Romanian Distribution Committe Magazine Volume 6 Issue 3

EDITORIAL BOARD

Romanian Distribution Committee Magazine

Volume: 6 Issue: 3 Year: 2015 Scientific Review of the Romanian Distribution Committee

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Romanian Distributtion Committee Magazine / September 2015 / www.distribution-magazine.eu

Ion Ababii, Chişinău

Aurel Iancu, Bucharest

Constantin Roşca, Craiova

Nicolae Albu, Brasov

Mitsuhiko Iyoda, Osaka

Analisa Romani,Turin

Ruxandra Andreea Albu, Bucharest

Mohamed Latib, Gwynedd

James Rowell, Buckingham

Levent Altinay, Oxford UK

Dong II Lee, Seoul

John Saee, Virginia Beach VA

Kathleen Andrews, Colorado Springs

Min-Sang Lee, Gyeonggi-Do

Cătălin Sfrija, Bucharest

Virgil Balaure, Bucharest

Claude Magnan, Paris

Adrian Socol, Strasbourg

Dan Barbilian, Bucharest

Radu Titus Marinescu, Bucharest

Eliot Sorel, Washington D.C.

Riccardo Beltramo, Turin

James K. McCollum, Huntsville

Mihaela-Luminița Staicu, Bucharest

Richard Beresford, Oxford Uk

Nicolae Mihăiescu, Bucharest

Radu Patru Stanciu, Bucharest

Dumitru Borţun, Bucharest

Dumitru Miron, Bucharest

John L. Stanton, Jr., Philadelphia

Leonardo Borsacchi, Turin

Dan Mischianu, Bucharest

Peter Starchon, Bratislava

Mihail Cernavca, Chişinău

John Murray, Dublin

Felicia Stăncioiu, Bucharest

Ioana Chiţu, Brasov

Alexandru Nedelea, Suceava

Marcin Waldemar Staniewski, Warsaw

Doiniţa Ciocîrlan, Bucharest

Hélène Nikolopoulou, Lille

Vasile Stănescu, Bucharest

Tudorel Ciurea, Craiova

Olguța Anca Orzan, Bucharest

Filimon Stremţan, Alba-Iulia

Alexandru Vlad Ciurea, Bucharest

Gheorghe Orzan, Bucharest

David Stucki, Fribourg

Maria Negreponti-Delivanis, Thessaloniki

Elena Mihaela Pahonțu, Bucharest

Ion Voicu Sucala, Cluj-Napoca

Jean-Sébastien Desjonqueres, Colmar

Rodica Pamfilie, Bucharest

Kamil Pícha, Ceske Budejovice

Aurel Dobre, Călăraşi

Iulian Patriche, Bucharest

Laurenţiu Tăchiciu, Bucharest

Luigi Dumitrescu, Sibiu

Carmen Păunescu, Bucharest

Emil Toescu, Birmingham

Mariana Drăguşin, Bucharest

Mircea Penescu, Bucharest

Simona Ungureanu, Bucharest

Ovidiu Folcuţ, Bucharest

William Perttula, San Francisco

Vlad Budu, Bucharest

Luigi Frati, Roma, Italy

Virgil Popa, Targoviste

Eva Waginger, Wien

Petru FILIP, Bucharest

Marius D. Pop, Cluj-Napoca

Léon F. Wegnez, Brussels

Victor Greu, Bucharest

Ana-Maria Preda, Bucharest

Răzvan Zaharia, Bucharest

Bernd Hallier, Köln

Monica Purcărea, Bucharest

Gheorghe Zaman, Bucharest

Sang-Lin Han, Seoul

Cristinel Radu, Călăraşi

Dana Zadrazilova, Prague

Florinel Radu, Fribourg

Sinisa Zaric, Belgrade

Gabriela Radulian, Bucharest

Hans Zwaga, Tornio

YOUNG EDITORIAL BOARD MEMBERS REVIEWERS

SCIENTIFIC COUNCIL

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Romanian Distributtion Committee Magazine / September 2015 / www.distribution-magazine.eu

Andreea Apetrei, Iasi Adalbert Lucian Banyai, Bucharest George Bobîrnac, Bucharest Roxana Codita, München Stefano Duglio, Turin Larisa-Diana Dorobat, Geneve Marinela-Filofteia Hostiuc, Bucharest Darius Ilincaş, London

Adrian Lală, Bucharest Irina Purcărea, Bucharest Ivona Stoica, Bucharest Dan Smedescu, Bucharest Constantin C. Stanciu, New York Radu Pătru Stanciu, Bucharest George Cosmin Tănase, Bucharest Oana Patricia Zaharia, Bucharest

Alexandru Ionescu, Romanian-American University Adriana Bîrcă, “George Bariţiu” University Brasov Nelu Florea, “Alexandru Ioan Cuza” University Iasi Ana Ispas, Transilvania University Brasov Irena Jindrichowska, University of Economics and Management in Prague Costel Iliuţă Negricea, Romanian-American University Adina Negruşa, “Babes-Boyay” University Cluj-Napoca Anca Purcărea, Academy of Economic Studies in Bucharest Monica Paula Raţiu, Romanian-American University Gabriela L. Sabau, Memorial University, Sir Wilfred Grenfell College Andreea Săseanu, Academy of Economic Studies in Bucharest

Vlad Barbu, Bucharest Gabriel Brătucu, Brasov Ion Bulborea, Bucharest Mircea Buruian, Targu Mures Iacob Cătoiu, Bucharest Jean Constantinescu, Bucharest Beniamin Cotigaru, Bucharest Radu Diaconescu, Iasi Valeriu Dulgheru, Chişinău Constantin Floricel, Bucharest Valeriu Ioan-Franc, Bucharest

Gheorghe Ionescu, Timisoara Christophe Magnan, Montréal Pompiliu Manea, Cluj Andrei Moldovan, Bucharest Dafin Fior Muresan, Cluj Neculae Năbârjoiu, Bucharest Constantin Oprean, Sibiu Dumitru Patriche, Bucharest Florian Popa, Bucharest Dumitru Tudorache, Bucharest Ion Smedescu, Bucharest Victor Părăuşanu, Bucharest

Hello, reader.

Our Readers are invited to submit articles for the 2016 (1&2) Issues of the Scientific Review of the Romanian Distribution Committee – „Romanian Distribution Committee Magazine”.

http://www.distribution-magazine.eu/submission

You can find out more about us just by clicking http://www.distribution-magazine.eu/about

Romanian Distribution Committee Magazine Volume: 6 Issue: 3 Year: 2015

The responsibility for the content of the scientific and the authenticity of the published materials and opinions expressed rests with the author.

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Romanian Distributtion Committee Magazine / September 2015 / www.distribution-magazine.eu

CONTENTS

page 8

The challenge of mastering the art of chaning quickly, the practice of search marketing and staying in sync with customer’s ever changing life stages.

Theodor Valentin PURCĂREA page 10

The Information Society Towards the Knowledge Based Society Driven by the Information and Communications Technologies From the Internet of Things to the Internet of … Trees (Part 3) Victor GREU page 20

Retailers: Facing the disruption of the traditional ways of doing business Theodor PURCAREA page 24

Business Intelligence and Performance Management George Cosmin TĂNASE page 28

Léon F. WEGNEZ (by courtesy of) - Le Comité Royal Belge de la Distribution célèbre son 60e anniversaire (1), “Distribution d’aujourd’hui,” 56ème Année, Mars-Avril 2015, Brussels

THE CHALLENGE OF MASTERING THE ART OF CHANGING QUICKLY, THE PRACTICE OF SEARCH MARKETING AND STAYING IN SYNC WITH CUSTOMER’S EVER CHANGING LIFE STAGES

In our previous editorial, the importance of properly understanding the consumer journey was underlined, from the

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moment the customer thinks about pur-

refusing to be passive recipients as

chasing and how digital (which is break-

customers was highlighted, striving to

ing all barriers) can enhance all of those

contribute to the courageous map of

experiences described within context.

this new world of consumer space by

While two years ago (http://crd-aida.

encouraging authentic partners in great

ro/RePEc/rdc/v3i2/1.pdf) the need of

tasks of transformation.

Romanian Distributtion Committee Magazine / September 2015 / www.distribution-magazine.eu

Recently, in August 2015, Ernan Roman (Ernan’s Blog, 2015) showed how in ERDM learnings from 15000+ hours of VoC Interviews it was reported that as engagement is now the critical competitive differentiator for companies, marketers need to make customer listening a part of every functional area and deliver high quality experiences in every channel, and in order to stay in sync with their customer’s ever changing life stages marketers’ customer insights must be in real time. He concluded that in order to influence both brand opinion and brand value, the first goal that every marketing plan needs to address is now listening and responding.

On the other hand, McKinsey’s representatives argued recently (Ewenstein, B., Smith, W. and Sologar, A., 2015) that as mastering the art of changing quickly (a new way of doing things becoming the way things are done) is now a critical competitive advantage, today leaders have to make decisions more quickly, managers having to react more rapidly to opportunities and threats, while employees on the front line having to be more flexible and collaborative, encouraging feedback from users. For example, in order to enhance the customer journey and shift consumer behavior, B2C are making substantial changes (using powerful digital tools, such as wearable technology, adaptive interfaces, integration into social platforms) by applying digital tools and techniques within the organization, considering for instance the time and geography constraints faced by management capacity to adequately engage with every employee.

We have also took notice of another recent opinion showing that most decision makers simply don’t know how they make choices, but they can tell, for example, whether they would prefer to do business with one brand over another. Rolf Wulfsberg, Siegel+Gale’s Global Director of Quantitative Insights (a recently constructed database of Siegel+Gale – having over 350 studies that use this technique to derive the impact of various attributes on brand decisions – includes over 4000 distinct attributes falling into 170 categories), explained in May this year (Wulfsberg, 2015) how together with his team developed an interesting technique (quantifying the impact each attribute has on the purchase decision, then computing the Impact Index for an attribute etc.).

According to a new Blue Nile’s research, (Blue Nile, 2015) there is also little known, for instance, about how searchers make decisions about how they form and phrase their queries (taking into account the trillions of distinct DNA combinations). Before developing a strategy, marketers need to find out how their audience chooses to search (while seeing visitors from a wide variety of search queries coming to marketers’ site) so as to adequately target their audience with the proper content. And this because of the complex cognitive process (Hearst, 2015) needed when trying to design successful search user interfaces, while considering the imperative of identifying people’s naturally inclination to conduct their searches.

Why is it important to properly understand the consumer journey from the moment the customer thinks about purchasing and how digital is enhancing all of those experiences within the above described specific context? Because, simply said – to paraphrase “Target Marketing” (Fletcher, 2015) – companies exist only if they matter to their customers.

Theodor Valentin Purcărea Editor - in – Chief

References

Ernan’s Blog - How Disney Influences Small Customer Segments for Major Impact, ernan@erdm-mail.com, 8/20/2015 Ewenstein, B., Smith, W. and Sologar, A. - Changing change management, July 2015, Retrieved from: http://www.mckinsey.com/insights/leading_in_the_21st_century/Changing_change_management?cid=digital-emlalt-mip-mck-oth-1507, 8/18/2015 Wulfsberg, R. - What Customers Really Think About Innovative Companies, May 22, 2015, Retrieved from: http://www.marketingprofs.com/opinions/2015/27719/what-customers-really-think-about-innovative-companies?adref=nlt052215, 7/8/2015 Blue Nile - Psychology of the Searcher: Patterns in How Searchers Formulate Queries, , Retrieved from: http://bluenileresearch.com/psychology-searcher/, 8/19/2015 Hearst, M.A. - Search User Interfaces, Cambridge University Press, 2009, Retrieved from: http://searchuserinterfaces.com/book/sui_ch3_models_of_information_seeking.html,8/23/2015 Fletcher, H. - Do You Exist? Only If You Matter to the Customer [Video], July 10, 2015, Retrieved from: http://www.targetmarketingmag.com/article/exist-matter-customer-video/#utm_source=today-%40-target-marketing&utm_medium=newsletter&utm_campaign=2015-07-10&utm_content=do+you+exist%3F+only+if+you+matter+to+the+customer+%5Bvideo%5D-7, 7/11/2015

Prof. Eng. Ph.D.

Victor GREU

T H E I N F O R M AT I O N S O C I E T Y T O WA R D S T H E K N O W L E D G E BASED SOCIETY DRIVEN BY THE I N F O R M AT I O N A N D C O M M U N I C AT I O N S T E C H N O L O G I E S FROM THE INTERNET OF THINGS TO THE INTERNET OF ‌TREES (Part 3)

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In Calarasi City, on the left shore of Borcea a Danube branch

Romanian Distributtion Committee Magazine / September 2015 / www.distribution-magazine.eu

Abstract:

The paper approaches the premises, applications, technologies and the main requirements of an emergent field of the information and communications technologies (ICT), the Internet of things (IoT), as an extension of the people’s need to communicate, which will bring, of course, a lot of data and unprecedented opportunities to improve life in the information society (IS) toward knowledge based society (KBS) context. The paper analysis shows that the main role of IoT is to collect data/information from life and environmental experience, helping people to optimize all Earth processes and finally add new value for a better and longer life for actual and next generations. One of the benefits of such a way to develop IoT is the „outcome economy”, like an expression of the intermediate steps to optimize IS/KBS, as the products and services (at most from ICT but not exclusively) will be designed and implemented to provide specific (costumized) results. The paper identifies two main categories of IoT applications, one (A) being focused on collecting and processing data concerning the operation/existence of a „thing”/device in order to optimize that thing/device operation/existence („things” could include industrial machines, home appliances, cars, toys, animals or ... trees). The second category (B) is focussed on individual/human body, in order to collect personal data useful for that person (generally for health, but not exclusively) or for entities interested in person’s behaviour. Both categories of IoT applications are further analyzed with relevant examples and revealing their main implications.

The second section of the paper analyzes the means to develop IoT on a rational base, showing that communications, as main part of ICT, must provide the „smart” support for developing IoT and generally optimize not only the „things” existence/operation, but the IoT as „network of networks” too. An important issue on developing such IoT complex systems is that it supposes a set of requirements to be accomplished, in order to reach the optimizations goals with efficiency on long term. The main requirement is an infrastructure to connect the World IoT devices, including broadband (high speed) communications support (wireless, optical fiber, satellite, cable), optimized to the application specific traffic.

Other analyzed requirements include IPv6 necessary addresses and new technologies for the energy (electric power) support. Predictive analytics will be in fact one of the main features of future IoT, intended to support optimization and added value, as generally it is not a problem to make software, but it is a huge challenge to make new performant algorithms for the optimization of the complex processes in IS/KBS. Including privacy, confidentiality and the intellectual property issues, the security requirements for IoT will be prominent and very difficult to comply, due to the complexity of interoperability conditions in a planetary network of networks. For implementing performant and complex IoT applications, the main ICT features which are required include: sensing and data collection capability; layers of local embedded processing capability;wired and/or wireless communication capability; software to automate tasks and enable new classes of services; remote network/cloud-based embedded processing capability; full security across all data and signal path.

Keywords: Internet of Things, communications and information technology, information society, knowledge based society, sensing technologies, outcome economy, nanogenerator, predictive analytics.

The paper main conclusion is that, in spite of the amazing performance of the automation/optimization processes implemented by „machines”, the risk of altering the human principles and values, by hazard or design errors, will increase and deep analyses and research must be further performed in order to assure a consistent and secure development of IoT applications for optimizing IS/ KBS on long term.

JEL Classification: L63; L86; M15; O13; O33

1. IoT extends communication as a core of humankind evolution and better life Starting from „We Evolve Because We Communicate”[4], we might go further, observing that, in fact, any segment of species evolution (including humankind) is essentially a communication of the genetic „message”.

Practically, the genetic message will consist of the „parent” message but it will include the acquired genetic information, i.e. the modifications determined by life and environmental experience.

Now we can observe, from the fundamental similarity between the general case of species evolution and the actual humankind evolution, the amazing role of Internet of Things (IoT) as a medium for collecting data/information from life and environmental experience, beyound the (very large) limits of usual communications of individuals.

The significance of this role could be also expressed by the Cisco definition [4] of IoT: „IoT is simply the point in time when more “things or objects” were connected to the Internet than people”.

In fact this definition shows us that IoT is an extension of the people’s need to communicate, which will bring, of course, a lot of data and unprecedented opportunities to improve life and eventually the humankind genetic message itself. This essential necessity is obvious, considering IoT as critical for human progression[4]: „As the planet’s population continues to increase, it becomes even more important for people to become stewards of the earth and its resources”.

The potential of IoT is already expressed [1][2][3][14][5][9][10][20] and its development is driven by a huge range of applications, but on the other hand is depending on the way the benefits and the risks will be controlled by appropriate technologies and management/politics.

In order to first evaluate the applications/benefits range, we have to identify the main fields/categories of applications which will influence the humankind evolution and better life.

Unfortunately it is not an easy job to analyze this influence, first because there is no common opinion about the „evolution” and even about „better life” there is more to discuss.

Here we have to recall the main challenges of the information and communications technologies (ICT) prominent role in the development of the information society (IS) toward knowledge based society (KBS) [17].

As a consequence, the faster and higher is the ICT development, the harder is the decision on what is positive or negative in the ICT influence on IS/KBS. To be more precise, the above „higher” includes, as an iceberg, a huge amount of influence on Earth, with vertical and horizontal extentions, in all humankind activity fields, in all life on Earth issues, in all environment resources and features.

Although one could consider „too much” the effect of ICT on „all”, there are many studies [1][2] [3][18][7][12] that provide amazing figures for these global extentions and effects (only for IoT - estimated benefits of USD trillions order in 2025), but it is worth to emphase that this is only what we can quantify as the visible part of iceberg.

When we above have mentioned genetic message evolution, or even part of „better life”, we refered to the qualitative aspects, including life quality, intelectual capacity/creative potential and eventually the behaviour profile of humankind – versus the changes in the Earth environment support for life on long term!

Speaking (so generally) about intelectual capacity and behaviour profile, perhaps a very relevant and surprising example is that people will have to learn, as a natural skill, to use everyday (after sophisticated smartphones) new augmented-reality technology devices.

All this context must be carefully considered, as IoT expansion will cover everything on Earth and ... more [5].

With other words, our analysis started by evaluating the complex and dynamic context where and how this extension is happening, in order to have a rational approach of the applications range, extent and features, with the purpose to avoid a possible unconscionable exponential development and consequences, as ICT and generally industry sometimes produced [17] – climate changes being the most relevant and simple example.

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Speaking of industry, anyway we have to point that, as was the case of all prominent technologies, most of IoT applications investments, benefits and consequences will be linked with industry.

That is why the most detailed and documented studies on IoT perspectives are focused on industry, although it is very difficult to cover all connections and implications of industry on all other humankind activity fields, including health, education and environment [1][2].

As a matter of fact, the industry has a trend in this direction, to offer not only products and services, but outcomes for customers, either organizations and individuals, so we will assist to a „convergence on the outcome economy” [12].

Our opinion is that such huge „project” (IoT at World scale) must be developed by a systemic approach and the first essential issue is of course: what we mainly expect from IoT on long term?

The answer will consider the practical fact that initially IoT applications will cover the natural extensions involving the existing devices connected to Internet, but step by step more devices will be connected, not necessary to Internet but in networks that will be eventually connected to Internet, as by some definitions IoT is a „network of networks”.

Now we have just arrived to the context we have mentioned above and this way it easy to conclude that IoT systemic purpose is to collect data/information from life and environmental experience, i.e. to help humankind to optimize all Earth processes and finally add new value for a better and longer life for actual and next generations!

This way we can observe that the IoT applications range is practically unlimited, with the above condition: to be part of an optimization process at planetary scale of IS/KBS. Because we have already approched the complex problem of ICT prominent role for IS/KBS optimization [13][15][19], now we may agree and point that any optimization sub-system (sub-criteria) have to be step by step integrated in a huge planetary optimization system/process that is IS/KBS itself, following with stricteness a check-act-balance strategy.

Of course, it is difficult to imagine a such huge optimization process, but practically it will be built, initially, simply by adopting some common goals, rules and standards when implementing IoT applications.

For example, the main rule is to obtain added value by processing the collected data, for goals like energy (power, fuel etc.) consumption reduction, health improvement and almost always ... money/resources savings.

In order to obtain added value, IoT devices/applications must be „smart”, this way providing the specific difference versus existent electrical/automation devices.

Without entering in technological details (as it will be presented in the paper next section), for being smart a device must have a processing unit and communications features to provide internal/external soft/data use for optimization processes.

The aim of „outcome economy”, like an expression of the intermediate steps to optimize IS/KBS, as the products and services, at most from ICT but not exclusively, will be designed and implemented to provide specific (costumized) results.

After analyzing the context and rules for developing IoT huge number of applications, we can better approach/identify the main categories of applications, starting from a simple obervation.

It is obvious that most of possible IoT applications (A) are focused on collecting and processing data concerning the operation/existence of a „thing”/device in order to optimize that thing/device operation/ existence. Such „things” could include industrial machines, home appliances, cars, toys, animals or ... trees[3] [18].

An other category (B) is focussed on individual/human body, in order to collect personal data useful for that person (generally for health, but not exclusively) or for entities interested in person’s behaviour.

Of course, these two categories (A;B) do not cover all possible scenarios and are not totally independent (orthogonal), but similar classificatios are confirmed [8].

For example, this way we agree (with formal regret) that „cow”[4][21] is a „thing” and people’s blood pressure monitoring is in the same category with the monitoring of person’s behaviour refering to the shops she/he is often visiting.

With other words B is human-centric and A is „non-human”-centric, although, for example, when we collect data about electric power home consumption we indirectly reflect people’s behaviour, but the application focus is oriented to optimize the cost/resources.

An other practical and important observation is that B applications are generally more complex and such applications will have initially a reduced pace but an explosive development on long term, as commercial and social applications/entities, based mainly on soft defined infrastructures, will be wide-spread in IS/KBS. On the other hand, B applications development will have complex challenges regarding legislation, privacy and security.

A similar classification could be done as a function of user [3]: IoT for individual; IoT for community (group of citizens); IoT for entreprise.

The above premises explain the amazing development of IoT (mainly A type) and the diversity of applications areas, which could be aproximatelly detailed in some main fields, as it is often expressed [3][1] [2][6][11][16][8].

Table 1. „A” IoT applications fields Field/areas

Field/areas

Industry M2M Applications Indoor Air Quality Temperature Monitoring Ozone Presence Indoor Location (ZigBee;UWB;RFID;NFC) Vehicle alarm/diagnosis

Retail Supply Chain Control NFC Payment (for public transport, gyms, theme parks etc.)

Smart Product Management in Warehouses

Energy Smart Grid Smart Grid Tank level Photovoltaic Installations Water Flow Silos Stock Calculation

Logistics Quality of Shipment/Container Conditions (vibrations, strokes, temperature etc.)

Item Location Storage Incompatibility Detection Fleet Tracking

Agriculture Wine/Soil Quality Enhancing Green Houses (micro-climate conditions) Selective irrigation in dry zones Meteorological/Forecast Station Network Compost (Control of humidity and temperature levels) Animal Farming Offspring Care Animal Tracking Toxic Gas Levels

Vehicles including cars/trucks/ aircraft/ trains Condition based maintenance Usage-based design Pre-sales analytics Mining, oil and gas, construction Operating efficiencies Predictive maintenance Health and safety

Community Smart Parking Structural health of constructions Noise Urban Maps Traffic Congestion Smart Lighting Waste Management Intelligent Transportation Systems

Environment Forest Fire Detection Air Pollution Landslide and Avalanche Prevention Earthquake Early Detection

Security Perimeter Access Control Liquid Presence Radiation Levels Explosive and Hazardous Gases 14

Water Water Quality Water Leakages River Floods Home Energy and Water Use Remote Control Appliances Intrusion Detection Systems Art and Goods Preservation

Romanian Distributtion Committee Magazine / September 2015 / www.distribution-magazine.eu

ships/

Table 2. „B” IoT applications fields Field/areas eHealth Person Fall Detection Patients Surveillance Ultraviolet Radiation Medical Freezers conditions Sportsmen Care

Field/areas People’s behaviour Smart and Customized Commerce/ Services/ Entertainment Social management

Obviously, Table 1 and Table 2 are far from being complete and on the other hand every area/field could be detailed and surely new technologies will be added to ZigBee, UWB(Ultra Wide Band), RFID(RF Identification), NFC(Near Field Communications) and GPS, as mobile communications 4G and even future 5G are planned to be implied in IoT long term development.

Further we have to shortly analyze some premises, design rules and technologies useful to implement the above IoT development/applications.

2. Using information and communications new technologies to develop IoT, for optimizing complex processes of IS/KBS From the first section we already have some contextual elements to further see which are the means to develop IoT on a rational base, having in mind the essential conditions we have just mentioned to be part of an optimization process and lead to added value in IS/KBS. Again communications, as main part of ICT, must provide the „smart” support for developing IoT and generally optimize not only the „things” existence/operation but the above mentioned „network of networks” too.

Developing such IoT complex systems supposes a set of requirements to be accomplished, in order to reach the optimizations goals with efficiency on long term.

The necessary infrastructure to connect the World IoT devices includes broadband (high speed) communications support (wireless, optical fiber, satellite, cable), optimized to the application specific traffic.

As a „network of networks”, at network level IoT will have very complex challenges, as potentially billions of new sensors will require unique IP addresses.

Adressing these challenges, the extension of IPv6 (replacing IPv4) will provide practically unlimited number of adresses and make the management of networks easier due to auto configuration capabilities, offering too improved security features [4][1][8][2].

An other systemic challenge for IoT is naturally the energy support, considering the diversity of the applications and the fact that more and more they will include „areas” of sensors – not one or a couple.

Lets imagine some environment applications, where large areas (forest, sea coasts etc.) must be monitored, so how could be changed the batteries for each sensor?

Consequently, researchers are looking for finding sensors to generate the necessary electricity from environmental elements such as vibrations, light or airflow.

On this topic the good news are already coming [4]:

„Scientists announced a commercially viable nanogenerator—a flexible chip that uses body movements such as the pinch of a finger to generate electricity ...This development [the nanogenerator] represents a milestone toward producing portable electronics that can be powered by body movements without the use of batteries or electrical outlets. Our nanogenerators are poised to change lives in the future. Their potential is only limited by one’s imagination”.

This result and similar achievements simply express the above mentioned „iceberg” and our estimation is confirmed by authorities like Gatner [4]: „Analyst firm Gartner recently declared that the Internet of Things (IoT) was the most hyped technology in 2014”.

Generally is agreed that IoT development on long term will be more and more based on „soft defined applications” and sophisticated software that can process/analyze more data than has ever been analyzed in order use the huge potential of IoT.

Recalling the „data deluge” we already analyzed [13][15], we must point here that IoT will rise at unprecedented level the critical necessity for new performant algorithms for processing and especially for analytics associate with what is called Big Data, Exa Data etc.

Predictive analytics will be in fact one of the main features of future IoT intended to provide the core of the above mentioned optimization and added value, as generally it is not a problem to make software, but it is a huge challenge to make new performant algorithms for the optimization of the complex processes in IS/ KBS. Such a firm appreciation could be justified by simple considering only two of the main goals (criteria) of the IoT optimization role on Earth: providing a better life and assist people to prevent/anticipate the changes (at individual, community and Earth levels).

Now it could appear easier to fulfill another essentiall requirement for the IoT development technologies: to provide IoT devices/applications more and more complex as purposes/functions, but very simple to operate/ understand by users.

Of course it could be a topic for an other paper (although we have mentioned above about genetic message changes and humankind evolution just for this reason), but the fact that the users must learn, step by step, to work and communicate with the new „smart machines”, leads us to the conclusion that „simple to operate/understand” will be a dynamic term.

Coming back to the ICT requirements necessary to develop IoT, most of the specialists include [8]:  Sensing and data collection capability (sensing nodes)  Layers of local embedded processing capability (local embedded processing nodes)  Wired and/or wireless communication capability (connectivity nodes)  Software to automate tasks and enable new classes of services  Remote network/cloud-based embedded processing capability (remote embedded processing nodes)  Full security across all data and signal path For example only the hard components of those requirements will have to provide a diversity of functions/devices like: Sensing (presence, gases etc.); Accelerometer; Magnetometer; Gyroscope; Pressure; Altimeter; Temperature; MCU (micro controller unit); MPU (micro processor unit); Hybrid MCU/MPU; Network Processor etc.

We know that ICT have an „Achile’s heel” even in the most performants achievements: privacy and confidentiality. It is easy to imagine how much the risks will increase when billions of IoT devices/applications will operate on Earth and the individuals/organizations concerns about their data privacy/confidentiality/ integrity are justified.

Adding here the intellectual property issues implied by generating data/information on a diversity of platforms and legal contexts, we complete the picture of security, as IoT will be a network of networks with obvious risk of attacks from Internet user and more, in spite of usual or new crypto-measures.

At a planetary scale of IoT it is essentiall to mention that security risks are not reduced at the data/ information aspects. As IoT applications will cover critical infrastructures like ICT, energy, transportation, food, health and more, we have a concerning picture of what the consequnces of vulnerabilities could bring ... on Earth!

This way the security requirements for IoT will be prominent and very difficult to comply, due to the complexity of interoperability conditions in a planetary network of networks.

Of course, as we progress with the automation/optimization processes implemented by „machines”, the risk of altering the human principles and values, by hazard or design errors, will increase.

As a consequence, deep analyses and research must be further performed in order to assure a consistent and secure development of IoT applications for optimizing IS/KBS on long term and facing all inherent challenges.

3. Conclusions The paper analyzed the premises of IoT emerging development, as an extension of the people’s need to communicate, which will bring, of course, a lot of data and unprecedented opportunities to improve life in IS/KBS context and eventually the humankind genetic message itself. The main role of IoT is then to 16

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collect data/information from life and environmental experience, i.e. to help humankind to optimize all Earth processes and finally add new value for a better and longer life for actual and next generations. This way the IoT applications range is practically unlimited, with the condition to be part of an optimization process at planetary scale of IS/KBS. One of the benefits of such a way to develop IoT is the „outcome economy”, like an expression of the intermediate steps to optimize IS/KBS, as the products and services, at most from ICT but not exclusively, will be designed and implemented to provide specific (costumized) results.

The paper identified two main categories of IoT applications, one (A) being focused on collecting and processing data concerning the operation/existence of a „thing”/device in order to optimize that thing/device operation/existence („things” could include industrial machines, home appliances, cars, toys, animals or ... trees).

The second category (B) is focussed on individual/human body, in order to collect personal data useful for that person (generally for health, but not exclusively) or for entities interested in person’s behaviour.

Both categories of IoT applications are further analyzed, with relevant examples and revealing their main implications.

The second section of the paper analyzed the means to develop IoT on a rational base, showing that communications, as main part of ICT, must provide the „smart” support for developing IoT and generally optimize not only the „things” existence/operation but the above mentioned „network of networks” too.

An important conclusion on developing such IoT complex systems is that it supposes a set of requirements to be accomplished, in order to reach the optimizations goals with efficiency on long term.

The main requirement is an infrastructure to connect the World IoT devices including broadband (high speed) communications support (wireless, optical fiber, satellite, cable), optimized to the application specific traffic.

Other analyzed requirements include IPv6 necessary addresses and new technologies for the energy (electric power) support.

IoT development on long term will be more and more based on „soft defined applications” and sophisticated software that can process/analyze more data than has ever been analyzed („data deluge”) in order use the huge potential of IoT.

Predictive analytics will be in fact one of the main features of future IoT intended to provide the core of the above mentioned optimization and added value, as generally it is not a problem to make software, but it is a huge challenge to make new performant algorithms for the optimization of the complex processes in IS/ KBS. Including privacy, confidentiality and the intellectual property issues, the security requirements for IoT will be prominent and very difficult to comply, due to the complexity of interoperability conditions in a planetary network of networks.

For implementing performant and complex IoT applications, the main ICT features which are required include: sensing and data collection capability; layers of local embedded processing capability;wired and/or wireless communication capability; software to automate tasks and enable new classes of services; remote network/cloud-based embedded processing capability; full security across the signal path.

Despite the amazing performance of the automation/optimization processes implemented by „machines”, the risk of altering the human principles and values, by hazard or design errors, will increase and deep analysis and research must be further performed in order to assure a consistent and secure development of IoT applications for optimizing IS/KBS on long term.

REFERENCES [1] Jacques Bughin, Michael Chui, James Manyika, An executive’s guide to the Internet of Things, McKinsey Quarterly, August, 2015.

[2] James Manyika, Michael Chui, Peter Bisson, Jonathan Woetzel, Richard Dobbs, The Internet of Things: mapping the value beyond the hype, McKinsey Global Institute, June, 2015.

[3] Ovidiu Vermesan, Peter Friess, Internet of Things: Converging Technologies for Smart

Environments and Integrated Ecosystems, 2013 River Publishers.

[4] Dave Evans, How the Next Evolution of the Internet Is Changing Everything, Cisco White Paper - The Internet of Things, April 2011.

[5] Victor Greu, The information society towards the knowledge based society driven by the information and communications technologies - from the Internet of Things to the Internet of …trees (Part 2), Romanian Distribution Committee Magazine, Volume 6, Issue2, Year 2015.

[6] Jim Chase, The Evolution of the Internet of Things, White Paper-Strategic marketing Texas Instruments, September 2013.

[7] Analysys Mason, Imagine an M2M world with 2.1 billion connected things http://www.analysysmason.com/about-us/news/insight/M2M_forecast_Jan2011/ [8] *** White Paper What the Internet of Things (IoT) Needs to Become a Reality, http://www. freescale.com/arm.com [9] *** Next Generation Networks- Frameworks and functional architecture models — Overview of the Internet of things, International Telecommunication Union — ITU-T Y.2060 — (06/2012) [10] *** Internet of Things — An action plan for Europe, (PDF). COM(2009)-278 final, Commission of the European Communities -18 June 2009.

[11] *** Internet of Things — ITU 2005 Report, www.itu.int/dms_pub/itu-s/opb/pol/ S-POL-IR.IT-2005-SUM-PDF-E.pdf [12] *** Industrial Internet of Things: Unleashing the Potential of Connected Products and Services, World Economic Forum’s IT Governors launched the Industrial Internet initiative at the Annual Meeting 2014 in Davos, Switzerland, January 2015.

[13] Victor Greu, The cognitive approaches of the communication and information technologies – a leverage for the progress of knowledge based society, Romanian Distribution Committee Magazine, Volume 3, Issue2, Year 2012.

[14] *** The Internet of Things, https://ec.europa.eu/digital-agenda/en/internet-things, 27/02/2015.

[15] Victor Greu, The Exponential Development of the Information and Communications Technologies – A Complex Process which is Generating Progress Knowledge from People to People, Romanian Distribution Committee Magazine, Volume 4, Issue2, Year 2013.

[16] Bari N., Mani G., Berkovich S., Internet of Things as a Methodological Concept, Computing for Geospatial Research and Application (COM.Geo), 2013 Fourth International Conference on.

[17] Victor Greu, Searching the right tracks of new technologies in the earth race for a balance between progress and survival, Romanian Distribution Committee Magazine, Volume 3, Issue1, Year 2012.

[18] Mark Harris, The Internet of Trees, IEEE Spectrum, Mar.2014.

[19] Greu, Context-aware communications and IT – a new paradigm for the optimization of the information society towards the knowledge based society (Part 2), Romanian Distribution Committee Magazine, Volume 5, Issue4, Year 2014.

[20] *** More Than 30 Billion Devices Will Wirelessly Connect to the Internet of Everything in 2020, ABI Research, London, United Kingdom - 09 May 2013, www.abiresearch.com/ press/more-than-30-billion-devices-will-wirelessly-conne/ [21] Victor Greu, The information society towards the knowledge based society driven by the information and communications technologies - from the Internet of Things to the Internet of …trees (Part 1), Romanian Distribution Committee Magazine, Volume 6, Issue1, Year 2015.

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Retailers: Facing the disruption of the traditional ways of doing business

Abstract As the way consumers shop continues to change, success is dependent on constant innovation in ensuring a convergent holistic experience, retailers being forced to consider e-commerce and mobile shopping, to align the right customer with the right product or service, while considering technology trends that would change how the businesses operate. As customers’ data sources are disparate, marketers need to use predictive models supported by the proper sources of information, using data integration and building the right analytical solutions, while facing inaccuracies due to time-consuming validation systems within the context of moving customer information collection to digital channels. Retailers can disrupt the traditional ways of doing business, better understanding that there is a possibility to look for new forms and mechanisms to create value by following the necessary steps of the reframing process, and adequately supporting their transformational aspirations by a skilled workforce sharing the learning environment.

Keywords:

Agile supply chain strategy; Technology trends; Reframing beliefs; Experiential learning 20

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JEL Classification: L81; L86; M31; O14; Q55

As the way consumers shop continues to change, success is dependent on constant innovation in ensuring a convergent holistic experience There is no wonder that within the context of the proliferation of digital, social, and mobile technologies coupled with increasing consumer adoption of these technologies retailers are challenged to embrace disruption and evolve. (Deloitte LLP, 2015) According to Alison Kenney Paul, Vice Chairman and US Retail and Distribution leader, Deloitte LLP, retailers need to put an increased emphasis on convergence and diversification, and to provide a blended online and in-store experience, in other words to ensure a convergent holistic experience (product assortment, pricing, shipping, return options, and promotional offerings, all this combined with a consistent aesthetic look and emotional feel, while minimizing cyber risk), thanks to an agile supply chain strategy which includes also services outside of their core competencies and diversifying talent portfolio and employee skill sets. US retail industry, for example, will be forced to evolve by the growing strength of internet and mobile retailing confirming the increasingly important role in the retailing landscape played by the e-commerce and mobile shopping. (Euromonitor, 2015) This fierce competition in the US retail industry (one of the largest industries in the world; estimated retail sales 2014 - $5.321 trillion, up about 5% for the year - consumer confidence being improved by the high stock market values and recovering house prices which have created a “wealth effect”; nearly 15.4 million employees in America alone - Plunkett Research, 2015) is confirmed by the evolution of the marketing strategies used by retailers to communicate with and attract consumers. Let us take a look, as another example, at the “2015 Top 50 Mailers and Emailers” (Target Marketing in collaboration with Who’s Mailing What!):

Aligning the right customer with the right product or service. Big data analytics and automation, technology trends that would change how the businesses operate In July this year eMarketer stated that Wal-Mart, (eMarketer, 2015) the world’s largest retailer, reported that roughly 70% of traffic to Walmart.com over the 2014 holidays (between Thanksgiving and Cyber Monday) was on a mobile device, while smartphone usage reaching 59.3% of the US population in 2015. According to a PricewaterhouseCoopers (PwC) survey - “Total Retail V Survey: United States”, Feb 9, 2015 (n=1,011 ages 18+; respondents chose their top 3) - the favorite retailers among US internet users were as follows: Amazon (), Wal-Mart (41%), Target (29%), Kohl’s (14%), eBay (13%), Best Buy (12%), Macy’s (11%), Costco (9%), JCPenney (8%), The Home Depot (8%). The criteria considered by the respondents were those used in a September 2014 PricewaterhouseCoopers (PwC) survey, when they were asked why they shopped at their favorite retailer: “good prices” (71%); “having items in stock” (50%).

But customers’ data sources are disparate. In order to target the right consumers with the right products and offers marketers are using predictive models which are supported by new and rich sources of information, (Gupta, 2015) the investigation starting from company’s customer data and interaction history (historical purchases, individual market baskets, common market baskets, ratings/reviews etc.), leveraging, in addition to these internal customer data: outside data sources to better associate customers to buying opportunities; search data, which can inform relationships between products and brands; third-party data, which are becoming a critical bridge in omnichannel marketing.

So, in this new age of data-driven targeting, the challenge is to use data integration and to build the right analytical solutions allowing leveraging the above mentioned disparate data sources. It is worth mentioning that a report from May 2014 of the US Federal Trade Commission (FTC) revealed the taxonomy of sources and activities currently available for sales by data vendors:

Figure no 1: Online and offline data points available for sale at the consumer level and collaboration between data providers to create even more powerful consumer-level data sets Source: Report from May 2014 of the US Federal Trade Commission, cited by Shiv Gupta - Predicting Profits With Models, July 10, 2015, Retrieved from: http://www.targetmarketingmag.com/article/predicting-profits/all/, 7/11/2015

According to a January 2015 study by EMC, (eMarketer, 2015) big data analytics (43%) and automation (37%) represent technology trends that would change how the businesses operate in the next five to 10 years in the opinion of business leaders worldwide. Fourth month later, in May 2015, a polling

by Acuant revealed that the majority of US companies have taken customer data input off consumers’ hands, the primary method used to capture customer information being card scanning technology (customer scanning an ID that then populating all key personal info). The companies’ problem, within the context of moving customer information collection to digital channels, is that they are facing inaccuracies due to timeconsuming validation systems: fewer respondents than three in 10 were very confident that they rarely made a mistake; nearly six in 10 were just somewhat confident; 10% were only fairly confident.

Can retailers disrupt the traditional ways of doing business?

Four places to innovate Allow us to remember that two years ago, in October 2013, McKinsey&Company (MacKenzie, Meyer and Noble, 2013) underlined that as a number of industry observers predicted the end of retail as we know it, big changes are inevitable (each of the well-known shifts unfolding faster than the one that preceded it, and each elevating new companies over incumbents), and retailers must act now to win in the long term. McKinsey’s representatives insisted on the major trends reshaping the retail landscape: demographic changes, multichannel and mobile commerce, personalized marketing, the distribution revolution, and emerging retail business models.

This summer, at the end of July, Marc de Jong (a principal in McKinsey’s Amsterdam office) and Menno van Dijk (cofounder and managing director of the THNK School of Creative Leadership 22

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and a former director in the McKinsey’s Amsterdam office) highlighted that now it’s time of reframing long-standing beliefs in every industry about how to make money, such as that the bottom line in retail is determined by purchasing power and format. (de Jong and van Dijk, 2015) In the opinion of these two specialists , this kind of beliefs considered inviolable reflect widely shared notions about customer preferences, the role of technology, regulation, cost drivers, and the basis of competition and differentiation.

And turning one of the notions that support these identified beliefs about value creation (reframing it) there is a possibility to look for new forms and mechanisms to create value, by following four steps of the reframing process: outlining the dominant business model in the industry; dissecting the most important long-held belief into its supporting notions; turning an underlying belief on its head; sanity-testing the reframe; translating the reframed belief into the industry’s new business model.

But as customer relationships, key activities, strategic resources, and the economic model’s cost structures and revenue streams represent core elements of the business model, the above mentioned specialists argue that a reframe seems to emerge for each one, the digitization of business (which upends customer interactions, business activities, the deployment of resources, and economic models) being the common denominator of these themes, that is why they are indicating four places to innovate (reframe): in customer relationships (from loyalty to empowerment; in activities (from efficient to intelligent); in resources (from ownership to access); in costs (from low cost to no cost).

Instead of conclusions According to McKinsey research, (Benkert and van Dam) transformational aspirations must be adequately supported by a skilled workforce (ready to achieve the change mission), that is why companies have a real need of experiential learning leveraging the intimate link between knowledge and experience thanks to an active and shared learning environment. We are convinced of the utility of applying this approach also in the retail industry following the typical staged process in experiential learning (experiencing and exploring: doing; sharing and reflecting: what happened? processing and analyzing: what’s important? generalizing: so what? applying: what works for me?), respecting, of course, all other McKinsey’s recommendations: resources sufficient to gain momentum and achieve rapid progress; clearly defined pivotal roles and responsibilities; fully engaged employees and leaders.

References Deloitte LLP - 2015 Retail Industry Outlook, Interview with Alison Kenney Paul, vice chairman and US Retail and Distribution leader, Deloitte LLP Retrieved from: http://www2.deloitte.com/us/en/pages/consumer-business/articles/2015-retail-outlook.html, 8/11/2015 Euromonitor - Retailing in the US, Jun 2015, Retrieved from: http://www.euromonitor.com/retailing-in-the-us/report, 8/11/2015 Plunkett Research - Retailing & Chain Stores Market Research, Retrieved from: https://www.plunkettresearch.com/industries/retailing-stores-market-research/, 8/11/2015 eMarketer - For Discount Retailers, Mobile Moves Onward and Upward, July 6, 2015, Retrieved from: http://www.emarketer.com/Article/Discount-RetailersMobile-Moves-Onward-Upward/1012681?ecid=NL1002,7/8/2015 Gupta, S. - Predicting Profits With Models, July 10, 2015, Retrieved from: http://www.targetmarketingmag.com/article/predicting-profits/all/, 7/11/2015 eMarketer - Can Companies Validate All of Their Customer Data? , July 7, 2015, Retrieved from: http://www.emarketer.com/Article/Companies-Validate-All-ofTheir-Customer-Data/1012693?ecid=NL1002, 7/8/2015 MacKenzie, I., Meyer, C. and Noble, S. - How retailers can keep up with consumers, McKinsey&Company, October 2013, 12/16/2014 de Jong, M. and van Dijk, M. - Disrupting beliefs: A new approach to business-model innovation, Mc Kinsey Quarterly, July 2015, 7/31/2015 Benkert, C. and van Dam, N. - Experiential learning: What’s missing in most change programs, Retrieved from: http://www.mckinsey.com/Insights/Operations/ Experiential_learning_Whats_missing_in_most_change_programs?cid=other-eml-alt-mip-mck-oth-1508, 8/14/2015

Business Intelligence and Performance Management

ABSTRACT GLOBALISATION, VOLATILE MARKETS, LEGAL CHANGES AND TECHNICAL PROGRESS HAVE AN IMMENSE IMPACT ON BUSINESS ENVIRONMENTS IN MOST INDUSTRIES. MORE AND MORE IT IS DEPLOYED TO MANAGE THE COMPLEXITY. AS A RESULT, COMPANIES AND ORGANISATIONS HAVE TO HANDLE GROWING VOLUMES OF DATA WHICH HAVE BECOME A VALUABLE ASSET. THE ABILITY TO BENEFIT FROM THIS ASSET IS INCREASINGLY ESSENTIAL FOR BUSINESS SUCCESS. THEREFORE, FAST STORAGE, RELIABLE DATA ACCESS, INTELLIGENT INFORMATION RETRIEVAL, AND NEW DECISION-MAKING MECHANISMS ARE REQUIRED. BUSINESS INTELLIGENCE (BI) AND PERFORMANCE MANAGEMENT (PM) OFFER SOLUTIONS TO THESE CHALLENGES.

Keywords: DATA STORAGE; RELEVANT KNOWLEDGE; ANALYTIC TOOLS; COMMUNICATION; TARGET VALUES; OPERATIONAL LEVEL; BUSINESS STRATEGY; HOLISTIC APPROACH; ENTERPRISE PERFORMANCE MANAGEMENT

JEL Classification C81, C82, M15, M21, G10, Q55

George Cosmin TĂNASE

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Current and Future Challenges

total-approach for managerial decision support”.

During the 21st century business environments have become more complex and dynamic than ever before. Companies operate in a world of change influenced by globalisation, volatile markets, legal changes, and technical progress. More and more IT is deployed to manage the complexity. As a result, growing volumes of data, for instance, provided by CRM systems, web shops or sensor technologies, have to be handled. Therefore, fast storage, reliable data access, intelligent information retrieval, and automated decision-making mechanisms, all provided at the highest level of service quality, are required.

Successful enterprises are aware of these challenges and efficiently respond to the dynamic environment in which their business operates. Business Intelligence (BI) and Performance Management (PM) offer solutions to the challenges mentioned above and provide techniques to enable effective business change. The corresponding instruments allow transparency of processes and their results on all management levels.

Based on this information, action can be taken as fast as possible in the case of sudden market changes or critical developments. Meanwhile, companies in many industries, including technology suppliers, have realised this point and act as players in the fields of BI and PM. Thus, it is not surprising that the IT environment has changed dramatically over the last decades, both in terms of new business as well as in soft- and hardware requirements.

From an application-oriented, logical perspective, a typical BI architecture consists of three layers. These three layers are based on operational sources, like Supply Chain Management (SCM), E-Procurement Systems, Enterprise Resources Planning (ERP) Systems, Customer Relationship Management (CRM), and external sources. The systems of the Data Support Layer are fed by ETL processes (extractiontransformation-loading). Data Support Layer The data support layer is responsible for storing transformed and harmonised, structured and unstructured data for decision support. Relevant data storing systems for unstructured data are document and content management systems. Structured data is stored in operational data stores (ODS), data

BI and PM are now well established and an important field for researchers as well as for professionals in all industries.

Whether activities in this field are successful or not depends on certain prerequisites. It is important to address aspects from different points of view, which cover the following issues:

Information Generation, Storage, Distribution Layer This layer provides functionality to analyse structured data or unstructured content and supports the distribution of relevant knowledge. The analytical functionality of this layer includes OLAP and data mining, in addition to functionality to generate (interactive) business reports, ad-hoc analysis, or to implement performance management concepts, like the Balanced Scorecard or Value Driver Trees. For the distribution of knowledge, tools from Knowledge Management and CSCW domains are used, e.g. workflow support or tools for information retrieval. Information Access Layer The information access layer offers the user convenient access to all relevant BI functions in an integrated environment—within the confines of defined user roles and user rights. Usually, the access layer is realised with some sort of portal software, which provides a harmonised graphical user interface. In initial discussions on enterprise-wide BI approaches, practitioners and researchers propagated the development of a single enterprise-wide reservoir with harmonised data for decision support. It was argued that only in this way “a single point of truth” could be established, which is able to guarantee consistent support for all managerial decisions made in companies. In recent years, the debate became more controversial. It is now argued that BI approaches have to support heterogeneous decisions in strategic business units with often highly specific information needs. Besides, the field of BI is expanding and is being vitalised by new concepts and technologies in the area of data gathering and process support. Since the requirements of the primary and secondary business and production processes involved often differ fundamentally, it becomes clear that monolithic company solutions can not lead to satisfactory solutions. Modern Business Intelligence solutions therefore normally consist of a set of different interacting data storing systems, diverse ETL procedures, domain-specific data granularities, adequate analytic tools, and appropriate BI process models, in order to meet all of the challenges for effectively supporting processes.

• BI/PM concepts to support business analytics, strategy and management • BI/PM applications to contribute to business development • methodologies • technologies

Business Intelligence (BI)

In 1996 the Gartner Group stated: “Data analysis, reporting, and query tools can help business users wade through a sea of data to synthesize valuable information from it—today these tools collectively fall into a category called ‘Business Intelligence”. Consequently, leading companies in the field of management support environment adopted the term and subsumed all their tools for Data Warehouses (DWH), Data Marts (DM), Online-Analytical Processing (OLAP), data mining, etc. under the umbrella term Business Intelligence. Hence, in the early days the term BI was only used to describe the heterogeneous conglomerate of isolated tools, supporting various tasks of managers. It took years to establish a common understanding of BI in research and practice. Meanwhile the various approaches merged into a common, rather inclusive understanding in the community that heavily focuses on aspects of integration and consistency. Based on this, BI is defined here as “an integrated, company-specific, IT-based

warehouses (DWH) and data marts, whereas ODS are reservoirs for transactional data, which are often stored realtime without complex historisation routines. DWHs are data management systems for integrated, non volatile, timevariant and subjectoriented data. Bigger DWH “hub-andspoke-architectures” have data marts, which are smaller data collections extracted from Core-DWHs, often based on multidimensional data models to support department-oriented ad-hoc reporting.

Performance Management (PM)

Since a few years, the term Performance Management (PM), which partially overlaps with BI, attracts attention in science as well as in industry. According to an independent multivendor study of the Business Application Research Center (BARC) more than 80 % of all surveyed companies which were from different countries and industries have recognised the necessity to improve their PM processes. They claim a growing need for integrated technology platforms which support PM. Subsequently, it is not surprising that the Gartner Group predicts a significant and growing demand for PM solutions. Unfortunately, there is no clear definition of PM and its different variants. In the literature, a huge variety of PM definitions can be found, for example. Sharma states that PM is based on “the process of assessing progress towards achieving predetermined goals”. It involves “the relevant communication and action on the progress achieved against these predetermined goals”. In contrast to that, Lebas calls PM a philosophy. It is obvious that these definitions differ in scope. Both remain a little bit imprecise. Geishecker’s and Rayner’s interpretation is more precise. They define PM as methodologies, metrics, processes and systems which are used to monitor and manage business performance. If the focus is set on PM in the context of enterprises, the terms Corporate Performance Management (CPM) or Enterprise Performance Management (EPM) can be used. They are subsets of PM. The term CPM is widespread in science as well as in industry. EPM is used by well-known software companies, such as Oracle or SAP. Because EPM and CPM exclude public institutions and non-profit organisations by definition, the term Business Performance Management (BPM) can be used in a more general context.

While the operational level deals with aspects of monitoring, controlling and the optimisation of work processes, the strategic level defines business objectives and strategic Key Performance Indicators (KPIs). The starting point is the analysis of the business and the subsequent definition of business objectives. Based on the business objectives, strategic KPIs are derived. They influence the process design and the definition of process-oriented indicators. It is important to align processes with strategic KPIs by defining operational KPIs. Operational KPIs periodically quantify the performance on the operational level. Compared to strategic KPIs their aggregation level is lower.

On the operational level, process performance has to be planned. In the case of automated processes, process execution can be monitored by business activity monitoring (BAM) tools. Of course, it is also possible to collect or add data manually. The data collected, which is processed by performance reporting tools, allows analyses of the process performance in terms of the objectives. The key data from the operational layer is monitored and analysed on a regular basis. The analyses make the actual performance transparent.

Indicators, such as “average operational hours per day”, are compared with planned values in order to identify possible issues in process execution. If the benefits overcome the effort, for example, if it is very important to recognise manufacturing problems as early as possible, real-time data monitoring can be desirable. BI components, as well as PM tools, can be used for further analyses. Abnormalities in the

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indicators denote issues which, for instance, can be caused by inefficient processes or exceptional market fluctuations. As a result, action can be taken, such as changing the process or revising the goals. In the ideal case, potential problems are avoided and identified before they arise. The positive or negative effects of those adjustments are measured in the next iteration, and a new cycle starts. Of course, the results which are achieved on the operational level have an impact on the strategic level. Aggregated data is used to analyse business or rather the corresponding strategic KPIs, regularly. Deviations of current parameters from target values can indicate alignment problems on the operational level or an inadequate business strategy. As a consequence, for instance, an adjustment of business objectives on the strategic level can be triggered.

This can result in a complete redesign of business processes.

The impacts on the operations are measured and analysed again by means of figures. Thus, the loop is closed and the strategic level is linked to the operational level.

The information generation, storage, and distribution layers of BI architectures include functionality to implement PM concepts and its components, such as Balanced Scorecards.

In this context, PM can be seen as an extension of BI. While BI applications are focused on the automated collection of data and the analyses by means of tools, such as data mining or OLAP, PM focuses on the process of systematic monitoring and on the control of business objectives on different management levels. The intention is to achieve sustainable success by means of continuous process improvements in terms of the company’s strategy.

Conclusions

BI and PM offer a rich set of concepts and tools to efficiently master the challenges which are caused by the dynamic environment of companies and organisations. The successful application of BI and PM requires a common understanding of all of the parties involved. The application-oriented view includes: • the data support layer, • the information generation, storage, and distribution layer, • and the information access layer.

Afterwards, different interpretations of PM were analysed.

It was shown that PM is based on the idea of the closedloop approach. Closed-loops can be established on different management levels and should be linked to achieve full transparency of processes and their results. This enables companies and organisations to respond quickly to current developments. The process-oriented view of PM contributes to continuous improvements in terms of the strategic goals, and the holistic approach is an important requirement for sustainable success. By means of integrated BI and PM components, the complete information chain, ranging from data supply to decision-making, can be supported and automated to a great extent. Thus, the growing amount of data can be processed efficiently. They are a valuable asset for companies and organizations. The ability to benefit from this asset is more and more essential for business success in

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competitive environments.

At the beginning of February 2015 Gartner’s Analyst Neil Chandler underlined that a primary investment focus in 2015 continues to be “Business analytics”, an umbrella term for analytics, business intelligence and performance management which organizations should rearchitect. He argued that IT leaders of analytic initiatives must adapt to deliver increased business value because processes, approaches and platforms are rapidly evolving, and in order to optimize decisions and manage performance are required leadership and organizational competencies to use information and analysis accordingly. Allow us to remember, within the context of the well-known Gartner’s “Business Intelligence and Performance Management Key Initiative Overview”, that the same Gartner’s Analyst Neil Chandler showed on 22 April 2014 how business value is continuing to be driven by the business intelligence and performance management. And in order to connect IT-related initiatives with business strategy and business transformation, it was recommended to execute business intelligence and performance management initiatives by using a structured approach (strategize and plan; develop governance; drive change management; execute; measure and improve).

On the other hand, it is worth mentioning the challenges faced by CFOs from the point of view of transforming their finance organizations and generating value for the business, by adequately using the Enterprise Performance Management (EPM) processes. According to its recent survey, Oracle identified seven trends (EPM embraces the cloud, speed is key; mobile goes beyond convenience to strategic; Big Data is creating a new signal for finance; modern planning practices are becoming a reality; detailed costing practices are needed to stay in the game or get ahead; finance departments need literacy as well as numeracy; organizations are not realizing the wider benefits of enterprise data governance), concluding with all that’s needed now (while driving digital transformation): predictive, data-driven analysis, continuous planning and budgeting, real-time decision making.

References

[1]

Anandarajan, M., Anandarajan, A., Srinivasan, C.A.: Business Intelligence Techniques. Springer, Berlin (2004)

[2] Baars, H., Kemper, H.G.: Management support with structured and unstructured data—an integrated business intelligence framework. Inf. Syst. Manag. 25(2), 132–148 (2008) [3]

BARC: Performance Management—Aktuelle Herausforderungen und Perspektiven (2009).

[4] Becker, D., Brunner, J., Bühler,M., Hildebrandt, J., Zaich, R.: Value-Based Performance Management. Gabler, Wiesbaden (1999) [5] Dinter, B., Bucher, T.: Business performance management. In: Chamoni, P., Gluchowski, P. (eds.) Analytische Informationssysteme, 3rd edn., pp. 23–50. Springer, Berlin (2006) [6]

Eddy, N.: BI, Performance management software market surpassed 12B in 2011.

[7] Geishecker, L., Rayner, N.: Corporate performance management: BI collides with ERP. Research note SPA 14-9282, Gartner, Inc., December 17 (2001) [8]

Hoffmann, O.: Performance management. Diss., Bern et al. (1999)

[9]

Inmon, W.H.: Building the Data Warehouse, 4th edn. Wiley, New York (2005)

[10] Kemper, H.G., Baars, H.: Business Intelligence und Competitive Intelligence. HMD, Prax. Wirtsch.inform. 43(247), 7–20 (2006) [11] Chandler, N. - Agenda Overview for Analytics, Business Intelligence and Performance Management, 2015, 06 February 2015, Retrieved from: https://www.gartner.com/doc/2978917/agenda-overview-analytics-business-intelligence, 8/17/2015 [12] Chandler, N. - Business Intelligence and Performance Management Key Initiative Overview, 22 April 2014, Retrieved from: https://www.gartner.com/doc/2715117/business-intelligence-performance-management-key, 8/17/2015 [13] Oracle - Enterprise Performance Management Top Trends for 2015, Retrieved from: http://www.oracle.com/us/ solutions/ent-performance-bi/business-intelligence/epm-top-trends-for-2015-2441101.pdf, 8/17/2015

Sharing with our distinguished Readers a well-known source of usable and useful knowledge…

Prof. Dr. h. c. Léon F. WEGNEZ is an Honorary Member of the Romanian Distribution Committee, and distinguished Member of the Editorial Board of our “Romanian Distribution Committee Magazine“. According to the announcement made by the European Retail Academy (ERA), the distinguished Léon F. Wegnez is the 2015 “Man of the Year” (the distinguished personalities who have been honored by ERA in the last four years were: Romano Prodi, Klaus Toepfer, Robert Aumann, and Mikhail Fedorov). Knowing our distinguished readers’ thirst for knowledge, we offer you, by courtesy of this remarkable personality, a short selection from “Distribution d’aujourd’hui”, 56ème année, Mars-Avril 2015, Brussels.

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