INFORMATION | 2017 International Edition by INFORM GmbH

INTELLIGENT OPTIMIZATION

Logistics networks in the Automotive Industry How intelligent planning processes can decrease cost and increase business agility

Airports: Where Rush and Relaxation Turnaround

How optimized scheduling reduces delays and saves airlines considerable cost

2017 international edition

Information 2017

Big data – the 12th man How data is used in football to make decisions for a successful game

aircraft line maintenance Mobile Ways of Communication

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Lifting Productivity How Agile Optimization is used to optimize

the movement of containers in and out of a terminal

Hartmut Giesen Publizistik Projekte

INDEX 2017 Adding value to jobs

Intelligent software gives greater transparency and more

04 – 07

responsibility to employees cover story

Intelligent computers – intelligent learning

08–11

Modern trainspotting

12–15

versus intelligent optimization

Latest advances in software technology can help cement producers to keep the business on track Big Data, the 12th man

How data is used in football to make decisions for

16–19

a successful game

Aircraft Line Maintenance

20–23

Logistics networks in the Automotive Industry

24–27

Mobile Ways of Communication

How intelligent planning processes can decrease cost and increase business agility Lifting Productivity

How Agile Optimization is used to optimize the movement of

28–31

containers in and out of a terminal

No congestion despite multi-stop course

How intelligent software optimizes the complex processes for

32–35

material deliveries in the chemical and process industry Airports: Where Rush and Relaxation Turnaround

How optimized scheduling reduces delays and saves airlines

36–39

considerable cost OR as art

40-41

INFORM Portrait

Information 2017

Â© Inform GmbH 2017

Adding value to jobs

Employees often fear that a higher degree of automation and information technology (IT) will have a negative impact on, or replace their job. The fact that the opposite is the case is demonstrated by many examples from different industries in which companies use intelligent software to optimize their business processes. “The introduction of such a system has an extremely positive effect on the daily

routines of the employees involved. Among other things, it leads to greater transparency and increased flexibility. The software relieves them of routine tasks and allows them to

focus their expertise on broader goals,” says Dr. Eva Savelsberg, SVP of the Logistics Division at INFORM.

IT only works with human expertise.

Information 2017

Relief from routine tasks This is the case, for example, for the employees in hubs and distri-

bution centers. “Parcel service providers such as Hermes, Swiss Post,

and UK Mail organize their hubs’ yard logistics using our SyncroTESS software,” says Savelsberg.

This type of IT-assisted processing not only increases the logistical

performance but also improves working conditions. The software calculates optimization proposals to determine which dock of the

sorting hall a container could be moved to for unloading. It also calculates which shunting vehicle would move the container and when. The yard scheduler can then accept these proposals or, if required,

override them at their discretion. “In the past, yard schedulers often

had to coordinate the transport orders directly with the drivers of the shunting vehicles and the team managers in the hall,” explains Savelsberg. “Today, the software performs these types of routine tasks, allowing the schedulers to focus entirely on increasing the value-added services of the hub.”

From plan creator to roster scheduling manager Personnel planning departments would not manage without IT support either. Here too, it is a matter of coordinating with the in-

dividual business drivers (e.g. flight schedules at airports, vessels

at seaports, customer sales in retail) to ensure that the right staff are always in the right place at the right time. Staff planning is a

logistical balancing act that has many influencing factors: seasonal fluctuations in workload demand, temporarily high employee ab-

sences, consideration of employee need, as well as statutory and collective agreement requirements. Whereas a planner can scarce-

ly try out more than just a few alternatives when faced with difficult

problems, our WorkforcePlus software can find the best solution from thousands of options while considering all constraints. The

result is a roster that always balances workload demand, business

requirements, and employee satisfaction. “This adds value to the

job and the planner goes from being the person who creates the plan to the roster scheduling manager," says Jörg Herbers, SVP of

Software performs routine tasks. 6

the Workforce Management Division at INFORM.

Improved work-life balance Employees in major industrial companies, with complex internal material flows, have also experienced substanti-

al improvements in the quality of their jobs. In the pre-IT

Employees focus much more on solving critical problems.

era, dispatchers and drivers worked in loud, hectic envi-

ronments. A dispatcher's time consisted of planning all tasks and communicating associated transport orders to drivers via radio. Driver's attention was split between

performing required tasks and monitoring constant radio chatter for relevant transport orders. In short - both roles were inefficient and stressful.

The implementation of IT systems in this environment

has shifted the focus of both roles leading to improved productivity, job satisfaction and ultimately, work-life

balance. “Companies and their employees benefit from the implementation of INFORM's optimization soft-

ware,” says Matthias Berlit, SVP of the Manufacturing

Time for strategic tasks

planning and communicating routine tasks to focusing

The effects in the area of inventory management for manufacturing

outside of the ordinary. The combination of IT-system

management by calculating optimized proposals for order quantities

Logistics Division at INFORM. Dispatchers shift from their expertise on resolving complex services that fall

automation and human expertise leads to further optimization of company assets. Drivers benefit from clearly

companies are similar. “Our add*ONE software automates inventory and times and displaying the need to intervene on an exception basis,”

says Ludger Schuh, SVP of the Inventory & Supply Chain Division at

communicated transport orders. Initial apprehension to

INFORM. Schedulers can therefore focus increasingly on the strategic

appreciation for the calm organization it delivers. “Since

ware, we always make it clear that the objective is not to let staff go

having a “computer” in their vehicle quickly gives way to implementing SyncroTESS, employees report substantial reductions in job-related stress,” says Matthias, “leading to an improved quality of work-life balance.”

direction of inventory management. “When implementing our soft-

reducing labor costs; rather, it is about changing the structure of the scope of duties in such a way that schedulers can now take on tasks from other areas.”

New scope for creativity

Interaction between people and IT

Besides dividing the work more equitably, adding value

The fears of many employees that their job might change or may

cision-making software solution. “Our FELIOS software

complex company processes only succeeds when human expertise

who produce highly-individual products in single-item

of responsibility of many jobs is expanded, as employees can focus

dis, SVP of the Production Division at INFORM. The sys-

gic corporate goals.

to jobs is one of the positive effects of an intelligent de-

was developed specifically for contract manufacturers

and small-batch manufacturing,” explains Andreas Glatem performs 80 to 90 percent of the, often tedious,

cease to exist are often unfounded in practice. True optimization of and information technology are combined. As a result, the scope

much more on solving critical problems or on implementing strate-

routine tasks. Planners and schedulers are then free to

focus precisely on the challenges for which they are qua© Inform GmbH 2017

lified and become optimization managers. Key figures and analyses are revealed to them early on allowing them to identify where there is waste, where capacity re-

quirements exist (and which ones), or where risks can be

minimized. “Using FELIOS, manufacturers can actively and profitably influence the interaction between personnel, machinery, and procurement.”

Intelligent computers â&#x20AC;&#x201C; intelligent learning versus intelligent optimization 8

ÂŠ Inform GmbH 2017

Information 2017

Until recently, computers have simply been solutions with pre-programmed 'smarts'. Now they have the capacity to learn on their own. The increasing intelligence of computers is one of the greatest technological debates of our time involving concepts such as artificial intelligence (AI)

and machine learning. These ideas promise to revolutionize the way we do business. However, making quick decisions in complex situations based on small amounts of data requires a different kind of computer intelligence.

INFORM has been offering intelligent optimization systems that

have been making decisions in highly complex, real-time situations

for more than 40 years. Further, AI has been a topic of discussion

ever since its forefather Alan Turing formulated his famous 'Turing test' nearly 70 years ago (e.g., a computer is intelligent if a human

interrogator cannot distinguish between its responses and a human's

responses in a question-and-answer game). So, why is the debate currently in the spotlight?

We are in fact dealing with a new quality of computer intelligence today â&#x20AC;&#x201C; intelligent learning, which emerges from the exponential progress of computing power and the development of self-learning

algorithms. Until recently, computers have simply been solutions with pre-programmed 'smarts'. Through the development of self-

learning algorithms, computers now have the capacity to learn on their own and go beyond their programmed parameters exhibiting intelligence.

Intelligent learning differs from intelligent solution-based methods,

specifically from intelligent optimization, in the way that INFORM uses them in logistics, production, and fraud prevention based on Operations Research (OR). While both concepts are complex in them-

selves, the difference can be simplified. Self-learning algorithms are

programmed to independently recognize objects from extremely large volumes of data, establish correlations, generate meaning,

and pursue objectives to create a unique model for the assessed data. Alternatively, intelligent optimization, in the form of OR, evaluÂŠ Inform GmbH 2017

ates alternative decisions in an attempt to find optimum solutions

for defined tasks such as: find the shortest route, produce the greatest output with the specified resources, or carry out all transports so

that all customers receive the best possible service with the available vehicles. Often operating in real-time, intelligent optimization

does this while taking into account a vast number of static and dynamic constraints.

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In principle, if sent away with the right questions, intelligent learning can find optimization solutions. However, to do this, it requires large amounts of data and an indefinite amount of time. In many real world situations, large amounts of

data and time do not exist. Instead, there are countless alternative options and a need for a decision now. For this reason, intelligent optimization provides the only real solution and will continue to be used.

This can be illustrated by the traveling salesman problem to which many business

problems can be attributed. The mathematical problem aims to find the shortest route between multiple geographic locations. In application, the same routing problem arises for forklift trucks in warehouses and factories, for managing out-

bound trucks, or for transporting new cars via the automotive supply chain. In all of these scenarios, there is no historical data for intelligent learning to start with;

instead, there are many possible routes of unknown length that can be made up of route segments of known length.

Continuing with the traveling salesman example, it is also possible to illustrate where intelligent learning and intelligent optimization may be combined in

the future. If the traveling salesman is a truck or an entire fleet of trucks that is controlled by an intelligent optimization algorithm all data related to deliveries

can be recorded and saved. This data might include: journey times, loading and unloading times, weather conditions, traffic conditions, etc. In turn, intelligent

learning can use this data as a basis for analyzing correlations and would gradually develop its own “delivery model” for the “shortest driving time” require-

ment. Improvements to the overall model can be achieved by combining the

information and insights the self-learning algorithm produces back into the optimization algorithm allowing for continuous improvement.

Intelligent learning, the question around which the current debate about intel-

ligent computers revolves, will not replace the tried-and-tested intelligent

solution methods, or intelligent optimization processes, but will further enhance

their performance by providing new information and insights.

In many real world situations, there are countless alternative options and a need for a decision now. ÂŠ Inform GmbH 2017

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MOdern trainsp

otting Many railway enthusiasts enjoy the sighting and recording of trains as a hobby. Cement producers, however, have a commercial interest in tracking and scheduling the whereabouts of their rolling stock. Latest advances in software technology can help to keep the business on track.

Information technology has hit the road: GPS, telematics, tracking & tracing,

smart routing, etc., have become an integral part of bulk material distribution by truck. And many cement producers combine real-time data insights into their

logistics planning software in order to find the right balance between transportation costs and customer service level.

Road and rail transports have different rules and requirements, but the same technology that helps truck dispatchers and schedulers to get their job done every day can also be used to optimize distribution by rail:

• Latest advances in mobile asset management balance the rail wagon fleet across the network and ensure that the right equipment is always available at the right time and at the right place.

• Algorithms create intelligent train loading schedules which improve overall plant utilization.

• Strategic network planning tools allow to shift road and rail capacities across all plants and depots, but also to tap the full potential out of modal split shipments by rail and truck.

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The same technology that helps truck dispatchers to get their job done can also be used to optimize distribution by rail.

Beyond GPS: Tracking & Balancing

Loading Point Optimization

Rail wagons are expensive to buy and to maintain, so

Freight trains are usually operated on a strict

possible. The challenge here is to balance the wagon

time, it will often be cancelled rather than

fleet across the network and to determine the location of a given wagon at a given time. Some sites might have an overabundance of idle wagons, while others have to push hard to meet capacity â&#x20AC;&#x201C; resulting into costly last-

minute shifting or leasing of wagons. On top of that, many

fleets suffer from an unaccounted number of wagons.

With each new unit valued from mid to high five figures depending upon type, construction, order size, etc., the costs of missing assets quickly add up. Different types and capacity of wagons make it even harder for a dispatcher to

risking to disrupt the entire network schedule. Before loading and departure, freight

trains need to be assembled. Wagon reshuf-

fling and consolidation, however, is a timeconsuming puzzle which has a deep impact on the efficiency and reliability of rail freight

services. Better resource planning avoids

all these hassles and improves overall plant utilization.

keep control of the rolling stock. Manual yard checks are

Conventional planning software packages

accurate for more than a day or two.

They automatically copy order data from the

time consuming and the information generated is never

GPS offers a field-proven technology to detect movements of wagons at selected points along the network,

providing tracking and tracing in real time. Geofences

around the plant, depots or terminals set up automatic

alerts when trains leave or enter those zones. But latest advances in mobile asset management can go one step further: Powered by algorithms, intelligent software

combines all four core topics into one process: demand forecast, tracking & tracing, distribution and transport organization.

Based on the current order book information, up-todate transactional information and historical data, the

software predicts wagon requirements for each location â&#x20AC;&#x201C; taking also seasonal fluctuations and statistical exceptions into account. The system automatically generates and schedules the corresponding transport orders in real

time and monitors transport execution. This creates a

continuous transparency and wagon loss is sustainably and significantly reduced. Automated processes replace costly, error-prone data entries.

timetable. If a train is not ready to depart on

can be a practical tool for the daily routine. ERP system (Enterprise Resource Planning), provide user-friendly graphical interfaces to

help assigning wagons to loading points, calculate schedules and turnaround times. But with a large wagon fleet on the track, several

production plants and depots spread across

a wider region, huge data volumes have to be handled in a very short time in order to

find a set of good solutions. Business rules (e.g. preferred service) and site constraints

like product availability at a specific loading

point add even more variables to the calculation. Even experienced schedulers and dispatchers are stretched to their limits if

they only work with software that does not have its own intelligence.

ÂŠ Inform GmbH 2017

operators try to minimize their numbers as much as

Specialized software is integrating rail and road distribution into a network-wide planning tool.

A giant leap forward in planning technology is the use of intelligent optimization software packages

They are equipped with algorithms that analyze a virtually endless number of

scheduling decisions in real-time and identify those that are ideal for minimizing costs and maximizing service quality – based on the business criteria defined.

The software calculates an optimized loading schedule based on the order book information, business rules and site constraints. Site constraints include: • Product availability at a specific loading point

• Loading point availability (opening hours, maintenance, etc.) • Equipment compatibility of the wagon and the loading point Business rules that may apply are for example: • Customer/contract profitability

• Premium service to specific customers • Timeline of orders

Depending on the software and algorithms used, an optimization run like this will take minutes. It is usually part of the tactical preplanning which helps the dispatcher and scheduler to prepare the next shift, working day or working week.

They can also compare several scenarios for the same data set by simply changing the objectives.

Strategic Network Planning Strategic planning prepares a distribution network for the season ahead. New

depots or plants, customer/depot allocation, plant product mix, haulier selection, fleet distribution – even complex configurations can be modelled effectively to © Inform GmbH 2017

find the best setup for the track to success.

Big data –

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the 12th man

How data is used in football to make decisions for a successful game.

Jean-Paul Sartre describes the complexity of a game of foot-

of transport and transport routes using a network plan,

the presence of the opposite team.” However, in any game

same way. They then devise a specific match plan for the

ball as follows: “In football everything is complicated by

scenario, it is not just the opponent, but the large total number of players that leads to a myriad of potential decisions whose consequences are difficult to predict.

In statistical terms, there is a 40% chance in football that a team will score a goal. To plan for success, this rate is too high. This is why scientific precision is being applied

to gain a better understanding of the game and reduce the significance of chance. Experts therefore collect more than three million data records per game and evaluate these. There is little to distinguish football teams pre-

paring for a match from companies planning a complex

football teams develop a fundamental style of play in the

next game. This has the same meaning as the advance planning of companies. Based on the available transport

orders, they define which resources are needed where, and when, and in what quantities. The real-time planning is carried out in the final phase, both in logistics and in

football. After all, in day-to-day operations and during the game, success depends on being able to adapt planned

procedures to unforeseen events in the shortest possible

time. Collecting data, using this data to make the best decisions from an endless number of options, and implementing this successfully – this key success factor in logistics is the 12th man in modern football, as it were.

logistics chain. While companies initially want to find the ideal configuration of production locations, hubs, means

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Using data to make the best decisions from an endless number of options this key success factor in logistics is the 12th man in modern football.

“Manni banana cross, me head – goal!” This game concept once described by Horst Hrubesch sounds promi-

sing – but it isn't. Studies indicate that only around two percent of all cross passes lead to goals. Findings such as these have a significant impact on a team's style of play. Similar to network planning in com-

panies, coaches and players first define their main game principles.

Coaches such as Joachim Löw or Pep Guardiola prefer a possessionbased style of play. Thus, the game should be controlled in the

knowledge that the chances of victory are much higher above a ball possession ratio of 70 percent. Other coaches rely on counter attacks

and the knowledge that most goals are scored after interceptions and quick counter strikes. For a special configuration, analyses are also used that show the pitch zones from which goals are best initiated or the passing sequence that is most suitable for beating the de-

fense. The style of play developed in this way is the basic framework for all upcoming matches.

“It's getting crowded at the top!” The content of this statement by former president of the German

Football Association (DFB) Hermann Neuberger is certainly true. Nowadays, there is very little difference between the top teams in

terms of performance. This makes a cleverly-devised match plan all the more important. "The opposing team always tries to bring its fast wingers into the game by switching players. We therefore need to

divert the opponent's attacking game away from the wings into the center, gain possession of the ball there and then counter ourselves using the wings. "This is what a simplified analysis of the upcoming

opponents received by national coach Joachim Löw during the Euro-

pean Championships in France might look like. In a similar way to how companies plan in advance, the coach also develops a match plan.

On which side should the attack preferably be made? Where does the opponent leave space for combining? How are your own players po-

session? In addition to these questions, a coach also needs to decide

who to use. He also uses data to do this. Using tracking systems, it is possible to draw accurate conclusions about a player's current form. How many kilometers does he run per match, how many intensive

sprints are included, which paths did he take, how much ball contact did he have, how many passes did he play, how many tackles did he win – all this data affects the decision as to which players to use.

sitioned when they have possession and when the opponent has pos-

There is little to distinguish football teams preparing for a match from companies planning a complex logistics chain.

“The truth is out there on the field!”

This statement by Otto Rehagel is timeless. It is only during the game that you see how good a plan is and, in particular, how flexibly it can

be adapted in real time. After all, despite the match plan, luck and incidents inevitably come into play: the opponent does not behave as anticipated, balls bounce badly or a teammate runs in the wrong direction. Particularly in moments such as these, the onus is on the player in

possession of the ball to quickly find a viable alternative for a pass. He has just one and a half seconds in which to do so. In the same way as a

dispatcher, he must think in advance, weigh up the options and assess the consequences of a decision. The dispatcher can rely on his experience and, in the best case scenario, on optimization software. The

player has only his experience gained through training and matches. The coach also influences his team's game by making adjustments. He

does this, for example, by changing the basic formation, substituting

players, or by giving specific instructions, e.g. fewer short passes, but play more long passes.

“Football is like chess, only with dice!” This variation of a witticism attributed to Lukas Podolski describes

football at its core. Despite all strategic planning, chance will always

be a decisive factor in football. At the end of the day, analyses also show: the more similar the strengths of two teams, the more the out© Inform GmbH 2017

come is affected by chance. This last element of uncertainty is what

makes football so fascinating. The fascination for logistics, however, is rather the opposite: chance and unforeseeable events should no

longer affect planned processes. Thanks to specialized optimization software, we have come extremely close to achieving the old pipe dream of being able to make the right decision at the right time in every situation.

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Â© Inform GmbH 2017

Aircraft Line

Maintenance MOBILE WAYS OF COMMUNICATION Â© Inform GmbH 2017

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Being well prepared for unplanned maintenance is fundamental to getting an aircraft back into service. When you think of aircraft maintenance, what is the first image that comes to mind? Do you

picture an aircraft standing in a big hangar being surrounded by a number of staff, equip-

ment, tools and parts? If so, your answer might

be consistent with the vast majority. But did you ever ask yourself what else lies behind the concept?

In general, aircraft maintenance can be divided into line and base maintenance. Whereas

base maintenance includes activities which

require the aircraft to be taken out of service for longer periods and which require special

equipment only available in a hangar, line maintenance activities are mostly carried out

during normal turnaround periods where the

aircraft is on the ground. During line mainte-

nance, the aircraft is not always taken out of service and the maintenance activities can be

done at the gate or stand itself (or in a hangar if time permits). Unlike base maintenance, which is always scheduled for longer ground times, line maintenance cannot always be scheduled, since it can imply dealing with

unplanned issues arising during normal flight operations.

Being well prepared for unplanned mainte-

nance is fundamental to getting an aircraft back into service quickly after experiencing a technical issue, avoiding delays and cancel-

lations. However, as many maintenance departments still communicate via radio or

conventional telephones, technical information necessary to perform an unplanned task isnâ&#x20AC;&#x2122;t take longer to complete their job. In order to

understand the situation better, letâ&#x20AC;&#x2122;s have a look at the current workflow in more detail.

ÂŠ Inform GmbH 2017

always easily available, causing engineers to

Line maintenance is at its best when sharing information in real time.

All information necessary to perform a maintenance task is to be found in the

The good news is: It can be done differently!

reviews all maintenance tasks for the day and assigns them to his or her team

Line maintenance is at its best when sharing information

the engineers are sent off to their first aircraft, working on routine and special

process of implementing a maintenance-geared mobility

maintenance information system. At the beginning of each shift, the lead engineer members. Equipped with their job assignments and all pertinent documentation checks. While out on the apron, everything goes according to plan. Nevertheless, unplanned maintenance is never far behind.

Inefficient information flows slow down reaction time When informed about a non-routine maintenance task, the lead engineer needs to act fast and assign the task efficiently. This means taking into consideration

the various skills of all team members, their location on the apron and the status

of their current job assignment. However, without any real-time information, things can get difficult. If the lead engineer wants to find out who is located near the aircraft requiring special assistance, he has no other choice but to call each

team member individually to check their position and availability. Although

in real time. This is why airlines such as SWISS are in the

solution. Line engineers are being equipped with smart-

phones on which modern applications allow them to see task assignments, task information, enter their job

status, report defects and interact with their maintenance department in real time and at any time during

the day. Unplanned maintenance can now be addressed quickly by the nearest engineer available having the

correct qualification. The app even allows them to access the information they need in order to perform the turnaround faster. At the same time, it reduces the need for

time consuming voice communication prone to mis-

understandings. An additional advantage is the reduction of paperwork for engineers. Thanks to these effi-

some engineers might be near the aircraft, they might already be working on a

ciency gains, airlines can greatly reduce maintenance-

down the reaction time, delaying the response to a technical issue. In a mission-

and increasing overall passenger satisfaction.

task that cannot be deferred. As a result, such inefficient information flows slow critical industry, where every minute counts, consequences can be severe.

related flight delays and cancellations, reducing costs

ÂŠ Inform GmbH 2017

Information 2017

ÂŠ Inform GmbH 2017

Logistics ne the Automot

tworks in ive Industry

ÂŠ Inform GmbH 2017

how intelligent planning processes can decrease cost and increase business agility 25

Information 2017

When it comes to outbound logistics in the automotive industry the question is not where are my cars going, but rather

where should they be built and what is the best way to get them to their future owners. These fundamental questions must

be answered by every car company as well as all businesses active in mass markets. Furthermore, they apply not only to individual countries, but also at continental and even global levels. It is the planning of logistics networks which solves these

issues. Depending on the particular region they serve, logistics networks may include manufacturing plants, ports, yards or terminals – giant parking areas where vehicles are held short term - and transit routes which allow the new cars to be distri-

buted to dealerships by ship, rail and truck. The aim of course is to keep the ensuing transport costs as low as possible on one hand, and on the other to shorten the total time it takes to get from supplier to dealer.

Complex network planning takes time

target market was extremely time consuming and taxing on person-

nel. Because of this and the complicated calculations required, car

model, and possible supply routes by ship, rail, and truck. This base-

line network is price weighted according to average rates or by using actual contract costs. The software works out a preliminary network

manufacturers only carry out logistical network planning every few

on this basis. The resulting transport routes generate proposal re-

ry volumes, transport costs, logistics providers etc., then corrections

for instance via a web portal. If and when the proposals become availa-

years. If mistakes occur in this process or there are variations in delive-

and network modifications if possible can only be achieved at the cost of both considerable time and effort.

Central Network Planning using intelligent techniques: from Project to Process

Intelligent, automatic planning procedures can dramatically accelerate “Central Network Planning” and add new dimensions to the logi-

stical agility of car companies. This in turn rapidly increases the potential for cost savings, and networks can adapt more quickly to changes in the market.

As part of a comprehensive Finished Vehicle Logistics System, Inform has developed and implemented an intelligent module for Central Network Planning for large car groups.

The process begins by laying out a preliminary network using present day manufacturing locations, dealer locations, transport volume per

quests to the hauliers and yard contractors, which are then published

ble, the system calculates a so-called ‘Least Cost Network’. The results

are illustrated on up-to-date maps, so that the visual element gives a

feel for the network topography as well as generating ideas for optimising the whole process.

When the costs of each potential transport route are known, the com-

plete recalculation of the network only takes a few minutes instead of several weeks or months. That means that the planning process ceases to be a major project, but one which can be used at any time for

the purpose of continuous improvement, cost cutting, or as the rationale behind strategic decisions.

Besides the time savings, automatic planning procedures enhance

network quality because they can take into account substantially more constraints than a planner who only has an Excel spreadsheet to work with. For example, Inform’s software is able to recognise whether it

is most cost effective to bring together two models manufactured in different factories into one hub to create “mixed” onward transport or whether a separate supply chain would work better.

Historically the process to develop and plan logistics networks for every

The potential for cost savings rapidly increases. Central Network Planning as a strategic tool The Central Network Planning Module can also help with strategic

productsâ&#x20AC;? looks significantly different from one which handles sim-

terminals or import and export ports. Scenarios can be run in order

in the car industry must be correspondingly specialised.

planning around central network hubs such as manufacturing sites,

to compare logistics costs using various means of transport as well as modelling of best ports, inland terminals, and final distribution loca-

ple products or components. So a tool for Central Network Planning

tions, which would in turn ensure well founded decisions about termi-

Initial practical findings

What now also becomes possible with Central Network Planning is the

Real world implementation of the Central Network Planning Modu-

pletely new creative formulations can be worked through, something

able to achieve a 20 percent cost reduction in a newly planned target

nal facilities and loading resources.

rapid simulation of logistical alternative scenarios. With these, comthat would have been too expensive and time consuming until now.

le shows what sort of results can be achieved: large car groups were market although transport costs had risen across the board.

Specialised tools for Automotive Central Network Planning

More intelligent use of network planning

At first glance the cross-sector demands made on Central Network

If we take Finished Vehicle Logistics as a whole we can see that what

becomes obvious that the logistical processes in the car industry are

there is untapped potential for shortening delivery times, reducing

Planning would appear to be similar. When you look again however, it

more complex than they are in other mass production areas such as textiles or technology. Since we are dealing with high-value products ÂŠ Inform GmbH 2017

which in some instances are individually customised, we need to be

able to identify each vehicle in the supply chain. For example, it is significant whether build-to-order car A, which has already been sold, or build-to-stock car B, which has not yet been sold, is being shipped currently or not. Over and above that, new vehicles may be modified

is most needed is an intelligent planning network. At this moment

cost, and creating competitive advantage by being agile and shortening the feedback loop. If we consider further the ever increasing diversity of models, the emergence of new vehicle categories such

as e-cars, new production methods, and last but not least a rising

demand in developing markets, algorithm supported planning will become indispensable.

in transit, for instance to adapt equipment to the requirements of

a particular country. A logistical network for â&#x20AC;&#x153;variable customised

Information 2017

Lifting Productivity 28

ÂŠ Inform GmbH 2017

How Agile Optimization is used to optimize the movement of containers in and out of a terminal.

Almost 400 meter long, 20 stories high and able to carry a staggering 19,000 standard containers (TEU) on board – a new generation of mega ships ploughs through the world’s busy sea routes. Fifteen years ago, when

vessels carried only around 8,000 TEUs, no one thought they could possibly get any bigger. But shipping lines

are building ever larger container ships, challenging not only the limits of marine engineering but also the infrastructure of the ports and terminals at which they call. When these ships come into a port, larger cranes

are needed to discharge them, more yard space is needed to stack the containers, and onward connections by road and rail need to be strengthened to handle the huge surge in traffic.

At the same time, a wave of automation and robotics has

hit the terminal industry. By using driverless automated

guided vehicles (AGVs) and automated cranes, a container can be moved from the yard stack onto a rail wagon with-

out any manned equipment in between. The data wave

that comes along with these new technologies can be used to optimize and speed-up decision-making in terminal operations. A Terminal Operating System (TOS) is

used to manage and control the movement of contai-

ners in and around a terminal. But the daily challenge of a yard manager is daunting. Here’s an example: One

container to stack, 30,000 available yard slots to choose © Inform GmbH 2017

from, hundreds of restrictions to consider, anticipating what the terminal will look like several moves ahead of the one you are about to make. Even a well-configured

TOS, operated by an experienced planner, will probably not pick the best available slot.

This is where Agile Optimization comes in. The software uses available container data to calculate an optimized yard location for each container. The real-time optimization ensures that space is used efficiently – even at high traffic

and peak cargo periods. Retrieval distances are kept short so that containers are immediately available for further transport, and the number of rehandlers is reduced by up to 50%.

The software can be used as an add-on to an existing

Terminal Operating System (TOS), following a best-of-

breed strategy for selecting the solution that is most suited for a particular task, e.g. rail crane operations, yard management, etc. To gain a better understanding of

how this works in practice, let’s take a closer look at some applications in rail yard operations.

Information 2017

Running an efficient rail yard is like running a relay race. Agility trumps speed Great teamwork

Agility training significantly improves an

Running an efficient rail yard is like running a

and quickly change direction with precise

relay race. The teams must be well-coordinated and able to pass a container from one

member of the team to the next while maintaining maximum speed. Agile Optimization

Software can help to ensure that each container transfer from yard to rail and from

rail to yard is perfectly timed and executed. Based on the data it receives from the TOS, the software quickly analyses all jobs within

athlete’s ability to accelerate, decelerate, timing and body control. The daily challen-

ge of a rail crane is similar: getting each container on and off the train as quickly as possible, immediately reacting to changing priorities or adjusted workload, constantly synchronizing all routing decisions with the neighbouring cranes, anticipating the next move’s impact on the following lifts, etc.

seconds and proposes optimized handover

Agile optimization software can act as a

Current work status and work load of each

times and locations, the software analyses

times and transfer points at the rail area. rail crane and handling equipment (straddle

carrier, reach stacker, AGV, terminal tractor, etc.) are also included in the calculations. Whenever the TOS provides updated data,

the optimization software automatically recalculates and updates the decisions to support the newoperative situation. This

real-time capability ensures that each container is moved at the right time and at the

right place, giving them a head start for their

all possible routing options within seconds

and identifies those that are ideal for highest productive operation. The best results

are immediately translated into optimized job order sequences for each crane – adapted to their grade of automation (manual, semi

or full). Double-cycling, i.e. loading trains as

they are unloaded is the ultimate challenge for any rail yard operation. It converts empty crane moves into productive ones. Double

cycles are proposed by the software whenever suitable.

next move.

drill instructor. Based on TOS data, handover

Perfect line-up Train load planning is a time consuming exercise. Dispatchers and schedulers usually spent hours figuring out which layout is best to maximize train utilization. With several trains per day to plan and numerous technical and logistical

requirements to consider, huge data volumes have to be handled in order to find a set of good layouts. And if just one item in the mix fails, the entire plan may collapse like a house of cards – with trains running late and costs way out of line.

Instead of hours, optimization software just needs a couple of seconds to come

up with an optimized plan. Once all information of incoming and outgoing trains

has been imported from the TOS, the software quickly creates optimized train

load plans at the click of a mouse. Plans usually have a limited shelf life. Daily challenges like containers arriving late, equipment breaking down, etc. disrupt

the loading/unloading schedule and will affect the efficiency of the operation

if no countermeasures are taken. The software reviews and adjusts all planning decisions – right up to the moment before execution – and updates the job order sequences for all rail cranes in real-time.

In some parts of the world, e.g. North America and Australia, double-stack freight

trains are used. Containers are placed two-high, one on top of the other, sharply reducing transport costs per container. If you add optimized train load planning on top, the benefits pile up, too.

Retrieval distances are kept short and the number of rehandlers is reduced by up to 50%. 31

Information 2017

ÂŠ Inform GmbH 2017

No congestion despite multi-stop course 32

How intelligent software optimizes the complex processes for material deliveries in the chemical and process industry.

An empty multi-chamber tanker truck rolls in for loading on the Salzbergen refinery site of H&R ChemPharm GmbH. From this moment, the clock starts ticking. The driver should spend a maximum of three hours in the yard loading several products on board. In Salz-

bergen, these products are industrial and automobile lubricants. Loading the truck involves making several rounds of the extensive site where there are 200 containers and tanks. For each product that is loaded into one of the truck's compartments, the truck

must make several stops. After registration in Dispatches, the driver heads to the central tanker control area, where the tanks are checked for cleanliness. The driver passes

through a weighing system, which measures the empty weight, before directing his truck to the loading point. Once the product has been loaded, the next stop is the sampling

point, where chemists test the quality of the sample taken. If it is okay, the driver steers his tanker back over the loading point's weighing system. The exact loading quantity is calculated using the difference between the weight before and after. The loading of the

next product can then begin. The process is repeated from scratch for every compartment to be filled.

ÂŠ Inform GmbH 2017

Information 2017

From a strategic perspective, it is especially important for companies to have a software system that can provide a clear overview of the delivery processes.

Congestion and no one knows why “Up until a year ago, we managed this process with lots of phone calls and internal

coordination, which led to long throughput times and intensive manpower”, explains Helmut Schröder, Operations Manager. At H&R, more than 700 products are loaded onto 60 trucks per day at five stations with 13 parallel loading lanes. “Mo-

reover, the processes were not transparent and could not be optimized. Although

we were aware of congestion and where it occurred, we did not know when or why.”

Constraints more complex than anywhere else In all industries, the logistics of material deliveries is a calculation with many unknowns. “In the chemical industry, however, the processes are even more complex”, says Matthias Berlit, SVP of the Manufacturing Logistics Division at INFORM.

This is due to a number of significant features. Many materials are classed as hazar-

dous goods, which are subject to special conditions during transportation. The va-

riety of vehicles is extremely wide – besides multi-chamber tankers, there is traffic

with trailers, container traffic, or intermodal traffic. The loading of liquids, pow-

ders or cargo requires different resources and loading times. In addition, the re-

quired qualifications for handling hazardous substances must be considered when allocating staff. “To guide the trucks manually through a loading process with so

XXXX

many constraints produces considerable losses”, explains Berlit.

The first chemical companies are therefore looking to the automotive industry

for guidance. “Over the past few years, the manufacturers there have optimized

their delivery processes using intelligent decision-making software systems. A system such as this accelerates the logistics processes while taking into account the

particular characteristics of the supply and loading processes in chemical plants”, explains Berlit.

Leading the way in the automotive industry

SUPPLIERS LAB

GATE

TESTING INSTRUCTIONS

LOADING distribution

CONTROL

Optimized from arrival through to loading and unloading

First of all, the software plans the delivery dates. As a

rule, it defines at least the date. The specific delivery time is communicated to the carriers via time slot allocation.

To do this, the software continuously forecasts the logi-

stics situation on a planned delivery date and allocates

SCALES

PRODUCTION

intelligent IT system allocates the limited resources and the suitably

qualified employees to the truck in such a way that performance is ma-

ximized. “The software also controls and coordinates the loading and

unloading processes. Since both the inbound and outbound processes lay claim to the same resources, the inbound and outbound traffic cannot be coordinated in isolation”, says Berlit.

the time slots in such a way that congestion at the gates

Complexity under control

react appropriately and quickly to unexpected delays.

H&R ChemPharm has the complexity of its delivery processes under

each time a new event occurs within a matter of seconds.

the throughput of trucks and the reliable supply to production are only

and inside the plant are avoided. The software can also This is because it reschedules the overall delivery process At the gates, the system provides an automated and pa-

perless check-in process. The required documents have already been transmitted electronically and therefore only need to be compared with the characteristics of the

arriving truck. The check-in process operates evenly and

control today, thanks to this optimization software. The increase in

superficially the objectives. “From a strategic perspective, it is espe-

cially important for companies to have a software system that can, for the first time, provide a clear overview of the delivery processes”, states Berlit. He expects that IT systems such as these will also become

standard in the chemical industry. “Intelligent optimization software

smoothly in this way throughout the day. The quality of

such as our SyncroSupply system therefore offers competitive advan-

suming laboratory testing. However, sample taking and

to have such a system.”

chemical products can only be determined by time-conanalyses are susceptible to problems, the waiting times of

tages today. In a few years' time, it will be more of a disadvantage not

the trucks cannot be predicted, and rescheduling is often

necessary. Optimization software maps these inspection processes completely. An essential aspect of quality is © Inform GmbH 2017

how optimally the system schedules the required resour-

ces. “Optimum” means maximizing the throughput of

trucks while taking into account the constraints described. The systems for loading and unloading chemical

materials are highly-specialized and have limited resources. Special vehicles are loaded at special systems, which

must only be operated by specially-trained employees. An

Intelligent optimization software offers competitive advantages today. 35

Airports: Where Rush and Relaxation Turnaround 36

Â© Inform GmbH 2017

Information 2017

Workday finished, your suitcase closed and you head off to the airport: According to the International Civil Aviation Organization (ICAO), in 2015 world-wide more than 3.5 billion passengers began their trip at the airport. In comparison to the global passenger traffic just 10

years ago, today the number has nearly doubled. Flying has become

the norm. For many of us the arrival at the airport already marks the beginning of long-awaited vacations as we become familiar with the

processes after buckling up a few times for takeoff. While passing

through the different areas at the airport almost blindfolded, have you ever asked yourself what’s going on behind the scenes?

120 minutes – That’s the average time a passenger who booked a long-haul flight usually has for check-in, the security check, proceeding to the gate, and boarding the plane. Often, there is even enough

time until boarding that can be bridged with a stop at restaurants,

cafés or duty-free shops, whereas at the apron the exact opposite is

true. A typical turnaround procedure for an aircraft such as Boeing 747, for example, takes around 90 to 120 minutes at the longest. © Inform GmbH 2017

During that time, between 30 to 40 services must be performed on the aircraft in order to meet flight schedules. Relaxation? No sign of it! Rush is a constant companion of all involved parties at the apron where every minute counts and costs.

Information 2017

With more than 50% of delays, the leading reason of all delayed flights can be traced back to circumstances within the airline’s control.

Interdependence of turnaround processes Similar to the stages a passenger passes through one by one, some of

the aircraft’s turnaround processes have to run one after another,

whereas others may run simultaneously. In theory, the aircraft is

unloaded, cleaned, reloaded, refueled, supplied with fresh water and food and, where necessary, maintained or deiced before the next set of passengers board. In practice, the processes are not only

interdependent and determine each other, they are also affected by external factors such as flight changes or bad weather conditions with the result that things go differently than originally planned.

Delays lead to billions in costs Flight delays are a serious challenge. Traditionally, external factors like the severe weather conditions mentioned before have been

the main cause for aircraft arriving late since the U.S. Bureau of Transportation (BTS) started collecting such data in 2003. However,

weather delays has been exceeded with more than 50%¹ of delays, the leading reason of all delayed flights can be traced back to circum-

stances within the airline’s control such as refueling, cleaning, aircraft maintenance, crew scheduling etc. Translated into minutes, that

means, that between 2005 and 2015 approximately 28 to 45 million minutes² of delay have been caused by the U.S. air carrier themselves each year, resulting in billions of delay-related costs³.

So, a small setback in one of the turnaround processes, if not identified and rectified, or at least limited early enough, can result in delays

that are almost impossible to make up. The knock-on effect can

impact other flights operated by the airline, which in turn could be

delayed as well, if they cannot arrive at the gate on the allocated time or have to wait for service providers who have been delayed

due to previous flights. Unfortunately, most of these delays are only detected after they have occurred, leading to frustration in the departure lounge and significant expenses.

² http://www.transtats.bts.gov/OT_Delay/ot_delaycause1.asp?pn=1&periodfrom=24193%20 &periodto=24200

³ http://airlines.org/data/cost-of-aircraft-delay-to-u-s-passenger-carriers/

¹ http://www.transtats.bts.gov/AircraftDelay/mainpage.aspx?fromYr=24188&toYr=24200

Intelligent and optimized scheduling improves efficiency and transparency, reduces delays and saves airlines considerable cost.

The key to success: Intelligent decision-support software systems The aviation business is still a fast growing but highly competitive industry. For this very reason it is even more important that airlines

work proactively on the improvement of their punctuality, which in turn would not only lead to satisfied passengers, but directly result

in cost savings per minute. The key lies in the airlineâ&#x20AC;&#x2122;s operational

expertise which can be achieved by optimizing the turnaround processes with the help of intelligent decision-support software systems.

An overview of the aircraftâ&#x20AC;&#x2122;s full dwell time at the airport, including

all turnaround activities, can provide the transparency required to be able to influence the overall situation. Ideally, not just for a single

aircraft but for all activities of all flights at the airport, day after

day. Intelligently optimized software for planning and coordinating aircraft turnaround procedures allows processes to be coordinated in a more integrated manner and guarantees perfectly executed

aircraft ground turnaround processes. All activities are planned at

an early stage and the development of each individual step is made

transparent so that experts can identify and intervene before delays occur. The potential of working with real rather than scheduled data not only optimizes execution; it also makes the data collected for future operational planning much more accurate.

Scheduling resources at an early stage and working with real-time

data enables a much more precise assessment of resources required. Consequently, intelligent and optimized scheduling improves efficiency and transparency, reduces delays and saves airlines considerable cost. For passengers an airport is the place where rush turns into relaxation as their journey begins. For airlines it is quite the opposite

as airports become their scene of competition where future success is written.

ÂŠ Inform GmbH 2017

Information 2017

The thin line between operations research and modern art

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2491

Visual image of a bill of materials structure from one of our supply chain projects. The structure consists of 5,564 nodes same color require the same production resource. The size of a node depends on which share of a resource's capacity the respective product consumes. For each week in one year, the utilization of production capacities was mapped in this way

and then aggregated. The goal was not, of course, to frame a piece of modern art, but to identify potential bottlenecks.

ÂŠ Inform GmbH 2017

and 4,074 edges. Each node represents a finished product or pre-product (raw material not included). Nodes of the

BDA

UTC-03:00

738_011 738_010 00:00

271 3 1443 3

00:00

271 4 1443 4

20:15 20:35

08:15 09:15

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BDL

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1721 3

1219 3 1735 3 08:55

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738_014 738_012 738_013 00:00

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2103 31525 3 1375 3

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271 7

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168h Check

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1721 48h 5 Check 1907 5 1735 5 06:40 08:00 08:55

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BWI

UTC-04:00

CUR

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DCA

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DTW

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EWR

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FLL

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GND

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IAD

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1597 3 1461 06:00

738_022 00:00

25 3

415 3

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2523 3

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3 33 13:10557 14:351281 15:00827

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993 3

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738_024 00:00

1597 4 1461 05:40

25 4

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1169 43744 4 11:201351

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807 3 07:05

1167 3

1231 3 3 3 12:05 10:55425 11:20877

08:15

10:10

738_069 00:00

664 3

11:45 12:10

15:40

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738_132 738_127 738_128 738_129 738_130 738_131 00:00

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168h Check

21:30 21:40 22:05 22:10 22:25

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487 3

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7 1397 7 1081 7 20:15 15:00827 18:35 20:40 15:40 16:40

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1597168h 1461 8 Check 25 8 05:40

415 8 8 08:40 09:402523 11:25

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1835 8

168h Check 82283 81397 8 15:00827 15:40 16:40 17:10

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910 8

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2045 7 19:00 19:1520:00

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25 9

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1835 9 9 9 92283 9 9 9 13:10557 14:351281 15:00827 18:351081 20:15 20:40 15:40 16:401397

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910 9

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467 9 1169 9 9 48h Check 9 985 9 09:0509:50 11:55199013:20593 14:10 14:40

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48h Check 807 48h 7 Check 1167 7 48h Check 1231 7 7 7 12:05 11:20877 06:45 07:55 09:50 10:55425

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2045 126199 19:00 19:1520:00 21:10

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48h Check 119 7 18:09 17:39 18:29

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168h Check48h Check 168h 547 1631 9Check 9 1269 1271 99 1 05:40 05:45 07:55

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8 8 8 8 11:201351 11:55199013:20593 14:10985

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168h Check 168h Check 547 433 1631 48h 8 8Check 1269 1271 88 1487 8 467 8 09:05 05:40 06:45 06:50

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48h Check 735 7 7 16:45867 17:0017:40

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168h 48h Check Check 168h 547 1631 7Check 7 1269 1271 77 1871 1487 7 467 7 1169 7 7 7 7 7 11:201351 11:55199013:20593 14:10985 05:45 06:50 07:55 09:0509:50

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2103 9

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807 8

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1167 8

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1231 8 8 8 12:05 10:55425 11:20877

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664 8

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807 48h 9 Check 1167 9 07:55

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9 10:5542512:05

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664 9

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07:15

13:15168h

08:20

04:30

1971 6 17:15

08:4009:20 09:30 09:35 09:45 09:50 10:10 10:15 10:20 10:30 10:35 10:45 10:50 10:55 11:1011:50 12:00 12:05 12:15 12:25 12:30 12:35 12:50

12:15

07:05

14:00 14:35

48h Check 969 7 06:45

22:20

391 9

06:0006:40 06:50 07:00 07:30

09:30 09:35 09:45 09:50 10:10 10:15 10:20 10:30 10:35 10:45 10:55 11:15 11:50 12:00 12:05 12:15 12:25 12:30 12:35 12:50

969 8

22:20

15:55 16:00 16:05 16:15 16:25 16:30 16:35 16:5017:35 17:55 18:15 18:20 18:30 18:45 18:55 19:05 19:15 19:40 19:45 20:00 20:20 20:25

48h Check 1743 9 07:55

17:151971

07:25 08:15

00:30

22:05

23:05

18:05

1623 9

12:25