Managing Disruptions in Inbound Logistics of the Automotive Sector

Proceedings,16th Proceedings,16th IFAC IFAC Symposium Symposium on on Proceedings,16th IFAC Symposium on Information Control Problems in Proceedings,16th IFAC Symposium on Available online at www.sciencedirect.com Information Control Problems in Manufacturing Manufacturing Information Control Problems in Manufacturing Proceedings,16th IFAC Symposium on Bergamo, Italy, Italy, June 11-13, 2018 Information Control Problems in Manufacturing Bergamo, June 11-13, 2018 Bergamo, Italy, June 11-13, 2018 Information Control in Manufacturing Bergamo, Italy, JuneProblems 11-13, 2018 Bergamo, Italy, June 11-13, 2018

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IFAC PapersOnLine 51-11 (2018) 376–381

Managing Disruptions Disruptions in in Inbound Inbound Logistics Logistics of of the the Automotive Automotive Sector Sector Managing Managing Managing Disruptions Disruptions in in Inbound Inbound Logistics Logistics of of the the Automotive Automotive Sector Sector Managing Disruptions in Inbound Logistics of the Automotive Sector ,, Mohammadtaghi Falsafi * ** Irene Marchiori*

Marchiori* Mohammadtaghi Falsafi *,** Irene Mohammadtaghi Falsafi Mohammadtaghi Falsafi **,,** ** Irene Irene Marchiori* Marchiori* Rosanna Fornasiero* Rosanna Fornasiero* Rosanna Mohammadtaghi Falsafi * ** Irene Marchiori*  Fornasiero* Rosanna Fornasiero*  Fornasiero* Rosanna *Institute of of Industrial Industrial Technologies Technologies and Automation, Automation, (ITIA-CNR), Milano, Milano, 20133 20133 ITALY ITALY *Institute and (ITIA-CNR), *Institute of Industrial Technologies and Automation, (ITIA-CNR), Milano,  *Institute of Industrial Technologies and Automation, (ITIA-CNR), Milano, 20133 20133 ITALY ITALY (e-mail: [email protected], [email protected], [email protected] [email protected] , [email protected] [email protected] (e-mail: , ))) (e-mail: [email protected], [email protected] ,, [email protected] of Industrial**Politecnico Technologiesdiand Automation, (ITIA-CNR), Milano, 20133 ITALY (e-mail:*Institute [email protected], [email protected] [email protected] ) Milano, Milano, 20156 ITALY **Politecnico di Milano, Milano, 20156 ITALY **Politecnico di Milano, Milano, 20156 ITALY (e-mail: [email protected], [email protected] , )[email protected] ) **Politecnico di Milano, Milano, 20156 ITALY (e-mail: [email protected] (e-mail: [email protected] ). (e-mail: ). **Politecnico di Milano, Milano, 20156 ITALY (e-mail: [email protected] [email protected] ). (e-mail: [email protected] ). Abstract: Management of the supply chain in the automotive sector is one of the most complex tasks Abstract: Management of the supply chain in the automotive sector is one of the most complex tasks Abstract: Management of the supply chain in the automotive sector is one of the most complex tasks Abstract: Management of the supply chain in the automotive sector is one of the most complex tasks since it involves numerous partners. Managing the inbound flow becomes more vital for large automotive since it involves numerous partners. Managing the inbound flow becomes more vital for large automotive since it involves numerous partners. Managing the inbound flow becomes more vital for large automotive Abstract: Management of the supply chain in the automotive sector is one of the most complex tasks since it involves numerous partners. Managing the inbound flow becomes more vital for large automotive manufacturers with hundreds of suppliers providing components according to just-in-time strategies. This manufacturers with hundreds of suppliers providing components according to just-in-time strategies. This manufacturers with hundreds of suppliers providing components according to just-in-time strategies. This since it involves numerous partners. Managing the inbound flow becomes more vital for large automotive manufacturers with hundreds of suppliers providing components according to just-in-time strategies. This paper presents a decision-support toolkit for monitoring and managing the disruptions in the inbound paper presents a decision-support toolkit for monitoring and managing the disruptions in the inbound paper presents a decision-support toolkit for monitoring and managing the disruptions in the inbound manufacturers with hundreds of suppliers providing components according to just-in-time strategies. This paper a decision-support toolkit for monitoring and managing the the disruptions the inbound flow of ofpresents the automotive automotive sector. When When a disruptive disruptive event happens, happens, it affects affects the dock and andintransportation transportation flow the sector. a event it dock flow of the automotive sector. When a disruptive event happens, it affects the dock and transportation paper presents a decision-support toolkit for monitoring and managing the disruptions in the inbound flow of the automotive sector. When a disruptive event happens, it affects the dock and transportation planning of of the the manufacturer. manufacturer. To To cope cope with with the the consequent consequent order order displacements, displacements, some some alternative alternative planning planning of the manufacturer. To cope with the consequent displacements, some flow of the sector. When a disruptive happens,order it affects dock and transportation planning of automotive the manufacturer. Toalternative cope withincurring theevent consequent order displacements, some alternative solutions can be applied, applied, with each each alternative incurring additional costs. Thethe paper proposes aalternative managing solutions can be with additional costs. The paper proposes a managing solutions can be applied, with each incurring additional costs. The paper proposes aaalternative managing planning ofwhich the manufacturer. Toalternative cope withdock the plan consequent order displacements, someorders solutions can be applied, with each alternative incurring additional costs. The paper proposes managing strategy in optimization models for the are utilized to deal with disruptive and to strategy in which optimization models for the dock plan are utilized to deal with disruptive orders and to strategy in which optimization models for the dock plan are utilized to deal with disruptive orders and to solutions can be applied, with each alternative incurring additional costs. The paper proposes a managing strategy in which optimization models for the dock plan are utilized to deal with disruptive orders and to minimize the negative impacts on time and cost in the supply network. minimize the negative impacts on time and cost in the supply network. minimize the negative impacts on time and cost in the supply network. strategy in which optimization models for the dock plan are utilized to deal with disruptive orders and to minimize the negative impacts on time and cost in the supply network. minimize the negative impacts on time and cost in the supply network. Keywords: Inbound logistics, Disruptive events, Automotive sector, Optimization, Dock planning © 2018, IFAC (International of events, Automatic Control) Hosting by Elsevier Ltd.Dock All rights reserved. Keywords: Inbound logistics,Federation Disruptive Automotive sector, Optimization, planning Keywords: Keywords: Inbound Inbound logistics, logistics, Disruptive Disruptive events, events, Automotive Automotive sector, sector, Optimization, Optimization, Dock Dock planning planning  Keywords: Inbound logistics, Disruptive events, Automotive sector, Optimization, Dock planning  R&D, with with resource-efficiency resource-efficiency being being one one of of its its major major focal focal R&D, 1. INTRODUCTION R&D, with with resource-efficiency beingonone one of its its major majormultifocal  1. INTRODUCTION R&D, resource-efficiency being of focal points. At the the same time, time, it it is is based based a complicated complicated 1. INTRODUCTION points. At same on a multi1. INTRODUCTION points. At the same time, it is based on a complicated multiR&D, with resource-efficiency being one of its major focal points.production At the same time,relying it is based onefficient a complicated multistage process on the collaboration The new era of manufacturing demands optimized plants and 1. INTRODUCTION stage process on collaboration The new era of manufacturing demands optimized plants and stage production production process relying on the the efficient collaboration The new era of manufacturing demands optimized plants and points. At the same time,relying it is based onefficient acorresponding complicated multistage production process relying on the efficient collaboration The new era of manufacturing demands optimized plants and among different manufacturing sites and value manufacturing chain networks, transforming them into among different manufacturing sites and corresponding value manufacturing chain networks, transforming them into among different manufacturing sites and corresponding value manufacturing chain networks, transforming them into stage production process relying on the efficient collaboration The new era of manufacturing demands optimized plants and among different manufacturing sites and corresponding value manufacturing chain networks, transforming them into chain partners. Considering that each car can have up to 6000 flexible factories that can be quickly "reprogrammed" to chain partners. Considering that each car can have up to 6000 flexible factories that can be quickly "reprogrammed" to chain partners. Considering that each car can have up to 6000 flexible factories that can be quickly "reprogrammed" to among different manufacturing sites and corresponding value manufacturing chain networks, transforming them into chain partners. Considering that each car can have up to 6000 flexible factories that can be quickly "reprogrammed" to components, the management of suppliers is very important provide faster time-to-market in response to global consumer components, the management of suppliers is very important provide faster time-to-market in response to global consumer components, the management of suppliers is very important provide faster time-to-market in response to global consumer chain partners. Considering that each car can have up to 6000 flexible factories that can be quickly "reprogrammed" to the management of suppliers is very important provide faster time-to-market in response to globalproduction consumer components, to assure the delivery of the products in due time. In that demand. This new model needs transparent to the delivery of the in due time. In demand. This new model needs transparent to assure assure the delivery ofenable the products products inextraction due time. In that that demand. This new model needs transparent production components, the management ofbig suppliers is very important provide faster time-to-market in response tounexpected globalproduction consumer to assure the delivery of the products in due time. In that demand. This new model needs transparent production regard, the system will data from the processes that are responsive to changes or events regard, the system will enable big data extraction from the processes that are responsive to changes or unexpected events regard, the system will enable big data extraction from the processes that are responsive to changes or unexpected events to assure the delivery of the products in due time. In that demand. This new model needs transparent production regard, the system will enable big data extraction from the processes are responsive to changes or unexpected events Internet Internet of of Things Things (IoT), (IoT), advanced advanced data data analytics analytics and and originatingthat throughout the value value chain. Moreover, Moreover, it requires requires originating throughout the chain. it Internet of Things (IoT), advanced data analytics and originating throughout the value chain. Moreover, it requires regard, the system will enable big data extraction from the processes that are responsive to changes or unexpected events Internet of Things (IoT), advanced data analytics and originating throughout the value chain. Moreover, it requires complex event event processing, processing, also also providing providing automated automated control control transparent logistics logistics processes processes that that ensure ensure aa just-in-time just-in-time complex transparent complexcyber-physical event processing, also providingdata automated control transparent logistics processes that ensure aa just-in-time Internet of Things (IoT), advanced analytics and originating throughout the material value chain. Moreover, it requires complex event processing, also providing automated control transparentguarantees logistics that processes that ensure just-in-time through gateways. paradigm flow is optimized just-inthrough cyber-physical gateways. paradigm guarantees that material flow is optimized just-inthrough cyber-physical gateways. paradigm guarantees that material flow is optimized just-incomplex event processing, also providing automated control transparent logistics processes that ensure a just-in-time through cyber-physical gateways. paradigm even guarantees that material flow is optimized just-in- In this paper, we describe the work done to design aa decision sequence when disruptive events occur. In we the work done design decision sequence even when disruptive events occur. In this this paper, paper, wefordescribe describe thethe work done to to events design in decision sequence even when disruptive events occur. through cyber-physical gateways. paradigm guarantees that the material flow isguarantee optimizedbusiness just-in- support In this paper, we describe the work done to design aa decision sequence even when disruptive events occur. system managing disruptive inbound This case stems from need to support system for managing the disruptive events in inbound This case stems from the need to guarantee business support system for managing the disruptive events in inbound This case stems from the need to guarantee business In this paper, we describe the work done to design a decision sequence even when disruptive events occur. support system for managing the disruptive events in inbound This case stems from the need to guarantee business logistics, a topic not elaborated extensively in the literature. continuity in the ever-changing contemporary manufacturing logistics, a topic not elaborated extensively in the literature. continuity in the ever-changing contemporary manufacturing logistics, a topic not elaborated extensively in the literature. continuity in the ever-changing contemporary manufacturing support system for managing the disruptive events in inbound This case stems from the need to guarantee business logistics, a topic not elaborated extensively in the literature. continuity in the ever-changing contemporary manufacturing The optimization model analyzes some new aspects related to environment, where production goals are often derailed by The optimization model analyzes some related environment, where production goals are often derailed by Thespecific optimization model analyzes some new new aspects aspects related to to environment, where production goals are often derailed by logistics, a topic not elaborated extensively in the literature. continuity changes, in the ever-changing contemporary manufacturing The optimization model analyzes some new aspects related to environment, where production goals are often derailed by the characteristics of the problem. late-cycle the use of unqualified and nonstandard the specific characteristics of the problem. late-cycle changes, the use of unqualified and nonstandard the specific characteristics of the problem. late-cycle changes, the use of unqualified and nonstandard The optimization model analyzes some new aspects related to environment, where production goals are often derailed by the specific characteristics of the problem. late-cycle changes, the use of unqualified nonstandard After aa review review of of the the related related literature literature in in Section Section 2, 2, Section Section 3 3 parts, unexpected unexpected plant floor events, andlow low supplier After parts, plant floor events, supplier After review of the the related related literature indisruptive Section 2, 2,events Section parts, unexpected plant floor events, low supplier specific of literature the problem. late-cycle changes, use unqualified nonstandard After aa review of Section Section parts, unexpected plant floor events, and low supplier explains thecharacteristics problem by classifying classifying thein and33 involvement and the thethe lack of of proper decision support tools to to the explains the problem by the disruptive events and involvement and lack of proper decision support tools explains the problem problem by classifying classifying the disruptive events and3 involvement and tools After a review oftothe related literature indisruptive Section 4, 2,events parts, unexpected plantof floor decision events, support low supplier explains the by the and involvement and the the lack lack of proper proper decision support tools to to the alternatives cope with them. In Section aaSection general handle the above. the alternatives to cope with them. In Section 4, general handle the above. the alternatives to cope with them. In Section 4, a general handle the above. explains the problem by classifying the disruptive events and involvement and the lack of proper decision support tools to the alternatives to cope withapproach them. Inis Section 4, afollowed general handle the applies above. to architecture of the solution explained, This case the automotive sector, and in particular, architecture of solution explained, followed This case applies to the automotive sector, and in particular, architecture of the the solution approach is Section explained, followed This case applies to the automotive sector, and in particular, the alternatives to cope withapproach them. Inis 4,5 adiscusses general handle thethe above. architecture of the solution approach is explained, followed This case applies to the automotive sector, and in particular, by the mathematical modelling. Finally, Section to one of largest European car manufacturers (employing by the mathematical modelling. Finally, Section 55 discusses to one of the largest European car manufacturers (employing by the mathematical modelling. Finally, Section discusses to one of the largest European car manufacturers (employing architecture of the solution approach is explained, followed This case applies to the automotive sector, and in particular, by the mathematical modelling. Finally, model. Section 5 discusses to onethan of the largestpeople European car manufacturers (employing the future improvements of the proposed more 200,000 in 2014), with multiple production the future improvements of more 200,000 people in 2014), with multiple production the the future improvements of the the proposed proposed model. more than 200,000 people in 2014), with multiple production by mathematical modelling. Finally, model. Section 5 discusses to onethan of the largest European car manufacturers (employing the future improvements of the proposed model. more than 200,000 people in 2014), with multiple production sites, including joint-ventures, license production and sites, including joint-ventures, license production and 2. STATE OF THE ART sites, including joint-ventures, license production and the future improvements of the proposed model. more than 200,000 people in 2014), with multiple production 2. STATE OF THE ART sites, including joint-ventures, license productionSupply and outsourced production facilities in several countries. 2. STATE STATE OF OF THE THE ART ART outsourced production facilities in several countries. Supply 2. outsourced production facilities in several countries. Supply sites, including joint-ventures, production and outsourced facilities in license several countries. Supply chains and and production plants need to optimize operations to Analyzing Analyzing the the inbound inbound logistics in the ART automotive sector sector is is an an 2. STATE OFin THE chains production plants need to optimize operations to logistics the Analyzing the the inbound logistics logistics in in the the automotive automotive sector sector is is an chains andincreasing productionproduct plants need to optimize operations to Analyzing outsourced facilities in to several countries. Supply chains and production plants need optimize operations to cope with with variability, uncertainties in the the important issue issueinbound since, on on the the one one hand, hand,automotive it accounts accounts for for 10% 10% an of cope increasing product variability, uncertainties in important since, it of important issue since, on the one hand, it accounts for 10% 10% ofa cope increasing product variability, uncertainties in chains and production plants need optimize operations to Analyzing the inbound logistics the automotive sector cope with increasing product variability, uncertainties in the the important issue since, on the it accounts for of the manufacturing costs. On one theinhand, other hand, this cost cost is isin inan supplywith chain, shorter lead-times lead-times andtoreduced reduced working capital. supply chain, shorter and working capital. the manufacturing costs. On the other hand, this is a the manufacturing costs. On the other hand, this cost is in supply chain, shorter lead-times and reduced working capital. cope with increasing product variability, uncertainties in the important issue since, on the one hand, it accounts for 10% of supply chain,optimization shorter lead-times and reduced working capital. trade-off the manufacturing costs. On the other hand, this cost isto trade-off with the the on-time on-time delivery of the the components tointhe theaa Continuous is required and needs adequate Continuous optimization is required and needs adequate with delivery of components trade-off with with the the on-time on-timeOn delivery of the the components tointhe thea Continuous optimization is required and needs supply chain,and shorter lead-times and reduced working capital. the the hand, thispredominant cost isto Continuous optimization is perform required and validation needs adequate adequate trade-off delivery of technology methods to early and to plantmanufacturing (Miemczyk etcosts. al., 2004). 2004). Theother latter is components more technology and methods to perform early validation and to plant (Miemczyk et al., The latter is more predominant plant (Miemczyk et al., 2004). The latter is more predominant technology and methods to perform early validation and to Continuous optimization is required and needs adequate trade-off with the on-time delivery of the components to the technology and methods to With perform early validation andthat to plant al., 2004). more predominant achieve company targets. aa generated turnover than (Miemczyk the former formeret since since mostThe oflatter the isinbound inbound flows to to achieve company targets. turnover than the most of the flows than (Miemczyk the former formeret since since mostThe oflatter the isinbound inbound flows to to achieve company targets. With aa generated generated turnover that technology and of methods to With perform early validation andthat to plant al., 2004). more predominant achieve company targets. With generated turnover that than the most of the flows represents 6.3% EU GDP and ripple effects throughout the automotive manufacturers are delivered by a Just-In-Time represents 6.3% of EU GDP and ripple effects throughout the automotive manufacturers are delivered by aa Just-In-Time automotive manufacturers are delivered by Just-In-Time represents 6.3% of EU GDP and ripple effects throughout the achieve company targets. With a generated turnover that than the former since most of the inbound flows to represents 6.3% of EUaa GDP and ripple effects throughout the automotive manufacturers are delivered by a Just-In-Time economy supporting vast supply chain and generating an (JIT) strategy, strategy, and more more recently recently by Just-In-Sequence Just-In-Sequence (JIS) economy supporting vast supply chain and generating an (JIT) and by (JIS) (JIT) strategy, strategy, and more more recently recently by Just-In-Sequence Just-In-Sequence (JIS) economy supporting vast supply chain and generating an represents 6.3% ofservices, EUaa GDP and ripple effects throughout the automotive manufacturers are delivered by a parts Just-In-Time economy supporting vastthe supply chain and generating an (JIT) and by (JIS) array of of business business the automotive industry is highly highly delivery methods which amount to 40% average volume delivery methods which amount to 40% average parts volume array services, automotive industry is delivery methods which amount to 40% average parts volume array of business services, the automotive industry is highly economy supporting a vast supply chain and generating an (JIT) strategy, and more recently by Just-In-Sequence (JIS) array of business services, the automotive industry is highly delivery methods which amount to 40% average parts volume competitive, and and innovation innovation is is its its driving driving force. force. The The of of aa car car (Wagner (Wagner et et al., al., 2012). 2012). Svensson Svensson (2002) (2002) analyzed analyzed the the competitive, of aa car carand (Wagner et al., al., amount 2012). Svensson (2002) parts analyzed the competitive, and innovation is its force. The array of business the automotive industry isresearch highly delivery methods which to 40% average volume competitive, and services, innovation isprivate its driving driving force. The time of (Wagner et 2012). Svensson (2002) analyzed the time functional dependencies among firms and automotive industry is the largest investor in automotive industry is the largest private investor in research and functional dependencies among firms and time and functional dependencies among firms and automotive industry is the largest private investor in research competitive, and innovation is its driving force. The of a car (Wagner et al., 2012). Svensson (2002) analyzed the automotive industry is the largest private investor in research time and functional amongmanufacturing firms and emphasized the role role of of dependencies JIT in in automotive automotive manufacturing and development in Europe, investing over €41.5 billion into and development in Europe, investing over €41.5 billion into emphasized the JIT emphasized the role role of of dependencies JIT in in automotive automotive manufacturing and in investing over €41.5 into automotive industry is the largest private investor in research and functional amongmanufacturing firms and and development development in Europe, Europe, investing over €41.5 billion billion into time emphasized the JIT 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. and development in Europe, investing over €41.5 billion into emphasized the role of JIT in automotive manufacturing Copyright © 2018 IFAC 376 Copyright ©under 2018 responsibility IFAC 376Control. Copyright 2018 IFAC 376 Peer review© of International Federation of Automatic Copyright © 376 Copyright © 2018 2018 IFAC IFAC 376 10.1016/j.ifacol.2018.08.322 Copyright © 2018 IFAC 376

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when the time dependency aspect is discussed. It analyzed the role of JIS by focusing on its importance in the elimination of inventory. Thun et al. (2007) showed three approaches for components delivery in a JIS system. In the second approach, where the supplier produces the sequence based on the manufacturer’s demand and then transports the products to the manufacturer’s site, it was emphasized that the most critical issue is the high cost of production rescheduling due to disruptions in sequence delivery. Accordingly, the disruptions in this area cause tremendous costs, not only because of re-scheduling of the supply chain plan but also, more importantly, the necessity for the rescheduling of the assembly line; this cost may be much higher than the costs of inbound logistics. Bode et al. (2015) defined the supply chain disruption as the combination of an unexpected triggering event that occurs somewhere in the upstream supply chain, the inbound logistics network, or the purchasing environment, and a consequential situation which presents a serious threat to the normal course of business operations for the focal firm. To overcome the disruptions in the most effective and efficient way, Meyer et al. (2017) proposed a solution framework in four steps: 1. Identifying the disruptive events 2. Analyzing the effect of the event 3. Mitigation actions (i.e. proposing alternatives) and 4. Communicating the corrective actions to the decision-maker. The events that cause disruptions are divided into two main categories: the problems with suppliers (e.g. in terms of quality or failure) and the disruptions in transportation modes (e.g. logistic integration or channel interruption) [Wagner et al. (2012) and El Abdellaoui et al. (2017)]. Meyer et al. (2017) analyzed the delays in transport modes and, as an alternative to cope with such interruptions, proposed the use of faster transport modes (an additional truck). Boysen et al. (2015) used four alternatives when a part is delayed. Among these alternatives, three of them are related to the assembly line; the only solution which is related to the inbound logistics is the use of the express delivery method. Wagner et al. (2012) stated that process optimization is one of the most effective ways to deal with disruptions. However, it concluded that there is a gap in the literature to quantify the impact of disruptions on the total costs for the company. Boysen et al. (2015) added another aspect of the problem which is related to the arrival of the delayed components to the dock doors of the plant. This problem is divided into the assignment of the trucks to the arrival times at the plant and the assignment of the docks to the trucks. It was mentioned that these aspects were not considered in the literature. However, this problem is partially dealt in the papers which analyze the problem of cross-docking. Boysen et al. (2010) emphasized the focus of both input and output docks in the literature of cross-docking problems. (refer to Yu et al., 2008 for an example). However, for automotive manufacturers, the problems in cross-docking are not the same as those in the arrival docks.

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In the analyzed supply chain, the flow of components moves from suppliers through the distribution centers and arrives at the docks of the assembly plants (Figure 1). Then, the arriving components are transferred to the inventory or directly to the assembly line. The correct feeding of the assembly line depends on the timely arrival of the components since a displacement of the parts may incur heavy costs for production re-scheduling. The main events that cause delays can happen at different points in the supply chain (Figure 1): A. Supplier: There might be problems at the suppliers’ warehouse due to events such as the quality of the components or the delayed delivery of the orders. B. Means of transport: The means of transport might experience a delay during the trip from suppliers to the assembly plant. In this case, the order is already on the container and is traveling when some problems occur (e.g. traffic or problems at the distribution center). C. Dock: Even though the components arrive on time, there might be some problems at the docks due to issues with handling and unloading equipment, or the dock situation.

Fig. 1: Flows in the inbound logistics and the corresponding delay events 3.2 Feasible Alternatives When one of the three delay events occurs, it is possible to apply two strategies to solve the problem, as follows: 1. Transport management. This alternative is considered when the supplier is late or there are problems during transportation (e.g., problems of loading / unloading at distribution centers). In such a case, the module for the optimization will evaluate the use of faster modes, e.g. express delivery, in order to speed up delivery and, if possible, achieve the planned arrival time taking into consideration trade-offs between cost and delivery time. 2. Dock management. This alternative is considered for any kind of disruptive event. The module will optimize the assignment of the arriving orders to the docks taking into consideration different strategies: a. Stay in the same dock, changing the time window. This offers the advantage that there is no change in the dock; once it has arrived, the truck waits at the dock until a free-time window arises. b. Change dock. This strategy has the advantage of avoiding further delays. If in a dock, different from the planned one, the time window is free when the truck arrives, the truck can be unloaded at the free dock. c. Open a reserved dock. In the set of available docks, the company always reserves one free dock during

3. THE PROBLEM 3.1 Inbound Logistics

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the entire time horizon. Based on necessity, this dock can be opened with the related costs of setup. Increase the number of resources. If the resources available are not sufficient, it is possible to add new resources for the unloading process. 4. SOLUTION METHODOLOGY

4.1 General Architecture To manage the disruptive events which occur in the inbound logistics process, different levels of analysis are required. In figure 2, a general architecture is presented to integrate the data which comes from four important different tools to minimize the impact of the disruptions on the entire supply chain. The tools are Complex Event Processing (CEP), database (DB), a simulation module, and an optimization module which is formed by two sub-modules: one for the transportation and dock management, the other for the production re-scheduling. 1. The CEP module, based on data collected from trucks, docks and suppliers, allows the detection of the delay events; the output of this module is an alert for each type of delay. 2. DB allows a firm to manage all the data derived from the enterprise information system (including ERP, SCM WMS) and collects all the information needed for the simulation and optimization. 3. The simulation module allows a firm to measure the impact of delays identified by CEP on the production plant KPIs. Simulation results are not applied to the optimization module, but comparing the KPIs with the company targets for those KPIs, it is possible to understand if it is necessary to activate the optimization module to mitigate the impact of the delays. 4. The optimization module allows a firm to take the following decisions: a. Transport and docks management based on input data such as unloading cost, transport cost, and dock assignment. The results are a new transportation plan and dock assignment. b. Re-scheduling production management based on input data such as re-scheduling cost, inventory costs, production planning, etc. The output is a new production plan. The two sub-modules for the optimization are linked to each other. In fact, the transport and dock management require the cost of the production re-scheduling while the second sub-module requires new transport and dock plans. Once the optimization module has calculated a solution to reduce the impact of delay, these results can be used as inputs for a new simulation to measure the impact of the new solutions on the KPIs. This architecture allows a firm to manage the disruptive events with real-time integration of the data from different sources. This strategy represents a novel use of digital platforms. In relation to the two sub-modules for the optimization, different use cases (UC) have been defined within the company to describe how the modules address the related use

Fig. 2. General architecture cases. These cases are useful to explain how the digital platform can communicate and interact with the user to support the decision process in the context of production rescheduling and inbound logistics.  UC_1: Provide alternatives for optimal production scheduling. The optimization module provides a holistic production scheduling tool that, given the input (events, horizon, shop-floor configuration), handles all the corresponding running flows and produces a feasible schedule that addresses the selected events based on userspecified optimization criteria.  UC_2: Provide alternatives for delays in delivery. In this use case, the optimization module produces feasible alternatives to handle delays in the delivery of components, in order to provide a new transportation and dock management plan, minimizing the impact of the delay on the production schedule. The strategies employed include the alternatives presented in section 3.2.  UC_3: Provide a combination of production schedule, transportation plan and plan for activities at docking stations. This use case enables a firm to select alternatives to handle delays in the delivery of components by providing alternatives in the transportation plan or the activities at the dock stations. Subsequently, this strategy allows a firm to obtain an optimal schedule given the changes proposed in the delivery of components by the various alternatives, as well as other events that the user may choose to handle.  UC_4 Evaluate the transportation plan and activities at docking stations. For this use case, optimization offers an evaluation of the set of feasible alternatives in the transportation plan or the activities at the dock stations to handle delays in the delivery of components. This evaluation includes the following impact estimation metrics: o New transportation and dock plan o Total cost of the implemented solutions o Number of solved delays o Number of delays not solved (treated by production re-scheduling) In the following sub-sections, we focus on the optimization module and, in particular, we propose a model to address the management of the dock’s assignment when some disruptive events occur. 378

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4.2 Dock Management Model The model implements the second alternative proposed in section 3.2. The following assumptions have been defined: 1. Each arriving truck is associated to one order. 2. Each time window is equal to one hour. 3. The unloading process for all the orders is fixed to one hour (15 minutes of preparation and 45 minutes of unloading). 4. To unload each truck, one resource is required. 5. Two types of docks are available: a. Standard docks, where the orders are normally unloaded. b. Reserved docks, where usually no order is assigned to them. These docks are reserved so that they are opened in case of necessity. The model manages the incoming disruptive events and minimizes the total cost to update the dock assignment in order to find a new optimal solution for the dock management. The most important aim is to respect the “actual production time” which indicates the time when the incoming order is required on the assembly line. Based on this time, it is possible to calculate when, at maximum, the order must be ready at the dock (due time) (Figure 3). Due time is the difference between the “actual production time” and the time needed to transport the order from the warehouse which is close to the docks to the assembly line. It is possible to manage the transfer of the components from the warehouse to the assembly line in different ways: the standard method (used to define the “due time”) and two faster strategies which feature more expedient transport and more costs and which are used to compensate for the delay when the delayed orders arrive after the “due time”. Therefore, the two strategies define two moments (acceptable due time_S1 and _S2): these are calculated as the difference between the “actual production time” and the respective transfer time by applying each transfer equipment. If the delayed orders arrive after these two moments, the only solution to accommodate the delay is production re-scheduling. Consequently, it is possible to define the buffer time as the time between the “due time” and the specific time window when the production re-scheduling is required. If the order arrives after the buffer time, it is considered to be within the re-scheduling time. When a disruption occurs, the order will arrive after the planned arrival time, a period known as the estimated arrival time. After the arrival of an order to the dock, it takes a specific amount of time to be unloaded and transferred to the warehouse (unloading duration). After unloading, the order is ready to be transferred to the assembly line (readiness time). If the estimated readiness time is before the due time, there is no additional cost. But if it is later than the due time, it would have the cost related to the buffer time or re-scheduling cost according to the delay amount (Figure 3). Based on this information, we list the set and parameters used in the model. The sets are defined as follows: i : orders ( i  1.. I ) k : docks ( k  1.. K )

Fig. 3: Calculation of the buffer and re-scheduling time : time window, the unit of time which the planning is based on ( t  1..T ) s : strategies for the transferring of the orders to the assembly line ( s  1, 2 ) Based on the dock assignment and the amount of delay, which respectively come from DB and CEP, it is possible to define the following parameters: PDA i , k ,t : Planned dock assignment for order i assigned to t

dock k at time t DDA i , k ,t : Dock assignment after the disruptive events; it shows the impact of the events A and B on the dock plan; it is possible to see some overlaps between orders since some orders could arrive at the same dock in the same time window AD i : Assigned dock for order i . It is calculated from PDA i , k ,t AT i :

Arrival time of order i ; it considers the amount of the delay per each order. If a specific order is affected by a disruptive event, it is calculated from DDA i , k ,t DP k , t :

Dock problem for dock k at time t

N A Rt :

Number of available resources at each time t

BT i , s , t

: Buffer time for time t of each order i with each of

the transferring strategies (S1, S2). It is one when, in the arrival of an order, it is required to use one of the strategies to speed up the transferring. RT i , t : Re-scheduling time at time t for each order i : Dock setup cost for each dock k ; this cost occurs when the order i is unloaded in a new dock (standard or reserved) which is different from the planned one. ARC : Cost of the additional resources required to guarantee the unloading process at each time window t WC : Waiting cost; it depends on how many time windows one order has to wait between its arrival time and the assigned time window in the new dock assignment because of the lack of free docks. BC s : Buffer cost for strategy s; the cost of transferring the DSC

k

orders from the warehouse to the assembly line for each buffer strategy. RC i : Re-scheduling cost for order i ; The main cost is due to the production re-scheduling and therefore, losses in job-per hour (JPH). 379

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If this condition is verified per each time window, the model can provide a solution; otherwise, the model sends the list of orders to be solved in the next time horizon.

The decision variable is defined as: N D Ai , k , t : New dock assignment for order i to dock k at time t The objective function minimizes the total cost:



M in

i , k  A D i ,t

 NDA

i , k ,t



i , k ,t



4.3 Preliminary results

( N D Ai , k ,t  D C S k ) 

1.1

  BC s  BTi , s , t  1    RC i  RTi , t  1       s 

A R C  m ax {  ( N D Ai , k , t )  N A R t , 0} 

1.2

1.3

i ,k

t

 (W C     t  N D A   A T  ) i , k ,t

1.4

i

t ,k

i

(1.1) shows the calculation of the setup cost. This cost is considered just when the assigned dock in the new dock assignment is different from the assigned dock in the planned dock assignment. (1.2) calculates the buffer and rescheduling costs. (1.3) calculates the additional resource cost when the number of available resources is not enough for the incoming orders in the new dock assignment. When the number of orders is less than the number of available resources in each time window, no additional resource cost should be considered, and therefore, this line should be zero. (1.4) calculates the waiting cost when, in the new dock assignment, the order is assigned to a later time than its arrival time. The constraints are as follows:

 NDA

i , k ,t

 1   1  D Pk , t 

k ,t

C. Number of time windows with more than one orders: indicates when there are overlaps between orders that have to be solved (it derives from DDA i , k ,t ) D. Initial delay: the total amount of delay of the disruptive orders (it derives from the comparison between PDA i , k ,t and DDA i , k ,t )

(2)

i

 NDA

i , k ,t

1

i

(3)

k ,t



N D Ai , k , t  0

(4)

i , k , t   A Ti  1 

Constraint (2) ensures that, in the new dock assignment, there should be maximum one order assigned to a specific dock in a specific time; but in case of dock problems, no order can be assigned. Constraint (3) ensures that one order cannot be assigned to more than one dock and one time window. The last constraint (4) ensures that each order cannot be assigned to an earlier time than its arrival time at the dock. To avoid the cases for which the model cannot find a solution, a condition has been developed which has to be checked before running the optimization. In the constraints (2), (3), and (4), the total number of orders assigned to a specific time window, considering the delay per each order, has to be less than or equal to the number of time windows available from that specific window to the end of the time horizon ( T ). It is also necessary to consider the problems at the docks which make them unavailable:



i , k , t1  t  T

DDAi , k ,t  k   T  t1  1   k ,t  t  T DPk ,t 1

In Table 1, the most important input data is summarized (from A to D): these data create an overview of the problem to deal with the proposed model and derives from the processing of the parameters listed in section 4.2. The input data are as follows: A. Number of arriving orders: Number of the total orders dealt with the model (represented by i ) B. Disruptive orders: Number of orders which have a delay caused by one of the three events listed in section 3.1. “b1” are the orders affected by event A, B and/or C, and “b2” are the orders affected only by event C (it derives from the comparison between PDA i , k ,t and DDA i , k ,t )

 t1  t

380

From the row E to L, the solution of NDA i , k ,t has been aggregated in appropriate KPIs to support the company in understanding the results and comparing different scenarios. In particular: E. Total waiting time: the sum of time that some orders have to wait once they arrive at the docks F. Final delay: the sum of D and E G. Total cost of solution: the cost to implement the new dock assignment provided by the model (derives from (1)) H. Number of orders solved in buffer time (S1 or S2): the total number of orders which are unloaded in a time window inside the buffer time implementing strategy 1 or 2 I. Number of orders solved with re-scheduling: the total amount of orders which are unloaded after the rescheduling time and for which production re-scheduling is required J. Number of orders assigned to the reserved dock: the total number of orders which are unloaded at the reserved dock K. Time windows with extra resources: the number of time windows when an extra resource is required to unload the orders assigned to a specific time window L. Number of extra resources: the total number of extra resources required to unload the arriving orders To test the model, we assume that the company uses the model to manage the disruptive events which affect the orders in one day. There are three standard docks and one reserved dock. In addition, the company works on two eighthours shifts, so the model manages 16 time windows per each dock. Cases 1, 2 and 3 are differentiated by the number of arriving orders: Case 1 is the worst because it has the maximum number of arriving orders (48, 3 standard docks per 16 time windows): the results show that (Figure 4) if the

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simplified problem; future research aims at increasing the dimension of the problem and measuring the impact on the most important KPIs of the company. The model, in fact, is expected to influence the following dimensions: job-per-hour (JPH), OEE, stock-out, and the capability to respond properly to disruptive events. Furthermore, in the proposed model in this paper, the dock management does not have strategies for reducing the delay caused by disruptive events. The model could be further refined by considering the transport management strategies to reduce this amount.

Table 1. Comparison of the results Input A B b1 b2 C D KPIs E F G H I J K L

case 1 48 21 20 1 13 33 case 1 21 54 3689 17 5 8 4 4

case 2 32 19 19 0 10 33 case 2 2 35 2482 13 5 2 -

case 3 36 23 23 0 10 33 case 3 3 36 2148 23 1 2 -

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case 4 36 21 19 2 10 61 case 4 10 71 4238 12 8 3 1 2

Acknowledgements: The work on this paper is funded mainly by the European Commission through the DISRUPT project H2020 FOF-11-2016, RIA project n. 723541, 20162018). The authors would like to thank the contributions of the different partners of the DISRUPT project. REFERENCES

company has to deal with the maximum number of orders, it becomes necessary to implement all the strategies listed in section 3.2 to manage the disruptive events and in particular the waiting cost, the re-scheduling cost and the cost for extra docks and resources are higher than the costs in the other two cases. In cases 2 and 3, for example, no extra resources are required and the waiting cost is very low.

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Fig. 4: Results comparison Comparing cases 3 and 4, the model manages the same number of arriving orders but the total delay in case 4 is almost doubled compared to case 3. This higher amount of delay causes the use of the re-scheduling strategy for most of the disruptive orders: in case 3 the percentage of orders solved with the re-scheduling is 4% whereas in case 4 it is 38%. 5. CONCLUSION Every day manufacturing flow interruptions caused by problems in the supply chain management of the analyzed company affect the overall productivity due to micro-stops and macro-stops, accounting for 2-4% of Overall Equipment Effectiveness (OEE). Moreover, changes in production schedule as an impact the plant resources (manpower, consumption, material scheduling) constitute 4% of operating costs. The main objectives for the company are to reduce manufacturing downtimes due to the external events which immobilize capital throughout the supply chain. For this reason, an optimization model is developed to manage the disruptive events that occur during the inbound logistics, and to optimize the alternatives defined according to the company disciplines. The model works with a 381