Development of a Method to Increase Flexibility and Changeability of Supply Contracts in the Automotive Industry

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52nd CIRP Conference on Manufacturing Systems 52nd CIRP Conference on Manufacturing Systems

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Development of a Method to Increase Flexibility and Changeability of 28th DesigntoConference, May 2018, Nantes, France DevelopmentSupply of a CIRP Method Increase Flexibility and Changeability of Contracts in the Automotive Industry Supply Contracts in theaAutomotive Industry b, Matthias A new methodology analyze the functional physical architecture of Jens Niemanna*,to Stephan Seisenberger , Andreas and Schlegel Putzb a*, Stephan Seisenbergera, Andreas Schlegelb, Matthias Putzb Niemannfor AG, Petuelring 130, 80788 München, Germany existingJens products an BMW assembly oriented product family identification a

bFraunhofer IWU, Reichenhainer Str. 88, 09126 Chemnitz aBMW AG, Petuelring 130, 80788 München, Germany

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

bFraunhofer * Corresponding author. Tel.: +49-89-382-78768. E-mail address: [email protected] IWU, Reichenhainer Str. 88, 09126 Chemnitz

* Corresponding author. Tel.: +49-89-382-78768. E-mail address: [email protected] École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France

Abstract

* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]

Abstract The electric mobility market is characterized by demand volatility and a changing supply chain with new to the industry suppliers. In order to cope with demand volatility, the production capacities must be synchronized with external supply chain partners through changeability and The electric mobility market is characterized by demand volatility and a changing supply chain with new to the industry suppliers. In order to flexibility measures. We define flexibility as the ability to quickly react to changing demand volumes within the current supply chain and Abstract cope with demand volatility, the production capacities must be synchronized with external supply chain partners through changeability and production structure. Changeability needs further investments to adapt to the changing environment. This paper presents a method for the design flexibility measures. We define flexibility as the ability to quickly react to changing demand volumes within the current supply chain and of flexible and changeable supply chains, predicated on an examination of two companies’ production systems and two different supply contract Inproduction today’s business environment, theneeds trendfurther towards more product variety andchanging customization is unbroken. Due to this development, of structure. Changeability investments to adapt to the environment. This paper presents a method forthe theneed design designs. We consider a quantity flexibility contract and a new developed contracting matrix. The method is being validated by a prototypical agile and reconfigurable production systems emergedontoancope with various products and product families. To and design optimize production of flexible and changeable supply chains, predicated examination of two companies’ production systems twoand different supply contract application in the production of electrified automotive powertrains. The results show an increase in supply chain flexibility for the quantity systems well as to choose the flexibility optimal product matches, product analysis methods are needed. of the knownby methods aim to designs.asWe consider a quantity contract and a new developed contracting matrix. The Indeed, method most is being validated a prototypical flexibility contract and we are able to introduce a changeable supply chain with the use of the contracting matrix. analyze a product one product on theautomotive physical level. DifferentThe product families, however, may terms of for the the number and application in theorproduction offamily electrified powertrains. results show an increase in differ supplylargely chain in flexibility quantity nature of components. fact impedes an efficient comparison choice product family combinations for the production flexibility contract and This we are able to introduce a changeable supplyand chain withof theappropriate use of the contracting matrix. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license system. new methodology is proposed to analyze © 2019AThe Authors. Published by Elsevier Ltd. existing products in view of their functional and physical architecture. The aim is to cluster (http://creativecommons.org/licenses/by-nc-nd/3.0/) these products inaccess new assembly oriented product foropen the optimization existing lines and the creation of future reconfigurable © 2019 The Authors. Published by Elsevier Ltd.families This is license an access articleofunder the assembly CC BY-NC-ND license This is an open article under the CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 52nd CIRP Conference on Manufacturing Systems. assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 52nd CIRP Conference on Manufacturing Systems. aPeer-review functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the under responsibility of the scientific committee of the 52nd CIRP Conference on Manufacturing Systems. Keywords: Production, Production Planning, Electric Vehicles, Supply Chain Management; similarity between product families by providing design support to both, production system planners and product designers. An illustrative Keywords: Production Electric Vehicles, methodology. Supply Chain Management; example of Production, a nail-clipper is usedPlanning, to explain the proposed An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. suppliers into existing supply chains while maintaining current 1. Introduction © 2017 The Authors. Published by Elsevier B.V. industry standards (e.g. quality). [1] while maintaining current suppliers existing supply chains Peer-review under responsibility of the scientific committee of the 28th CIRP Design into Conference 2018. 1. Introduction

Demand fluctuations cause uncertainties for companies, A new generation of electric vehicles is gaining momentum, industry standards (e.g. quality). [1] which they attempt to counter through volume flexibility benefitted by high emission standards, rising fuel costs, Keywords: Design method; Family identification Demand fluctuations cause uncertainties for companies, A newAssembly; generation of electric vehicles is gaining momentum, measures and changeability in their own production. environmentalism and political influence (e.g. through buyer’s which they attempt to counter through volume flexibility benefitted by high emission standards, rising fuel costs, Coordination the OEM in and their its suppliers managed premium or free public parking for electric vehicles). measures andbetween changeability own is production. environmentalism and political influence (e.g. through buyer’s through supply contracts. Presently, the automotive industry Paralleling these dynamics, factors like customer behavior, Coordination between the OEM and its suppliers is managed or free public parking for electric vehicles). primarily uses quantity flexibility contracts that fulfil a certain 1.premium Introduction of the product range and characteristics manufactured and/or infrastructure, new competitors and technological through supply contracts. Presently, the automotive industry Paralleling these dynamics, factors like customer behavior, volume flexibility corridor. Yet, due to the high demand assembled in this system. In this context, the main challenge in improvements cause high volatility in forecasted market primarily uses quantity flexibility contracts that fulfil a certain infrastructure, new competitors and technological fluctuations, the corridor is often undercut and/or exceeded in Due to the fastvehicles development in forecasts the domain ofa modelling and analysis is now not only to cope with single volumes for electric [1]. Current predict volume flexibility corridor. Yet, due to the high demand improvements cause high volatility in forecasted market many years, which can lead to increased costs or even supply communication and an between ongoing 3trend of digitization and products, a limited product or existing product families, share of electric vehicles % and 24 % in 2025 [2–4]. fluctuations, the corridor is range often undercut and/or exceeded in volumes for electric vehicles [1]. Current forecasts predict a bottlenecks. digitalization, manufacturing enterprises are facing importanta but also to be able to analyze and to compare products to define In line with the changing market environment, many years, which can lead to increased costs or even supply share of electric vehicles between 3 % and 24 % in 2025 [2–4]. Inproduct order families. to avoid Ithigher costs increasing challenges in today’s market environments: a structure continuing new can be supply observed that while classical existing transformation of thethe supply chain and the supplier isa bottlenecks. In line with changing market environment, flexibility and supply security, new concepts and methods must tendency towards reduction of product development times and product families are regrouped in function of clients or features. currently taking place. Suppliers with a high share of added In order to avoid higher supply costs while increasing transformation of the supply chain and the supplier structure is be developed that meet present volatility requirements in the shortened product lifecycles. Inwill addition, there is an production increasing However, assembly oriented product families are hardly to find. value for conventional cars lose substantial flexibility and supply security, new concepts and methods must currently taking place. Suppliers with a high share of added production of electrified powertrains. In this paper, we demand of to customization, beingofat the same timecomponents. in a global On the product family level, products differ mainly in two volume manufacturers electronic be developed that meet present volatility requirements in the value for conventional cars will lose substantial production introduce a new method in which we optimize a quantity competition with competitors all challenge over the world. This trend, main characteristics: (i) the number of components and (ii) the Furthermore, OEMs face the to integrate new production of electrified powertrains. In this paper, we volume to manufacturers of electronic components. flexibility as well present optimize a new which is inducing the development from macro to micro type of components (e.g. mechanical, electrical, electronical). introduce acontract new method in aswhich we and optimize a quantity Furthermore, OEMs face the challenge to integrate new markets, results in diminished lot sizes due to augmenting Classical methodologies considering mainly single products flexibility contract as well as present and optimize a new product varieties (high-volume to low-volume production) [1]. or solitary, already existing product families analyze the 2212-8271 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license To cope with this augmenting variety as well as to be able to product structure on a physical level (components level) which (http://creativecommons.org/licenses/by-nc-nd/3.0/) 2212-8271 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license an efficient definition and identify possible optimization potentials in the existing causes regarding Peer-review under responsibility of the scientific committee of the 52nd CIRP Conference on difficulties Manufacturing Systems. (http://creativecommons.org/licenses/by-nc-nd/3.0/) production system, it is important to have a precise knowledge comparison of different product families. Addressing this Peer-review under responsibility of the scientific committee of the 52nd CIRP Conference on Manufacturing Systems. 2212-8271 © 2019 The Authors. Published by Elsevier Ltd. This is an©open article Published under theby CC BY-NC-ND 2212-8271 2017access The Authors. Elsevier B.V. license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of scientific the scientific committee theCIRP 52ndDesign CIRPConference Conference2018. on Manufacturing Systems. Peer-review under responsibility of the committee of the of 28th 10.1016/j.procir.2019.03.045

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contract type. For the optimization of quantity flexibility contracts, we optimize contract volumes based on possible scenarios and the customer's production system. Furthermore, we analyze the production systems of the customer and supplier. The analysis allows us to create a new concept for a changeable supply chain. Finally, both contracts are compared in terms of costs, flexibility, changeability and applicability. We have structured the paper as follows: The next section discusses the recent literature in the research areas supply chain flexibility, changeability and supply contracts. In the third section we present our developed method. We validate the method using an example from the production of electrified powertrains. Lastly, a conclusion and an outlook close this paper. 2. Literature Review Previous literature often defines the terms flexibility and changeability differently. Therefore we start with a definition of volume and supply chain flexibility as well as changeability. The first is also referred to as capacity flexibility [5]. Both describe the ability to operate a production system economically with different capacity utilizations and changing production volumes. [6] Secondly, the adaptability of a customer-supplier relationship to changing demand volumes or deviating delivery conditions defines supply chain flexibility [7]. STEVENSON and SPRING expand this definition to include the aspect of consistent quality [8]. Alternative definitions according to KUMAR ET AL. additionally integrate the restructuring of the supply chain [9] and the inclusion of new supply chain partners [10]. Contrarily, GOSLING ET AL. understands supply chain flexibility as the flexibility that results from all involved companies [11]. In this paper we follow STEVENSON'S definition. According to SEEBACHER, changeability is primarily applied in production planning. It begins to take impact when the flexibility corridor is exceeded and is generally referred to as the potential for change. MÖLLER develops it further in his definition and calls it "the ability of a production system to adapt quickly to changes in the environment by changing its structure" [12]. The changeability comprises various drivers: universality, mobility, scalability, modularity, compatibility. In our paper we focus on scalability, since it is the only driver, which deals with volumes. In this context, scalability refers to the technical, local and personal possibility to expand and reduce factory elements and production systems [13]. Analyzing previous research, we find papers on methods to integrate technical and economic aspects into the planning [14]. There are further papers on the assessment of the economic efficiency [12] and of the changeability of a production system based on simulations [15] or on ERP data [16]. However, we do not find any work that considers the supply chain. In the area of supply chain research, CHRISTOPHER and HOLWEG, for example, see the answer to dealing with a volatile environment in an adaptable supply chain which can be quickly reconfigured [17]. Reconfigurability means a change in supply chain structures and partners. It misses the goal of the paper to

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create a changeable supply chain by adapting the supply contract under the assumption of fixed structures and partners. Supply contracts represent an elementary component of our work. Supply contracts determine the prices, delivery conditions and quantities between the supply chain partners and are thus jointly responsible for the coordination of the supply chain [18]. The literature review by HEZARKHANI, identifies eight different supply contract types and many derivatives. The choice which supply contract should be used depends on the existing supply chain and challenges. These include the supply chain topology, contract length, decision variables (e.g. price, capacity), agent characteristics, supply chain environment and the information structure [18]. We compare the existing supply contracts with respect to the challenges of the automotive industry and thus focus on the quantity flexibility contract. The quantity flexibility contract was defined in 1999 by TSAY and LOVEJOY. They see the contract as a method of coordinating material and information flows in the supply chain. The flexibility corridor is defined by a negotiable percentage of the contract volume and a minimum purchase commitment of the customer. The supplier is obliged to cover all requests within this flexibility corridor [19]. In our case, the minimum purchase quantity is determined by a maximum percentage of volume reduction. In addition to the quantity flexibility contract, SEISENBERGER develops a quantity-range and time flexible contracting matrix. The approach of this model is to uncouple the customers’ internal decision process based on uncertain volume information from contractual binding price, time and volume conditions. In view of volatile volume scenarios, the goal is to contractually agree on the best possible offer in terms of costs and flexibility in a competitive environment. For this purpose, changeable, in terms of scalability, production systems are requested from the suppliers within a very broad volume range. The supplier offers their products within different volume steps as well as unit prices per year and volume step. With a certain lead time, the customer has the option of switching between the steps as required. The changeable production system, the volume steps and the relevant prices are contractually binding. An example can be seen in Table 1. Table 1. Example of the contracting matrix. Volume [k Units] 9

29

Year 1 303 €

Year 2

Year 3

Year 4

Year 5

290 €

288 €

288 €

285 €

30

58

288 €

281 €

278 €

275 €

59

115

288 €

278 €

275 €

272 €

116

173

273 €

269 €

266 €

174

231

269 €

266 €

263 €

232

289

269 €

266 €

263 €

290

318

266 €

263 €

260 €

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3. Method In the paper, we develop a method for adjusting supply contracts by volume in order to achieve a changeable supply chain. The method consists of five consecutive steps (see Fig. 1) and differs by using a specific optimization for each contract category. In this work we focus on the quantity flexibility contract and on SEISENBERGER’s contracting matrix and we are temporally before the binding contract assignment of the component to a supplier. Generation of volume scenarios (3.1)

Evaluation of costs and flexibility (3.2)

Optimization of contract volumes (3.4)

Analysis of production capacity (3.3)

Evaluation and selection of the supply contract (3.5)

Fig. 1. Overview of the developed method.

3.1. Generation of volume scenarios We start the method by determining the volume uncertainty of the considered product. To ensure an unbiased perspective, it is therefore necessary to examine different external and internal volume forecasts. Yet, as external studies often only show punctual predictions that are not available for each of the considered periods, we interpolate absent values. For each period we have the predicted volumes from the various forecasts and determine whether there is a normal distribution. Therefore, we use the Anderson-Darling test, other test methods may also be applied. We recommend to use the possible volumes in corridor of the mean value plus/minus the standard deviation (µ±1σ) for the method. In this volume corridor, we consider either each volume scenario or we increase the granularity by using e.g. only every tenth volume scenario in order to shorten the necessary computing time. 3.2. Economic and flexibility-based evaluation For the contracting matrix we require supplier quotes to execute the next steps. In this step, we evaluate costs and flexibility of these supplier quotes. With the quantity flexibility contract, we can omit this step as we do not need it for the method. Relevant criteria for the evaluation are total costs, price per unit and flexibility in comparison to the OEMs production system and flexibility within the supply contract. The total costs are the unit price multiplied with the ordered quantity. We recommend the price per unit as a parameter. For quantity flexibility contracts, these are an indication of how high the penalty costs are outside of the volume corridor. Flexibility optimization is one of the key aspects of the method. We evaluate both the flexibility of the supplier in comparison to our production system and the flexibility that is still contractually available for each volume scenario.

3

3.3. Analysis of the OEM’s and the supplier’s production system Together with Step 3.4, the analysis represents the core of the developed method. In this step, the production system of the OEM and that of the supplier must be analyzed. In order to create a changeable supply chain, production capacities in particular must be coordinated and all possible influencing factors examined. • Analysis of the OEMs production capacity We recommend a bottom-up approach for the analysis of the production system, starting with the analysis of the production type. For example, this involves determining whether it is an assembly line or a highly automated production line. Next, we examine the products to be manufactured. Since different variants are often produced on the identical line, we analyze (a) whether the products are produced in parallel, if this is technologically and logistically possible, (b) to what extent they differ technologically and (c) which cycle times the individual variants have. Using these aggregated information, we calculate the possible product mix. Information about this enables us to assess the considered product’s proportion of total machine capacity. In addition to the annual capacity, we have to ascertain how many units can be produced per hour, as these may have to be covered by the supplier. The shift model and flexibility will be taken into account to calculate the machine capacity. Our method bases on a 1, 2 or 3 shift model; flexibility measures can be for example additional shifts or break passages. In addition to flexibility, changeability is another aspect to be investigated. We have to find out whether changeability measures are planned or already defined. We recommend to include all existing and scheduled changeability measures in the capacity calculation. Critical for the method is the precise determination of the planned capacity per time interval. We calculate it using the planned shift model, machine capacity and capacity proportion for the considered component per time interval. • Analysis of the supplier’s production capacity It is more complex to analyze the production capacity of the supplier, since there is only an insufficient database due to the lack of transparency between the two companies. Only the contracting matrix provides us with more information about the planned production system. The analysis of the supplier is structured analogous to our previous OEM’s analysis. Since the component to be purchased can differ technologically from the product in which it is installed, we start with the analysis of the production technology and the type of production. In many cases, suppliers try to maximize their capacity utilization, which often results in products from several customers being produced on one line. If this is the case, capacity synchronization becomes even more difficult. Using the contracting matrix, we can very well find out how capacity is composed. The specified shift model and volume levels allow us to determine the planned quantity. By specifying the employees per step, we can determine what influence personnel

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flexibility measures have on the production system. Furthermore, the steps indicate the volumes for which an investment in changeability is necessary and what the individual measures per step look like. 3.4. Calculation of different contract volumes Due to the differences between the quantity flexibility contract and the contracting matrix, we will present both optimization problems separately. We focus on cost and flexibility. • Quantity flexibility contract The goal of the optimization is finding an optimal contract volume. The quantity flexibility contract includes a flexibility corridor around the contract volume. All volumes within this corridor are purchased at the fixed unit price. In the event of an under- or overrun, penalty costs are incurred, which result in an increase in unit price p. For this reason, we consider the unit costs in the optimization (1). We assume that in the event of a volume reduction (s < blow n), the result of the division of the contract volume n by the scenario volume s will be multiplied to the unit price. If the quantity exceeds the corridor (s > bhigh n), we assume that the supplier will have to invest in his production system. The investment sum I is allocated to the unit price. Following our goal of a changeable supply chain, we maximize the flexibility of the quantity flexibility contract in relation to the customer's changeable production system (2). Furthermore, we want to integrate the company's opinion whether the volumes will increase or decrease (3). As volumes increase, we focus on maximizing scenario coverage in the µ+1σ range. In the case of declining volumes we concentrate on the µ-1σ range. We also supplement the constraint, that µ must always be part of the flexibility corridor. This ensures that the most likely scenario is always purchased at contract unit prices. We scale every formula and use weighting factors to adapt the multi-criteria optimization to the company's goals. The costs are minimized, whereas we maximize flexibility and the scenario coverage. We introduce the following objective functions that we aim to optimize: (1)

For the calculation we formulate the maximization into minimization problems. To find the objective functions optimum we summarize Eq. (1) - (3) to a combined function Eq. (4) with three weighting factors g. Afterwards we use a nonlinear solver to find the optimal values for n. Depending on the choice of the weighting factors, the contract volumes are either cost-optimal or flexible. • Contracting Matrix With the contracting matrix, we have introduced a fundamentally new concept for a changeable supply chain. This section deals with the optimization and synchronization of this contract model. Meaning that we align the levels that are optimal from the supplier's point of view with the capacity levels of the customer's production system. Optimization focuses on costs on the one hand and flexibility on the other. The goal is to minimize total costs and maximize flexibility in comparison with the customer's production system. From the analysis of the supplier's production system, we know the steps in which its production system is structured. We optimize the size of the steps. As a constraint, we define that the resulting steps must be mapped by the steps of the original contracting matrix. At the beginning we define all possible capacity step combinations. For this purpose, we select each year the permitted capacity step, which is closest to the median, the highest scenario volume and the customer's production system. The cost and flexibility is then calculated for each scenario as follows: (5)

(6)

(2) (3) (4)

In the cost function Eq. (5) we assume that the unit costs from the original contracting matrix, whose upper capacity limit is identical, are valid. The two functions are then scaled.

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Weighting factors can also be used to integrate the company's preferences. Each possible level combination is then processed with all possible volume scenarios and the result multiplied by the probability of the respective volume scenarios. In the case of several optimal step combinations, it is up for negotiation with the supplier. The solution achieves a slight cost reduction and a large increase in flexibility. 3.5. Economic and flexibility-based evaluation of the new contracts and selection of the more favorable supply contract In the final step, the new contract volumes or contract steps are assessed in monetary and flexibility terms. We recommend concentrating on the total costs and the flexibility in comparison to the customer’s production system. Both the quantity flexibility contract and the contracting matrix can have several contract volumes. Depending on the used weighting factors, they can either be cost or flexibility optimal. In this case, the company must decide whether costs or flexibility should be prioritized higher. The method ends with the selection of one of the two contract types. The supplier is included in the decision. As one of the contracting parties, it is significantly involved in the drafting of the contract and possibly prefers either the quantity flexibility contract or the contracting matrix. 4. Verification of the Method We have taken a two-tier topology with two nodes example of the production of electric powertrains to validate our method. The customer is an automotive OEM and we consider the production of an electric drive and the supplier for a corresponding component. In our example, we have a quantity flexibility contract and a contracting matrix. The quantity flexibility contract has a contractual flexibility of 20 % per year and a price per unit of 248 €. If an additional investment is required in the production system, the total investment sum amounts to 10 million €. The contracting matrix consists of 7 steps and covers a maximum volume of 318,000 units. The first two and step 7 have a size of approximately 29,000 units, whereas the remaining steps each have a size of 57,000 units. The unit price starts at 303 € in the first step in the year 2020. The unit price decreases per year and per step (see Table 1). The first step of our method is the generation of volume scenarios. In our example, we look at the period from 2020 to 2025 by considering various external scenarios from 2014 to 2018. We have further supplemented these with five internal scenarios in order to obtain a comprehensive picture of the environment. The Anderson Darling Test confirmed a normal distribution each year. The example already includes two different contract models, both of which we evaluate in advance with regard to their volume flexibility and costs. The results show that the quantity flexibility contract has a cost advantage in a scenario with the mean value. As soon as the volume scenarios are no longer covered by the volume corridor, the contracting matrix has lower costs. With regard to volume

5

flexibility, we compare the flexibility of both contract types with the OEMs flexibility of the production system. Concerning the contracting matrix, only the upper limit of the current step is considered and the option of the further steps is not included. Table 5 shows the results of the quantity flexibility contract and the contracting matrix for three volume scenarios. The volume flexibility is given as an average value for the 5 years considered. A total of four electric drive variants are manufactured on the OEMs production lines. The component under consideration is only relevant for one of these products. This is the most frequently manufactured product and requires approx. 75 % of the capacity. The production system of the OEM is scalable and consists of two lines, each comprising four expansion steps. Meanwhile, the expansion of one step is accompanied by a doubling of capacity. There is already a timetable for the expansion of production. The production is designed for a 3-shift operation and capacity can be increased by up to 28 % through additional measures, e.g. extra shifts (compare Table 2). Table 2. Planned capacity of the OEM’s production system. OEM [k Units]

Year 1

Production Capacity

Year 2

31

62

Year 3 135

Year 4 187

Year 5 249

The component produced by the supplier requires two production technologies: electronics production and final assembly. The electronics manufacturing produces the component as well as other products for different clients, whereas the final assembly is only designed for the component of the electric drives. In the case of the quantity flexibility contract, investments of 10 million € are required for a possible expansion of production. In the case of the contracting matrix, the investments cumulate to 20 million €. Through the step-bystep structure, we know that the supplier initiates flexibility measures from a production increase of approximately 29,000 units and expands its production from a production increase of 57,000 units. We use Eq. (4) to optimize the quantity flexibility contract and calculated a flexibility oriented optimization. For the flexibility optimized calculation, we weighted the costs with 0.2, the flexibility with 0.7 and the scenario coverage with 0.1. The weighting factors must be selected according to the company's preferences. The results can be taken from Table 3. Table 3. Overview of the various contract volumes. Contract Volumes [k Units] Status Quo Flexibility oriented

Year 1

Year 2

Year 3

Year 4

Year 5

9

37

94

149

213

10

38

117

167

215

We have calculated the optimum of the contracting matrix according to Eq. (5) and (6). Here we have balanced the flexibility and the costs, since in our example an increase in flexibility and a reduction in costs pass together. The new contracting matrix has 5 volume levels, which are larger for it

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(e.g. compare Table 4 column “Volume”). Thus they allow more flexibility. Due to the lower number of volume levels, the planning effort for the supplier is also reduced, which justifies setting the lowest possible unit price per volume step and year. Table 4. Overview of the new contracting matrix. Volume [k Units] 9

29

Year 1

Year 2

303 €

Year 3

Year 4

Year 5

290 €

288 €

288 €

285 €

288 €

30

58

281 €

278 €

275 €

59

173

273 €

269 €

266 €

174

260

269 €

266 €

263 €

261

318

266 €

263 €

260 €

An overview of the three examples can be seen in Table 5. For the µ - σ scenario, the contracting matrix has the lowest flexibility because only the lowest step is built. When considering the flexibility of the contracting matrix, it should not be forgotten that the changeability is contractually guaranteed. We evaluate the cost reduction and flexibility increase across all possible scenarios in the considered corridor from (µ ± σ) weighted with their respective occurrence probability. We find that the quantity flexibility contract achieves cost savings of 14 million € and an 8 % flexibility increase. The contracting matrix can be improved by 1.8 million € and 7 % flexibility. Table 5. Evaluation and comparison of the original and optimized contracts. Scenarios Cost [mio. €] Flexibility [ø-%]

µ-σ µ µ+σ

Quantity flexibility contract

Quantity flexibility contract optimized

Contracting Matrix

Contracting Matrix optimized

Cost

217

236

102

100

Flex.

-14

-11

-33

-24

Cost

196

195

214

212

Flex.

-30

-23

-19

-8

Cost

382

382

317

315

Flex.

-45

-34

-10

-6

In our case we achieve an improvement of the quantity flexibility contract regarding its flexibility and the costs by the optimization. A changeable supply chain, which enables synchronization of different production systems, can only be ensured by our optimized contracting matrix. 5. Conclusion and Outlook The goal of this paper is to develop a method for a changeable supply chain. We look at a two-tier topology with two nodes. The analysis of the literature shows that there are no concepts for this yet. Our research in this paper is based on a quantity flexibility contract and the developed contracting matrix. Our method shows that we achieve an increase in flexibility in both the quantity flexibility contract and the contracting matrix. Due to the fixed flexibility corridors,

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optimization of the quantity flexibility contract is only possible to a limited extent and this concept is not suitable for creating a changeable supply chain. The optimized contracting matrix is ideal for synchronizing changeable production systems. This enables us to create a changeable supply chain and to achieve a strongly improved result in a volatile environment with high demand fluctuations. Furthermore, we see a need for research in the integration of our method into a capacity simulation to apply our method to further products. In the future, we will examine the application of different optimization procedures. Acknowledgements We would like to express our gratitude to the employees of the purchasing and production planning at BMW and the supplier for providing us information to develop this method. References [1] Kampker, A. Elektromobilproduktion. Berlin: Springer Vieweg; 2014. [2] Amsterdam Round Tables, McKinsey&Company. Evolution: Electric vehicles in Europe: gearing up for a new phase?; 2014. [3] IHS AutoInsight. „Light Vehicle Production Forecast“; 2018. [4] Lazard, Roland Berger. Global Automotive Supplier Study 2018: Transformation in light of automotive disruption; 2017. [5] Seebacher G. Ansätze zur Beurteilung der produktionswirtschaftlichen Flexibilität. Berlin: Logos-Verlag; 2013. [6] Browne J, Dubois D, Rathmill K, Sethi S, Stecke K. Classification of Flexible Manufacturing Systems. The FMS Magazine 1984; 2 p.114-117. [7] Das SK, Abdel-Malek L. Modeling the flexibility of order quantities and lead-times in supply chains, International Journal of Production Economics 2003; 85. p.171-181. [8] Stevenson M, Spring M. Supply chain flexibility: an inter‐firm empirical study 29, International Journal of Operations & Production Management 2009; p.946-971. [9] Kumar V, Fantazy KA, Kumar U, Boyle TA. Implementation and management framework for supply chain flexibility. Journal of Enterprise Information Management 2006; 19 p.303-319. [10] Lun Choy K, K. H. Chow H, Tan K, Chan CK, Mok E, Wang Q. Leveraging the supply chain flexibility of third party logistics - Hybrid knowledge-based system approach. Expert Syst Appl 2008; 35 p.1998 - 2016. [11] Gosling J, Purvis L, Naim M. Supply chain flexibility as a determinant of supplier selection. International Journal of Production Economics 2010; 128 p.11-21. [12] Möller N. Bestimmung der Wirtschaftlichkeit wandlungsfähiger Produktionssysteme. München: Utz; 2009. [13] Westkämper E, Zahn E. Wandlungsfähige Produktionsunternehmen: Das Stuttgarter Unternehmensmodell. Berlin: Springer-Verlag; 2009. [14] Heger C. Bewertung der Wandlungsfähigkeit von Fabrikobjekten. Garbsen: PZH Produktionstechn. Zentrum; 2007. [15] Albrecht F, Kleine O, Abele E. Planning and Optimization of Changeable Production Systems by Applying an Integrated System Dynamic and Discrete Event Simulation Approach. Procedia CIRP 2014; 17 p.386-391. [16] Potente G, Schuha T, Fuchs S, Hausberg C. Methodology for the Assessment of Changeability of Production Systems Based on ERP Data 3, Procedia CIRP 2012; 3 p.412-417. [17] Christopher M., Holweg M.”Supply Chain 2.0”: managing supply chains in the era of turbulence. Int J Phys Dist & Log Manage 2011; 41, p. 63-82. [18] Hezarkhani B, Kubiak W. Coordinating contracts in SC: A review of methods and literature. Decision Making in Manufacturing and Services 2010; 4, p.5-28. [19] Tsay AA, Lovejoy WS. Quantity Flexibility Contracts and Supply Chain Performance. Manufacturing & Service Operations Management 1999; 3 p.89-111.