Balistreri, 2015). The higher these costs are, the more likely it is that AM can offer a cheaper production alternative and reduce the costs. Conceivably, the most expensive parts are also the most complex and therefore could be infeasible for AM, but these parts are excluded on the basis of the Go/No-Go criterion ‘complexity’. Design ownership is mentioned in the first two papers as being a relevant criterion. In order to print a spare part, the CAD-design of this part is required. Acquiring or developing such a design can be difficult and expensive. If the RNLA already possesses a design, this saves time and money (reduce costs).

Attributes to discard Knofius further lists the agreed response time as a relevant attribute. This response time is the service level agreement between a company and another party. However, the RNLA has no such agreements with logistical providers, so this attribute is irrelevant. Balistreri notes that installed base accessibility is irrelevant for his research as no remote locations are considered. This research does consider these remote (on-site) locations, but still 44


the parameter is seen as irrelevant. As discussed, the project is scoped to the ILM level. The spare parts used on this level are stored at the MOB, a base which is by definition accessible relatively easily. One could argue that special circumstances, such as a sandstorm, could limit this accessibility, in which case the attribute would become relevant. However, this limited accessibility would then apply to all spare parts of the assortment and so the parameter would lack distinctiveness. The three papers agree that if there currently is a high inventory cost, the potential for AM is higher. The logic behind this is that AM can reduce the lead time and, consequently, reduces the need for keeping a high stock. The main cause of these high inventory costs are expensive items with long lead times and a variable demand. The attributes “lead time”, “demand variability” and “purchasing costs” therefore already account for part of this suggested parameter. Furthermore, as explained in Section 3.2.3, there is not one methodological way in which the RNLA determines its stock levels. Also, RNLA does not measure its inventory costs like ordinary companies do (it works with budgets). In conclusion, this attribute is not expected to be a reliable indicator of the AM potential of spare parts and its most important aspects are already covered with other attributes. Therefore, it is discarded.

Attributes to change Knofius et al. and Balistreri have used several different attributes to address two topics, one economical and one regarding availability. Both topics are relevant and included as criteria in this research. They are listed as numbers 1) and 2) below. 1) Economical issue – Using AM for a part requires an investment (e.g. to acquire the CAD-designs) and depreciating this over ten years makes the investment more interesting than doing this over two years. Therefore, the longer the system will be in use, the higher the potential for AM to reduce costs. Three attributes/terms relate to this first issue. The ‘item in initial lifecycle phase’ measures whether or not the system is new (Balistreri, 2015). The ‘remaining usage period’ indicates for how long the system is still to be used (Knofius et al., 2016). The RNLA uses the term End Life of Type (ELOT) to describe the date until which the system is used. This final term is used in this thesis as well. 2) Availability issue – If an OEM discontinues the production of spare parts of a system that is still in use, the RNLA needs to find an alternative way of acquiring the parts. AM could be a solution here. The sooner the spare parts will stop being supplied, the higher the necessity for an alternative acquisition method to secure supply.

45


The ‘supply risk’ assesses the need for an alternative production method (Knofius et al., 2016). This relates to the second issue. The RNLA uses the term obsolescence to describe this risk: a system is considered obsolete when the spare parts can no longer be supplied. Balistreri misused the term ELOT to measure this risk; he wrote that the ELOT indicates when the OEM stops supplying parts, which is an incorrect definition. Figure E.1 shows how the terms described above relate to each other. To conclude, the criterion relating to the reduce costs objective is End Life of Type, and the criterion relating to the secure supply objective is obsolescence.

Figure E.1 - Time-related attributes

Another suggested attribute is the number of supply options. Knofius et al. state that if there are only a few supply options, AM may offer a chance to reduce order costs because an additional supply option improves the negotiation position (Knofius et al., 2016). In the context of the RNLA this does not seem very relevant, because there is often only one OEM for the systems. However, the number of emergency supply options could be an interesting attribute for the RNLA. This does not have anything to do with financials or negotiating, but with the system readiness (objective = reduce downtime). If a system breaks down due to the failure of a spare part and this part is not on stock, it is relevant to know what the options are for acquiring it. It is easier to obtain a part of a militarized civilian vehicle on the local market of the Host Nation, than it is to acquire parts of military vehicles.

Attributes to add A parameter that is relevant in the context of the RNLA but has not been mentioned before is the mission criticality. If a spare part is critical to the mission, i.e. it has a direct effect on the functional usage of the system, it is important that this part becomes available as soon as possible so that downtime is reduced. The (German) phrase used to describe this functional usage is “fahren, funken, schießen, navigieren” (drive, send radio signals, shoot, navigate).

46


Result of adaptations Section E.1 Table E.2 shows the weighted attributes that have been selected so far. Table E.2 - Spare part attributes assortment, before considering data

Attribute

Improvement potential Reduce costs Reduce downtime Secure supply

Weighted spare part attributes

Lead time

Long

Long

Demand variability

Low

Low

Purchasing costs

High

Time to ELOT

Long

Time to obsolescence

Short

Emergency supply options

Low

Mission criticality Design ownership

Yes Yes

Read: If spare part attribute ‘x’ belongs to value level ‘y’ then this indicates improvement potential ‘z’.

E.2

Adaptations due to data availability

Two Go/No-Go attributes and nine weighted attributes have been defined. However, some of the required data might not be available, in which case an alternative parameter needs to be formulated or the attribute should be discarded. A visit was made to the Systems & Analysis department of MatLogCo in order to find out what data can be retrieved from the information systems. As expected, not all attribute data can be retrieved from the system. However, this can sometimes be resolved by using a slightly different parameter that is available in the system to approximate the desired attribute. In other cases, a more creative solution has to be thought of to apply the parameter or, in the worst case, the parameter has to be discarded. This section describes the adaptations that were done due to the imperfections of the available data. The three attributes that can be extracted directly from the system (SAP) without requiring any further adaptations are: lead time, purchasing cost and mission criticality. Demand variability cannot be retrieved, so the attribute demand rate is introduced to approximate this attribute. A low demand rate often indicates a high variability, so spare parts with low demand rates are more interesting for AM than spare parts with high, predictable demands. The time to ELOT of each system cannot be retrieved via SAP, but it is documented in the system plan, which can be found on Defence’s intranet. The number of emergency supply options is also not in the system but it this information is available on the internet. 47


The system does not indicate whether or not the design is owned already by the RNLA. Therefore, the parameter design ownership needs to be adapted. It is conceivable that the supplier base can indicate the difficulty of obtaining a design. For example, a Dutch or German supplier with a strong relationship with the RNLA might be more willing to cooperate than an American supplier. The time to obsolescence is not in the system. This is a troubling fact, because it means that units do not find out that an OEM no longer supplies a spare part, until they try to order it. The information is available somewhere (in the contract with the OEM), but it could not be obtained within the timeframe of this thesis. Therefore, we do not use this parameter. The final weighted attributes assortment is presented in Table 5.2.

48


Appendix F)

Applied AHP

In this appendix, the AHP process that is described in Appendix D) is gone through to determine the attribute weights for the RNLA’s spare parts. Seven respondents with different backgrounds were chosen to participate in this process (Table F.1) Table F.1 - Respondents AHP process

Organisation/department

Function

Royal Netherlands Army, Recovery Platoon

Platoon Commander

NLR, Metal Additive Manufacturing Technology

R&D Engineer

Centre European External Action Service (EEAS),

Branch Chief Logistic Policy

European Military Staff (EUMS), Logistics Directorate Royal Netherlands Army, Army Maintenance &

Regional manager South (former

Logistics Command (MatLogCo)

commander recovery platoon)

Royal Netherlands Army, Section S4: Logistics

Commander redeployment unit

Ministry of Defence, Defence Materiel

Engineer

Organisation (DMO) Royal Netherlands Army, Expertise Centre

Staff officer operational logistics

Logistics These respondents were asked to fill out three pair-wise comparison forms; one to determine the weights of the three main goals, one to determine attribute weights with respect to the reduce cost goal, and one to determine the weights with respect to the reduce downtime goal. The third goal, secure supply, has only one underlying attribute, which automatically receives a 100% weight. The averages of their responses are shown in Table F.2, Table F.3 and Table F.4. Table F.2 - Pair-wise comparison Company Goals

Company goals Reduce cost

Secure supply

Reduce downtime

1

0.26

0.20

Secure supply

3.89

1

0.59

Reduce downtime

4.98

1.70

1

Reduce cost

49


Table F.3 - Pair-wise comparison Reduce Cost

Reduce cost Purchasing cost

Time to ELOT

Lead time

Supplier base

Demand rate

1

2.62

1.27

2.39

2.83

Time to ELOT

0.38

1

1.06

1.62

1.74

Lead time

0.79

0.94

1

3.48

2.55

Supplier base

0.42

0.62

0.29

1

2.14

Demand rate

0.35

0.58

0.39

0.47

1

Purchasing cost

Table F.4 - Pair-wise comparison Reduce Downtime

Reduce downtime Lead time

Demand rate

Mission criticality

1

1.43

0.88

Demand rate

0.70

1

0.48

Mission criticality

1.14

2.08

1

Lead time

The mathematical process of Appendix D) results in the weights that are presented in Figure 5.3. The Consistency Ratios of the three AHP-procedures (in the order of the tables) are 0.0077, 0.0404 and 0.0058. All ratios are below 0.1, which means they are consistent enough to be used in the decision process. The hierarchy presented in Figure 5.3 can be reduced to a model in which every parameter is used once and there are no intermediate levels. For example, the attribute ‘lead time’ is used twice, but in total it weighs 26% of 10% (reduce cost) + 35% of 55% (reduce downtime) = 22%. Such a model simplifies the further calculations that are made in Excel. The total weight of each parameter is shown in Table F.5. Naturally, the sum of these weights equals 1. Table F.5 - Total attribute weights

Attribute

Total weight

Purchasing cost

0.0331

Time to ELOT

0.0184

Lead time

0.2159

Supplier base

0.0129

Demand rate

0.1319

Criticality

0.2341

Emergency supply options

0.3537

50


Appendix G)

Data cleaning

In this appendix, the spare part data that was retrieved from SAP on April 19th, 2017 is cleaned and prepared so that it can be used in the application of the model that has been developed in Chapter 5. This is done for the Boxer, the Fennek and the Mercedes-Benz 290GD vehicles.

G.1

Boxer data cleaning

The statistics of the raw data of the Boxer spare parts are shown in Table G.1. Histograms of this data are given in Figure G.1a-d. Table G.1 - Spare part data Boxer, raw

N

Valid Missing

Criticality 2188 0

Mean Mode Std. Deviation Minimum Maximum

Statistics Lead time 2122 66 94,25 90 25,786 0 270

Demand rate Purchasing cost 445 2188 1743 0 32,81 1509,8545 2 ,69 172,387 11086,13868 0 ,01 2866 301790,00

Figure G.1 - Boxer data, raw

Figure F.1a - Lead time in days, Boxer (1)

Figure F.1b - Yearly demand rate, Boxer (1)

51


Figure F.1c - Purchasing cost in â‚Ź, Boxer (1)

Figure F.1d - Criticality, Boxer (1)

As can be read from, there are 2188 raw data entries for Boxer spare parts. However, some entries (66) have no value for the lead time, and many entries (1743) have no entries for demand rate. There are two reasons for this: 1) the Boxer is a new vehicle and for many parts, there has not been any demand so far, 2) many of the spare parts that are in the system would not be replaced even if they failed; for example because they are part of a bigger spare part that would then be replaced. The entries with empty values are removed, because the full information of all attributes is required for the framework to work. There are also quite some entries with a negative or zero value for one of the attributes lead time, demand rate and purchasing cost. These values pollute the data and should therefore be removed. Applying these two filters results in the statistics given in Table G.2. Table G.2 - Spare part data Boxer, cleaned

Statistics Criticality N

Valid Missing

Mean Mode Std. Deviation Minimum Maximum

Lead time

Demand rate

Purchasing cost

380

380

380

380

0

0

0

0

93,74 92 25,309 31 270

38,42 2 186,005 1 2866

1715,3411 25,00a 16826,43933 ,01 301790,00

a. Multiple modes exist. The smallest value is shown The new frequency graphs are given in Figure G.2a-d.

Figure G.2 - Boxer data, cleaned 52


Figure F.2a - Lead time in days, Boxer (2)

Figure F.2b - Yearly demand rate, Boxer (2)

Figure F.2c - Purchasing cost in €, Boxer (2)

Figure F.3d - Criticality, Boxer (2)

From these figures, two problems can be identified. The attributes ‘lead time’, ‘demand rate’ and ‘purchasing cost’ are to be scored linearly. This means that the maximum (or minimum) value is scored ‘1’ and the minimum (or maximum) value is scored ‘0’. Everything in between is receives a value based on a linear graph. If this method is applied to the demand rate, there will be very little differentiation in the scores. The majority of the demand rates are between 0 and 100, however, there are some outliers above a thousand. Because of this, all demand between 0 and 100 will get a score between 0.9 and 1.0 (note that a low demand rate indicates a high potential). This is undesirable. The same problem exists for the attribute ‘purchasing cost’. The majority of spare parts is cheaper than €1000, however, the maximum price is as high as €301790. As a result, the spare parts between 0 and 1000 euros vary in scores from 0 to about 0.04. To solve this problem, all entries with demand rates above 100 or purchasing costs above €1000 are removed. It is unlikely that these removed spare parts would have been interesting for AM anyway; low demand rates are more interesting for AM and very expensive items are likely to be too large or complex to print. The resulting data is presented in Section 6.1.

53


G.2

Fennek data cleaning

The statistics of the raw data of the Fennek spare parts are shown in Table G.3. Histograms of this data are given in Figure G.3a-d. Table G.3 - Spare part data Fennek, raw

Statistics Criticality N

Valid Missing

Lead time

Demand rate

Purchasing cost

5948

5946

1368

5948

0

2

4580

0

96,45 90 32,035 5 659

18,18 0 58,593 -5 939

679,1330 ,69 8063,82823 ,01 273290,00

Mean Mode Std. Deviation Minimum Maximum

Figure F.3a - Lead time in days, Fennek (1)

Figure F.3b - Yearly demand rate, Fennek (1)

Figure F.3c - Purchasing cost in â‚Ź, Fennek (1)

Figure F.3d - Criticality, Fennek (1)

Figure G.3 - Fennek data, raw

54


As can be read from Table G.3, there are 5948 raw data entries for Fennek spare parts. First, the negative, zero and empty entries are removed. This results in the statistics in Table G.4 and the graphs in Figure G.4a-d. Table G.4 - Spare part data Fennek, cleaned

Statistics Criticality N

Valid

Lead time

Demand rate

Purchasing cost

1072

1072

1072

1072

0

0

0

0

111,07 90 57,730 7 659

23,20 2 65,307 1 939

1448,8604 ,69 8647,52590 ,01 180000,01

Missing Mean Mode Std. Deviation Minimum Maximum Figure G.4 - Fennek data, cleaned

Figure F.4a - Lead time in days, Fennek (2)

Figure F.4b - Yearly demand rate, Fennek (2)

Figure F.4c - Purchasing cost in â‚Ź, Fennek (2)

Figure F.4d- Criticality, Fennek (2)

Finally, for reasons explained in Section G.1, parts more expensive than â‚Ź1000 and parts with a demand rate above 100 are removed. The resulting data is presenting in Section 6.2.

55


G.3

Mercedes-Benz 290GD data cleaning

The statistics of the raw data of the Fennek spare parts are shown in Table G.5. Histograms of this data are given in Figure G.5a-d. Table G.5 - Spare part data MB 290GD, raw

Statistics Criticality N

Valid Missing

Lead time

Demand rate

Purchasing cost

4579

4454

2134

4577

0

125

2445

2

110,39 90 53,409 5 633

76,29 2 1079,889 -1 47628

173,5024 ,69 841,11582 ,00 22621,90

Mean Mode Std. Deviation Minimum Maximum Figure G.5 - MB 290GD data, raw

Figure F.5a - Lead time in days, MB 290GD (1)

Figure F.5b - Yearly demand rate, MB 290GD (1)

Figure F.5c - Purchasing cost in â‚Ź, MB 290GD (1)

Figure F.5d - Criticality, MB 290GD (1)

As can be read from Table G.5, there are 4579 raw data entries for MB 290GD spare parts. First, the negative, zero and empty entries are removed. This results in the statistics in Table G.6Table G.4 and the graphs in Figure G.6a-d. 56


Table G.6 - Spare part data MB 290GD, cleaned

Statistics Criticality N

Valid Missing

Lead time

Demand rate

Purchasing cost

1872

1872

1872

1872

0

0

0

0

124,91 140 62,361 5 633

86,87 2 1152,625 1 47628

202,6110 ,69 761,88311 ,01 11527,42

Mean Mode Std. Deviation Minimum Maximum Figure G.6 - MB 290GD data, cleaned

Figure F.6a - Lead time in days, MB 290GD (2)

Figure F.6b - Yearly demand rate, MB 290GD (2)

Figure F.6c - Purchasing cost in â‚Ź, MB 290GD (2)

Figure F.6d- Criticality, MB 290GD (2)

Finally, for reasons explained in Section G.1, parts more expensive than â‚Ź1000 and parts with a demand rate above 100 are removed. The resulting data is presented in Section 6.3.

57


Appendix H)

Results

This appendix lists some of the results of the applied method. Table H.1 gives the top 50 spare parts, 48 of which are Fennek articles. Table H.2 and Table H.3 give the top 20 parts of the Boxer and the top 15 MB 290GD parts respectively. Table H.4 gives an overview of the percentages of Go’s and No-Go’s. Table H.1 - Top 50 spare parts

# 1 2

Article number

Description

Score

Go/No-Go

Comment

10000062668

T-STUK, PIJP

0,9059

No-Go

10000107403 10000107623 10000110878

STEUN, COMPRESSOR ONDERLEGRING, VLAK O-RING

0,8783 0,8388 0,8349

No-Go No-Go No-Go

10000112972 10000114324

PAKKING SENSOR, DRUK (DEUTZ)

0,8342 0,8277

No-Go No-Go

10000113297

PAKKING KOPPAKKINGSET TBV LUCHTCOMPRESSOR FENNEK

0,8274

No-Go

Need a metal printer Too much heat and force Need a metal printer Need rubber printer Cannot print this material Cannot print sensors Cannot print this material

0,8270

No-Go

10000115406 10 10000120132 11 10000120611 12 10000118246 13 10000123524 14 10000124447 15

ONTLUCHTINGSSLANG HITTESCHILD POTENTIOMETER M/KABEL W358 SCHROEF,INWENDIGE AANDRIJFVOORZIENING SPATSCHERM, BOVEN LINKS COMBINATIEMETER

0,8237 0,8199

No-Go No-Go No-Go

Cannot print this material Need two print heads to print PVA Too much heat Too complex

0,8192

No-Go

Need a metal printer

0,8165 0,8134

No-Go No-Go

10000124146 16 10000124833 17 10000125174 18 10000129018 19 10000124075 20 10000129053 21 10000126411 22 10000125204 23 10000134755 24 10000129880

AFDEKPLAAT LINKS COMBINATIEMETER STICKER, SCH. SPERDIFFERENTIEEL KABELSAMENSTEL, W31 KNIPPERLICHTAUTOMAAT KABELSAMENSTEL, W32

0,8131 0,8122

? No-Go ?

Too large Too complex Shape and size is possible, but forces are unknown Too complex No drawing

0,8115 0,8104 0,8103 0,8101

No-Go No-Go No-Go

STOELBEDIENING SCHAKELAAR AFDICHTING, ONBEMANTELD KABELSAMENSTEL, W348

0,8099 0,8097

No-Go ?

Cannot print copper Too complex Cannot print copper Can only print the housing No drawing

0,8094 0,8094

Go No-Go

Cannot print copper

3 4 5 6 7 8

10000114499 9

0,8194

58


25 26 27 28 29

10000135167 10000135414 10000135198 10000131456 10001703494 (BOXER) 30 10000135502 31 10000138701 32 10000138876 33 10000142507 34 10000142786 35 10000144018 36 10000143904 37 10000143436 38 10000147218 39 10000146586 40 10000150842 41 10000152740 42 10000152778 43 10000152869 44 10000150581 45 10001495582 (BOXER) 46 10000148120 47 10000154994 48 10000157079 49 10000156928 50 10000157048

LAGER BUS VULRING AANPASSTUK, RECHT PERISCOOP, GEPANTSERD VOERTUIG BUS, LAGER

0,8092 0,8082 0,8082 0,8071

? ? Go ? No-Go

No drawing No drawing

0,8067 0,8059

?

KNIE, PIJP

0,8052

?

MEMBRAANCILINDER

0,8041

No-Go

KOPPELSTANG WIELSTANDINDICATOR CPL DEKSEL, INSPECTIEGAT SCHROEF, PASGRENDELNOK LAGER, BUS ONDERLEGRING, BORGOPVULSCHUIM, RUGLEUNING COMMANDANTSLUIK CPL BATTERIJKLEM SCHAKELAAR, DRUK BUS, NAAFREDUCTIE ABGASROHR

0,8038

No-Go

0,8036 0,8026 0,8022 0,8009 0,8008 0,8006

No-Go Go No-Go Go Go Go

No drawing Unknown effects of brake fluid on nylon Too complex and too much stress Too much stress and risk of accidents Part of it can be printed (the siphon)

0,7994 0,7994 0,7994 0,7993 0,7993

No-Go No-Go No-Go ? ? Go

KNOP BEVESTIGINGSPROFIEL SENSOR, TOERENTAL PAKKING, VOORGEVORMD SENSOR, DRUK

0,7990 0,7986 0,7986 0,7973 0,7972 0,7970

Unclear drawing Too complex

Too much stress

Material Too much stress Need a metal printer No drawing No drawing Complicated, print in two parts due to size

Go ? No-Go No-Go

No drawing Cannot print sensors Material

No-Go

Cannot print sensors

59


Table H.2 - Top 20 spare parts, Boxer

# 1

Article number 10001703494

2

10001495582

Description PERISCOOP, GEPANTSERD VOERTUIG ABGASROHR

3

10001663905

DOP, VULOPENING

4

10001663333

SCHERM, HITTE, UITLAAT

Score

Go/No-Go No-Go

Comment Too complex

Go

Complicated, print in two parts due to size Depending on the pressure Cannot withstand the heat Pressure too high

0,8067 0,7990 Go 0,7961 No-Go 0,7931

5 6

10001727331 10001751712

ADAPTER DROGE LUCHT SYSTEEM REMBLOKKENSTEL

No-Go 0,7924 No-Go 0,7919

7

10001663126

8

10001663553

9 10 11 12

10001495629 10001495630 10001663363 10001663669

13 10001705268

VENSTER, LICHTARMATUUR SPIEGELSAMENSTEL, ACHTERUITKIJKBOLZEN SPINDEL GABE BOLZEN HEBEARM HI SLANG, LUCHTCIRCULATIE SLANGSAMENSTEL, GELAAGD, NIET-METAAL SPIEGEL ACHTERKLEP

No-Go 0,7902 No-Go 0,7882 0,7874 0,7874 0,7869

No-Go No-Go No-Go No-Go

0,7849 No-Go No-Go

Reflecting material required Pressure too high

No-Go

Pressure too high

?

No drawing

?

No-Go No-Go

Temperature could be too high, uncertain Material is too hard Material is too hard

No-Go

Too complex

0,7843 14 10001663269 15 10001663268 16 10001692306 17 10001663915

18 10001663590 19 10001751779 20 10001656582

SLANGSAMENSTEL, GELAAGD, NIET-METAAL SLANGSAMENSTEL, GELAAGD, NIET-METAAL FILTERELEMENT, LUCHTBEHANDELING COVER

GASKET AFDICHTING, NIET-METAAL SPECIAAL PROFIEL ZWAAILAMP, ORANJE, 24V, INCL. EMC FILTER

Cannot withstand the heat Surface needs to be too smooth Reflecting material required Too much stress Too much stress Pressure too high Pressure too high

0,7842 0,7837 0,7826

0,7825 0,7808 0,7807 0,7805

60


Table H.3 - Top 15 spare parts, MB 290GD

# 1 2 3 4

5 6

Article number 10000044307 10000126432 10000145790 10000263045

10000204513 10000157567

Description SCHAKELGAFFEL HALTER HALTER SCHOKBREKERSTEUN LINKSACHTER TBV MERCEDES HALTER HINTERFEDER

Score 0,5644 0,5628 0,5325

0,5124 0,5014

Go/No-Go ? ? Go No-Go

Comment No drawing No drawing

? No-Go

No drawing Material stiffness cannot be approximated Some parts of it may be printed Impractical because it is a large part Copper cannot be printed Too large and too much stress Requires too much precision and several materials Impractical because it is a large part Can be done with a 2D printer Textile cannot be printed No drawing

0,4928 7

10000165339

GURTSCHLOSS

No-Go 0,4917

8

10000150955

RUGLEUNING

Go 0,4890

9

10000311923

KABELSAMENSTEL

10

10000204476

BUEGEL

11

10000145234

HOOFDREMCILINDER

12

10000205280

BUMPER

13

10000154289

TYPEPLAAT

14

10000166841

BEKLEDING

No-Go 0,4862 No-Go 0,4862 No-Go 0,4855 Go 0,4829 Go 0,4802

15

10000269319

No-Go 0,4772 0,4769

KONSOLE

?

See case study Chapter 8

Table H.4 - Percentages Go/No-Go

Top 50

Top 20 Boxer

Top 15 MB 290GD

Go

8

16%

2

10%

4

27%

No-Go

31

62%

16

80%

7

57%

Unknown

11

22%

2

10%

4

27%

61

Rooijakkers (2017) impact of on location temporary fix am of spare parts on service supply chains at  
Rooijakkers (2017) impact of on location temporary fix am of spare parts on service supply chains at  
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