EVALUATION OF THE BENEFITS TO EUROPEAN LOGISTICS OPERATIONS BY ADOPTING INDUSTRY 4.0 APPROACHES

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DIPARTIMENTO DI INGEGNERIA DELL’ENERGIA DEI SISTEMI,

DEL

T

ERRITORIO E DELLE

C

OSTRUZIONI

RELAZIONE PER IL CONSEGUIMENTO DELLA LAUREA MAGISTRALE IN INGEGNERIA GESTIONALE

Evaluation of the benefits to European logistics

operations by adopting Industry 4.0 approaches

RELATORI CANDIDATO

Prof. Ing. Gino Dini Chiara Andreotti

Dipartimento di Ingegneria Civile e Industriale, Università di Pisa chiaraandreotti93@hotmail.it

Dr. Patrick McLaughlin

Manufacturing department, Cranfield University

Sessione di Laurea del 04/09/2017 Anno Accademico 2016/2017

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SOMMARIO

Questo lavoro di tesi è stato sviluppato secondo l’accordo di Doppia laurea con l’Università di Cranfield ed è stato sponsorizzato da DPDgroup UK Ltd.

Per quanto pubblicato fino ad oggi risulta evidente la mancanza di una revisione sistematica dello stato dell’arte dell’Industria 4.0 nella supply chain, ed in particolare nella logistica. Di conseguenza, l’obiettivo di questa tesi è sviluppare un framework strategico a supporto delle aziende logistiche europee verso le possibilità offerte da questo nuovo paradigma. Per realizzare ciò è stata scelta una metodologia basata sulla Grounded theory, in accordo con la quale sono stati identificati gli aspetti che possono risultare migliorativi nei processi delle aziende logistiche attraverso un’intensa analisi della letteratura e delle interviste condotte con esperti del settore.

Da questo studio sono emerse le tecnologie abilitanti, i relativi benefici dal punto di vista puramente logistico, i fattori di successo e le sfide legate all’adozione dell’Industria 4.0.

Infine, il framework è stato validato da manager di DPDgroup i quali hanno analizzato il lavoro svolto e fornito interessanti feedback, contestualizzando il modello proposto nel loro business.

ABSTRACT

This thesis work has been developed according to the Double Degree Agreement with Cranfield University and it has been sponsored by DPDgroup UK Ltd.

In the current literature, there is a lack of effort to systematically review the state of the art of Industry 4.0 in the supply chain, and this is even more noticeable in logistics. Therefore, the objective of the thesis is to address this gap by developing a strategic framework to support European logistics companies towards the possibilities that Industry 4.0 offers. A grounded theory approach was adopted where an analysis the existing literature and empirical examples from industry experts led to identify the aspects that are likely to benefit logistics businesses.

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Enabling technologies, related benefits purely for logistics, success factors and the challenges surrounding adopting Industry 4.0 emerged from this research.

Finally, the framework was validated by industry experts from DPDgroup and their criticisms and opinions were collected.

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ACKNOWLEDGEMENTS

I would like to express my deep and sincere thanks to my supervisors Dr. Patrick McLaughlin and Professor Gino Dini for their guidance and precious life advice. I would like to thank also the DPD managers, in particular my industrial supervisor Mr Adrian Souto Moure, for their interest in this project.

I am grateful to my family for their support toward my education and for their unfailing love. A very special gratitude goes out to Mathias for his enthusiasm and for believing in me since the beginning of this experience.

I would like to thank all my friends at home, especially Giulia and Frediana, and in Cranfield, in particular Javier, for their encouragement throughout this year.

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LIST OF FIGURES

Figure 2-1 The four stages of the Industrial Revolution (DFKI, 2011 reproduced by Bauer et al., 2013) ... 4 Figure 2-2 Hype Cycle for emerging Technologies, 2016 (Gartner, 2016) ... 5 Figure 2-3 Industry 4.0 across the globe (I-SCOOP, 2017) ... 6 Figure 2-4 Industry 4.0 framework and contributing digital technologies

(Geissbauer, Vedso and Schrauf, 2016)... 7 Figure 2-5 Enabling technologies related with Industry 4.0 (Melanson, 2015) ... 7 Figure 2-6 Traditional supply chain model vs digitally enabled supply ecosystem

(Schrauf and Berttram, 2016) ... 18 Figure 2-7 Percentage of respondents indicating priority to invest in the supply

chain areas in the next 3 years (Industry 4.0 Insights, 2017, p. 3) ... 19 Figure 2-8 7 megatrends affecting transportation & logistics (Hausmann et al.,

2015) ... 20 Figure 3-1 Methodological approach based on grounded theory ... 31 Figure 4-1 Success factors for Industry 4.0 adoption ... 45

Figure A-1 Europe transportation services industry category segmentation: % share, by value, 2016 (MarketLine, 2016) ... 85 Figure A-2 Europe transportation services industry geography segmentation: %

share, by value, 2016 (MarketLine, 2016) ... 86 Figure B-1 Gantt chart ... 89

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LIST OF TABLES

Table 2-1 Enabling technologies ... 12

Table 2-2 Industry 4.0 in supply chain ... 17

Table 2-3 Challenges in the logistics sector ... 21

Table 2-4 Summary of challenges in logistics, I4.0 technologies and benefits . 26 Table 4-1 Summary of European logistics challenges ... 36

Table 4-2 Companies interviewed during data collection ... 37

Table 4-3 Results of interviews for industry 4.0 enabling technologies ... 39

Table 4-4 Frequency technologies mentioned during the interviews ... 42

Table 4-5 Summary of success factors for Industry 4.0 adoption ... 44

Table 4-6 Summary table of literature review and interviews for Industry 4.0 enabling technologies ... 47

Table 4-7 Summary table of literature review and interviews for Industry 4.0 success factors ... 51

Table 4-8 Summary table of literature review and interviews for Industry 4.0 adoption challenges ... 53

Table 5-1 Applicability of Industry 4.0 enabling technologies to logistics challenges ... 55

Table 5-2 Competitive advantage of Industry 4.0 enabling technologies in logistics operations ... 57

Table 5-3 DPD managers validation ... 63

Table C-1 Interviews: Industry 4.0 enabling technologies ... 90

Table D-1 Interviews: success factors for Industry 4.0 adoption ... 99

Table E-1 Interviews: adoption challenges for Industry 4.0 ... 102

Table F-1 Merging of I4.0 benefits VS logistics challenges ... 103

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LIST OF ABBREVIATIONS

3PL Third party logistics AGV Automatic guided vehicle AR Augmented reality

BCG The Boston Consulting Group CIM Computer Integrated Manufacturing CoBot Collaborative robot

CPS Cyber-physical system

DFKI Deutsches Forschungszentrum für Künstliche Intelligenz DPD Dynamic Parcel Distribution

ESD Electrical static discharge ETA Estimated time of arrival I4.0 Industry 4.0

IoT Internet of Things IT Information Technology M2M Machine to Machine PwC PricewaterhouseCoopers RFID Radio-Frequency IDentification SC Supply chain

SMEs Small and medium enterprises SMO Smart manufacturing object VR Virtual reality

WIP Work in progress

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1 INTRODUCTION

This study is an extensive review of the literature surrounding Industry 4.0 and an analysis of primary qualitative data extrapolated from several interviews with industry experts. The focus is on the logistics sector as the project sponsor is DPD Group UK Ltd, one of the UK’s leading time-critical parcel carriers. As this company wants to continue being the pioneer in this sector, it is interested in this new paradigm and its potentialities.

Industry 4.0 will change business processes, business models and entire supply chains while the assessment of the multiplicity of developments and concepts included in the term Industry 4.0 is a challenge that companies face before starting developing their own corporate strategies. Furthermore, it is unclear what important factors affect the potentialities of Industry 4.0 as there is a lack of research in this field, and in the logistics sector this gap is even more significant.

Therefore, this thesis aims to provide empirical information on the successful technologies and approaches of Industry 4.0 for European logistics companies. Along these lines, it will cover the academic research gap and it will help logistics practitioners to identify their steps towards Industry 4.0.

1.1 Background to the research

Industry 4.0 is recognised to be the fourth industrial revolution and the opportunities related to this new paradigm are comparable with the ones brought by the previous industrial revolutions. To sustain the competition on the market it is fundamental, for the most innovative companies, to be ready for the future paradigm shift.

Industry 4.0 holds huge potential for the next few years. The resource productivity and efficiency, which mean reduction of costs and increase of profit, together with the technological competitive advantage, will lead the pioneers to an increase of turnover enlarging the boundaries of the current market.

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1.2 Problem statement

The competition in the logistics sector and the aim of integrated systems through the enabling technologies in order to stay competitive on the market and lead it, are heading the companies to the Industry 4.0 concept which offers lots of opportunities but which are not properly documented.

In fact, in the light of the extant literature there is not a strategic approach neither model nor framework regarding the approaches logistics companies are taking for adopting Industry 4.0. Therefore, there is space and need for a comprehensive framework which supports logistics companies to visualise and understand the great possibilities, potentialities and enabling technologies that this new paradigm is offering.

1.3 Thesis scope, aim and objectives

1.3.1 Scope

The scope of this thesis is the evaluation of the approaches and enabling technologies of Industry 4.0 from a strategic point of view for European businesses where logistics operations are their core competency.

1.3.2 Aim

The aim of this thesis is to develop a framework that captures the enabling technologies of Industry 4.0 and transforms them into potential competitive advantage for European logistics organisations.

1.3.3 Objectives and research question

To meet the aim of this thesis, a list of objectives was defined:

1 - Literature review of Industry 4.0 enabling technologies and related benefits, of the challenges faced by European logistics businesses and of Industry 4.0 in logistics

2 - Data gathering among Industry 4.0 industrial practitioners to identify competitive advantages created by Industry 4.0 enabling technologies

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3 - Development of a framework that identifies the benefits of Industry 4.0 in European logistics operations

4 - Evaluation of the framework by European logistics experts

The research question this thesis is going to answer is: what are the benefits in terms of competitive advantage to European logistics operations by adopting Industry 4.0 approaches?

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2 LITERATURE REVIEW

2.1 Industry 4.0

Previous industrial revolutions created disruptive changes in manufacturing, represented in Figure 2-1 (Bauer et al., 2013; Kang et al., 2016). Industry 4.0 is defined as the fourth industrial revolution and it is described as the new paradigm where the production organisation is based on technology and autonomous communication among devices along the value chain to face increasing business challenges as rapid market changes (Barbosa et al., 2016; Maslarić, Nikoličić and Mirčetić, 2016; Musil, Laskovsky and Fialek, 2016), diversification of product life-cycle and smaller lots to deal with customisations for clients (Barbosa et al., 2016).

Figure 2-1 The four stages of the Industrial Revolution (DFKI, 2011 reproduced by Bauer et al., 2013)

The term “Industrie 4.0” was coined in 2011 by the German government fostering that physical structures are integrated into information networks, enabling both horizontal and vertical integration (Bauer et al., 2013). However, the basis of

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Industry 4.0 is not entirely new but it exploits and integrates the technologies individually adopted in Industry 3.0, readapting concepts as Computer Integrated Manufacturing (CIM), Machine to Machine (M2M) and Industrial IoT, present since 1980s (Cervelli, Pira and Trivelli, 2017). Therefore, some researchers renamed Industry 4.0 as “CIM Reloaded” which nowadays is gaining attention because of more advanced, reliable and cheaper technologies that could lead to integrate people, machines and products (Cervelli, Pira and Trivelli, 2017). Figure 2-2 shows in which phase of the Hype Cycle several Industry 4.0 technologies are located.

Figure 2-2 Hype Cycle for emerging Technologies, 2016 (Gartner, 2016)

Industry 4.0 (I4.0 in short) has different names and strategic approaches around the world (Figure 2-3).

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Figure 2-3 Industry 4.0 across the globe (I-SCOOP, 2017) 2.1.1 Characteristics of Industry 4.0

Industry 4.0 is characterised by several main features (Kovács and Kot, 2016; Smit et al., 2016). The first one is interoperability of a system to work with parts of another system. In I4.0 it is represented by cyber-physical systems (CPSs), conceiving a new level of socio-technical interaction. Furthermore, virtualisation (capability of connecting physical systems to virtual models) and decentralisation (ability of CPSs to make decisions by themselves, acquiring data from sensors applied in smart factories) are fundamental towards real-time responsiveness. Flexible adaptation of smart industries to changing requirements on the basis of automatic detection of a real situation is central. Finally, horizontal network connection of smart products, communicating with the equipment and the devices together with vertical network connection, where the communication is extended to the whole supply chain, enhance efficiency of resources.

2.1.2 Pillars of technological advancement

There is no complete agreement on the number of pillars enabling Industry 4.0. For instance PwC (Geissbauer, Vedso and Schrauf, 2016) defines 11 (Figure 2-4) while BCG defines 9 (Figure 2-5). However, there is consensus on that

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companies need to apprehend them first to obtain benefits. Nevertheless, some of the pillars identified by PwC can be incorporated as technologies or applications of broader categories: for example, mobile devices and location detection technologies can be included into IoT. Therefore, the BCG framework will be used as reference for presenting the enabling pillars, as it captures broader and consistently the I4.0 technologies.

Figure 2-4 Industry 4.0 framework and contributing digital technologies (Geissbauer, Vedso and Schrauf, 2016)

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2.1.2.1 Autonomous robots

Autonomous robots have a fundamental role in smart factories, leading to improve production flow quickly, automatically and efficiently (Lutz, Verbeek and Schlegel, 2016), combining local individual planning and centralised coordination. CoBots are collaborative robots that lead to higher flexibility, increased operators’ productivity and job satisfaction, relieving operators from non-ergonomic and repetitive tasks (Romero et al., 2016).

2.1.2.2 Simulation

Simulation is used in several fields to analyse, diagnose, optimise, validate and test operations and entire systems in order to support decision-making (Rosen et al., 2015; Shao, Shin and Jain, 2015). Leveraging real-time data in virtual models allows to test and optimise machine settings before compromising the physical changeover, driving down setup times and increasing quality (The Boston Consulting Group, 2017). The next wave in simulation and modelling is the Digital twin which is an exact copy of a real asset. In this context, the challenge has shifted from gathering data to analysing and selecting useful information from huge amounts of available data (Shao, Shin and Jain, 2015).

2.1.2.3 System integration

The majority of today IT systems are not fully integrated neither among enterprises nor within a company. With I4.0 a universal data-integration across companies networks will enable integrated value chains leading horizontal and vertical system integration (The Boston Consulting Group, 2017). Vertical procedure fully integrates internal manufacturing systems, leading high flexibility and adaptability towards a full customisation, whereas horizontal integration enables worldwide value networks, engaging value chain partners in order to achieve cooperation between enterprises (Zhou, Taigang and Lifeng, 2015; Marques et al., 2017) This new transparency of value chains faces challenges as information accessibility, standardisation and security (Lu, 2017).

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2.1.2.4 Internet of Things

The Internet of things consists in interconnected devices that through sensors and interfacing modules, can communicate each other and with a central server. A foundational technology of IoT is the RFID technology which has been widely utilised in logistics and supply chain management since 1980s but only in the recent years it is gaining momentum due to lower cost and increased capability of RFID tags (Sun, 2012). By using RFID readers, it is possible to automatically identify, track and monitor any smart manufacturing objects (SMOs) which are able to autonomously regulate and react, minimising human intervention and external triggers, allowing a decentralisation of decision-making and real-time responses (The Boston Consulting Group, 2017). Furthermore, a SMO knowing its design and usage history, can be able to suggest predictive maintenance, increasing the process efficiency (Barbosa et al., 2016). Many international organisations are involved in IoT standardisation, security and scalability which are the main critical components for enabling IoT widespread adoption, guaranteeing interoperability, compatibility and reliability (Miorandi et al., 2012; Xu, He and Li, 2014).

2.1.2.5 Cybersecurity

The current global operations are asking for more stringent security and privacy requirements as connectivity and standard communication protocols are linking more and more the actors of value chains globally. As a consequence, reliable identity and access management is fundamental and cybersecurity is critical for smart factories success. Cyber threats include theft of intellectual property, alteration of trade transactions and denial of process control (Albert, 2015). The role of computational intelligence is to identify, track, and analyse digital security threats as blackmail, stock market manipulation and espionage (He et al., 2016). Blockchain is a new research area and it is a distributed and electronic database that holds any information, records and transactions, organised in blocks and characterised by cryptographic hash functions (Sikorski, Haughton and Kraft, 2017).

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2.1.2.6 Cloud computing

Cloud computing is a platform that provides Internet services as software, hardware, storage sites and IT infrastructure resources as required by users through on-demand access (Zhou, Taigang and Lifeng, 2015). Industry 4.0 will require a higher sharing of data among sites and enterprises to achieve reaction times of milliseconds for data-driven decisions (The Boston Consulting Group, 2017). Products integrated in this cloud can lead to predictive maintenance and analytics as well as production optimization (Schmidt et al., 2015). However, nowadays security against external attacks cannot be provided only by physical measures, as access restriction or centralised security systems, instead IT security architectures and standards need to be developed (Bauer et al., 2013).

2.1.2.7 Additive manufacturing

Additive manufacturing, commonly called also solid free form fabrication, rapid manufacturing or 3D printing, is a new layer-by-layer fabrication technology, which confers enormous potentialities to products that can be produced and delivered more quickly, reducing time-to-market, and at a lower cost thanks to shape and geometries flexibility, material usage reduction, and lighter components (Larkin, 2016). Other benefits are arising in terms of design and complexity geometries feasibility, weight savings, smaller batches and product customisation. Furthermore, knowledge of product manufacturing processes is not essential anymore, as the part can be directly manufactured from 3D CAD files without manufacturing experience (Angioletti et al., 2016).

2.1.2.8 Augmented reality

This term refers to the technology that supplements the real space with virtual objects, enhancing the sensorial perception through mobile devices (e.g. tablet, phone), wearables (glasses, watch) or other visual assistance systems that guide operators in real-time. Significant benefits are in terms of time saving, job safety and productivity (Blümel, 2013). As it has to deal with humans, it is important to consider ergonomics aspects, a proper training as well as social acceptance issues (Van Krevelen, 2007).

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2.1.2.9 Big data

Ubiquitous sensors have generated enormous datasets, increasing difficulties in completing storage, analysis and management of volume, velocity and variety of Big data. Big data analysis will benefit manufacturing companies optimising processes, reducing costs, understanding better consumer spending patterns, improving operational efficiencies and equipment service (Mazzei and Noble, 2017; The Boston Consulting Group, 2017). However, information protection and privacy issues are the main challenges (Zhou, Taigang and Lifeng, 2015). Half of the existing small and medium-sized enterprises (SMEs) are dealing with Big data projects but the adoption is limited by complex and continually changing technologies, lack of platforms and limited packages providing algorithms which usually work for specific domains and tasks (Gil et al., 2017).

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Table 2-1 Enabling technologies

Technology Characteristics and Benefits Applications Challenges Source

Autonomous robots Autonomous Adaptive Flexible Self-maintenance Cooperative Collaborative Availability 24/7

Increase operator productivity

Robots CoBots

Safety

Energy autonomy

(Lutz, Verbeek and Schlegel, 2016) (Romero et al., 2016)

Simulation SC optimisation

Time-to-market reduction Reaction to market instability Support decision-making Setup times reduction

Modelling Simulation Digital twin Data gathering Data collection Information extrapolation

(Shao, Shin and Jain, 2015)

(Longo, Nicoletti and Padovano, no date) (Rosen et al., 2015) (The Boston Consulting Group, 2017) System integration

Value chains integration Flexibility Adaptability Customisation Transparency Interoperability Vertical integration Horizontal integration Accessibility Standardisation Security Multilingualism (The Boston Consulting Group, 2017) (Marques et al., 2017) (Lu, 2017)

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(Zhou, Taigang and Lifeng, 2015)

IoT Unique identification Localisation Interconnectivity Communication Tracking Self-regulation Self-organisation Decentralised decision-makings Real-time responses

Resource and energy monitoring Predictive maintenance RFID Wireless devices Mobile devices Sensors devices GPS Standardisation Security Scalability

(Xu, He and Li, 2014) (Zhang, Chen and Lu, 2012) (Miorandi et al., 2012) (The Boston Consulting Group, 2017) (Barbosa et al., 2016).

Cybersecurity Identity management

Access management

Automatic detection of malware Tracking, analysing, identifying digital security threats

Security by design Security by architecture Blockchain

Theft of intellectual property Alteration of data

Denial of process control

(Albert, 2015) (He et al., 2016) (Bauer et al., 2013) (Sikorski, Haughton and Kraft, 2017) Cloud computing On-demand access Global access Sharing data Data collection Supporting decision-making Scalable compute capacity

Software Hardware Platforms IT infrastructure resources Storage capability Security Privacy Idle data Data mining

(Schmidt et al., 2015) (The Boston

Consulting Group, 2017)

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Predictive maintenance Predictive analytics Additive manufacturing Costs saving Material savings Energy savings Design flexibility Smaller lots

Flexible manufacturing strategies Reducing time-to-market Product customisation 3D scanning techniques (Larkin, 2016) (Angioletti et al., 2016) Augmented reality Costs saving Time saving

Increasing job safety

Increasing operators’ productivity Improving work procedures Presentation of complex systems Improving decision-making Mobile devices Wearables Visual assistance systems Operators’ training User interaction Technological limitations Social acceptance (Blümel, 2013) (Van Krevelen, 2007)

Big data Optimising processes

Reducing costs

Improving operational efficiencies Saving energy

Improving equipment service Supporting real-time decision-making Customised marketing Social media analysis Information protection Privacy issues Lack of platforms

Algorithms work in specific domains

Lack of technical knowledge Filtering valuable information

(The Boston Consulting Group, 2017)

(Gil et al., 2017) (Mazzei and Noble, 2017)

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2.1.3 Key requirements towards Industry 4.0

As this phenomenon is worldwide, and will involve networking of several companies through collaborative partnerships (Wende and Kiradjiev, 2014; Maynard, 2015; Mazak and Huemer, 2015), common standards are fundamental for systems interoperability. However, sharing knowledge with other companies could be difficult and strategic acquisition could be preferred (Geissbauer, Vedso and Schrauf, 2016).

Furthermore, employees’ competence profile required will be more related with critical thinking capability and soft skills, while decision-making will be part of the work of machines. Additionally, a key requirement for the success of Industry 4.0 is reliable and high-quality communication networks also from the safety and security point of view, with information protection against misuse and unauthorised access (Bauer et al., 2013; Cervelli, Pira and Trivelli, 2017). In conclusion, standardisation, work organisation, new business models and skilled workers are essential preconditions for the successful implementation of Industry 4.0 (Maslarić, Nikoličić and Mirčetić, 2016). Clearly the economic investment required for the digital transformation is quite intensive and an estimation of the return on investment should be carried out (Sanders et al., 2016).

2.1.3.1 Strategy development

In order to take advantage from Industry 4.0 opportunity, it is vital a correct strategic decision, aligning firm’s strategy with its competitive goals. Two main strategic approaches are get-ahead and catch-up whether the top management wants to be a pioneer or a follower in the new technology business, respectively (Garrett, Covin and Slevin, 2009). In the first case the firm will obtain first-mover competitive advantages in terms of reputation, cumulative learning, economies of scale and preferred channels to suppliers. On the other hand, catch-up strategy consists in a set of actions that aim to copy the successful cases, attacking leaders on their failures (Li et al., 2012). However, although awareness of technology potentialities is vital in the new paradigm (Basl, 2017), adding technologies without a clear business strategy and organisational changes will

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not lead to the expected benefits, whereas if digital initiative is driven by clear business drivers and requirements, it will improve the likelihood of a successful investment (Peppard, 2016). Furthermore, some researchers state that successful implementations of specific Industry 4.0 strategies principally depend on appropriate internal capabilities, and in particular, knowledge will become the only source of competitive advantage (Sher and Lee, 2004). Furthermore, early practical experiments, trials and pilot projects are key in Industry 4.0 (Wende and Kiradjiev, 2014).

2.1.3.2 Employee engagement

Talent development and employee engagement at all levels are key aspects of I4.0, whereas the simple implementation of new technologies can easily fall flat (Kane et al., 2016). Furthermore, a more engaged workforce is aware of business context, can increase job satisfaction and willingness to accept changes, increasing productivity by 22% (Baldoni, 2013). Failures in engaging shop floor employees lead to a lack of changing mind-set and attitude, representing one of the major obstacles in lean transformation (Chay et al., 2015). A participation-oriented approach should be taken into consideration in the implementation of Industry 4.0 and change management initiatives should be considered from the beginning (Acatech, 2016). According to Blue (2017) “smart manufacturing starts with people, not with machines”. The author states that a company culture based on the synergy between innovation, commitment, community and teamwork provides a sustainable competitive advantage more powerful than faster machines. In this sense the fourth revolution has to start with a smart workforce that is engaged, empowered and that believe in the culture of the organisation and its leaders before starting with technologies.

2.1.4 Industry 4.0 in the supply chain: benefits

Industry 4.0 involves all phases and actors of supply chains. Some of the benefits of I4.0 are presented in Table 2-2 (Hofmann and Rüsch, 2017). Additionally, BCG (Rüßmann et al., 2015) identifies factory floor logistics as the area where the greatest cost savings (about 50%) are expected in the next five to ten years

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through production and logistics processes integration and autonomous consignment systems.

Table 2-2 Industry 4.0 in supply chain

Phase Key aspects of 4.0 Benefits

Production Planning

Real-time traceability Real-time consumption SC integration

Accurate production planning Reduction of bullwhip-effect

Production order

Integrated material and information flow

Transparency

Requesting the exact amount needed Disposition and production Real-time data Decentralised decision-making Improved suppliers’ production planning

Suppliers additional flexibility Reduced cycle time

Delivery Localisation Condition monitoring Simulation Integrated systems Just-in-time delivery Route optimisation

The transition from traditional linear supply chains to digitally integrated supply chain ecosystems is allowed by transparency, communication, collaboration, flexibility and responsiveness (Schrauf and Berttram, 2016) (Figure 2-6).

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Figure 2-6 Traditional supply chain model vs digitally enabled supply ecosystem (Schrauf and Berttram, 2016)

The Industrie 4.0 Working Group identified the potentialities of this new paradigm from a supply chain point of view (Bauer et al., 2013): meeting individual customer requirements, manufacturing even one-off items profitably through more dynamic engineering processes, allows to face the volatility of customer demand. Furthermore, innovative services and ways of creating value are leading toward servitisation of products, leading companies towards service-based competitive strategies. To achieve these results, companies are aware of the need to prioritise the areas to invest in. According to Figure 2-7 (Industry 4.0 Insights, 2017), the first area to investigate is IT vertical integration, followed by horizontal integration with supply chain partners which is essential mostly for companies working globally.

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Figure 2-7 Percentage of respondents indicating priority to invest in the supply chain areas in the next 3 years (Industry 4.0 Insights, 2017, p. 3)

2.2 Logistics

According to Kovács and Kot (2016), “logistics is the planning, organising and coordinating of the flow materials, information, energy, money and values inside a logistic system”. The aim of logistics is to provide products in adequate quality, quantity and time from a specific origin to a given destination at minimal cost. During the last years, globalisation and uncertainty about how markets will evolve has led logistics companies towards the need of being aware of the supply chain they participate and their roles inside it (Kot, 2014). The growing competition in the global trade, shorter lifecycle of more complex products and constant cost reduction targets (Slusarczyk and Golnik, 2015) together with fluctuating customer demand, drove the sector towards new technologies, business processes and the concept of a global supply chain (Gereffi, 2011; Stojanović, 2012). One part of logistics is transportation and in 2021 the European one, is expected to increase by 19.6% since 2016 (MarketLine, 2016). For further analysis of this specific market please refer to Appendix A.

Logistics function is changing from purely operative to strategic, becoming one of the most important tiers of the supply chain. As e-commerce purchases and international shipping are increasing, distances are becoming longer, while volume locally delivered decreases and frequency increases, and consequently,

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the need of supply chain integration and cooperation arises (Kovács and Kot, 2016). On McKinsey report (Hausmann et al., 2015), seven megatrends are identified (Figure 2-8). In particular, the digital trend is expected to reduce costs and add more value to services through automation and big data analysis, requiring collaborations with data providers.

Figure 2-8 7 megatrends affecting transportation & logistics (Hausmann et al., 2015)

Furthermore, during the past decades, most companies tended to focus their business on core competencies, outsourcing logistics issues to third parts, referring to it as “vertical disintegration” (Stojanović, 2012). Cost reduction, service improvement, operational flexibility and business focus are the main motivators. The level of outsourcing varies thus, relationships with logistics providers goes from spot contracts to long-term agreements and strategic alliances (Stojanović, 2012). The main strategic alliance is the Third party logistics (3PL) and according to Mininno (2016), a commonly cited drawback of this strategy is the loss of control by manufacturers over the process. Despite abundance of research studies in this field, the body of literature generally

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supports that there is not an ideal partnership type but it depends on the given business conditions.

2.2.1 Logistics challenges

The transition from mass production to unique production or small batches (push to pull approach), results in some challenges across supply chain as shorter lead times, uncertainty of customer demands, need of flexible reaction and smaller stock (Kovács and Kot, 2016). Particularly, logistics companies are facing challenges related with higher expectations and clients’ preferences in terms of short time and costs.

According to McKinsey, KPMG and PwC, the challenges identified in logistics are the ones presented in Table 2-3. In particular, McKinsey (Joerss et al., 2016) highlights that a growing group of consumers desires cheaper forms of home delivery and at the same time, the acceptable shipping time has decreased to same day or instant delivery, asking for reliability of the time window. According to KPMG (2016) increasing operational efficiencies and strategic partnership are fundamental, while PwC (2013, 2014) identifies as main issues driver training, cost of fuel and having old fleet on roads. Costs and time-to-market reduction, together with meeting customer requirements, are common challenges identified by all three consultancy companies. In Table 2-4 these challenges are categorised and merged in 8 groups according to the findings of Industry 4.0 in logistics.

Table 2-3 Challenges in the logistics sector

Challenges Source

Cheaper home delivery

Mckinsey&Company (Joerss et al., 2016) Same day delivery

Time window reliability Instant delivery

Customer demand volatility

(McKinsey&Company, 2010)

Complex customer demand patterns Improving customer service

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Challenges Source Increasing consumer expectations

Environmental concerns

Improving products/services quality Cost pressure in logistics/transportation Increasing pressure from global competition Reducing operating costs

Reducing inventory level Time-to-market

Reducing capital costs Tracking material

Consistent and stable IT performance

(KPMG, 2016) Tracking material

Improving business processes Increasing operational efficiencies Enabling business change

Saving costs

Strategic partnerships

Accelerate product development Agility and responsiveness On time delivery Data Security Customer engagement New products/services Budget constraints (PwC, 2014) Driver recruitment Fuel cost

Internal communication breakdown Driver training

Manage old fleet Time constraints H&S

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Challenges Source Increasing customer requirements

(PwC, 2013) Preparing SC for up/downwards volume flexibility

Competitive pressures Skills shortage

Automate and increase transparency Emerging markets growth

SC security and risk Sustainability

2.2.2 Industry 4.0 in logistics

As in manufacturing sector the steps towards Industry 4.0 are becoming more and more real and tangible, a transformation in logistics processes is fundamental to exploit the benefit of Industry 4.0 from the point of view of the entire supply chain. Analysing, communicating, designing, understanding and optimising are the aspects that can be improved through Industry 4.0 applications in logistics (Maslarić, Nikoličić and Mirčetić, 2016). Furthermore, global competition and dynamic customer demand require efficient logistical processes to face the challenges of this sector (Kovács and Kot, 2016; Chen and Tsai, 2017; Majeed and Rupasinghe, 2017) such as growing customised and flexible production that lead towards modular logistics (Hachmann, Keßler and De La Torre, 2016) and autonomous delivering (Hofmann and Rüsch, 2017). Therefore, digitalisation of logistics processes leads to “logistics transformation” and “smart logistics” solutions creation (Maslarić, Nikoličić and Mirčetić, 2016).

Condry and Nelson (2016) confirm that the logistics sector has been involved in the fourth industrial revolution where IoT and smart devices allow an interoperability, connectivity and transparency between devices and systems. IoT is playing an important role in logistics as more and more bar codes, RFID tags and sensors are located on Smart Manufacturing Objects allowing real-time monitoring of freight across supply chains. Furthermore, the increasing sensing, networking and communication of vehicles can be used to share under-utilised resources among vehicles in parking space or on road, providing driving

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directions (Xu, He and Li, 2014) and to monitor temperature and humidity in refrigerator trucks (Zhang, Chen and Lu, 2012). The related generated data will then support decision-making as logistics planning and scheduling (Röschinger et al., 2015; Zhong et al., 2015, 2017). Additionally, planning for maintenance of transport means could become more predictable as RFID and sensors are gathering huge amount of data. For example, Lufthansa airlines applied RFID to improve airplane maintenance and the Chinese ministry is studying sensors on key parts of high-speed trains (Fraga-Lamas, Fernández-Caramés and Castedo, 2017). However, it is clear a lack of implementation strategies together with a shortage of operative professionals (Straub et al., 2016; Willeke and Kasselmann, 2016).

Growing traffic volumes on the road and increasing request for short time delivery by clients, has led to new solutions as autonomous trucks, drones and vehicles telematics. According to Pwc (Schmahl, Tipping and Elliot, 2017), Amazon Prime Air will deliver parcels by drones within 30 minutes of order placement while, internally, advanced robotics to optimise loading/unloading operations are already experimented by UPS, DHL and FedEx (Schmahl, Tipping and Elliot, 2017). In addition, logistics providers can take over final assembly and product customisation with additive manufacturing technologies, delivering closer to the point of demand, offering new logistics services as postponement. In this sense, logistics providers can set up a global 3D printing infrastructure coupled with a software database of digital models, printing spare or customised parts only on-demand at the nearest 3D printing facility in the local distribution centre (e.g., a hub or airport). For example, Amazon has patented mobile 3D printing delivery trucks (Amazon Technologies, 2015): when a shopper selects a product from Amazon, the nearest truck is triggered to 3D print and deliver the product, removing the need for any storage (DHL, 2016).

These innovations together with augmented reality, cloud computing and IoT devices will save costs in terms of labour and training, scheduling efficiency and transportation. For example, tyres manufactured by Michelin and embedded with sophisticated telematics can save €3,200 annually for long-haul trucks and at the

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same time a reduction of 8 metric tons of CO2 emission is achieved (Accenture, 2016).

Mckinsey (Hausmann et al., 2015) states that forecasting capability is improved by real-time information about fleet location and traffic, allowing routes optimisation, logistics planning and scheduling, while RFID, NFC and beacons will be beneficial for monitoring and tracking products (Accenture, 2016). An efficiency optimisation can be obtained by these technologies: in particular, according to Mckinsey (Baur and Wee, 2015; Hausmann et al., 2015) autonomous trucks will reduce the number of accidents by 50%, augmented reality will help operators for more precise picking, decreasing error rates by 40%, and at the same time blockchain will enable transparent communication and transactions, reducing frauds and possibly cutting out distributors which add costs for clients.

Table 2-4 shows a summary of the findings from both academic papers and consultancy companies, some case studies and quantitative benefits. The author grouped the information and associated them with the challenges logistics companies are currently facing.

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Table 2-4 Summary of challenges in logistics, I4.0 technologies and benefits

Challenges Technologies Benefits Case study Source

Lead time reduction

Drones Packages shipped within 30 minutes of order placement

Amazon Prime Air DHL

(Schmahl, Tipping and Elliot, 2017)

(Joerss et al., 2016) (DHL, 2016)

Autonomous truck Delivery time reduced by 30% Uber (Schmahl, Tipping and Elliot, 2017)

(Hausmann et al., 2015) (Hofmann and Rüsch, 2017)

Vehicle telematics Real-time logistics (Schmahl, Tipping and Elliot, 2017)

Advanced robotics Cooperation with human operators

UPS, DHL, FedEx (Schmahl, Tipping and Elliot, 2017)

Additive manufacturing

Ultra-express delivery Amazon: mobile 3D printing delivery trucks

(DHL, 2016)

Piece picking robot Quicker and more precise picking

(Hofmann and Rüsch, 2017)

Security

Blockchain Possibility to skip intermediaries (distributors) which increase costs for customer

Skuchain (Schmahl, Tipping and Elliot, 2017)

RFID Better monitoring of products, inventory and assets

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NFC Beacon

Cost saving

Autonomous truck Transport costs reduced by 25 to 40%

(Hausmann et al., 2015)

Advanced robotics Reduction of labour costs UPS, DHL, FedEx (Schmahl, Tipping and Elliot, 2017)

(DHL, 2016) Augmented reality Reduction of training time for

seasonal workers

Knapp AG (Baur and Wee, 2015)

Cloud computing Scheduling efficiency maximisation

IoT €3,200 annual saving for long-haul trucks

Michelin (Accenture, 2016)

Sensors 10% of maintenance bill reduction

Trenitalia Frecciarossa (Fraga-Lamas,

Fernández-Caramés and Castedo, 2017)

Additive manufacturing

Cut inventory costs

Highly individualised products New market segments creation Value creation opportunities (digital warehouses, trusted service provision of 3D data hosting and exchange)

Amazon: mobile 3D printing delivery trucks

(DHL, 2016)

Blockchain Disintermediation decreasing costs for customers

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Traceability

Vehicle telematics More efficient fleet, routes optimisation, traffic forecasting

Cloud based analytics

Truck location (Schmahl, Tipping and

Elliot, 2017) RFID

NFC beacon

Better monitoring and tracking inventory

Truck location

BMW (iDrive system) (Accenture, 2016) (Zhong et al., 2017)

Blockchain Record of all transactions (Schmahl, Tipping and Elliot, 2017)

Forecasting capability

IoT Real-time information Local Motors: IBM IoT devices implemented in driverless trucks

(Röschinger et al., 2015) (Condry and Nelson, 2016)

Big data Capacity planning

Support decision-makings as logistics planning and

scheduling

(Hausmann et al., 2015) (Zhong et al., 2015)

Vehicle telematics More efficient fleet, routes optimisation and traffic forecasting

Environmental sustainability

Cloud computing Sharing economy model Convoy: its software match deliveries coming into an area with trailers available from that area

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Big data Real-time data on fuel

consumption saving millions of dollars

U.S. Xpress (Hausmann et al., 2015)

IoT Fuel consumption reduction of 2.5 litres every 100 km, cutting 8 metric tons of CO2 emission

Michelin (Accenture, 2016)

Efficiency optimisation

Autonomous truck Number of accidents reduced by 50%

Uber (Hausmann et al., 2015)

Augmented reality Quicker and more precise picking

Decreased error rate by 40% 25% performance increase Real-time devices connectivity to WMS

Knapp AG: Pickers wear a headset with vital information on a see-through display helpful to locate items, build more efficient pallets DHL: order picking device

(Baur and Wee, 2015) (DHL, 2016)

Piece picking robots

Quicker picking (Hofmann and Rüsch,

2017) Blockchain Transparent communications

between carriers and shippers

Skuchain (Schmahl, Tipping and Elliot, 2017)

Maintenance

RFID Sensors IoT

Key parts predictive maintenance Real-time data Predictive models Actions triggered Lufthansa airline Trenitalia’s Frecciarossa Siemens (Zhang, 2012) (Fraga-Lamas, Fernández-Caramés and Castedo, 2017)

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2.3 Knowledge gap and research location

The extensive literature review indicated that there is no much published about Industry 4.0 in logistics. For example, Scopus database generated 37 papers, 25 of which were considered not appropriate due to the subject area. A further research on the major consultancy companies’ reports was carried out noticing that the main publications are about manufacturing.

Furthermore, although a deep research about challenges faced in logistics company and validated by consultancy companies had been carried out in all the databases accessible from Cranfield Library, there were minimal results. For this reason, the author relied on the major consultancy companies reports.

Therefore, it can be concluded that there is very little published in this area and it is evident a significant research gap, where evidently the impact of technologies and the related benefits in logistics is missing.

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3 METHODOLOGY

Figure 3-1 shows the methodology process adopted. This is structured in 5 phases based on grounded theory. The Gantt chart that illustrates the project schedule is shown in Appendix B.

Figure 3-1 Methodological approach based on grounded theory

3.1 Grounded theory

In order to conduct an exploratory research, the author chose the grounded theory developed by Glaser and Strauss in 1967. It is based on the progressive identification and integration of categories of meaning from a low level (descriptive) to an higher level (analytic) of abstraction, starting from data, with the purpose of identifying the links among the instances and group them, to recognise the shared central features (Willig, 2013). The aim of this approach is to analyse the experience and insights of the interviewees on a specific topic without preconceptions, where categories emerge from data. This qualitative method allowed to move from the data gathered from literature and interviews to the theory embedded in the framework. After an initial research, a quantitative approach was discarded because this thesis represents an exploratory study,

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and consequently a selected group of interviewees from different industry sectors with professional insights regarding Industry 4.0, was considered appropriate.

There are different versions of grounded theory applicable, however, the “abbreviated version”, which consists in implementing the theory within the only texts analysed, was chosen as time constraints prevented the implementation of the full version (Henwood and Pidgeon, 1992, 1995). This approach was based on a diverge-converge attitude where during the first phase, the author interacting with qualitative data, modified the research questions to explore new issues that arose from a particular answer to collect further information, in order to identify a wide range of descriptive categories. This was followed by the “Theoretical sampling” phase to refine the progressively analytic categories until the theoretical saturation was achieved as no new categories emerged (Willig, 2013). This was achieved in the seventh interview.

3.2 Literature review: systematic approach

As a qualitative research, this thesis contains information about the logical and rational approach towards the study. In the first phase, conducting an extensive literature review was of paramount importance to collect existing secondary data about Industry 4.0. Furthermore, it was fundamental to identify a systematic and reproducible search strategy performed on ABI Inform Complete (ProQuest), Scopus, Ebsco and Emerald. For this reason, the basic string used was: (TITLE-ABS-KEY (("industrie 4.0" OR "industry 4.0" OR "smart factory" OR " smart manufacturing" OR "fourth industrial revolution" OR "4th industrial revolution")) AND TITLE-ABS-KEY (“logistic*” OR "transport*")) AND (LIMIT-TO (DOCTYPE, "ar ")) AND (LIMIT-TO (SUBJAREA, "ENGI") OR LIMIT-TO (SUBJAREA, "BUSI") OR LIMIT-TO (SUBJAREA, "DECI")) AND (LIMIT-TO (LANGUAGE, "English") OR LIMIT-TO (LANGUAGE, "German")). As it is shown, the available information was limited to peer reviewed articles and the field selected was “Business” AND “Engineering” AND “Decision Sciences”.

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3.3 Interview strategy

This was the primary and empirical data collection phase which was carried out in parallel with the literature review. According to grounded theory the questions to the interviewees were open-ended avoiding “yes/no” answers and as the interview progressed, questions became narrower to invite detailed discussion of the topic (Charmaz, 2014). A semi-structured approach was chosen, as it allowed to conduct qualitative interviews, which are directed conversations that enable researchers to access information about phenomena otherwise difficult to understand from the respondent’s prospective (Easterby-Smith, Thorpe and Jackson, 2015).

As grounded theory is a comparative method that looks at the same event from different situations, it was consistent to study a number of different interviews handled by managers working in different organisations.

3.3.1 Interview best practices

Interview approach was structured to involve clear, short and easy to understand questions avoiding abstract concepts and jargon, giving time to interviewees to answer (Bryman and Bell, 2015). At the beginning of the interviews a brief explanation of the purpose of the research and an introduction to Industry 4.0 was essential to create a common understanding of the field (Collis and Hussey, 2014).

3.3.2 Avoiding bias

In order to gain unbiased data and preventing predictable answers, there were some important precautions (Collis and Hussey, 2014; Easterby-Smith, Thorpe and Jackson, 2015):

- Avoid questions that ask more than one question

- Repeat the last words of the interviewee before asking more details

- Mirroring and expressing in the interviewer’s words what the interviewee has just said to force him rethinking and amplifying the answer

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- Show attention when the respondent is answering, but do not show approval or disapproval

- Read each question slowly, using the same intonation and emphasis

- Good questions are “What did you mean by that?” and “What makes you say that?”

An additional insight was given by Sackmann (1991), who suggests to the interviewer to imagine the answer before the interviewee’s answer in order to prove the understanding of the interviewee’s perspective.

The initial question asked to all the interviewees was: “In which areas do you think Industry 4.0 could be applied to improve the performance of your business?”. After this first question, interviewees started speaking and the further questions usually asked were: “What are the things the company would need to do to make that work effectively?”; “What do you think are the challenges of implementing this technology?”; “What type of improvement do you envisage coming from this implementation?”.

3.4 Data analysis

Grounded theory coding is made of two phases: an initial phase where each word, line or segment of data is named remaining open to all possible theoretical directions; this is followed by a focused phase that selects the most significant codes to sort, integrate and organise large amounts of data (Charmaz, 2014). The author decided to interactively code segments of data contained in the recorded and transcribed interviews (using Google Speech to text API), choosing the words that constituted the author’s core conceptual code in order to create an analytic frame from which it was possible to build the analysis. Categories were identified through a colour-coding method, which helped to quickly move from the open coding phase to the focused coding, especially suited for visually-minded researchers (Stottok, Bergaus and Gorra, 2011). Memo-writing during the interviews was a pivotal step to increase the level of abstraction of ideas, capturing connections between information and making the work concrete and manageable. The data analysis was conducted in parallel with data gathering in

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order to have an efficient time management and to control the saturation achievement.

3.5 Framework proposition

The final framework was the outcome of a process made of several table creation, where primary and secondary data were compared, matched and merged. The findings were organised around the key categories identified through a discussion with industrial professionals, and the appropriate technologies were selected against the requirements for logistics.

3.6 Framework validation

The validation of the proposed framework was carried out through a workshop with DPD managers and their criticisms and opinions were collected.

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4 DATA COLLECTION AND ANALYSIS

4.1 Challenges in European logistics company

To understand the daily challenges faced by European logistics companies, two interviews were conducted with DPD managers. The results were compared with the literature findings and are summarised in Table 4-1.

Table 4-1 Summary of European logistics challenges Challenges

Literature Head of DPD Hubs DPD Workforce planning manager

Lead time reduction

Fast delivery service; quicker last-mile delivery

Loading trailer faster

Security Cost saving Traceability

Volume forecasting Unpredictability of: arrival time of trailers; number of parcels arriving; number of trailers needed; number of workers needed

Environmental sustainability

Using proper technologies to make the work of the

operators easier, enhancing their performance and reducing error rate;

coordination and visibility of operations on the shop floor

Loading trailer faster;

optimisation of space inside trailers

Maintenance

4.2 Opportunities of Industry 4.0 from empirical examples

In pursuance of challenges that companies are facing and the opportunities that Industry 4.0 could offer, seven face-to-face and semi-structured interviews were carried out to important industry experts of this new paradigm. Saturation was achieved in the seventh interview. The presentation of the interviewees is

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introduced in Table 4-2. Due to confidentiality purposes, the names of the companies are anonymised throughout the document.

Table 4-2 Companies interviewed during data collection Company

industry

Respondent’s

role Qualification for interview

Interview 1 Technology consulting Business development manager 2 years of experience in Cyber physical systems and IoT to industrial use cases

Interview 2 Consulting Innovation analyst

10 years of experience in autonomous systems, sensors and ICT Interview 3 Consulting Head tech

facilitation team

6 years of experience in I4.0

Interview 4 Research

centre Digital engineer

1 year of experience in simulation of manufacturing processes Interview 5 Paper industry Digitalisation and technology specialist 1 year of experience in I4.0 Interview 6 Electronics company Head of factory operations 10 years of experience in I4.0 for manufacturing processes Interview 7 Retail Technical delivery manager 10 years of experience in automation and I4.0 for retail sector

4.3 Results of interviews

A step of reflection to merge the collected data was carried out in order to identify the main categories. In the majority of the interviews the challenges and the strategic steps for Industry 4.0 came out as capital elements before and during the implementation phase. Therefore, the categories identified are:

 enabling technologies

 success factors, and

 Industry 4.0 adoption challenges. 4.3.1 Enabling technologies

Several challenges are addressed by Industry 4.0 enabling technologies. The challenges discussed during the interviews, with the exception of “environmental

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sustainability” which has never been mentioned, are the same found in the literature review (lead time reduction, security, cost saving, traceability, forecasting capability, efficiency optimisation, maintenance). However not all the challenges resulted to be important in the same way. In fact, “cost saving” and “efficiency optimisation” were discussed in all the interviews, while “forecasting capability” only during two of them. Nonetheless those challenges can be principally addressed by the main technologies cited which are IoT, simulation and advanced robotics. The results of the interviews are presented in Table 4-3 where the enabling technologies have been associated with the challenges the interviewees referred them to. The numbers in parenthesis denotes the serial number of the interview from Table 4-2. For further details see Appendix C.

With the hypothesis that the relevance of enabling technologies for a specific company is proportional to the times the interviewees discussed them during the interviews, the most widely cited by every interviewee is IoT. Simulation and advanced robotics are in the following positions and they are mentioned only by three interviewees. However, cybersecurity and additive manufacturing are mentioned very few times.

Table 4-4 shows the incidences of the nine enabling technologies in the seven interviews.

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Table 4-3 Results of interviews for industry 4.0 enabling technologies

Lead time reduction

Agv, Cobot, Robot (Shuttle): cooperation with human operators, increasing productivity (2) Autonomous truck: reduced delivery time (2)

3D printing: same day manufacturing at the local place (3)

Simulation: increased visibility and feasibility test within the company, better decision-making, higher awareness of capability and faster response to market (4)

Smart sensors: smart racking with scales underneath Vauxhall bins and direct connectivity/integration trough 4G network with suppliers to notify when level of parts is low, vertical systems integration (6) RFID: Data sharing with suppliers to have automatic refill of parts through sensors and automatic alarms, enabling visibility along the value chain (3) (4)

AR: 3D glasses for cell design reducing time by 50% (6)

Optimal interplay of components (roads/warehouse), containers (pallets) and people (order pickers, truck drivers) interacting real-time, creating intelligent systems (3)

VR cave: maintenance problems detection before buying and installing new machines (6)

13 Smart-Storage-System rack lines, 16 OSR Shuttle rack lines with 320 shuttles connected to the conveyor network by 20 high-speed lifts for faster-moving goods developed in collaboration with Knapp, reducing time for delivery to stores from 96 hours to 36 hours (7)

Vehicle telematics: transparency along the value chain, better decision making, lead-time reduction (5) Digital twin: higher flexibility and quicker product to market (6)

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RFID tags and tracking system on products to control them, production and products availability. Doors of shop floor with embedded RFID readers to know what comes in (6)

Cybersecurity to protect intellectual property and data (4)

Cost saving

Digital twin: of products and systems to maximise asset availability and streamline spare parts procurement (1)

Simulation: feasibility tests reduce costs. Jack tool to simulate operators' performance, reducing rework costs. VR cave maintenance problems prediction (4) (6)

RFID: data sharing with suppliers to have automatic refill of parts and automatic alarms, enabling visibility along the value chain. Automatic stock availability, reducing headcount (3) (7) (4)

Data & IoT: data sharing with suppliers, higher transparency in SC, reduced WIP and system integration (3) (4) (5)

Smart racking with scales in the box and direct connectivity/integration with suppliers to notice when level of parts is down (6)

Advanced robotics: cobots facilitate workers' operation, saving labour costs. One robot saves 3 workers (2) (6) Shuttle (2)

Agv: 24/7 operation, headcount reduction (2)

Blockchain: disintermediation decreasing the cost for the customer (4) Additive manufacturing: small prototypes and spare parts of machines (6) AR: 3D glasses instruction (6)

Traceability

Connected fabrics: in the textile industry sorting capabilities are essential as it is characterised by fragmentation of items and fabrics on large scales. Knowing exactly products location is vital (1) (2) Connected human tissues: as diabetic devices that release insulin (1) (2)

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RFID: product localisation, warehouse availability. RFID portals tracing ESD with 99.99% accuracy (4) (6)(7) Blockchain: recording all the transactions (4)

Vehicle telematics: trucks location for route optimisation and to identify delayed trucks and process earlier vehicles arrived prematurely (3)

Big data and data analytics: better forecasting and planning, reducing bullwhip effect e.g. vehicle telematics. Accuracy improvement (4) (5)

Warehouse automation interpreting data of sales and schedule in/out from warehouse (4)

Environmental

sustainability No significant results from interviews

Digital twin of systems: maximisation of asset availability, streamlined spare parts procurement, process visibility. Real-time KPIs monitoring (1) (2)

IoT: automotive sector is “uberising” so there is a transition from transferring ownership of means, offering a service of mobility without buying a car (1)

Big data: data analytics enables better understanding of processes and clients’ needs and therefore market positioning (1)

Big data & IoT: route optimisation outside and inside to avoid traffic congestion and optimise docking (5) (3) Data Connectivity to not manipulate it multiple times (6)

AR: intelligent sequences the operator has to follow avoiding mistakes (6) Screens on the shop floor with real-time performance indicators (6)

Simulation: space and assets optimisation (6)

EVALUATION OF THE BENEFITS TO EUROPEAN LOGISTICS OPERATIONS BY ADOPTING INDUSTRY 4.0 APPROACHES

DIPARTIMENTO DI INGEGNERIA DELL’ENERGIA DEI SISTEMI,

T

C

Evaluation of the benefits to European logistics

operations by adopting Industry 4.0 approaches

SOMMARIO

ABSTRACT

ACKNOWLEDGEMENTS

TABLE OF CONTENTS

LIST OF FIGURES

LIST OF TABLES

LIST OF ABBREVIATIONS

1 INTRODUCTION

1.1 Background to the research

1.2 Problem statement

1.3 Thesis scope, aim and objectives

2 LITERATURE REVIEW

2.1 Industry 4.0

2.2 Logistics

2.3 Knowledge gap and research location

3 METHODOLOGY

3.1 Grounded theory

3.2 Literature review: systematic approach

3.3 Interview strategy

3.4 Data analysis

3.5 Framework proposition

3.6 Framework validation

4 DATA COLLECTION AND ANALYSIS

4.1 Challenges in European logistics company

4.2 Opportunities of Industry 4.0 from empirical examples

4.3 Results of interviews