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Identifying Key Performance Indicators to be used in Logistics 4.0 and Industry 4.0 for the needs of sustainable municipal logistics by means of the DEMATEL method
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Transportation Research Procedia 39 (2019) 534–543 www.elsevier.com/locate/procedia
Green Cities 2018 Green Cities 2018
Identifying Key Performance Indicators to be used in Logistics 4.0 Identifying Key Performance Indicators to be used in Logistics 4.0 and Industry 4.0 for the needs of sustainable municipal logistics by and Industry 4.0 for the needs of sustainable municipal logistics by means of the DEMATEL method means of the DEMATEL method a a a a
Witold Torbackia*, Kinga Kijewskaa Witold Torbacki *, Kinga Kijewska
Maritime University of Szczecin, Faculty of Economics and Transport Engineering, 11 Poboznego St., 70-507 Szczecin, Poland; Maritime University of Szczecin, Faculty of Economics and Transport Engineering, 11 Poboznego St., 70-507 Szczecin, Poland;
Abstract Abstract The article presents the issues covering the transformation of logistics within the currently developed concepts of both Industry 4.0 and Logistics 4.0.the Inissues this approach, research problem arose as to which anddeveloped characteristics of both logistics and The article presents covering athe transformation of logistics withinparameters the currently concepts of both Industry 4.0 and Logistics 4.0. In this research problem to which parameters and characteristics of both logistics and manufacturing processes are approach, the most aimportant from thearose pointas of view of sustainability. In addition, mutual relationships manufacturing the established. most important from the point of view of DEMATEL sustainability.analysis In addition, mutual between these processes parametersarewere In the article, a multi-criteria was used to relationships assess these characteristics. article can be useful to people in modern solutionsDEMATEL in the production and was logistics between these The parameters were established. In interested the article, a multi-criteria analysis usedindustries. to assess these characteristics. The article can be useful to people interested in modern solutions in the production and logistics industries. © 2018 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection under responsibility of of Selection and peer-review under responsibility of the the scientific scientific committee of Green Green Logistics Logistics for for Greener Greener Cities Cities 2018. 2018. Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. Keywords: Industry 4.0; Logistics 4.0; Sustainability; DEMATEL; KPI Keywords: Industry 4.0; Logistics 4.0; Sustainability; DEMATEL; KPI
1. Introduction 1. Introduction The current – fourth – industrial revolution is associated with the idea of Industry 4.0. The previous, i.e. the third The current – fourth – industrial revolution is associated withuse theofidea of Industry The previous, i.e. the third industrial revolution began in the 1970s and relied on the wide electronics and 4.0. automation in production. The industrial revolution began in 4.0 the 1970s relied on the (Bauernhansl, wide use of electronics automation in2014) production. The latest concept of Industry is notand homogeneous Hompel,and Vogel-Heuser, or (Liao, latest concept of Industry 4.0 is Anot homogeneous Hompel, Vogel-Heuser, 2014) or (Liao, Deschamps, Loures, Ramos, 2017). substantial merit of(Bauernhansl, the new approach may be that the idea of Industry 4.0 is Deschamps, Ramos, 2017). merit of the new approach may be that the ideamanagement, of Industry 4.0 is predicated onLoures, the assumption that itAissubstantial supposed to make a significant contribution to logistics thus predicated on the assumption that it related is supposed to make of a contemporary significant contribution to logistics management, thus making it possible to solve problems to complexity manufacturing and logistics. making it possible to solve problems related to complexity of contemporary manufacturing and logistics.
* Corresponding author. Tel.: +48-91-4809689; fax: +48-91-4809757. address:author. [email protected] * E-mail Corresponding Tel.: +48-91-4809689; fax: +48-91-4809757. E-mail address: [email protected] 2352-1465 © 2018 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2352-1465 © 2018 Thearticle Authors. Published by Elsevier B.V. Selection under responsibility of the scientific of Green Logistics for Greener Cities 2018. This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 2352-1465 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 10.1016/j.trpro.2019.06.055
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In parallel to the concept of Industry 4.0, the term “Logistics 4.0” was coined (Barreto, Amaral, Pereira, 2017) or (Strandhagen, Vallandingham, Fragapane, Strandhagen, Stangeland, Sharma, 2017), denoting flexible implementation of logistic processes aimed at handling the production output. The currently observed complexity of production and of the network of logistic connections between business partners, as well as the increasing necessity to take into account the sustainability concept in goods and products distribution within metropolitan areas, constitute significant barriers to development and optimisation of production and logistics processes. What is also very important in terms of sustainable development is the process of freight distribution planning within metropolitan areas. In this context, a research problem emerges as to which parameters of production and logistics processes, and to what extent, are the most vital from the point of view of a manufacturing process compliant with the idea of Industry 4.0 and a logistics process under the Logistics 4.0 concept. Both processes are often connected with implementation of intensified transport processes within metropolitan areas. This article presents a research study consisting in establishing those parameters and evaluating them by means of multiple criteria. Key performance indicators (KPIs) were formulated from the point of view of three areas: Industry 4.0, Logistics 4.0 and sustainable development in the area of products distribution to and from production plants. In the end, by means of the DEMATEL methodology, the relations between the parameters were determined by specifying their importance within the three areas. 2. Industry 4.0, Logistics 4.0, sustainable freight and manufacturing processes 2.1. Industry 4.0 The idea of Industry 4.0 combines production processes and application of latest technologies, including, but not limited to, the use of cyber-physical systems (Parvin, Hussain, Hussain, Thein, Park, 2013), as well as extensive use of the internet. Other state-of-the-art technologies used in production processes monitoring and optimising include e.g. the internet of things (Nolin, Olson, 2016), the internet of services (Andersson, Mattsson, 2015), 3D printing (Sehwan, 2014), cloud computing (Scavo, Newton, Longwell, 2012), robotics (Kehoe, Patil, Abbeel, Goldberg, 2015), smart factory (Brettel, Friederichsen, Keller, Rosenberg, 2014), smart manufacturing (Zhong, Xu, Klotz, Newman, 2017), digital transformation (Ustundag Emre, 2017), artificial intelligence (Kumar, 2017), augmented reality (Paelke, 2014), man-machine interfaces (Gorecky, Schmitt, Loskyll, 2014) and machine to machine communication (Verma, Verma, Prakash, Agrawa, Naik, Tripath, et al., 2016). Cyber-physical systems enable registering and monitoring of actual, “physical” processes by IT systems. These processes, in turn, affect data processing. In the context of production and logistic cycles it means that “physical” information registered by, inter alia, various sensors in recipients’ warehouses, initiate production and distribution of products via IT systems. The internet of things (IoT) increases the capabilities of items which via connecting to the internet become “smart things” with new capabilities and functions. The internet of services (IoS) enables provision of services via the internet, which are often offered in the SaaS (Software as a Service) rental model. This makes it possible for service providers to e.g. combine various services and to offer them to customers in packages. In the production context, both the ordering parties and the contractors have reciprocal access to their respective IT resources for the purposes of the manufacturing process. Cyber-physical systems as well as the internet of things and the internet of services enable “smart manufacturing” under the Industry 4.0 concept. In this context, by using the internet, Industry 4.0 combines products, services, production and its optimisation to create a process that is initiated on the basis of data registered by cyber-physical systems, without a need to involve a human. Another characteristic feature of the idea is the close connection between products, machines, transport systems and people within the framework of the so-called “smart factories”. A modern production process enables implementation of the so called “individualised” mass production, and also introduction of complicated production processes which are simultaneously easy to manage and take into account the principles of production and logistics sustainability also within metropolitan areas (Kijewska, Iwan, 2016; Iwan et al., 2018).
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2.2. The connections between Industry 4.0 and Logistics 4.0 The new approach to production processes results in new needs regarding logistics. In this context, in the wake of the idea of Industry 4.0, a proposal for a new Logistics 4.0 concept is being more and more often raised. Both terms are interconnected. The characteristic features of the new approach to logistics are: extensive use of the internet of things, constant transparency/ visibility of a complete supply chain for all participants of the process, a possibility of unambiguous verification of the supply chain coherence (making use of the blockchain technology), a possibility of dynamic optimisation of supply chains and suppliers. Logistics 4.0 also comprises the process aspect (BPM handling) and the technological aspect (supporting the logistics by means of the latest IT solutions). The new approach is supposed to increase effectiveness and efficiency of logistic services in view of developing the concept of Industry 4.0. The effective approach to Logistics 4.0 should include solutions (Barreto, Amaral, Pereira, 2017) regarding: supporting logistics processes planning, warehouse operation and deployment of goods in warehouses, management of intelligent transport system, and also management of secure information flows. Solutions in the area of Logistics 4.0 include e.g. sensors in cyber-physical systems (RFID, LIDAR, accelerometers, GPS, cameras, humidity sensors) (Berger, Hees, Braunreuther, Reinhart, 2016; Forczmański, Małecki, 2013; Małecki, Kopaczyk, 2013), Intelligent Transportation System usage (Kovalský, Mičieta, 2017), telematics technology usage (Iwan et al., 2013; Iwan, Małecki, 2012; Kijewska et al., 2016), support for blockchain technology (Abeyratne, Monfared, 2016), the use of drones, robots and autonomous vehicles (Bechtsis, Tsolakis, Vlachos, Srai, 2018), using electronic marketplace platforms (Wanga, Potter, Naim, Beevor, 2011) and augmented reality (Cirulis, Ginters, 2013). 2.3. Sustainable freight and manufacturing process within Industry 4.0 and Logistics 4.0 concepts Application of solutions under Logistics 4.0 for the purposes of handling production processes may be a perfect way to improve effectiveness of logistics processes, concurrently enabling fulfilment of sustainability principles in logistics in metropolitan areas (Kauf, 2016). This may be attained e.g. via smart management of: heavy goods vehicles, delivery areas, multimodal transport, and car park areas; as well as via estimation and monitoring of air pollution, promoting eco-driving among drivers in order to decrease fuel consumption and consequently to lower CO2 emissions, application of Intelligent Transport Systems (ITS) which make it possible to e.g. decrease the presence of heavy vehicles in urban areas. Linking Industry 4.0 and Logistics 4.0 will also make it possible for production plants to serve the “local” area more and more often, as a result of shortening the distances between them and their customers. Also, locations of storage facilities will be subject to change (to be closer to major logistic nodes), while their number and area will be decreased. Summing up, the aforementioned issues of the three perspectives (P1 – Industry 4.0, P2 – Logistics 4.0, P3 – Sustainability) may be illustrated by a sample process of handling of orders, production and distribution of products, taking into account the concepts of Industry 4.0/Logistics 4.0/sustainability: Perspective P1 – Industry 4.0 - IT systems initiate production planning, based on the analysis of rotation, data mining, online data on the flow of products and materials, and also of data registered and monitored by cyber-physical systems. - A dual process of order handling is initiated: on the one hand, sales orders from customers are taken, on the other hand, purchase orders are placed with suppliers to replenish any missing and indispensable production ingredients. - IT systems reserve the resources (equipment, labour and materials) and also supervise relevant production. Perspective P2 – Logistics 4.0 - The products are distributed to customers, taking into account intelligent routing of deliveries which are performed also by autonomous vehicles. Perspective P3 – Sustainability - The production and distribution process is carried out according to the concept of sustainable development, decreasing the presence of heavy vehicles in urban areas.
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2.4. KPI for the assessment of manufacturing and sustainable metropolitan logistics In the context of the above described issues of Industry 4.0, Logistics 4.0 and sustainable distribution of merchandise in metropolitan areas, a research problem emerges, regarding the importance and network of interconnections between the parameters from the above described three perspectives. Table 1 presents the features of these three perspectives. The concept presented in Table 1 combines frequent approaches to sustainability in the current literature with the trends combined with Industry 4.0 and Logistics 4.0. Table 1. Three perspectives and ten KPIs adopted for the analysis. Perspective KPI P1 – Industry 4.0 Hofmann, Rüsch, 2017), (Hermann, Pentek, Otto, 2015), (Wagner, Herrmann, Thiede, 2017) and (Oliff, Liu, 2017) P2 – Logistics 4.0 (Barreto, Amaral, Pereira, 2017)
P3 – Sustainability (Kayikci, 2018)
Description
P11 – Supply chain
Digital supply chain uses basic Industry 4.0 components (cyberphysical systems, IoT, IoS, etc.)
P12 – Production planning
Intelligent production planning based on real-time consumption data
P13 – Reconfigurable manufacturing
Introducing of flexible, reconfigurable manufacturing system
P21 – IoT in logistics
Support for planning logistic processes using IoT
P22 – Warehouse
Warehouse management and distribution of goods in warehouses
P23 – Intelligent Transportation System
Management of Intelligent Transportation System
P24 – Data safety
Management of secure information flow
P31 – Economy
An affordable system that operates efficiently, offers collaborative solutions and a mix of transport mode choices, and supports the local economy
P32 – Environment
Energy use indicator, reduced greenhouse gas emissions, pollution and waste, minimized consumption of non-renewable energy sources and application of technologies that reuse and recycle its components.
P33 – Society
Meeting individuals’ basic mobility needs, ensuring traffic safety, supporting good lifestyles, equally to all people
3. The DEMATEL method Multi-criteria expert methods are often used to assess the validity and interrelations between individual parameters. In this article, the DEMATEL (Decision Making Trial and Evaluation Laboratory) method is used (Gabus, Fontela, 1972) or (Torbacki, 2017). This one is also applied to identify a correlation between sets of KPIs for Industry 4.0, Logistics 4.0 and sustainability. In the beginning, a group of n 8 experts was established. Each of them was asked to assess the direct influence between different coefficients based on a numeric scale from 0 to 4, where: ‘no impact’ – 0, ‘low impact’ – 1, ‘distinct impact’ – 2, ‘big impact’ – 3 and ‘extreme impact’ – 4, respectively. Then the values of mutual interactions within respective pairs of all the criteria were determined. It was assumed that each of the k factors may directly influence another factor, but it cannot influence itself. At the end of this step, eight partitive initial direct influence matrices Z m were created for each expert. In matrices Z m , the principal diagonal elements are equal to zero and
Z m z ijm , k k
(1)
m where z ij represents the assessment provided by the m th expert regarding the degree to which parameter i influences parameter j . A cluster of partitive matrices was thus obtained. Finally, aggregation of the matrices resulted in direct influence matrix Z .
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The direct influence matrix Z was computed based on the following equation:
Z
1 n m zij , i , j 1,2 ,3 ,...k . n m1
(2)
Next, normalized direct influence matrix X was determined, by using equations (3)-(4):
X xij sZ , k k
(3)
1 1 s = min , k k . max zij max zij 1 j k i 1 1i k j 1
(4)
In the next step, matrix of total relations T was derived:
T = lim ( X + X 2 + + X m ) = X ( I X )1 , m
(5)
where I is the identity matrix. Based on T [tij ] matrix, the influential relation diagram in the ( ri ci , ri c j ) layout could be built. Sums of individual rows were calculated ri – which mirror the sum of indirect and direct i influences criteria on other criteria (equation 6) and sums of all c j columns – which in turn showed the sum of direct and indirect influences criterion j received from the other criteria (equation 7): k R = ri k1 = t ij , i 1 k1
(6)
k = tij . j 1 1k
(7)
C = cj
1 k
It is worth noticing that ri means the sum of all direct and indirect influences dispatched from factor i to other factors. It is called the degree of influential impact. On the other hand, c j means both direct and indirect impacts that factor j receives. It is called the degree of influenced impact. Next, a ri c j – relation indicator was determined, which is also called a net influence and a ri c j – position indicator, which is also called an overall influence. If i j then the value ri ci indicates the sum of criteria values, which both influence the other criteria, and are under the influence of other criteria. The value ri ci 0 means that criterion i influences other criteria, and the entire system as well. The value ri ci 0 means that other criteria influence criterion i , hence criterion i is not a source of influence on the remaining criteria in the system. When it comes to ri ci it is a measure of importance degree of criterion i in the entire system. Taking into consideration the above position and relation indicators, the influential relation diagram can be plotted in the ( ri ci , ri ci ) layout, using ri ci as the horizontal axis and ri ci as the vertical axis. When analysing the values of ri ci and ri ci indicators, the DEMATEL technique identifies the degree of interdependences of the criteria on one hand, and on the other hand it determines the criteria which influence other
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criteria, as well as the criteria which depend more on other criteria, and which are the recipients of influence of other criteria. Next, the analysis of each of the performance criteria based on both indexes ri , c j , ri ci , ri ci and the influential relation diagram should be made. According to the position of each criterion in the diagram, the ones which have a great effect on other criteria or have a complicated relationship with other criteria can be found. 4. Case study – Logistics 4.0, Industry 4.0 and sustainability In order to evaluate the parameters shown in Table 1, eight expert interviews were conducted. The experts had a vast knowledge in the area of logistics and industry (including Industry 4.0 and Logistics 4.0). However, some of them did not consider themselves to be specialists in the area of the sustainability concept. Based on the set of criteria presented in Table 1, ten KPIs were qualified for further analysis, as shown in Table 1 within the 3 perspectives. A 5-grade scale of mutual influence of the criteria was adopted, and the values of interactions between pairs of all the criteria were determined. Based on the experts’ responses, eight partitive initial direct influence matrices Z m were obtained – see equation (1). Superposition of all the matrices, based on equation (2), resulted in direct influence matrix Z (Table 2). Table 2. Direct influence matrix Z. Z P11 P12
P13
P21
P22
P23
P24
P31
P32
P33
3.54
3.76
3.12
3.54
3.12
2.88
2.24
2.62
0.68
P11
0
P12
3.46
0
3.34
3.64
3.82
3.24
3.44
3.12
2.46
0.54
P13
3.72
3.54
0
3.12
3.22
2.88
2.68
2.32
2.42
0.44
P21
3.12
3.56
3.24
0
3.52
3.56
3.72
2.12
2.68
0.24
P22
2.98
3.12
3.14
2.86
0
3.46
3.52
1.88
2.12
0.42
P23
2.68
3.08
2.88
3.12
3.46
0
3.62
1.46
1.36
0.52
P24
3.62
3.14
2.26
3.14
2.88
3.46
0
0.82
2.64
0.62
P31
2.18
2.72
2.08
1.84
1.68
1.24
1.72
0
1.42
0.88
P32
2.56
2.42
2.20
1.42
1.12
1.46
1.32
1.24
0
0.94
P33
0.88
0.22
0.68
0.42
0.44
0.72
0.54
0.34
0.82
0
Next, in accordance with formula (3), we computed normalized direct influence matrix X (Table 3). Table 3. Normalized direct influence matrix X. X P11 P12 P13
P21
P22
P23
P24
P31
P32
P33
0.131
0.139
0.115
0.131
0.115
0.106
0.083
0.097
0.025
P11
0
P12
0.128
0
0.123
0.135
0.141
0.12
0.127
0.115
0.091
0.02
P13
0.137
0.131
0
0.115
0.119
0.106
0.099
0.086
0.089
0.016
P21
0.115
0.132
0.12
0
0.13
0.132
0.137
0.078
0.099
0.009
P22
0.11
0.115
0.116
0.106
0
0.128
0.13
0.069
0.078
0.016
P23
0.099
0.114
0.106
0.115
0.128
0
0.134
0.054
0.05
0.019
P24
0.134
0.116
0.084
0.116
0.106
0.128
0
0.03
0.098
0.023
P31
0.081
0.101
0.077
0.068
0.062
0.046
0.064
0
0.052
0.033
P32
0.095
0.089
0.081
0.052
0.041
0.054
0.049
0.046
0
0.035
P33
0.033
0.008
0.025
0.016
0.016
0.027
0.02
0.013
0.03
0
Further on, on the basis of equation (5), matrix of total relations T was determined (Table 4).
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Table 4. Matrix of total relations T. T P11 P12
7
P13
P21
P22
P23
P24
P31
P32
P33
P11
0.546
0.673
0.641
0.612
0.648
0.62
0.619
0.43
0.498
0.136
P12
0.689
0.587
0.656
0.654
0.684
0.651
0.664
0.476
0.515
0.137
P13
0.649
0.654
0.502
0.595
0.621
0.595
0.596
0.421
0.478
0.124
P21
0.661
0.685
0.636
0.52
0.659
0.644
0.656
0.433
0.508
0.123
P22
0.612
0.626
0.59
0.574
0.5
0.599
0.607
0.396
0.457
0.12
P23
0.583
0.604
0.563
0.563
0.594
0.468
0.592
0.369
0.418
0.118
P24
0.613
0.608
0.548
0.565
0.579
0.583
0.475
0.351
0.46
0.123
P31
0.418
0.441
0.397
0.383
0.393
0.369
0.388
0.224
0.309
0.102
P32
0.406
0.407
0.379
0.348
0.352
0.353
0.352
0.253
0.241
0.099
P33
0.138
0.118
0.126
0.115
0.12
0.126
0.121
0.081
0.109
0.022
Matrix of total relations T can be viewed as submatrix TP based on the three categories from Table 1 and submatrix TK based on the ten KPIs. Table 5 presents submatrices TP and TK and respective position and relation indicators defined by equation (6)-(7). Table 5. Submatrices TP and TK and respective position and relation indicators. TP R C R+C R-C TK P1
P2
P3
1.609
1.507
0.759
1.547
1.448
0.840
3.156
2.966
1.600
0062
0.019
-0.081
R
C
R+C
R-C
P11
5.442
5.314
10.736
0.107
P12
5.713
5.314
11.027
0.398
P13
5.234
5.037
10.271
0.197
P21
5.525
4.929
10.454
0.597
P22
5.081
5.150
10.231
-0.069
P23
4.872
5.008
9.881
-0.136
P24
4.904
5.070
9.974
-0.166
P31
3.424
3.434
6.858
-0.009
P32
3.189
3.993
7.181
-0.804
P33
1.077
1.105
2.182
-0.028
Based on submatrix TP , the influential relation diagram, for three perspectives (Table 1) is presented in Figure 1.
Fig. 1. The influential relation diagram of submatrix TP; ri-ci – relation indicator, ri+ci – position indicator, P1-P3 – analysed perspective.
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In Fig. 1, perspective P1 – Industry 4.0 shows the highest value of the indicator for position ri ci , which means that it is the most strongly interconnected with the other processes, taking up the central position in the network of interconnections. Perspective P3 – Sustainability shows the lowest level of the indicator. The indicator for relation ri ci , in turn, makes it possible to determine the impact level of the analysed parameter on the other parameters. Concurrently, it is assumed that it makes it possible to reflect the parameter priority among the other studied interdependencies. In Fig. 1, perspective P1 – Industry 4.0 shows the highest positive value of the indicator for relation ri ci , which means that the process has a dominating, causative influence on the other processes and simultaneously it is the most important out of the discussed three processes. Perspective P3 – Sustainability, in turn, with the highest negative value of the relation indicator, shows the highest degree of being receptive to influence exerted by other perspectives. Based on submatrix TK , the influential relation diagram, in the ( ri ci , ri ci ) layout, for ten KPIs (Table 1) is presented in Figure 2.
Fig. 2. The influential relation diagram of submatrix TK; ri-ci – relation indicator, ri+ci – position indicator, P11-P33 – analysed KPIs.
In Fig. 2, the highest value of the position indicator is shown by criterion P12 – Production planning, from perspective P1, thus taking up the central position in the network of connections with the other criteria. Criterion P33 – Society shows the lowest value of this indicator. Criterion P21 – IoT in logistics, with the highest positive value of the relation indicator exerts the strongest influence on the other criteria and is the most important criterion for logistic companies that handle production processes under the Industry 4.0 concept. On the other hand, criterion P32 – Environment, with the highest negative value of the relation indicator, is the largest recipient of influence exerted by the others, and its priority is the lowest among the 10 criteria formulated for the logistic process under the Industry 4.0 concept, while accounting for sustainability. 5. Conclusions The Industry 4.0 concept is currently considered to be potentially capable of changing production processes and related logistics processes to a significant extent. This article presents the analysis of a set of characteristic logistic parameters connected with production processes, taking into account the principles of the concepts of Industry 4.0, Logistics 4.0, and sustainability. It should be noted that any negative aspects of implementing these concepts were outside the scope of this study, and they constitute the object of further research in this regard. Within the framework of three basic perspectives, ten characteristic KPIs were determined. Then, applying the DEMATEL methodology, the mutual influence matrices for all pairs of the criteria were specified. Matrix of total relations T was devised; position and relation indicators were established for the three perspectives as well as for the ten KPIs. Both perspectives and criteria with the highest and the lowest (gross and net) influence were determined.
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Acknowledgements The results of the research studies presented in this article were accomplished as part of the research project No 1/S/IZT/18 titled ”Analysis of logistic determinants of sustainable urban development“ financed from the grant of the Ministry of Science and Higher Education to finance statutory activities. References Abeyratne, S. A., Monfared, R. P., 2016. Blockchain Ready Manufacturing Supply Chain Using Distributed Ledger. International Journal of Research in Engineering and Technology 5.9, 1–10. Andersson, P., Mattsson, L.-G., 2015. Service innovations enabled by the internet of things, IMP J. 9.1, 85–106. Barreto, L., Amaral, A., Pereira, T., 2017. Industry 4.0 implications in logistics: an overview. Procedia Manufacturing 13, 1245-1252. Bauernhansl, T., Hompel, M., Vogel-Heuser, B., 2014. Industrie 4.0 in Produktion, Automatisierung Und Logistik. Wiesbaden: Springer. Bechtsis, D., Tsolakis, N., Vlachos, D., Srai, J. S., 2018. Intelligent Autonomous Vehicles in digital supply chains: A framework for integrating innovations towards sustainable value networks. Journal of Cleaner Production, 181, 60-71. Berger, C., Hees, A., Braunreuther, S., Reinhart, G., 2016. Characterization of Cyber-Physical Sensor Systems, Procedia CIRP 41, 638-643. Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M., 2104. How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective. Int. J. Mech. Ind. Sci. Eng. 8.1, 37–44. Cirulis, A., Ginters, E., 2013. Augmented Reality in Logistics. Procedia Computer Science 26, 14-20. Forczmański P., Małecki K., 2013. Selected Aspects of Traffic Signs Recognition: Visual versus RFID Approach. In: Mikulski J., (Ed.) Activities of Transport Telematics. TST 2013. Communications in Computer and Information Science, vol 395. Berlin: Springer, 268-274. Gabus, A., Fontela, E., 1972. World problems, an invitation to further thought within the framework of DEMATEL. Geneva: Battelle Geneva Research Center. Gorecky, D., Schmitt, M., Loskyll, M., 2014. Human-Machine-Interaction in the Industry 4.0 era. IEEE International Conference on Industrial Informatics, INDIN, 289-294. Hermann, M., Pentek, T., Otto, B., 2015. Design Principles for Industrie 4.0 Scenarios: A Literature Review. Working Paper. Dortmund: Technical University of Dortmund. Hofmann, E., Rüsch, M., 2017. Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry 89, 23–34. Iwan, S., Kijewska, K., Johansen, B. G., Eidhammer, O., Małecki, K., Konicki, W., Thompson, R. G., 2018. Analysis of the environmental impacts of unloading bays based on cellular automata simulation. Transportation Research Part D: Transport and Environment 61, 104-117. Iwan, S., Małecki, K., Korczak, J., 2013. Impact of Telematics on Efficiency of Urban Freight Transport. In: Mikulski J., (Ed.) Activities of Transport Telematics. TST 2013. Communications in Computer and Information Science, vol. 395. Berlin: Springer, 50–57. Iwan, S., Małecki, K., 2012. Data Flows in an Integrated Urban Freight Transport Telematic System. In: Mikulski J., (Ed.) Telematics in the Transport Environment. TST 2012. Communications in Computer and Information Science, vol 329. Berlin: Springer, 79-86. Kauf, S., 2016. City logistics – A Strategic Element of Sustainable Urban Development. Transportation Research Procedia 16, 158-164. Kayikci, Y., 2018. Sustainability impact of digitization in logistics. Procedia Manufacturing 21, 782-789. Kehoe, B., Patil, S., Abbeel, P., Goldberg, K., 2015. A Survey of Research on Cloud Robotics and Automation. IEEE Transactions on Automation Science and Engineering 12.2, 398-409. Kijewska, K., Iwan, S., 2016. Analysis of the Functioning of Urban Deliveries in the City Centre and its Environmental Impact Based on Szczecin Example. Transportation Research Procedia 12, 739-749. Kijewska, K., Małecki, K., Iwan, S., 2016. Analysis of Data Needs and Having for the Integrated Urban Freight Transport Management System. In: Mikulski J., (Ed.) Challenge of Transport Telematics. TST 2016. Communications in Computer and Information Science, vol 640. Cham: Springer, 135-148. Kovalský, M., Mičieta, B., 2017. Support Planning and Optimization of Intelligent Logistics Systems. Procedia Engineering 192, 451-456. Kumar, S. P. L., 2017. State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing. Engineering Applications of Artificial Intelligence 65, 294-329. Liao, Y., Deschamps, F., Loures, E. F. R., Ramos, L. F. P., 2017. Past, Present and Future of Industry 4.0 - a Systematic Literature Review and Research Agenda Proposal. International Journal of Production Research 55.12, 3609-3629. Małecki, K., Kopaczyk, K., 2013. RFID-Based Traffic Signs Recognition System. In: Mikulski J., (Ed.) Activities of Transport Telematics. TST 2013. Communications in Computer and Information Science, vol. 395. Berlin: Springer, 115-122. Nolin, J., Olson, N., 2016. The internet of things and convenience. Internet Res. 26.2, 360–376. Oliff, H., Liu, Y., 2017. Towards Industry 4.0 Utilizing Data-Mining Techniques: A Case Study on Quality Improvement. Procedia CIRP 63, 167-172. Paelke, V., 2014. Augmented reality in the smart factory: Supporting Workers in an Industry 4.0 Environment. 2014 IEEE Emerging Technology and Factory Automation, ETFA, 1-4.
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Parvin, S., Hussain, F., Hussain, O., Thein, T., Park, J., 2013. Multi-cyber framework for availability enhancement of cyber-physical systems. Computing 95.10-11, 927–948. Scavo, F., Newton, B., Longwell, M., 2012. Choosing Between Cloud and Hosted ERP, and Why it Matters. Computer Economics Report 34.8, 1-12. Sehwan, P., 2014. 3D Printing Industry Trends. The International Journal of Advanced Culture Technology 2.1, 30-32. Strandhagen, J. O., Vallandingham, L. R., Fragapane, G., Strandhagen, J. W., Stangeland, A. B. H, Sharma, N., 2017. Logistics 4.0 and emerging sustainable business models. Advances in Manufacturing 5.4, 359–369. Torbacki, W., 2017. DEMATEL method in ERP systems for TSL branch. Transport Problems 12.4, 27-36. Ustundag, A., Emre, C., 2017. Industry 4.0: Managing The Digital Transformation. Berlin: Springer. Verma, P. K., Verma, R., Prakash, A., Agrawa, A., Naik, K., Tripath, R., et al., 2016. Machine-to-Machine, M2M communications: A survey. Journal of Network and Computer Applications 66, 83-105. Wagner, T., Herrmann, C., Thiede, S., 2017. Industry 4.0 Impacts on Lean Production Systems. Procedia CIRP 63, 125-131. Wanga, Y., Potter, A., Naim, M., Beevor, D., 2011. A case study exploring drivers and implications of collaborative electronic logistics marketplaces. Industrial Marketing Management 40.4, 612-623. Zhong, R. Y., Xu, X., Klotz, E., Newman, S. T., 2017. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering 3.5, 616– 630.
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Green Cities 2018 Green Cities 2018
Identifying Key Performance Indicators to be used in Logistics 4.0 Identifying Key Performance Indicators to be used in Logistics 4.0 and Industry 4.0 for the needs of sustainable municipal logistics by and Industry 4.0 for the needs of sustainable municipal logistics by means of the DEMATEL method means of the DEMATEL method a a a a
Witold Torbackia*, Kinga Kijewskaa Witold Torbacki *, Kinga Kijewska
Maritime University of Szczecin, Faculty of Economics and Transport Engineering, 11 Poboznego St., 70-507 Szczecin, Poland; Maritime University of Szczecin, Faculty of Economics and Transport Engineering, 11 Poboznego St., 70-507 Szczecin, Poland;
Abstract Abstract The article presents the issues covering the transformation of logistics within the currently developed concepts of both Industry 4.0 and Logistics 4.0.the Inissues this approach, research problem arose as to which anddeveloped characteristics of both logistics and The article presents covering athe transformation of logistics withinparameters the currently concepts of both Industry 4.0 and Logistics 4.0. In this research problem to which parameters and characteristics of both logistics and manufacturing processes are approach, the most aimportant from thearose pointas of view of sustainability. In addition, mutual relationships manufacturing the established. most important from the point of view of DEMATEL sustainability.analysis In addition, mutual between these processes parametersarewere In the article, a multi-criteria was used to relationships assess these characteristics. article can be useful to people in modern solutionsDEMATEL in the production and was logistics between these The parameters were established. In interested the article, a multi-criteria analysis usedindustries. to assess these characteristics. The article can be useful to people interested in modern solutions in the production and logistics industries. © 2018 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection under responsibility of of Selection and peer-review under responsibility of the the scientific scientific committee of Green Green Logistics Logistics for for Greener Greener Cities Cities 2018. 2018. Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. Keywords: Industry 4.0; Logistics 4.0; Sustainability; DEMATEL; KPI Keywords: Industry 4.0; Logistics 4.0; Sustainability; DEMATEL; KPI
1. Introduction 1. Introduction The current – fourth – industrial revolution is associated with the idea of Industry 4.0. The previous, i.e. the third The current – fourth – industrial revolution is associated withuse theofidea of Industry The previous, i.e. the third industrial revolution began in the 1970s and relied on the wide electronics and 4.0. automation in production. The industrial revolution began in 4.0 the 1970s relied on the (Bauernhansl, wide use of electronics automation in2014) production. The latest concept of Industry is notand homogeneous Hompel,and Vogel-Heuser, or (Liao, latest concept of Industry 4.0 is Anot homogeneous Hompel, Vogel-Heuser, 2014) or (Liao, Deschamps, Loures, Ramos, 2017). substantial merit of(Bauernhansl, the new approach may be that the idea of Industry 4.0 is Deschamps, Ramos, 2017). merit of the new approach may be that the ideamanagement, of Industry 4.0 is predicated onLoures, the assumption that itAissubstantial supposed to make a significant contribution to logistics thus predicated on the assumption that it related is supposed to make of a contemporary significant contribution to logistics management, thus making it possible to solve problems to complexity manufacturing and logistics. making it possible to solve problems related to complexity of contemporary manufacturing and logistics.
* Corresponding author. Tel.: +48-91-4809689; fax: +48-91-4809757. address:author. [email protected] * E-mail Corresponding Tel.: +48-91-4809689; fax: +48-91-4809757. E-mail address: [email protected] 2352-1465 © 2018 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2352-1465 © 2018 Thearticle Authors. Published by Elsevier B.V. Selection under responsibility of the scientific of Green Logistics for Greener Cities 2018. This is an and openpeer-review access article under the CC BY-NC-ND licensecommittee (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 2352-1465 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of Green Logistics for Greener Cities 2018. 10.1016/j.trpro.2019.06.055
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In parallel to the concept of Industry 4.0, the term “Logistics 4.0” was coined (Barreto, Amaral, Pereira, 2017) or (Strandhagen, Vallandingham, Fragapane, Strandhagen, Stangeland, Sharma, 2017), denoting flexible implementation of logistic processes aimed at handling the production output. The currently observed complexity of production and of the network of logistic connections between business partners, as well as the increasing necessity to take into account the sustainability concept in goods and products distribution within metropolitan areas, constitute significant barriers to development and optimisation of production and logistics processes. What is also very important in terms of sustainable development is the process of freight distribution planning within metropolitan areas. In this context, a research problem emerges as to which parameters of production and logistics processes, and to what extent, are the most vital from the point of view of a manufacturing process compliant with the idea of Industry 4.0 and a logistics process under the Logistics 4.0 concept. Both processes are often connected with implementation of intensified transport processes within metropolitan areas. This article presents a research study consisting in establishing those parameters and evaluating them by means of multiple criteria. Key performance indicators (KPIs) were formulated from the point of view of three areas: Industry 4.0, Logistics 4.0 and sustainable development in the area of products distribution to and from production plants. In the end, by means of the DEMATEL methodology, the relations between the parameters were determined by specifying their importance within the three areas. 2. Industry 4.0, Logistics 4.0, sustainable freight and manufacturing processes 2.1. Industry 4.0 The idea of Industry 4.0 combines production processes and application of latest technologies, including, but not limited to, the use of cyber-physical systems (Parvin, Hussain, Hussain, Thein, Park, 2013), as well as extensive use of the internet. Other state-of-the-art technologies used in production processes monitoring and optimising include e.g. the internet of things (Nolin, Olson, 2016), the internet of services (Andersson, Mattsson, 2015), 3D printing (Sehwan, 2014), cloud computing (Scavo, Newton, Longwell, 2012), robotics (Kehoe, Patil, Abbeel, Goldberg, 2015), smart factory (Brettel, Friederichsen, Keller, Rosenberg, 2014), smart manufacturing (Zhong, Xu, Klotz, Newman, 2017), digital transformation (Ustundag Emre, 2017), artificial intelligence (Kumar, 2017), augmented reality (Paelke, 2014), man-machine interfaces (Gorecky, Schmitt, Loskyll, 2014) and machine to machine communication (Verma, Verma, Prakash, Agrawa, Naik, Tripath, et al., 2016). Cyber-physical systems enable registering and monitoring of actual, “physical” processes by IT systems. These processes, in turn, affect data processing. In the context of production and logistic cycles it means that “physical” information registered by, inter alia, various sensors in recipients’ warehouses, initiate production and distribution of products via IT systems. The internet of things (IoT) increases the capabilities of items which via connecting to the internet become “smart things” with new capabilities and functions. The internet of services (IoS) enables provision of services via the internet, which are often offered in the SaaS (Software as a Service) rental model. This makes it possible for service providers to e.g. combine various services and to offer them to customers in packages. In the production context, both the ordering parties and the contractors have reciprocal access to their respective IT resources for the purposes of the manufacturing process. Cyber-physical systems as well as the internet of things and the internet of services enable “smart manufacturing” under the Industry 4.0 concept. In this context, by using the internet, Industry 4.0 combines products, services, production and its optimisation to create a process that is initiated on the basis of data registered by cyber-physical systems, without a need to involve a human. Another characteristic feature of the idea is the close connection between products, machines, transport systems and people within the framework of the so-called “smart factories”. A modern production process enables implementation of the so called “individualised” mass production, and also introduction of complicated production processes which are simultaneously easy to manage and take into account the principles of production and logistics sustainability also within metropolitan areas (Kijewska, Iwan, 2016; Iwan et al., 2018).
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2.2. The connections between Industry 4.0 and Logistics 4.0 The new approach to production processes results in new needs regarding logistics. In this context, in the wake of the idea of Industry 4.0, a proposal for a new Logistics 4.0 concept is being more and more often raised. Both terms are interconnected. The characteristic features of the new approach to logistics are: extensive use of the internet of things, constant transparency/ visibility of a complete supply chain for all participants of the process, a possibility of unambiguous verification of the supply chain coherence (making use of the blockchain technology), a possibility of dynamic optimisation of supply chains and suppliers. Logistics 4.0 also comprises the process aspect (BPM handling) and the technological aspect (supporting the logistics by means of the latest IT solutions). The new approach is supposed to increase effectiveness and efficiency of logistic services in view of developing the concept of Industry 4.0. The effective approach to Logistics 4.0 should include solutions (Barreto, Amaral, Pereira, 2017) regarding: supporting logistics processes planning, warehouse operation and deployment of goods in warehouses, management of intelligent transport system, and also management of secure information flows. Solutions in the area of Logistics 4.0 include e.g. sensors in cyber-physical systems (RFID, LIDAR, accelerometers, GPS, cameras, humidity sensors) (Berger, Hees, Braunreuther, Reinhart, 2016; Forczmański, Małecki, 2013; Małecki, Kopaczyk, 2013), Intelligent Transportation System usage (Kovalský, Mičieta, 2017), telematics technology usage (Iwan et al., 2013; Iwan, Małecki, 2012; Kijewska et al., 2016), support for blockchain technology (Abeyratne, Monfared, 2016), the use of drones, robots and autonomous vehicles (Bechtsis, Tsolakis, Vlachos, Srai, 2018), using electronic marketplace platforms (Wanga, Potter, Naim, Beevor, 2011) and augmented reality (Cirulis, Ginters, 2013). 2.3. Sustainable freight and manufacturing process within Industry 4.0 and Logistics 4.0 concepts Application of solutions under Logistics 4.0 for the purposes of handling production processes may be a perfect way to improve effectiveness of logistics processes, concurrently enabling fulfilment of sustainability principles in logistics in metropolitan areas (Kauf, 2016). This may be attained e.g. via smart management of: heavy goods vehicles, delivery areas, multimodal transport, and car park areas; as well as via estimation and monitoring of air pollution, promoting eco-driving among drivers in order to decrease fuel consumption and consequently to lower CO2 emissions, application of Intelligent Transport Systems (ITS) which make it possible to e.g. decrease the presence of heavy vehicles in urban areas. Linking Industry 4.0 and Logistics 4.0 will also make it possible for production plants to serve the “local” area more and more often, as a result of shortening the distances between them and their customers. Also, locations of storage facilities will be subject to change (to be closer to major logistic nodes), while their number and area will be decreased. Summing up, the aforementioned issues of the three perspectives (P1 – Industry 4.0, P2 – Logistics 4.0, P3 – Sustainability) may be illustrated by a sample process of handling of orders, production and distribution of products, taking into account the concepts of Industry 4.0/Logistics 4.0/sustainability: Perspective P1 – Industry 4.0 - IT systems initiate production planning, based on the analysis of rotation, data mining, online data on the flow of products and materials, and also of data registered and monitored by cyber-physical systems. - A dual process of order handling is initiated: on the one hand, sales orders from customers are taken, on the other hand, purchase orders are placed with suppliers to replenish any missing and indispensable production ingredients. - IT systems reserve the resources (equipment, labour and materials) and also supervise relevant production. Perspective P2 – Logistics 4.0 - The products are distributed to customers, taking into account intelligent routing of deliveries which are performed also by autonomous vehicles. Perspective P3 – Sustainability - The production and distribution process is carried out according to the concept of sustainable development, decreasing the presence of heavy vehicles in urban areas.
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2.4. KPI for the assessment of manufacturing and sustainable metropolitan logistics In the context of the above described issues of Industry 4.0, Logistics 4.0 and sustainable distribution of merchandise in metropolitan areas, a research problem emerges, regarding the importance and network of interconnections between the parameters from the above described three perspectives. Table 1 presents the features of these three perspectives. The concept presented in Table 1 combines frequent approaches to sustainability in the current literature with the trends combined with Industry 4.0 and Logistics 4.0. Table 1. Three perspectives and ten KPIs adopted for the analysis. Perspective KPI P1 – Industry 4.0 Hofmann, Rüsch, 2017), (Hermann, Pentek, Otto, 2015), (Wagner, Herrmann, Thiede, 2017) and (Oliff, Liu, 2017) P2 – Logistics 4.0 (Barreto, Amaral, Pereira, 2017)
P3 – Sustainability (Kayikci, 2018)
Description
P11 – Supply chain
Digital supply chain uses basic Industry 4.0 components (cyberphysical systems, IoT, IoS, etc.)
P12 – Production planning
Intelligent production planning based on real-time consumption data
P13 – Reconfigurable manufacturing
Introducing of flexible, reconfigurable manufacturing system
P21 – IoT in logistics
Support for planning logistic processes using IoT
P22 – Warehouse
Warehouse management and distribution of goods in warehouses
P23 – Intelligent Transportation System
Management of Intelligent Transportation System
P24 – Data safety
Management of secure information flow
P31 – Economy
An affordable system that operates efficiently, offers collaborative solutions and a mix of transport mode choices, and supports the local economy
P32 – Environment
Energy use indicator, reduced greenhouse gas emissions, pollution and waste, minimized consumption of non-renewable energy sources and application of technologies that reuse and recycle its components.
P33 – Society
Meeting individuals’ basic mobility needs, ensuring traffic safety, supporting good lifestyles, equally to all people
3. The DEMATEL method Multi-criteria expert methods are often used to assess the validity and interrelations between individual parameters. In this article, the DEMATEL (Decision Making Trial and Evaluation Laboratory) method is used (Gabus, Fontela, 1972) or (Torbacki, 2017). This one is also applied to identify a correlation between sets of KPIs for Industry 4.0, Logistics 4.0 and sustainability. In the beginning, a group of n 8 experts was established. Each of them was asked to assess the direct influence between different coefficients based on a numeric scale from 0 to 4, where: ‘no impact’ – 0, ‘low impact’ – 1, ‘distinct impact’ – 2, ‘big impact’ – 3 and ‘extreme impact’ – 4, respectively. Then the values of mutual interactions within respective pairs of all the criteria were determined. It was assumed that each of the k factors may directly influence another factor, but it cannot influence itself. At the end of this step, eight partitive initial direct influence matrices Z m were created for each expert. In matrices Z m , the principal diagonal elements are equal to zero and
Z m z ijm , k k
(1)
m where z ij represents the assessment provided by the m th expert regarding the degree to which parameter i influences parameter j . A cluster of partitive matrices was thus obtained. Finally, aggregation of the matrices resulted in direct influence matrix Z .
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The direct influence matrix Z was computed based on the following equation:
Z
1 n m zij , i , j 1,2 ,3 ,...k . n m1
(2)
Next, normalized direct influence matrix X was determined, by using equations (3)-(4):
X xij sZ , k k
(3)
1 1 s = min , k k . max zij max zij 1 j k i 1 1i k j 1
(4)
In the next step, matrix of total relations T was derived:
T = lim ( X + X 2 + + X m ) = X ( I X )1 , m
(5)
where I is the identity matrix. Based on T [tij ] matrix, the influential relation diagram in the ( ri ci , ri c j ) layout could be built. Sums of individual rows were calculated ri – which mirror the sum of indirect and direct i influences criteria on other criteria (equation 6) and sums of all c j columns – which in turn showed the sum of direct and indirect influences criterion j received from the other criteria (equation 7): k R = ri k1 = t ij , i 1 k1
(6)
k = tij . j 1 1k
(7)
C = cj
1 k
It is worth noticing that ri means the sum of all direct and indirect influences dispatched from factor i to other factors. It is called the degree of influential impact. On the other hand, c j means both direct and indirect impacts that factor j receives. It is called the degree of influenced impact. Next, a ri c j – relation indicator was determined, which is also called a net influence and a ri c j – position indicator, which is also called an overall influence. If i j then the value ri ci indicates the sum of criteria values, which both influence the other criteria, and are under the influence of other criteria. The value ri ci 0 means that criterion i influences other criteria, and the entire system as well. The value ri ci 0 means that other criteria influence criterion i , hence criterion i is not a source of influence on the remaining criteria in the system. When it comes to ri ci it is a measure of importance degree of criterion i in the entire system. Taking into consideration the above position and relation indicators, the influential relation diagram can be plotted in the ( ri ci , ri ci ) layout, using ri ci as the horizontal axis and ri ci as the vertical axis. When analysing the values of ri ci and ri ci indicators, the DEMATEL technique identifies the degree of interdependences of the criteria on one hand, and on the other hand it determines the criteria which influence other
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criteria, as well as the criteria which depend more on other criteria, and which are the recipients of influence of other criteria. Next, the analysis of each of the performance criteria based on both indexes ri , c j , ri ci , ri ci and the influential relation diagram should be made. According to the position of each criterion in the diagram, the ones which have a great effect on other criteria or have a complicated relationship with other criteria can be found. 4. Case study – Logistics 4.0, Industry 4.0 and sustainability In order to evaluate the parameters shown in Table 1, eight expert interviews were conducted. The experts had a vast knowledge in the area of logistics and industry (including Industry 4.0 and Logistics 4.0). However, some of them did not consider themselves to be specialists in the area of the sustainability concept. Based on the set of criteria presented in Table 1, ten KPIs were qualified for further analysis, as shown in Table 1 within the 3 perspectives. A 5-grade scale of mutual influence of the criteria was adopted, and the values of interactions between pairs of all the criteria were determined. Based on the experts’ responses, eight partitive initial direct influence matrices Z m were obtained – see equation (1). Superposition of all the matrices, based on equation (2), resulted in direct influence matrix Z (Table 2). Table 2. Direct influence matrix Z. Z P11 P12
P13
P21
P22
P23
P24
P31
P32
P33
3.54
3.76
3.12
3.54
3.12
2.88
2.24
2.62
0.68
P11
0
P12
3.46
0
3.34
3.64
3.82
3.24
3.44
3.12
2.46
0.54
P13
3.72
3.54
0
3.12
3.22
2.88
2.68
2.32
2.42
0.44
P21
3.12
3.56
3.24
0
3.52
3.56
3.72
2.12
2.68
0.24
P22
2.98
3.12
3.14
2.86
0
3.46
3.52
1.88
2.12
0.42
P23
2.68
3.08
2.88
3.12
3.46
0
3.62
1.46
1.36
0.52
P24
3.62
3.14
2.26
3.14
2.88
3.46
0
0.82
2.64
0.62
P31
2.18
2.72
2.08
1.84
1.68
1.24
1.72
0
1.42
0.88
P32
2.56
2.42
2.20
1.42
1.12
1.46
1.32
1.24
0
0.94
P33
0.88
0.22
0.68
0.42
0.44
0.72
0.54
0.34
0.82
0
Next, in accordance with formula (3), we computed normalized direct influence matrix X (Table 3). Table 3. Normalized direct influence matrix X. X P11 P12 P13
P21
P22
P23
P24
P31
P32
P33
0.131
0.139
0.115
0.131
0.115
0.106
0.083
0.097
0.025
P11
0
P12
0.128
0
0.123
0.135
0.141
0.12
0.127
0.115
0.091
0.02
P13
0.137
0.131
0
0.115
0.119
0.106
0.099
0.086
0.089
0.016
P21
0.115
0.132
0.12
0
0.13
0.132
0.137
0.078
0.099
0.009
P22
0.11
0.115
0.116
0.106
0
0.128
0.13
0.069
0.078
0.016
P23
0.099
0.114
0.106
0.115
0.128
0
0.134
0.054
0.05
0.019
P24
0.134
0.116
0.084
0.116
0.106
0.128
0
0.03
0.098
0.023
P31
0.081
0.101
0.077
0.068
0.062
0.046
0.064
0
0.052
0.033
P32
0.095
0.089
0.081
0.052
0.041
0.054
0.049
0.046
0
0.035
P33
0.033
0.008
0.025
0.016
0.016
0.027
0.02
0.013
0.03
0
Further on, on the basis of equation (5), matrix of total relations T was determined (Table 4).
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Table 4. Matrix of total relations T. T P11 P12
7
P13
P21
P22
P23
P24
P31
P32
P33
P11
0.546
0.673
0.641
0.612
0.648
0.62
0.619
0.43
0.498
0.136
P12
0.689
0.587
0.656
0.654
0.684
0.651
0.664
0.476
0.515
0.137
P13
0.649
0.654
0.502
0.595
0.621
0.595
0.596
0.421
0.478
0.124
P21
0.661
0.685
0.636
0.52
0.659
0.644
0.656
0.433
0.508
0.123
P22
0.612
0.626
0.59
0.574
0.5
0.599
0.607
0.396
0.457
0.12
P23
0.583
0.604
0.563
0.563
0.594
0.468
0.592
0.369
0.418
0.118
P24
0.613
0.608
0.548
0.565
0.579
0.583
0.475
0.351
0.46
0.123
P31
0.418
0.441
0.397
0.383
0.393
0.369
0.388
0.224
0.309
0.102
P32
0.406
0.407
0.379
0.348
0.352
0.353
0.352
0.253
0.241
0.099
P33
0.138
0.118
0.126
0.115
0.12
0.126
0.121
0.081
0.109
0.022
Matrix of total relations T can be viewed as submatrix TP based on the three categories from Table 1 and submatrix TK based on the ten KPIs. Table 5 presents submatrices TP and TK and respective position and relation indicators defined by equation (6)-(7). Table 5. Submatrices TP and TK and respective position and relation indicators. TP R C R+C R-C TK P1
P2
P3
1.609
1.507
0.759
1.547
1.448
0.840
3.156
2.966
1.600
0062
0.019
-0.081
R
C
R+C
R-C
P11
5.442
5.314
10.736
0.107
P12
5.713
5.314
11.027
0.398
P13
5.234
5.037
10.271
0.197
P21
5.525
4.929
10.454
0.597
P22
5.081
5.150
10.231
-0.069
P23
4.872
5.008
9.881
-0.136
P24
4.904
5.070
9.974
-0.166
P31
3.424
3.434
6.858
-0.009
P32
3.189
3.993
7.181
-0.804
P33
1.077
1.105
2.182
-0.028
Based on submatrix TP , the influential relation diagram, for three perspectives (Table 1) is presented in Figure 1.
Fig. 1. The influential relation diagram of submatrix TP; ri-ci – relation indicator, ri+ci – position indicator, P1-P3 – analysed perspective.
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In Fig. 1, perspective P1 – Industry 4.0 shows the highest value of the indicator for position ri ci , which means that it is the most strongly interconnected with the other processes, taking up the central position in the network of interconnections. Perspective P3 – Sustainability shows the lowest level of the indicator. The indicator for relation ri ci , in turn, makes it possible to determine the impact level of the analysed parameter on the other parameters. Concurrently, it is assumed that it makes it possible to reflect the parameter priority among the other studied interdependencies. In Fig. 1, perspective P1 – Industry 4.0 shows the highest positive value of the indicator for relation ri ci , which means that the process has a dominating, causative influence on the other processes and simultaneously it is the most important out of the discussed three processes. Perspective P3 – Sustainability, in turn, with the highest negative value of the relation indicator, shows the highest degree of being receptive to influence exerted by other perspectives. Based on submatrix TK , the influential relation diagram, in the ( ri ci , ri ci ) layout, for ten KPIs (Table 1) is presented in Figure 2.
Fig. 2. The influential relation diagram of submatrix TK; ri-ci – relation indicator, ri+ci – position indicator, P11-P33 – analysed KPIs.
In Fig. 2, the highest value of the position indicator is shown by criterion P12 – Production planning, from perspective P1, thus taking up the central position in the network of connections with the other criteria. Criterion P33 – Society shows the lowest value of this indicator. Criterion P21 – IoT in logistics, with the highest positive value of the relation indicator exerts the strongest influence on the other criteria and is the most important criterion for logistic companies that handle production processes under the Industry 4.0 concept. On the other hand, criterion P32 – Environment, with the highest negative value of the relation indicator, is the largest recipient of influence exerted by the others, and its priority is the lowest among the 10 criteria formulated for the logistic process under the Industry 4.0 concept, while accounting for sustainability. 5. Conclusions The Industry 4.0 concept is currently considered to be potentially capable of changing production processes and related logistics processes to a significant extent. This article presents the analysis of a set of characteristic logistic parameters connected with production processes, taking into account the principles of the concepts of Industry 4.0, Logistics 4.0, and sustainability. It should be noted that any negative aspects of implementing these concepts were outside the scope of this study, and they constitute the object of further research in this regard. Within the framework of three basic perspectives, ten characteristic KPIs were determined. Then, applying the DEMATEL methodology, the mutual influence matrices for all pairs of the criteria were specified. Matrix of total relations T was devised; position and relation indicators were established for the three perspectives as well as for the ten KPIs. Both perspectives and criteria with the highest and the lowest (gross and net) influence were determined.
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Acknowledgements The results of the research studies presented in this article were accomplished as part of the research project No 1/S/IZT/18 titled ”Analysis of logistic determinants of sustainable urban development“ financed from the grant of the Ministry of Science and Higher Education to finance statutory activities. References Abeyratne, S. A., Monfared, R. P., 2016. Blockchain Ready Manufacturing Supply Chain Using Distributed Ledger. International Journal of Research in Engineering and Technology 5.9, 1–10. Andersson, P., Mattsson, L.-G., 2015. Service innovations enabled by the internet of things, IMP J. 9.1, 85–106. Barreto, L., Amaral, A., Pereira, T., 2017. Industry 4.0 implications in logistics: an overview. Procedia Manufacturing 13, 1245-1252. Bauernhansl, T., Hompel, M., Vogel-Heuser, B., 2014. Industrie 4.0 in Produktion, Automatisierung Und Logistik. Wiesbaden: Springer. Bechtsis, D., Tsolakis, N., Vlachos, D., Srai, J. S., 2018. Intelligent Autonomous Vehicles in digital supply chains: A framework for integrating innovations towards sustainable value networks. Journal of Cleaner Production, 181, 60-71. Berger, C., Hees, A., Braunreuther, S., Reinhart, G., 2016. Characterization of Cyber-Physical Sensor Systems, Procedia CIRP 41, 638-643. Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M., 2104. How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective. Int. J. Mech. Ind. Sci. Eng. 8.1, 37–44. Cirulis, A., Ginters, E., 2013. Augmented Reality in Logistics. Procedia Computer Science 26, 14-20. Forczmański P., Małecki K., 2013. Selected Aspects of Traffic Signs Recognition: Visual versus RFID Approach. In: Mikulski J., (Ed.) Activities of Transport Telematics. TST 2013. Communications in Computer and Information Science, vol 395. Berlin: Springer, 268-274. Gabus, A., Fontela, E., 1972. World problems, an invitation to further thought within the framework of DEMATEL. Geneva: Battelle Geneva Research Center. Gorecky, D., Schmitt, M., Loskyll, M., 2014. Human-Machine-Interaction in the Industry 4.0 era. IEEE International Conference on Industrial Informatics, INDIN, 289-294. Hermann, M., Pentek, T., Otto, B., 2015. Design Principles for Industrie 4.0 Scenarios: A Literature Review. Working Paper. Dortmund: Technical University of Dortmund. Hofmann, E., Rüsch, M., 2017. Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry 89, 23–34. Iwan, S., Kijewska, K., Johansen, B. G., Eidhammer, O., Małecki, K., Konicki, W., Thompson, R. G., 2018. Analysis of the environmental impacts of unloading bays based on cellular automata simulation. Transportation Research Part D: Transport and Environment 61, 104-117. Iwan, S., Małecki, K., Korczak, J., 2013. Impact of Telematics on Efficiency of Urban Freight Transport. In: Mikulski J., (Ed.) Activities of Transport Telematics. TST 2013. Communications in Computer and Information Science, vol. 395. Berlin: Springer, 50–57. Iwan, S., Małecki, K., 2012. Data Flows in an Integrated Urban Freight Transport Telematic System. In: Mikulski J., (Ed.) Telematics in the Transport Environment. TST 2012. Communications in Computer and Information Science, vol 329. Berlin: Springer, 79-86. Kauf, S., 2016. City logistics – A Strategic Element of Sustainable Urban Development. Transportation Research Procedia 16, 158-164. Kayikci, Y., 2018. Sustainability impact of digitization in logistics. Procedia Manufacturing 21, 782-789. Kehoe, B., Patil, S., Abbeel, P., Goldberg, K., 2015. A Survey of Research on Cloud Robotics and Automation. IEEE Transactions on Automation Science and Engineering 12.2, 398-409. Kijewska, K., Iwan, S., 2016. Analysis of the Functioning of Urban Deliveries in the City Centre and its Environmental Impact Based on Szczecin Example. Transportation Research Procedia 12, 739-749. Kijewska, K., Małecki, K., Iwan, S., 2016. Analysis of Data Needs and Having for the Integrated Urban Freight Transport Management System. In: Mikulski J., (Ed.) Challenge of Transport Telematics. TST 2016. Communications in Computer and Information Science, vol 640. Cham: Springer, 135-148. Kovalský, M., Mičieta, B., 2017. Support Planning and Optimization of Intelligent Logistics Systems. Procedia Engineering 192, 451-456. Kumar, S. P. L., 2017. State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing. Engineering Applications of Artificial Intelligence 65, 294-329. Liao, Y., Deschamps, F., Loures, E. F. R., Ramos, L. F. P., 2017. Past, Present and Future of Industry 4.0 - a Systematic Literature Review and Research Agenda Proposal. International Journal of Production Research 55.12, 3609-3629. Małecki, K., Kopaczyk, K., 2013. RFID-Based Traffic Signs Recognition System. In: Mikulski J., (Ed.) Activities of Transport Telematics. TST 2013. Communications in Computer and Information Science, vol. 395. Berlin: Springer, 115-122. Nolin, J., Olson, N., 2016. The internet of things and convenience. Internet Res. 26.2, 360–376. Oliff, H., Liu, Y., 2017. Towards Industry 4.0 Utilizing Data-Mining Techniques: A Case Study on Quality Improvement. Procedia CIRP 63, 167-172. Paelke, V., 2014. Augmented reality in the smart factory: Supporting Workers in an Industry 4.0 Environment. 2014 IEEE Emerging Technology and Factory Automation, ETFA, 1-4.
10
Witold Torbacki et al. / Transportation Research Procedia 39 (2019) 534–543 Witold Torbacki, Kinga Kijewska / Transportation Research Procedia 00 (2018) 000–000
543
Parvin, S., Hussain, F., Hussain, O., Thein, T., Park, J., 2013. Multi-cyber framework for availability enhancement of cyber-physical systems. Computing 95.10-11, 927–948. Scavo, F., Newton, B., Longwell, M., 2012. Choosing Between Cloud and Hosted ERP, and Why it Matters. Computer Economics Report 34.8, 1-12. Sehwan, P., 2014. 3D Printing Industry Trends. The International Journal of Advanced Culture Technology 2.1, 30-32. Strandhagen, J. O., Vallandingham, L. R., Fragapane, G., Strandhagen, J. W., Stangeland, A. B. H, Sharma, N., 2017. Logistics 4.0 and emerging sustainable business models. Advances in Manufacturing 5.4, 359–369. Torbacki, W., 2017. DEMATEL method in ERP systems for TSL branch. Transport Problems 12.4, 27-36. Ustundag, A., Emre, C., 2017. Industry 4.0: Managing The Digital Transformation. Berlin: Springer. Verma, P. K., Verma, R., Prakash, A., Agrawa, A., Naik, K., Tripath, R., et al., 2016. Machine-to-Machine, M2M communications: A survey. Journal of Network and Computer Applications 66, 83-105. Wagner, T., Herrmann, C., Thiede, S., 2017. Industry 4.0 Impacts on Lean Production Systems. Procedia CIRP 63, 125-131. Wanga, Y., Potter, A., Naim, M., Beevor, D., 2011. A case study exploring drivers and implications of collaborative electronic logistics marketplaces. Industrial Marketing Management 40.4, 612-623. Zhong, R. Y., Xu, X., Klotz, E., Newman, S. T., 2017. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering 3.5, 616– 630.