This work investigates company performance measurement in case of a merger by applying a composite indicator for measuring company performance from economic and environmental perspectives.
The recent merger between Motor Vehicle Manufacturers (MVMs) Peugeot Société Anonyme (PSA) and Fiat Chrysler Automobiles (FCA) has drawn attention due to its potential positive impact on company performance. The aim of this research is to contribute to the existing knowledge by adding value to the field of company performance measurement in the context of mergers.
To achieve this goal, the current situation regarding the merger between automotive companies PSA and FCA was investigated and the state-of-the-art in composite indicators for measuring company performance and merger performance measurement was presented.
Furthermore, the relevance of adding Market Capitalization as independent variable to the comprehensive set of measures of the composite indicator for company performance of motor vehicle manufacturers (I_MVM) was investigated. The I_MVM was extended to assess both historic and future company performance for a hypothetical market of motor vehicle manufacturers in the case that two of these motor vehicle manufacturers were fictitiously merged into one company.
A case study focusing on the merger between PSA and FCA is conducted according to the methodology of theory-testing research. The application of the extended model I_MVMMC provides valuable insights into merger performance.
Moreover, a method for measuring future merger performance was designed. This method provides a framework to evaluate the potential success of mergers and an index quantified this potential. The results showed the potential of value through growth and the potential of the merger between PSA and FCA.

This study entails the development of a planning model utilizing Centralized Model Predictive Control (CMPC) to optimize the flow of physical goods throughout a network of supply chain nodes, utilizing a Mixed-Integer Linear Programming (MILP) approach to determine the optimal decision variables. Specifically, a Current State CMPC model was created to reflect the current outbound logistic network at Heineken Zoeterwoude, where information asymmetries are known to impact the accuracy of the outbound logistic planning tool. The Current State model was compared against a Future State model, where real-time data is available, thereby eliminating the aforementioned information asymmetries. By assessing four key performance indicators, it was found that the Future State model enables considerably better performance of the logistic network, even during peak production.

Fully autonomous bin handling systems are relatively new and examples of existing systems are hard to find. This makes designing such a system more difficult. The goal of this research is to provide insight in the effects of different design strategies of autonomous bin handling systems. This is done through a quantitative optimization model of an existing autonomous bin handling system. With this model, modifications to the system are made and evaluated. Optimal solutions for bill of material and floorspace, for different demand quantities are generated and compared.

This master thesis focuses on the effect of variabilities in a Power & Free conveyor system on the flow continuity by an analysis on the product, process and production level (3P), a discrete-event simulation model and the theory of Cyber-Physical Production systems (CPPS) to control the product flow. Scenarios were evaluated with this model and the results show the effect of performance increase when variability decreases. Requirements are set to come to real-time control by developing this model into a digital twin as part of a CPPS. It can be concluded that the discreteevent model can be used to predict the performance and take control measures accordingly, subsequently real-time control is possible by implementing real-time sensor measurements in the model, thereby developing the model into a digital twin.

This research aim was to design a centralised model that redefines definitions, standardises maintenance by categorisation, and reduces waste by introducing new slot lengths, leading to an equilibrium between flights and maintenance. This equilibrium enhances the airline’s capacity to manage maintenance efficiently and ensures greater uptime. The model addresses the important aspects of airline planning, including maintenance, flight and operation planning. Lean Six Sigma theories serve as a framework to combine the different plannings. The key importance is that the model considers all the stakeholders instead of only one so that the equilibrium between excessive flying and excessive maintenance can be established. In striking such a balance, uptime is assumed to increase.

Big data analytics (BDA) and supply chain resilience are both present and important topics, but research on the relation between the subjects is limited. This also holds for the translation of the subject for a specific industry. This research therefore addresses this relation and translates it to a comprehensive partial resilience assessment tool that provides a benchmark for the Fast Moving Consumer Goods (FMCG) industry. The tool is based on a deterministic model that incorporated 14 resilience enablers and their corresponding interdependence. Results show that current industry BDA based resilience levels are, compared to a theoretical optimum, on average 48% and have a better practice of 66%. It is recommended that the tool is further implemented within the industry to gain more reliable and substantiated results.

Tummers Food Processing Solutions is an internationally operating company based in the Netherlands, that sells factory lines for processing tuberous products like potatoes. Before being processed into various products, the incoming tuber batches are always washed and cleaned from waste particles like clods, stones and haulm. For this purpose washing lines consisting out of different machines are used. Every processing application, customer and kind of input product results in different requirements regarding the machines, options and their configuration. Currently for every customer a tailor made machine line is designed, consisting out of customised machines based on the expertise of the corresponding sales agent and the engineer that designs the machines. This results in individually designed machines, friction between different departments and mistakes in drawings, parts lists, manufacturing, documentation and others. Once installed on site, the machine line is controlled by machine operators that keep the factory operational but are not optimising the performance and efficiency of the machine line nor conduct preventive maintenance. To tackle both the issues on the company and the customer side, the strategy for designing these tuber washing lines has to be turned into adaptive machine line design. Adaptive machine line design is a combination of modular design and smart factory engineering. Machine lines are customer specifically configured out of standardised modules and controlled by smart systems to optimize performance and efficiency and predict maintenance.

Because potatoes are by far the most commonly processed product, followed up by carrots, this research focusses on potato washing lines with some specific side notes for processing carrots. Based on sales records and customer preferences, a set of standardised machine models and options is defined as the machine portfolio. This portfolio consists out of ten different machines, each available in different sizes, with waste and product streams to different directions and other standardised options. A jigsaw puzzle model can be made where this machine portfolio is build up as a set of puzzle pieces. For every different application a set of these puzzle pieces can be chosen from the portfolio and connected in different configurations. To determine which machines with which options to choose from the portfolio in which order, a model can be used. This model is based on a set of input- output relations and first set up as a decision scheme after which it is modeled in LabView as a basic machine line configurator. The model is then used to configure machine lines for five different examples. Each example is an existing order with differences in customer specific wishes, input and output characteristics and boundary conditions regarding factory layout and peripheral equipment. With help of these five examples the model is iteratively checked and improved after which the amount of customer specific engineering is determined that still needs to be performed after standardising of the machines.

A first layer of a Digital Twin for smart control of the washing line is designed. This control strategy is based on the Key Performance Indicators of the washing line and their relations between them. Based on open or closed loop control, a set of control schemes is created for each machine that requires smart control.

A thorough analysis of the costs and benefits of implementing a modular design strategy for the tuber washing lines shows that huge amounts of man hours can be saved after implementation. Next to these savings in labour, less mistakes will be made in the design and manufacturing of the machines which improves the professional image of the company and lowers friction between different departments and stress on the workfloor. Due to significant savings in replacing wear sensitive parts, reduced factory downtime, loss of product in waste streams and reduced operator wadges, installing a system for smart control can easily be earned back in the first year of operations. All and all changing the design strategy for tuber washing lines to adaptive machine line design is very promising for both the machine line manufacturer as for the customer.

In this day and age sustainability is becoming increasingly important. Even though the airline business is not the first thing which comes to mind when regarding sustainability there are huge efforts in play to extend the life of airplanes and its corresponding components. This is done via remanufacturing. Remanufacturing can here be defined as:” Returning a used product to at least its original performance with a warranty that is equivalent to or better than that of the newly manufactured product” (Gunasekara, et al., 2018). Even though numerous attempts using traditional techniques like Lean have been tried at KLM E&M, none have yielded the long term desired results, which is why a different approach is needed. Remanufacturing brings numerous problems with it, unknown to traditional manufacturing (Guide Jr & Daniel, 2000). These problems result in the operational level of the organisation not having control over the process, which leads to subpar performances. Furthermore, a gap originates between higher level management and the operations, which leaves a lot of potential uncovered as well as discourage operational level progress. The proposed model is designed to help gain control over an uncontrolled process, improve its performance and sustain the newly found control, which is also the purpose of this research. This is all achieved using a more bottom-up integral approach. This proposed model is combined with the operational lean maturity model developed by Maasouman (Maasouman, 2015), in order to create a Remanufacturing Process maturity model, which indicates the maturity level of each aspect used in the Remanufacturing Process control model, resulting in a model which has both quantitative and qualitative aspects.

Covid-19, the Suez canal blockade and a decreasing amount of larger shipping companies have changed the container shipping industry. A power shift from the exporter towards the shipping companies is occurring, paired with capacity constraints and surging container transportation prices indicating a global container shortage. Exporters of large volume mainly experience this with contract negotiations and are left with a choice for higher prices or less flexibility in contractual service conditions whilst striving to become more sustainable. This study answers the following research question: ‘How should large exporters like HNS weigh off transportation costs, flexibility in service conditions and environmental effects for contractual agreements in times of a global container shortage?’. This study poses several designs through the DMADE methodology to anticipate the aforementioned changes. It does so by modelling several designs based on contract variables, a modal shift and a production division based on geographical locations of breweries for Heineken Netherlands Supply. The study found that higher standard tariffs for container transportation should be preferred above less flexibility. Next to that a modal shift towards road transport is advised if the service conditions in contractual agreements become less flexible.

This thesis looks into the link between the Volkswagen emission scandal, or dieselgate, and the strong rise of electric vehicles in the years following that event. In order to investigate this, a framework is build that describes the process of an innovation (the electric vehicle), taking over from an incumbent technology (fossil fuel cars), in measurable factors. This framework combines several theories on these subjects, including innovation diffusion, standard battles, disruptive innovation and radical innovation. The addition of manners to measure the underlying factors is one of the new aspects this thesis brings to both science and society. Through the application of the framework in case study concerning the automotive industry, it was concluded that dieselgate has indeed influenced the rise of electric vehicles, both directly and indirectly through Company Strategy, the reputation of a company, the opinion of consumers on technology, and government regulations and incentives.

Nowadays many companies still conceive their logistic operations as a simple material replenishment of the production plants and do not invest money and time in projects to structure their Supply Chain and bring more eciency to the production process. In addition, the high complexity of the automotive industry and the emerging uncertainties that are characterizing a more globalized, dynamic and interconnected world give companies a huge incentive to research and innovate the management of their supplier network. Over the last years, businesses have experienced several issues along their logistic flows. Unexpected events such as the pandemic and the semiconductor crisis have put companies in research for solutions that look to improve and strengthen the partnership with their suppliers.
Digitization represents one of the most innovative and disruptive challenges in today’s Supply Chains. Indeed, the increasing amount of data retrievable from logistic and production processes today is yet not exploited enough in comparison with its potential benefits. Companies still work by silos and prefer to hide their information rather than sharing them with their partners.
In this research paper, the role of data visibility is put under attention, in order to demonstrate its practical benefits in a complex automotive Supply Chain. By collaborating with Ferrari on a Supplier Relationship Management (SRM) project, this research presents the design of a Supply Chain control tower through Model Predictive Control. By simulating a MPC optimization model on a small part of Ferrari’s supplier network, the coordination, eciency and sustainability of the Supply Chain are assessed through a comparison with the current state and by evaluating the network’s performances in di↵erent logistic scenarios. Although this solution is presented as a decision-support tool, it is thought as a key technology for the future development of autonomous Supply Chain operations.

Port performance is under pressure due to high congestion levels and a need to reduce GHG emissions. The industry is looking for ways to increase efficiency in ports. A promising concept is known as port call optimization where waiting times and turn-around times can be reduced. This research contributes to the body of port call optimization literature by identifying the importance of performance attributes for innovations. Identifying the performance attributes and how stakeholders weight these has never been explored before in literature. The relative importance has been derived using the Bayesian Best-Worst Method. The performance attributes: incidents, near misses, robustness and added value, are most important for evaluating an innovation for port call management. Results on micro level show heterogeneity and homogeneity in preferences for port attributes. Both ports with complex nautical restrictions prefer performance attributes related to safety. The port with centralised management is more commercially driven and there is homogeneity within a port. Furthermore, heterogeneity is seen between stakeholder groups and between port authority (PA). PAs from centralised port value attributes relatively different than a PA with decentralised and licensing activities. Some differences and similarities can be explained by the port contexts. The findings can be used for the design of innovations in port call management. Research could be repeated in other port cases to validate the significance of these results.

Social and political pressure has made emissions an increasingly important subject for companies. The extension of the sustainability reporting mandate by the EU has increased the necessity for carbon transparency for companies in Europe. Warehousing and distribution account for 13\% global supply chain emissions and can be an important factor in reducing company emissions. The traditional trade off in warehousing storage systems between cost, service level and emissions has mostly been in favor of cost and service level. Now however, emissions are becoming just as important to companies. This paper investigates the emission levels of manual and automated storage system, specified to aviation standard parts. Aviation standard parts are chosen as these are commonly stored using automated systems, due to the high degree of accuracy needed and the quantities involved in this sector. Current knowledge does not provide insight into the environmental footprint of automated and manual storage systems for small parts. This paper aims to fill this gap by combining literature research and expert consulting to gain insight into the carbon footprint of storage systems and modelling to gain insight into operational energy consumption. Finally, a case study is performed at an aviation parts dealer for empirical evidence on storage system emissions.

This thesis project features a design method of an automated warehouse in an insect rearing system, where pallets are transported from storage to a feeding machine and back into storage. From seven material handling equipment concepts, the most promising concepts are selected through a combined expert panel evaluation of 7 criteria and a basic design evaluation of four criteria. Inspired by the automated generation of design paradigm, a performance based parametric design approach is taken, where a parametric description of the warehouse lay-out is coupled to a pallet flow simulation. Iterative simulations are then performed until a lay-out design is obtained that provides the required handling capacity with minimal material handling equipment. Lay-out designs are generated for three operational scenarios with a shuttle and orbiter, and one scenario with an automated forklift.

Container liner shipping organisations facilitate trade between all regions globally by deploying vessels in a network with a corresponding sailing schedule. Since the container shipping network is part of a larger network, it is favourable for the continuation of the supply chain to maintain this schedule. Although the importance of the continuation is recognised, the industry is not known for a high degree of schedule reliability. This is due to the presence of regular and irregular uncertainties in the network. It is found that more than 90% of these uncertainties find their origin in the port area. By deploying the LSS methodology, the causes and effects of port uncertainties are further analysed. It is found that the port uncertainties lead to transportation waste at sea in the form of fuel costs and emissions. The shipping sector is held accountable for approximately 3% of the global carbon dioxide emissions. A Dynamic Arrival Time (DAT) design is proposed to recover a schedule after a delay takes place to avoid waiting times and arrive JIT in the port. This differentiates from the current state where a First Come First Serve policy is maintained. An Agent-Based Discrete Event Simulation model in Anylogic is proposed to test the effects of a DAT implementation in a Short Sea Shipping (SSS) network that is subject to regular and irregular port uncertainties. It is found that a fuel and emissions reduction of up to 6.1% is achieved when a DAT is implemented under irregular uncertainties in the network. If the DAT incorporation is tested for regular uncertainties, the effect is equal to a 0.5% reduction in fuel and emissions. Furthermore, it is found that the design leads to a decrease in the port turnaround time due to improved arrival predictability. This improved predictability enables the shipping line (or agent) to recover the schedule and avoid drastic fuel increase during the departure transit. This study's findings are important for the policy-making for shipping companies due to the substantial energy savings that are measured. Furthermore, this study contributes to the MEPC.323(74) resolution, where voluntary cooperation between the port and shipping sectors is encouraged to reduce the GHG emissions from the shipping industry.

To mitigate global warming, worldwide CO2-emissions have to be cut. Similarly, the shipping industry has to drastically decrease it's emissions. Rules and regulation on emissions are becoming more stringent. Moreover, the public and clients are demanding less pollution from ship operators. While many shipping operators are looking at a suited fuel alternative, no consensus has been reached on the best alternative to entirely dispose of harmful emissions from shipping in the long-term. Fugro is one of the ship operators interested in entirely bringing down the emissions of their vessels that are built from 2030 onwards and therefore this research is conducted. This thesis will research how and if Fugro can achieve the IMO target of 70 percent CO2 reduction in 2050, by deploying net zero-emission fuels in Fugro vessels as of 2030. An evaluation will be done to assess which alternative fuels are feasible and most suitable, based on current technologies and considering future scenarios around technology. The research will approach this problem by firstly conducting an extensive literature research into alternative fuels and Multiple Criteria Decision Analysis methods. Thereafter the Fugro fleet, potential future vessel developments, future scenarios and alternative fuels are researched to set up a technical framework for this case problem specifically. With this technical framework in place, the method itself is conducted. This is done in two ways. First, by conducting a qualitative approach using the Analytic Hierarchy Process, also involving stakeholders from Fugro and the industry. Second, by carrying out a case application of the four best alternatives on a representative Fugro vessel. This approach, including a qualitative approach extended with a quantitative one results in a substantiated advise. Both methods complement each other on points where the other falls short. The AHP includes stakeholders and takes into account criteria that are difficult to quantify. The application approach tests the feasibility of the proposed alternatives and tests the potentially subjective or intuitive outcome of the AHP method. With methanol scoring well in both approaches, the conclusion of this research is that methanol is the advised alternative fuel for future Fugro vessels. Liquid hydrogen is the highest rated fuel alternative from the AHP. The application example shows that this fuel isn't able to comply with the required Fugro operability however. Contrary to that, ammonia is a highly rated alternative in literature and is also scoring well in the application part of this thesis, but is not rated as a suitable alternative by stakeholders in the AHP. The last considered alternative in both parts of the method is synthetic diesel, which is comparable to MDO. This fuel scores well in both methods but is considered to remain too expensive to become a viable alternative in the coming ten years. In this thesis, estimates of different fuel options are presented and some design concepts and considerations on the different alternative fuel applications are given. The next step for Fugro would be to work out a methanol concept, by going through the design spiral more than once and work out the design choices and challenges this fuel brings. With alternative fuels being more expensive than MDO, this thesis pointed out that the economic speed will change when using alternative fuels, which is another point that requires more research. While the AHP proved to be a suitable way to score fuel alternatives while including stakeholders and assessing criteria qualitatively, it also pointed out that it is difficult to solely base an alternative fuel advise on this method. Potentially, the AHP results would improve when the questionnaire is simplified by reducing the amount of considered criteria.

In recent years market expansion and globalization have led to new market requirements, which resulted in more complex supply chains. Within these complex supply chains, the lack of visibility and cross-enterprise data-exchange on the whereabouts of goods has led to the occurrence of undesired dwell-time of products between processes, resulting in a decrease in supply chain performance with regards to throughput times. This paper addresses the current lack of visibility and cross-enterprise data-exchange in supply chains by designing a Digital Chain that is based on Internet of Things (IoT)-sensor technology and blockchain technology. Through use of a Digital Chain, undesired dwell-time of goods is diminished and therefore supply chain performance is improved concerning Service-Level Objectives regarding Throughput Times (SLOTT). A Digital Chain is defined as a digital representation of a physical supply chain that mimics its counterpart in great detail through continuous updating of state changes of the physical counterpart via sensor-based data. Digital Chains are expected to improve supply chain SLOTT performance as they provide for upfront, iterative forecasting of arrival times of goods. In this paper the following research question is answered: ‘What are the design characteristics to develop a Digital Chain, based on combining IoT-sensors with blockchain technology, to improve supply chain SLOTT performance?’

Prysmian Netherlands as a cable producer uses a cable drum to deliver products to their customers. However, these customers do not always participate in returning this returnable packaging material (RPM). Customer willingness and reverse supply chain (RSC) visibility are mentioned as the main contributors. The study aims to develop improvement strategies and assess their impacts through a discrete event simulation (DES). The strategies consider the approach from logistics system design, technological implementation, and compliance policy. The findings suggest that an RPM with a high product residual value such as drums promises a high financial return if recovered. However, maintaining the returned drum condition is of the utmost importance to ensure the recovery operations are fruitful. Moreover, a shorter RL cycle time does have a substantial effect on the RSC efficiency.

Due to the European Union renewable energy targets for 2030 and beyond, increasing trends of installed offshore wind energy production capacity and increasing wind turbine size emerge. Therefore, the offshore wind maintenance demand increases and becomes increasingly more complex. The most applied maintenance strategy is reliability-centred maintenance, which is a form of preventive maintenance that uses prediction models to determine future ocean and weather states and asset health states. Based on those predictions, maintenance demand is scheduled. The processes that lead up to the generation of the maintenance schedule ranging from asset condition monitoring, to data analysis, to future state predictions, to maintenance scheduling are currently highly automated. The last part of the maintenance organisation cycle, matching and contracting of maintenance supply for the demand is currently still done manually via email and phone. Offshore wind maintenance operations predominantly are multi-party operations, requiring vessels, teams of technicians, spare-parts and ports supplied by the WPP itself, shared WPP inventory, third-party service providers and OEMs. The increasing maintenance demand puts a lot of pressure on the already complex task of the Asset Manager that is processing all this information and communication manually.
The problem that is preventing automation of the matching and contracting process, is the lack of a system of demand and supplier systems, that processes commercially sensitive information, such as maintenance demand schedules and supplier availability schedules, in a trustworthy privacy preserving manner. The main research question for this design research therefore becomes:
How to design a technical feasible decentralized system-of-systems that enables automated matching and contracting of maintenance supply for scheduled demand through privacy preserving processing of commercially sensitive data?...

The Heineken Company, known as Heineken, is a global and family-owned brewing company of Dutch heritage. With more than 300 different high quality beers and ciders and 167 breweries around the world, consumers are enjoying their products in more than 190 countries. Heineken Netherlands Supply (HNS) is a regional Operating Company (OPCO) and with three breweries in the Netherlands, HNS supplies Heineken products to their customers, including Heineken Germany (HGER). The supply chain between HNS and HGER is used as a case study for this thesis.

This supply chain is a Closed-Loop Supply Chain (CLSC) in which Returnable Packaging Materials (RPM) such as crates, are circulating through the supply chain. Heineken, including HNS and HGER, aspires to continuously eliminate inefficiencies and charge future growth through strategic investments and initiatives.

Nowadays, production volumes for the German market are growing increasingly and supply chains are under pressure since they are required to be faster, more flexible, more efficient and consumers have high expectations regarding product availability. Hence, strategic decisions regarding supply chains have become more important and featuring reliable data to measure Key Performance Indicators is essential. Therefore, Heineken is planning on introducing Radio Frequency Identification (RFID) gateways to measure actual crate cycle times, since they are currently based on assumptions.

Scope
The HKR Cluster crate will be the first RPM Stock-Keeping-Unit (SKU) which is going to be tracked through the supply chain, since the largest beer volume of the total volume for the German market (33%) are kept in this SKU. The initial idea is to install the RFID gateways and at 3 main locations:

• Brewery in Den Bosch (Netherlands)
• LCDB: Logistic center in Den Bosch (Netherlands)
• Warehouse in Werne (Germany)

For the purpose of this master thesis a scope and system boundaries have to be defined. A figure is presents the CLSC between HNS and HGER. The scope is limited to the three main locations in the Reverse Logistics (RL) flow of the CLSC. Since the HKR Cluster crate is considered to be the first RPM SKU to be tracked, this research will focus on this type of RPM.

Objective and Method
From the initiative to implement RFID gateways, it can be concluded that there is not enough RPM visibility and information transparency in the supply chain, which results in lack of integral RPM flow control. Improvement in this area can make the supply chain intelligent and more efficient. In literature, digital twins are found to be explored as a means of improving performance of physical entities. A digital twin is a virtual representation of a real entity and the concept has gained much interest over the years. The world of supply chain and logistics is lagging behind when it comes to adapting digital possibilities to current conditions. Therefore, the objective of this master thesis is to enable supply chain control of RPM flows using data provided by the RFID gateways, from a Digital Twin design perspective. The research is driven by the ambition and visions for digital transformation in supply chains.

To obtain the research objective the following main research question is defined: How can real-time control in the reversed supply chain be enabled, with use of RFID data?. To answer the main research question multiple sub research questions are defined and are used as guidance through the thesis. A current state analysis will help to understand how the supply chain currently operates and performs. Based on this analysis a Digital Supply Chain Twin (D-SC-T) framework for the current supply chain is proposed. Then, a mathematical model for control is proposed and simulations are done in MATLAB. The impact of control will be assessed and evaluated by comparison of financial Key Performance Indicators (KPI's) in the current state (no control) with the future state financial KPI's (control).

Current state analysis
Before systems are modelled or designed, a current system states analysis is performed to determine how the current supply chain operates. The analysis confirms the earlier found inefficiencies in the current state. The cycle times are based on assumptions and approximate to be 25 weeks, which is considered to be high. This is because the average time spent at the locations is high due to large inventories. There are large safety stocks to avoid out of stock situations, while there is limited storage capacity. It is very common that at the inventory locations, LCDB and warehouse, the storage space is at full capacity and an external storage location has to be rented. Furthermore, the planning for production at the brewery is made a relatively long time in advance and therefore lacks flexibility. It can be concluded that there is little RPM visibility throughout the supply chain and data availability for planning departments. Change in demand, weather and events can cause inaccurate forecasting. In conclusion, there is no centralized control of inventory levels in the current supply chain.

Heineken’s reversed supply chain, driven by returnable packaging, is defined to be a push-based supply chain. Crates are pushed through the channel from the location where it is returned by the customer up to the brewery. Every supply chain agent has its own priorities and inventory management preferences. This can lead to unnecessary inventory costs.

Design
Digital twins are found to be explored by means of improving performance of physical entities by using models combined with various data to interpret and to predict the behavior of a real system. Therefore, digital twins have the potential to increase the intelligence of a specific environment. This leads to the motivation of digital twins of supply chains. First steps towards D-SC-T creation are done by proposing a framework according to its functions and requirements.

The prediction function includes analysis of the behaviour of the supply chain before actual run-time. Planned crate flow processes are simulated prior to the actual transportation decisions in the supply chain system (pro-active planning). Consequently, supply chain parameters can be tested, while potential impacts on the supply chain performance can be evaluated. The monitoring function enables optimization when models are enriched with real-time data from physical sources, such as RFID. Therefore, the RFID gateways allow tracking and supervision of the current states of crate flow and inventory at the main locations. The RFID data, including live positional data from the crates, can be fed into the digital twin. If the current state measurements deviate from the preferred state, transportation decisions in the supply chain system can be calculated (re-active planning). Supply chain performance and behaviour diagnosis is usually enabled after an event and is done by data analysis.

Model Predictive Control (MPC) is a control strategy, by means of controlling a process based on some form of model. Literature shows that the digital twin and MPC have similarities in the way they capture and interpret the current state of the physical system and being able to use that current state to change the future state. Therefore, MPC seems a very suitable option for control of the inventory levels of the supply chain within the digital twin framework. A centralized MPC control model for the control of inventory levels and crate flows within Heineken's reversed supply chain is proposed.

The described MPC control model has the objective to optimize the supply chain performance by reducing Operating Expenditures (OPEX) and Capital Expenditures (CAPEX). The controller will accurately keep track of where crates are located in the supply chain and calculate the related OPEX and CAPEX, while meeting the requirements.

Results
Simulation experiments are done to be able to quantify the impact of control on the supply chain in OPEX and CAPEX. In the experiments, the controller reacts to disturbances and unforeseen events, while optimizing inventory levels and meeting demand. This is demonstrated by 3 scenarios.

• Scenario 1: Current supply chain with actual events LCDB: Logistic center in Den Bosch (Netherlands)
• Scenario 2: Current supply chain with disturbance: peak in demand
• Scenario 3: Supply chain with additional RFID gateway location with disturbance: capacity limitation

A Table presents the simulation results for all scenarios and are presented including the results for the same scenarios with no control. When comparing the results of current supply chain with actual events, the most remarkable result is the difference in the location where inventory is allocated. In the base case scenario, the crate inventory levels are much higher at the LCDB, while with MPC control, the results show higher inventory levels at the warehouse. These more detailed results are shown in Chapter \ref{chap:Simulation and Results}. In the second scenario the simulated event is an unforeseen high beer demand due to weather changes. The controller reacts to the occurring event and meets the demand at the brewery in time. In the third scenario the effect of having more supply chain information by including one additional RFID gateway location is determined. More detailed results and explanations on how the controller reacts to various events are provided in Chapter \ref{chap:Simulation and Results}.


Conclusion, Discussion and Recommendations
This thesis has created insights on what a digital supply chain is and what the effect of control can be on the supply chain performance. The controllability of the crate flows in Heineken’s reversed supply chain driven by returnable packaging can be improved, using a centralized MPC control model within the proposed digital twin framework, combined with RFID data from the proposed gateways. These gateways measure crate positions and quantities per time. The controller uses this data to interpret and predict the supply chain behaviour. The RFID data of the current supply chain states are fed into the model. The controller interprets the states and calculates which actions lead to less CAPEX and OPEX, while meeting the modeling requirements. Due to the RFID measurements, data is visible and transparent for planning departments and other stakeholders and better coordination along supply chain agents can be made possible.

For this research MPC is the chosen control method for the control part within the digital twin. Therefore it only covers a small part of the wide variety of different control methods which could have been investigated and tested. Other control methods are still to be investigated.

This thesis offers a theoretical digital twin solution for the problems they have at Heineken’s CLSC. But only a solution for the measure and control part has been brought forward. A digital twin also carries out big data analytics and machine learning possibilities. How these growing technologies fit into the digital twin concept could be interesting for further research.