supply chain planning in the german automotive industry · supply chain planning in the german...

DOI: 10.1007/s00291-004-0168-4OR Spectrum (2004) 26: 447–470

c© Springer-Verlag 2004

Supply chain planningin the German automotive industry

Herbert Meyr

Institute of Transport Economics and Logistics, Vienna University of Economicsand Business Administration, Nordbergstraße 15, 1090 Wien, Austria(e-mail: [email protected])

Abstract. Following the evolution in the computer industry, quite a lot of carmanufacturers currently intend to move from a built-to-stock oriented productionof standardized cars towards a customized built-to-order (BTO) production. In thepremium segment of Germany’s automotive industry, the share of customized BTOcars traditionally is comparatively high. Nevertheless, German car manufacturershave spent a lot of efforts in recent years to further increase this share in orderto realize short delivery times, high delivery reliability and a fast responsiveness.Surprisingly, comprehensive overviews of the short- and mid-term planning land-scape of car manufacturers cannot be found in the scientific literature. Thus, thefirst part of the paper discusses supply chain planning, as traditionally establishedin the premium segment of the German automotive industry, and reviews methodsof Operations Research (OR) that are able to support the various planning tasksinvolved. In the second part, the major change in strategy, currently to be observedin the German automotive industry, is briefly summarized in order to derive itsimpacts for the planning system and for the respective planning methods. In thisway, challenges for a future application of OR methods in the automotive industrycan be identified.

Keywords: Supply chain planning – Operations research – Automotive industry

1 Introduction

Mass customization [42] that aims at offering customized products in a high varietybut for still low prices and within short delivery times gains increasing importancein various branches of business and, in the meantime, also captivates the automotiveindustry. The BMW Group, for example, spent $ 55 million on its new Europeanonline-ordering system [24] to cut order-to-delivery times by 20 days on the average.At the same time, BMW offers up to 1032 variants (at least theoretically), several

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thousands of them actually being demanded [51, p. 42]. Other manufacturers alsodeclared their intention to decrease order-to-delivery times from an average of 40days to about 15 days [22] and try to make the transition from “build-to-stock“(BTS) to “build-to-order” (BTO) that has successfully been demonstrated by thecomputer industry, and first and foremost by its paragon Dell.

The transition to BTO in the computer industry caused a reorganization ofplanning processes and led to an increased use of “Advanced Planning Systems”(APS, [29]), i.e. of computer-based decision support systems, which – at least partly– rely on sophisticated methods of Operations Research (OR). Thus the questionsarise, whether and how the transition of the automotive industry changes theirrespective planning tasks and planning processes, and to what extent planning andOR methods are and will be affected. Since mutual interrelations are particularlyimportant for operational planning tasks, the discussion will concentrate on mid-and short-term supply chain planning, and here especially focusing on the carmanufacturers’ point of view. But before discussing changes it has to be shownwhat the planning landscape of automotive industries traditionally looks like. Thereare, of course, discussions of various individual planning tasks (see Sect. 3) andsome overviews of the order-to-delivery process (see e.g. [23,51]). However, tothe author’s knowledge, in scientific literature no comprehensive overviews of theshort- and mid-term planning landscape of car manufacturers can be found.

Due to this lack of literature and since common scientific approaches like ques-tionnaires and structured interviews did not seem to be very promising becausequite a lot of confidence is needed to get such a sensitive information, the followingcharacterization of the planning system of car manufacturers mainly builds on var-ious joint projects with German car manufacturers and communication with theirresponsible planners and with employees of automotive consultancies. In order toverify the conclusions drawn, a working paper has been written, sent to skilledpeople in these companies and they have been asked for statements about its va-lidity. The results of this process are presented in the following. To sum up, thecontribution of this paper is

– first, that the planning systems of German car manufacturers are analyzed,described and thus made available to the academic literature,

– secondly, that OR methods suitable for planning within the automotive indus-tries are reviewed, categorized with respect to the planning tasks of (German)car manufacturers and that insufficiently supported planning tasks are disclosed,and

– thirdly, that the challenges of the managerial changes from BTS to BTO areoutlined that arise for the planning tasks, the planning systems and for the ORmodels/methods involved.

Due to this broad scope of the paper, a review of OR methods – even thoughrestricted to short- and mid-term planning – cannot be comprehensive. This paperrather intends to give an idea where (i.e. at which subsection within the overallplanning system of a car manufacturer) OR methods already contribute or maycontribute in the future.

Supply chain planning in the German automotive industry 449

Long-term, strategic planning provides potentials, which mid-term planninghas to further develop and short-term planning has to implement. Of course, alsolong-term planning tasks are supported by OR methods. Concerning the productdesign, for example, the optimal commonality of automotive components (e.g. wireharnesses) is determined [53] or the impact of product variety on the performance ofmixed model assembly lines is analyzed [6,16]. It is even worth to include assemblysequencing issues into product design decisions [52]. Analytical and simulationmodels provide general hints (“chaining strategies’’) how to assign products tomanufacturing plants so that high process flexibility is achieved for both single stage[28] and multi stage [5,19] automotive supply chains. Linear Programming (LP) orMixed-Integer linear Programming (MIP) models are, for instance, used by APS todesign the inbound system of assembly plants [20] or car distribution networks [2](see also [36] without use of APS). Concerning the inside of assembly plants, theplanning of the physical layout and of buffer sizes of assembly shops, in general,and of body shops [41,49, p. 20 ff. & 73 ff.], in particular, can be supported bysimulative, analytical and combinatorial optimization methods. A comprehensiveoverview of OR methods for the well-known assembly line balancing, which is arather strategic than mid-term task in the automotive industry, is given by Scholl[47]. A recent survey of heuristic methods for cost-oriented assembly line balancingcan be found in [1].

In order to understand why automotive planning systems are organized the waythey are, Section 2 describes the characteristics of automotive supply chains. Thesevary substantially for car manufacturers in different parts of the world (North Amer-ica, Japan/Korea, Europe/Germany), operating on different market segments. Thispaper mainly concentrates on premium brands (like BMW, Mercedes, Audi) but notluxury cars (like Rolls-Royce, Maybach, Bentley) and on the German automotiveindustry. Nevertheless, quite a lot of the statements and findings of this paper can betransferred to car manufacturers in other parts of the world tackling related productsegments because (even though beginning with different starting points) many ofthem similarly intend to change to a BTO production. Section 3 then presents thetraditional short- and mid-term planning system and – after introducing the respec-tive planning tasks – points to appropriate planning and OR methods. After brieflysummarizing the measures to improve BTO assembly currently being implementedin the German automotive industry (Sect. 4), their impact on the planning systemis discussed in Section 5. Thus changed requirements for planning methods can bederived and challenges for future research can finally be identified (Sect. 6).

2 Automotive supply chains

Cars are sold to final customers either directly via sales subsidiaries of the car man-ufacturer or indirectly via legally separate retailers. The bill-of-material (BOM) isstrictly convergent, i.e. assembly processes are dominant. Cars often are thoughtto be standard products. However, in the premium segment of this line of busi-ness, there is a high degree of customization. This allows the customer to specifyobligatory features like the color of the car and type of upholstery or optionalfeatures like air conditioning or a navigation system, to name only a few. In the

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following both obligatory and optional features are just referred to as “options”.A car manufacturer usually offers several types of cars (e.g. the E-class or C-classof DaimlerChrysler), which again differ in several body-in-white variants (coupe,convertibles, etc.). Not every customer needs his car immediately. According toStautner [51, p. 38] the order lead time desired by a final customer is normallydistributed with a mean value of 4 to 6 weeks.

The sales organization and distribution network of a car manufacturer have adivergent structure, which comprises several stages like the central sales departmentof the manufacturer, sales persons responsible for different world regions (also at theheadquarter), sales companies in different countries or local areas and a rather highnumber of further retailers and sales subsidiaries. This type of customized premiumcars can only be assembled “to order”, i.e. there has to be an “order” available –either by the final customer, a retailer or a sales department of the manufacturer– that specifies the options of the car. Current SCM initiatives in the automotiveindustry try to increase the share of final customers’ orders and to decrease theshare of retailers’ and sales departments’ orders (see Sect. 4.1).

Commonly, manufacturer and retailers communicate in two types of interac-tion rounds: In the first one, a retailer sends his mid-term requests for cars to themanufacturer. Both “negotiate” the number of cars (so-called “quota”) the retailerwill get during the next year. Usually, this “negotiation” process is clearly domi-nated by the manufacturer so that – due to the preferences of the manufacturer –the agreed quota may be less or even higher than the original requests. Since thesequotas are, for example, defined for the next year on a monthly basis, only body-in-white variants and the type of engines (referred to as “models” in the following)are considered, but the options are not specified at this point in time.

In a second round, about three to five weeks before planned production, theretailer has to specify the options for all cars of his quota, which are due and havenot been assigned to final customer orders that had arrived in the meantime. Froma retailer’s point of view, these cars are “built to stock” (BTS-cars), based on a sortof forecasting process for options. From the manufacturer’s point of view, an orderof the retailer exists, thus justifying the term “built to order”.

Figure 1 illustrates the different states of demand information that are impliedby these two interaction rounds. The curve (I) shows the cumulated share of fullyspecified orders of final customers with respect to the overall number of orders(incl. forecasts) considered by planning. It can be computed by calculating thedistribution function of the order lead times, that are desired by final customers(see [51, p. 38]). This distribution function is drawn backward in time, starting withthe delivery of cars to final customers.

For the section above the curve, no information about the preferences of finalcustomers is available. This lack of information has to be replaced with forecasts.Concerning the options of BTS-cars, this is done by retailers with a lead time of3–5 weeks before production (II). Beforehand, with a lead time of one year at amaximum, only the retailers’ requests for models are known (III), which also arethe result of a forecasting process of the retailer. For yet earlier planning tasks, acar manufacturer has to rely on his own pure forecasts for models (IV).


pureforecast

for models(IV)

requestsfor modelsby retailers

(III)

sending requestsfor models

optionsspecified

byretailers

(II)

specification of optionsby retailers

share of final customers‘ orders

100 %

time

50 %

delivery tofinal customer

optionsspecified by

final customer(I)

„built-to-stock“of retailers

Fig. 1. Demand information available to a car manufacturer

The production system in a car assembly plant usually comprises the four stagespressing of metal or aluminium sheets, welding the body-in-white from the mouldedsheets in the body shop, painting it in the paint shop and final assembly, wherepainted body, engine, transmission and the further equipment are brought togetheror built in. For the final assembly one or several production lines are used. Aproduction line consists of quite a lot of serially arranged assembly stations, betweenwhich cars are conveyed with a fixed belt rate. The processing time at an assemblystation depends on the option chosen for the car to be assembled. Therefore, theoverall utilization of a station is determined by the sequence in which cars/ordersare assembled on a line (the so-called “model mix”). If too many cars requiring thesame options are following one another, some of the stations may be overloadedwhereas others are underloaded. Thus a “balanced” model mix has to be found,almost equally utilizing the various stations of an assembly line.

Because of the convergent BOM and ten thousands of components to be pur-chased, a procurement network with several hundreds direct and an enormous num-ber of indirect suppliers has to be coordinated. For the delivery of incoming goodsnormally several transport modes are applied. Voluminous and expensive compo-nents are – as far as possible – delivered “just in time” (JIT) at the day of assembly,partly even directly to the assembly line and thus arranged in the sequence of plannedassembly (“sequence-in-line supply”, SILS). The remaining incoming goods arecollected by regional carriers, consolidated and brought to intermediate warehousesof the car manufacturers, which are close to their assembly sites.

The structure of an automotive supply chain is characterized by a convergentflow of material upstream of the assembly plants of the car manufacturer and adivergent flow of finished cars downstream. An automotive SC is difficult to co-ordinate, because not only production capacity and manpower may turn out to bebottlenecks, but also incoming goods. For reasons of flexibility, high-volume carmodels can sometimes be produced at several assembly plants. Because of theseconstraints, the promised delivery dates cannot always satisfy the expectations of

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the final customers. Furthermore, final customers need a reliable delivery date be-cause important further activities (like selling the old car, making money available)have to be synchronized with the arrival of the new car. Thus, “order promising”not only has to aim at setting a delivery date close to the customer’s wishes, butalso at promising a reliable date considering as many of the above constraints aspossible.

Besides a significant intra-organizational information flow between differentplanning units/departments of the car manufacturer itself (as will be discussed inSect. 3), there is also a vital inter-organizational exchange of information betweenthe different members of the SC. Commonly, car manufacturers prepare a roughmid-term supply plan of the next year for their (first-tier) suppliers in order to drawearly attention to potential capacity bottlenecks. In the short term, daily supplyplans are sent to the suppliers. These include binding orders for the next day, butalso quite reliable “forecasts” for the next days/weeks and even rough forecasts forthe next months.

3 Traditional planning processes

To cope with the various planning tasks of automotive supply chains, quite a lot ofplanning units/departments have to be involved. These planning tasks and the re-spective decisions can be assigned to several planning levels (e.g. strategic, tactical,operational) comprising different planning horizons (e.g. long-, mid-, short-term).Depending on the planning horizon and the lead time necessary to make a certaindecision – different phases of the time axis of Figure 1 are relevant and thus adifferent state of knowledge about actual customer demand is available. Therefore,from a manufacturer’s point of view, one may distinguish between forecast-drivenlong- and mid-term planning (phases (III) and (IV) of Fig. 1) and order-drivenshort–term planning (phases (I) and (II)). In Section 3.1 forecast-driven mid-termplanning tasks and their information flows, which are more or less common forthe German automotive industry, will be discussed first. Order-driven planning willthen be the concern of Section 3.2. Within both sections organizational issues areleft aside. This will be covered in Section 3.3.

3.1 Forecast–driven planning

Figure 2 summarizes the forecast-driven planning activities. Planning tasks aremarked by rectangles, arcs illustrate the information flows in between. From thebottom to the top, the level of aggregation and the planning horizon are increasing,the frequency of planning is decreasing, however. The planning tasks are roughlyassigned to the logistical functions procurement, production, distribution and sales,again. Of course, not all of the mid-term planning tasks of a car manufacturer willbe discussed. Only the most important ones which show a close interrelation havebeen selected.

The annual budget planning determines the overall monetary budgets of thecar manufacturer’s departments and assembly plants for the next year. For this,


retailerssuppliersplants

demandplanningMRP

allocationplanning

take rates

forecasts,take rates

requestsfor models

requests ofregions

volume goals,earning goals,

annualworking time

productionplans

(weekly)production

plans,overtime

(weekly)aggregate quotas

detailed quotas

supplyplans

procurement production distribution sales

budget planning

pro-duction

sales

master production pl.

pro-duction

sales

(monthly)production& salesplans

(monthly)aggregate

quotas

Fig. 2. Overview of (mainly) forecast-driven planning

production plans for the respective plants and the sales plans for the respectivesales regions have to be calculated, too. This is done once per year, for the nextyear, by deciding about production and sales quantities of car models (per plantand world region, for example) on a monthly basis. The overall yearly quantitiescan be considered as “volume goals” of the next year for both sales and production.From these, the expected production costs and earnings can be derived (“earninggoals”).

A further result of the annual budget planning is the usage or reservation ofadditional capacities, as far as these can still be influenced on a mid-term basis.Because of the long lead times (e.g. two years or more to install an assembly lineor a plant), usually capacities of production resources are adapted to customerdemand in the long term and thus are a concern of strategic planning. However,agreements about the extent and flexibility of the yearly working time, for example,are also a task of mid-term planning. A lot of further constraints have to be respectedlike potential bottlenecks of suppliers, model mix restrictions (capacities of crucialoptions, minimum utilization) and upper or lower bounds of the sales in certainmarkets. Lower bounds, for example, result from strategic directives about thepresence in important markets, upper bounds may be due to marketing analysesabout final customer demand.

Input data for the annual budget planning mainly are forecasts for final cus-tomers’ demand (see also Fig. 1), which result from the demand planning. These aremade on basis of historical sales data of the few already known and fully specifiedorders from final customers (e.g. car rentals), of the retailers’ annual requests formodels, of the sales companies’ decentral knowledge about the local preferences oftheir customers (“requests of regions”) and on basis of information about marketingcapabilities to influence final customer demand.

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Since budget planning has to decide about car models on the one hand andto anticipate potential bottlenecks of suppliers on the other hand, the componentdemand needs to be estimated, too. One way to do this is to forecast take ratesdirectly, i.e. to calculate the probability that a certain option or even component isdemanded (in a specific customer region) and to multiply it with the total numberof car models planned (for this region).

The task master production planning is similar to the annual budget planning.Again, production and sales plans have to be determined and coordinated. However,both now require a higher level of detail (e.g. weekly instead of monthly quantities)and they are not used to derive budget goals any further. The planning horizonof a monthly rolling horizon planning varies between three months and one year.Nevertheless, only the weekly quantities of the first month or the first two months(depending on the lead times of planning) are put into practice.

Input data (see Fig. 2) are the already mentioned sales forecasts for models andforecasts for take rates. Because of the high share of final customers’ orders, thatis available for this shorter planning period (see Fig. 1), these monthly forecastsare more reliable than the annual forecasts used for budget planning. Further inputdata are the production and sales quantities per month that have been agreed uponin the budget planning, or the respective volume and earning goals (e.g. per year).One objective of the master production planning is to meet these targets as closeas possible in the short term. Constraints to be respected are quite the same aswere relevant for the budget planning. However, again a higher level of detail isnecessary.

Results of the master production planning are the updated and more detailed(e.g. weekly) production plans of the assembly plants and sales plans. The latter onesinclude the quotas for the different sales regions. Because of the above mentionedconstraints, these quotas may exceed or fall below the requests for car models,originally demanded by the regions. A similar setting of (monthly instead of weekly)quotas for sales regions may possibly also be part of the annual budget planning.For both budget and master production planning LP or MIP models seem to beappropriate. However, for reasons to be explained in Section 3.3, they are not usedin practice at the moment. Planning usually is only supported by simple spreadsheetmodeling.

The production plans for car models, which are a result of the annual budgetand master production planning, are the basis to derive the component demand in afurther material requirements planning (MRP) procedure. The component demandis communicated to the first-tier suppliers as a preview of the quantities to bedelivered within the next months. As the options of the cars are just specified forthe 3–5 weeks before production (see Fig. 1, phases (I) and (II)), and since the shareof final customers’ orders decreases rapidly for longer lead times (phases (III) and(IV)), this component demand becomes more and more unreliable, the longer theforecast horizon is.

On the sales side, the allocation planning has to allocate the aggregate quotas,which are known as a result of the budget planning on a monthly basis and as aresult of the master production planning on a weekly basis, to the lower levels of thesales system. Depending on the organizational structure of the car manufacturer,


this planning task may occur on several hierarchical levels, e.g. first an allocation ofquotas of world regions to different countries, and afterwards an allocation of thesemore detailed quotas to the countries’ respective retailers and sales subsidiaries.As an example, in the following only the relation “world region → countries” isconsidered: After the annual budget planning, the respective monthly quotas (salesplans) of the world regions have to be allocated to the countries with respect totheir original requests. If the requests cannot all be satisfied, it has to be decided,whose demand will only be fulfilled partly. This “shortage planning” may followsome predefined rules (so-called “fair share rules”, see e.g. [30, p. 169 f.]), which,for example, might reflect the purchase behavior of a country in the past, or moreor less be based on “negotiations” between representatives of the world regions andof the respective countries. Furthermore, a region has to balance the deviations ofthe countries’ actual demands from their former requests between all the differentcountries assigned to the region. For this purpose, the region may also (call for and)hold a “regional” pool of cars, originally not having been requested by one of thecountries.

3.2 Order-driven planning

Until now planning tasks have been discussed, which mainly build on forecasts foroptions. In other words, only a few fully specified orders are known at the time ofplanning. In this section planning tasks will be considered which are exclusivelytriggered by fully specified orders, either of final customers or of sales subsidiariesand retailers. Figure 3 gives an overview of these order-driven planning tasks andtheir interrelations.

finalcustomerssuppliers

allocationplanning

order promisingline assignment &

model mix planning

sequencing

plant assignment

MRP &lot-sizing

MRP distribution

customerorders

customer &retailer orders

specified orderswith due dates

(weekly)production ordersper plant

daily buckets

daily buckets

procurementlot-sizes

JIT-callsSILS car

sequence cars

specificationchanges

(weekly) production plans,

overtime

aggregate quotas

promiseddates

detailedquotas

procurement production distribution sales

Fig. 3. Overview of order-driven planning

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Direct buying of cars via the Internet is not (yet) worth mentioning. Normallyprivate customers order their cars via the sales subsidiaries or retailers of the carmanufacturer. The respective sales personnel tells the final customers the expecteddelivery dates of their desired cars. Usually, a granularity of weeks is sufficientfor the customer, who e.g. has to provide the money on time and to synchronizethe delivery with the selling of his used car. Thus order promising, i.e. promisingreliable delivery dates to the customer, is an important task. If a free quota of thesales subsidiary or retailer is available, the final customer gets his desired deliverydate promised. Otherwise, the next free quota is recommended or a standard deliverytime is proposed (if quotas are not available in sufficient detail). The customer mayaccept the promised date, change the options of his desired car or even the modeltype (in order to get an earlier delivery date), or try his luck with another retailer.

Furthermore, retailers and sales subsidiaries have to specify the options forthat part of their quotas that has not been filled up with final customers’ ordersuntil the agreed date of specification (see phase (II) of Fig. 1). In order to reduceinventories at the retailers’ sites, the desired options of potential customers haveto be anticipated as precisely as possible. Because of the rather small number ofcustomers and large number of options, this is an almost unsolvable problem fora single retailer. Thus, Stautner [51] suggests central support of the manufacturerfor these decentral forecasts of the retailers (see Sect. 5.1) and Holweg and Pil [24]even propose a central pool of BTS cars.

Traditionally, these fully specified orders are collected by the respective salesorganization, responsible for a certain retailer, and sent in bulk (e.g. all orders ofa week) to the next higher level of the sales hierarchy. A central order manage-ment department of the car manufacturer finally has to select an assembly plant,able to produce the car model requested by a certain order. This plant assign-ment has to consider the production quantities and capacities per plant, that havebeen agreed upon in the master production planning. If the actually requested caroptions significantly deviate from the ones assumed within master production plan-ning (e.g. when anticipating bottlenecks of components or model mix constraints),some orders have to be fulfilled earlier and others have to be delayed, thus resultingin a re-assignment of orders to weeks.

The selection of an assembly plant was not a big problem so far because tradi-tionally car manufacturers had little flexibility in assigning cars to plants and thusthis task has (up to the author’s knowledge) not directly been addressed in the ORliterature. However, recently body shop and assembly have become flexible enoughto allow model swap and thus the degrees of freedom and the need for intelligentplanning methods grow. In [17], the more important assignment of customer ordersto discrete time buckets with respect to promised due dates and to material/capacityconstraints is introduced as a planning task called “demand supply matching” andcorresponding LP/MIP models are formulated. However, the specific requirementsof the automotive industry (e.g. several assembly plants, model mix constraints)are not considered. Lovgren and Racer [33] make a first step towards mixed modelassembly line sequencing with respect to given due dates of orders. They calculatedetailed sequences of cars for a single assembly line. Thus, their model is ratherdesigned for the short-term line sequencing (see below) than the more aggregate


plant assignment. However, the problem of early or late demand fulfillment in theautomotive industry is at least generally addressed.

After this assignment, the decentral short-term production planning departmentsof the assembly plants have production orders available, that ought to be assembledwithin (or up to) their pre-defined week of production (ideally still the promisedweek minus a standard lead time for delivery to the respective customer). Theshorter the planning horizon is, the more restrictive the model mix constraints are.Thus, the line assignment & model mix planning have to distribute the productionorders among the possibly parallel assembly lines and to assign days of productionto the orders. Doing this, the most important model mix constraints (e.g. “at amaximum 300 air conditionings per day”) have to be considered, but the assemblysequence of a day is not yet determined. Scholl [47, p. 108f.] denotes this task as“Master Sequencing” and suggests, for reasons of complexity, a further aggregationof individual orders to families of cars. Again, this planning task has not adequatelybeen tackled in the literature. Only Mergenthaler et al. [35] and Ding and Tulani[11] address the single line (sub-)problem directly. The former ones try to smooththe daily workload of a week by modifying a bin packing algorithm in order tominimize the quadratic model mix deviation in a greedy manner, whereas the latterones apply simple neighborhood operations like “switching models of differentlyutilized days” in a two-phase greedy algorithm.

As compared to mid-term planning, car options are now known with a highreliability. Since the daily assembly buckets are also known as a result of the lineassignment & model mix planning, the daily demand of components can directlybe derived. For components and material, that are collected by regional carriers andtemporarily stored in an intermediate warehouse (see p. 451), an MRP & lot-sizingprocedure is appropriate that balances the trade off between inventory holding costsand degressive transportation costs of the regional carriers and determines adequatesupply frequencies.

The daily buckets of the line assignment & model mix planning are also guide-lines for the daily sequencing of the assembly lines. Here, the sequences of theproduction orders on the final assembly lines are determined on a rolling horizonbasis with a planning horizon of one to two weeks. The level of detail again is higherthan in model mix planning. Now all potential bottlenecks have to be considered,for example, the availability of all of the components and “distance” restrictions ofthe lines like “no two cars with air-conditioning are allowed to follow each other ”.For this reason, sometimes the earlier assignment to days of production cannot bemaintained. However, it should be avoided to postpone an order to another weekthan the planned (and promised) one. To use flexible workforce or to work duringlunch breaks are short-term measures to extend capacity.

Undoubtedly, most scientific research on planning aspects of automotive supplychains has been done in the fields of balancing and sequencing mixed-model assem-bly lines. In the sequencing literature, usually it is assumed that orders have alreadybeen assigned to a certain period (e.g. a day) of production, so that promised duedates need not to be considered any further. The various sequencing approachesdiffer with respect to their different objectives. Besides cost-oriented objectives,mainly time related or JIT-objectives and combinations thereof are pursued (see

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e.g. [32, p. 44 ff.] and [47, p. 98 ff.]). A comprehensive literature review of modelsand exact/heuristic solution methods is given by Scholl [47]. Summing up, prioritybased (greedy) heuristics [47, p. 205 ff.] are – for reasons of complexity – clearlyfavored over exact (mainly branch and bound) methods [47, p. 199 ff.]. Newerheuristic approaches also apply multi agent systems [9] or local search methodslike simulated annealing or genetic algorithms (see e.g. [35],[25], [43],[46, p. 40]).

For a review of models and methods with respect to the different objectives,the reader is referred to Lochmann [32]. Models with time related objectives [32,p. 58 ff.] try to smooth the work load and minimize the overload of the various sta-tions of a line. For this, usually MIP models are formulated. JIT-objectives attemptto smooth the material supply at the stations in order to keep the inventory of com-ponents constantly low. The usage rates of components are either leveled directly[32, p. 81 ff.] or, in case the cars require a similar number and mix of components,the mix of cars is leveled instead [32, p. 86 ff.]. The latter “level scheduling” wasintroduced by Miltenburg [37] and commonly pursues nonlinear goals. Thus bothtime related objectives and JIT-objectives directly address the model mix constraintsdiscussed so far.

The car sequencing problem (CSP), originally introduced by Parretto et al. [40],allows to model the above mentioned minimum distances between orders with thesame options and further separation rules like a “maximum number of identicaloptions within a car sequence of predefined length”. The CSP in not widely knownwithin the OR community, but one of the classical problems in the literature onconstraint satisfaction problems [7]. Brailsford et al. [7] review this kind of litera-ture, showing that these “soft” constraints also pursue time-related objectives andthat JIT- and some further objectives can also be modeled as soft constraints of aCSP. They report that – by using a hybrid approach combining simulated anneal-ing and constraint logic programming – David and Chew [10] are able to obtaingood solutions for a practical problem at Renault involving 7500 cars with 50–100options each.

Recent approaches of Drexl et al. combine the classical CSP with level schedul-ing [12] and solve it in a two stage approach [13]. Also Monden [38, Chapt. 17]extends his JIT-oriented “goal-chasing” heuristics in order to respect CSP distanceobjectives (denoted as “continuation control” and “interval control”). Zeramdini etal. [55] propose a two-stage approach, smoothing the components’ usage first andthe workload secondly, to optimize the bicriteria sequencing problem. Kormazeland Meral [31] reformulate the same combined problem as an assignment problemwith weighted objectives and develop heuristics for it. Hyun et al. [25] and later onPonnambalam et al. [43] consider “minimization of setup costs” as a third strivingobjective and find (near-) Pareto optimal solutions for the multi-objective prob-lem by using a genetic algorithm. A further overview of models and methods forcombined objectives is given in [32, p. 92 ff.]. Concluding this brief discussion ofsequencing, it can be stated that there is a trend in recent literature on mixed-modelassembly line sequencing to consider several objectives, simultaneously.

The frozen car sequence is then the basis to derive the component demand forJIT calls and SIL supply. This short-term material requirements planning (MRP) isnot a “real” planning task because there is nothing left to be decided about. Just the


BOM has to be exploded as late as possible before the scheduled delivery (usuallyseveral times per day). It is just mentioned to provide a complete picture of supplierrelationships.

If final customers do not pick up their cars at the assembly sites directly, the fin-ished cars have to be brought to the customers or their respective retailers and salessubsidiaries. There again are some decisions to be made concerning the distributionof the finished cars. For example, the actual carrier has to be chosen, and transportfrequencies (how often to deliver to a retailer) and vehicle routes (sequence of re-tailers within a tour) have to be determined. Some of these tasks are in the planningdomain of logistic service providers [8].

3.3 Organizational issues

One has to be aware that in the preceding sections only “abstract” planning tasksof car manufacturers have been described, but organizational issues have been leftaside. In reality, often several different planning departments are involved in a singleplanning task. Then there are several “coordination rounds“ whose result is a com-mon plan. Within each coordination round, a single department has to contribute itsown (locally “optimal”) partial plan until some predefined date. Such a (temporarilyvalid) partial plan is a sort of self-commitment of the respective department andprovides input for the next planning activity of another department. This procedureiterates until the common plan hopefully respects all relevant constraints and fulfillsthe various and sometimes conflicting objectives of the different departments to anacceptable level.

The mutual arcs between production and sales in the budget planning andmaster production planning boxes of Figure 2 ought to indicate that in practice therespective planning task usually is not tackled in a single, simultaneous planningprocedure, but in the above mentioned coordination rounds. This is one reasonwhy LP and MIP models are not used for a simultaneous budget planning or asimultaneous master production planning as it is common practice in other typesof industries like consumer goods manufacturing, for example [45]. Wahl [54]proposes appropriate models to – at least individually – optimize the planningdecisions of the sales department in this way. But even such a local application ofLP and MIP has not been implemented in practice for reasons like missing (IT)infrastructures, inappropriate forms of organization or mostly a lack of acceptanceand understanding of OR methods.

4 Current trends in the German automotive industry

As Figure 1 shows, “to move from BTS to BTO” is a somewhat imprecise formu-lation. The task is rather to increase the share of final customers’ orders. Furtherstrategic goals, currently pursued in the German automotive industry, are to shortencustomer order delivery times of customized cars, to keep promised delivery dateswith a high reliability and to allow customers to change their car options also in thevery short term [51, p. 31 ff.]. In order to reach these goals in addition to supply

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chain collaboration (see e.g. [18]) two major bundles of measures, online orderingand late order assignment, have been and are still being implemented.

4.1 Online ordering

The total order-to-delivery lead time (OTD) can be shortened by reducing the leadtimes of all individual processes (like order entry and processing, manufacturing,distribution) involved. Since manufacturing and distribution only comprise a verysmall percentage of the OTD (about 16 % according to [22, Figure 3]), the highestpotential can be found in order entry and processing. “Online ordering” initiativesaim at simplifying and accelerating the circumstantial and timely collecting and(weekly) bulk processing of orders within the multi-stage sales hierarchy. Thusretailers send fully specified ordering requests of final customers via the Extranetor Internet directly to a central order processing system, where the requests areonline (i.e. within seconds or minutes) checked for technical feasibility and pro-vided with a promised delivery date. In case of final customer’s acceptance of thepromised date, the final order is processed with the same speed on the same route.By implementing such a system, the car manufacturer BMW tries to reduce thelead time of order entry from 13–17 days to a single day [44], for instance. Figure4 graphically illustrates how online ordering reduces demand uncertainty. In this(fictitious) example, cutting the lead times of order entry in half triples the share offinal customers’ orders known. Thus the forecast-based BTS inventory of retailers(phase (II), see also Fig. 1) can be reduced significantly.

Fig. 4. Example of lead time reduction by online ordering

4.2 Late order assignment

Traditionally each body-in-white, physically processed within the body shop, isalready assigned to a customer order (“order assignment”) and a re-assignment to


another order is only rarely practicable. Following the pull-principles of the just-in-time philosophy the final assembly as the last production stage has to be planned firstand synchronizes all direct suppliers and upstream production stages, especially thepaint shop and the body shop. In the light of “lean thinking” the work-in-processbuffers (body store and painted body store) should be small and thus body and paintshop ideally produce in the same sequence of customer orders as is planned for thefinal assembly. However, these buffers are still necessary because process failuresin the body and paint shops occur frequently [49, p. 29 f.]. According to Holweg[21] the rework rate is even up to 40–50 %. For this reason, a planned assemblysequence can only be considered to be reliable, when the respective orders’ paintedbodies have left the paint shop. Thus the sequence can only be transmitted to theSIL suppliers a few hours before planned assembly, depending on the assemblystation and the respective component.

In order to guarantee more reliable assembly plans, which can be fixed for alonger time interval (about 4–6 days), the order assignment nowadays is postponedto the final assembly stage (“late order assignment” or “late order tagging”, see[21]), i.e. the bodies in the body and paint shops are no longer identified by customerorders. Body and paint shops still get the information about the customer orders tobe assembled, but are free to deviate from the planned assembly sequence. Althoughthere is no demand uncertainty, safety stocks have to be installed for each body-in-white variant and paint color. These safety stocks exclusively hedge againstthe process failures in the body and paint shops. In order to limit the total amountof safety stock required and to restrict buffer sizes, the number of body-in-whitevariations and paints (the so-called “internal complexity”, [21]) should be low. Forthis reason, BMW reduced the number of body-in-white variations from 40 000 to16 for their new 3 series when introducing late order assignment [21]. The higherstability of assembly plans is expected to increase the radius of JIT/SIL deliveryand the share of JIT/SIL-suppliers significantly.

5 Impacts on planning

Online ordering and late order assignment have been and still are being introducedby BMW (project title “Kundenorientierter Vertriebs- und Produktionsprozess”[44]) and DaimlerChrysler (project titles “Global Ordering” and “Perlenkette”[18]). Further car manufacturers intend to follow. These two types of measuresconsiderably influence the traditional planning landscape as discussed in Section 3.Thus it is necessary to check how planning requirements and information flowschange (some planning tasks may loose importance whereas others win) and whichnew planning tasks arise.

5.1 Impacts of online ordering

Online ordering and online order promising require extremely short response timesfor incoming customer requests. If highly reliable promised delivery dates shall

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be achieved, the capacities of all potential bottlenecks (material or production re-sources) have to be checked. Thus the formerly decentral order promising has tobe automated and centralized. The changes in the planning landscape depend onthe level of delivery reliability aspired. In the following only two extreme scenar-ios, denoted as quota-available-to-promise (QATP) and capable-to-promise (CTP)scenario, are discussed as examples. Of course, there are various intermediatesconceivable between these extremes.

5.1.1 QATP scenario. The QATP scenario is more or less an automation of al-ready existing processes. As Figure 5 shows, the general logic of planning staysthe same. The quotas for retailers and sales subsidiaries, which have been deter-mined on a weekly basis anyway and have been synchronized with capacities inthe medium term, are (as far as they have not yet been assigned to final customers’orders) considered to be “available to be promised”. Incoming customer requestsand customer orders, respectively, are checked for technical feasibility [26], first,and according to simple precedence rules [30] for free quotas, secondly. Such aproceeding is known from material constrained industries like the computer indus-try and successfully applied there [29]. In contrast, however, material availabilityis not yet checked in the simple QATP scenario. The installation of an online order-ing system (OOS) is technically lavish and costly, but hardly changes the planninglogic. When comparing Figure 5 with Figure 3, the major differences are that spec-ified orders (and their due dates) are directly transmitted to the plant assignmentinstead of using the multi-stage sales hierarchy and that specification changes canbe sent faster (and thus later) to the model mix planning.

promiseddates

line assignment &model mix planning

plant assignment

finalcustomers

allocationplanning

onlineorder promising

specified orderswith due dates

aggregate quotas

detailedquotas(QATP)

production ordersper plant


daily buckets

daily

buckets

retailers

requests,orders,


promiseddates

promiseddates

production sales

(weekly) production plans,

overtime

Fig. 5. QATP scenario

However, because the mid-term capacity check, on which free quotas (QATP)are based, had no detailed information about the customers’ choice of car options,there is a high probability that the promised delivery dates do not fit the model mixconstraints and thus cannot be kept on the short-term.


5.1.2 CTP scenario. In order to achieve a higher delivery reliability, a shorter-term and more detailed capacity check is necessary, which motivates the other,more challenging extreme, the capable-to-promise (CTP) scenario. When accept-ing orders and confirming delivery dates, the customer orders are directly booked[22] to a day of production or week of production (if a late delivery is desired by thefinal customer) of an adequate assembly plant. In contrary to the QATP scenario,all or at least the most crucial constraints, relevant for model mix planning (likeoptions of the orders, material required, production capacity, quotas of the respec-tive sales hierarchy), are considered. The order promising is extended such thatproduction orders can automatically be generated. Thus the online order promisingtakes on planning tasks of the short-term production planning or – at least – limitsits scope. Furthermore, also the plant assignment has to be integrated into such acomprehensive online ordering.

Questions, which have to be answered online, are for example: Is a BTS carphysically available somewhere in the supply chain, which fits the requirements ofthe new customer order to a very high degree? Is a similar BTS car planned and canits options be changed so that the order still can be assigned to it? Which plant hasto be chosen if a new production order has to be generated? Should be producedearlier or later than the desired date, if this is already (over)booked? If model mixconstraints are limiting, which car specifications should a customer change in orderto still get his desired delivery date promised?

However, one has to keep in mind that the computational burden to update allthe necessary data and the desired response times of the OOS are conflicting. Themajor problem is to find the right trade off between modeling capacities as detailedas necessary (increases delivery reliability) and updating as few data as possible(in order to guarantee short response times).

Figure 6 shows the embedding of a CTP online order promising into the plan-ning landscape. The online order promising needs free quotas (QATP) and notyet assigned net capacities of material (“material-available-to-promise“, MATP)and assembly resources (CTP), e.g. expressed by a maximum number of cars witha specific (combination of) option(s) per day, as inputs. The results of the orderpromising are weekly delivery dates, which are promised to the customers, and“production orders” with a promised delivery date and a planned day (or week)of production, which are sent to the line assignment & model mix planning of therespective plants.

A decentral model mix planning is still necessary for several reasons. For ex-ample, the preliminary production plans of order promising have to be updatedwith respect to (for complexity reasons) still unconsidered capacity constraints anda line assignment has to be made. Production orders, which have only been allo-cated to a week of production because of rather long customer order lead timesbeing desired, have to be assigned to a day of production. The more detailed thecapacity constraints of order promising are, the less changes of its plans should benecessary later on in the model mix planning because the most crucial potentialbottlenecks have already been anticipated. However, short-term failures in supplyand production can never be avoided and thus make a re-planning necessary.

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line assignment &model mix planning

netting

finalcustomers

allocationplanning

sequencing

production ordersper plant

and day / week


aggregate quotas

detailed quotas(QATP)

retailers

onlineorder promising

(incl. plant assignm.)

promiseddates

promiseddates

promiseddates

MATP, CTP

dailybuckets

dailybuckets

production sales

(weekly) productionplans, overtime

requests,orders,


Fig. 6. CTP scenario

The results of the model mix planning are daily buckets, which again are sent tothe sequencing, but are – in a further netting procedure – also used to calculate the(net) MATP and (net) CTP for the online order promising. Further input for the net-ting is up-to-date information about plant capacities and projected material supply,which have been synchronized in the master production planning in the mediumterm (see Fig. 2). Fleischmann and Meyr [17] illustrate the interaction between“order entry” (online order promising) and “MATP/CTP (re)calculation” (nettingand model mix planning) by means of two more detailed workflows and discuss theplanning tasks of demand fulfillment for various positions of decoupling points.They also propose LP and MIP models for order (re-) promising, which are usefulif several customer requests/orders can be processed in a batch. However, if eachcustomer request has to be answered immediately, the degree of freedom is ratherlow. Consequently, the importance and impact of previous planning tasks, like mas-ter production and allocation planning, grows. The APS vendor SAP [46] offers asoftware module called Realtime-Positioning, which has especially been designedfor the online order promising in the CTP-scenario, and Ohl [39, p. 207 ff.] dis-cusses the advantages of “code rules”, describing the interrelations between variouscar options, for a capacitated BOM-explosion of online queries. However, formalmodels for the MATP/CTP calculation and search are not presented. Similarly tothe approach of Ohl, Bertrand et al. [4] propose to use a hierarchical pseudobill ofmaterial for the MATP check in case of strong interdependencies between differentoptions (so-called “non-modular products”).

Besides newly arriving customer requests/orders also changes of the specifica-tion of already accepted orders can be processed and checked for capacity usingthe online ordering system. Furthermore, the specification of not yet fulfilled quo-tas by retailers (see phase (II) of Fig. 1) can be checked with respect to modelmix constraints. Online ordering accelerates order processing and increases theshare of final customers’ orders (see Fig. 4), but BTS cars cannot completely be


avoided [51, p. 6]. In order to decrease the times in inventory of the remaining BTScars, final customers’ desired options should be anticipated more precisely. Centralstatistics about the final customers’ preferences and about frequently purchasedoptions can comfortably be made available to retailers by means of the OOS. Theywiden the local view of the retailers and promise a higher quality of forecasts forBTS specifications [51, p. 176 ff.]. These proposals for BTS options and the moredetailed MATP/CTP capacity check can be seen as new potentials that arise due tothe centralization of order promising and the online connection to retailers.

5.2 Impacts of late order assignment

Late order assignment undoubtedly has its major impacts on strategic planning.Products have to be re-designed so that a high number of options (high externalvariety) can be kept up while simultaneously reducing the number of body-in-whitevariations (low internal complexity [21]). There is a rich OR literature on designfor postponement and modularization (see Sect. 1 and [3], for instance), which triesto support such issues. Furthermore, the re-dimensioning of the (body store and)painted body store is a strategic planning task.

But also for the operational planning of the body and paint shops and their re-spective stores new challenges arise because of the higher degrees of freedom. Thesafety stocks of the body store and the painted body store have to be refilled withrespect to the failure probability of the respective production processes. Becauseof the rather loose coupling to the assembly sequence and because of the increasedbuffer sizes, lot-sizing issues can now be considered easier in the paint shop. Al-though changeover times are negligible, batching lots is economically desirablebecause a change of the paints incurs costs between � 10 and � 30 [49, p. 30].Taking cars out of the body store is a Sequential Ordering Problem [15], a specialvariant of the Traveling Salesman Problem. For paint shops as a practical applica-tion, Spieckermann [49, p. 126 ff.] proposes a branch-and-bound approach whichtakes advantage of special knowledge about common structures of body stores inthe automotive industry (see also [50] for earlier approaches to the same problem).Engel et al. [14] propose a heuristic for workload leveling which can be extendedfor the batch sequencing of paints of the same color.

Inman and Schmeling [27] prove the operational advantages of late order assign-ment by means of simulation. They compare the traditional irreversible coupling oforders and physical vehicles at the body shop with a flexible assignment procedure(“Agile Assemble-to-Order” (AAO) system) that is able to assign and re-assign or-ders to vehicles before the body shop, paint shop and the final assembly are entered.The objective of the AAO system is a weighted function comprising penalty termsfor violating lead time, paint colour, spacing and levelness constraints. Orders areselected by the AAO system in a greedy manner with the weights varying accordingto preferences of the production stage under consideration.

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6 Conclusions and outlook

Concerning forecast-driven planning it can be stated that the quotas of the tra-ditional master production and allocation planning had a detrimental effect onmeeting final customers’ demand on time. This gets even worse if the same quotasare directly taken over to an OOS with automatic booking and without the pos-sibility of human intervention (see Sect. 5.1.1). Thus, if still necessary to smooththe workload in the medium term, one has to think about more flexible allocationmechanisms incorporating the increased knowledge (see Sect. 4.1) about final cus-tomers’ demand. Virtual, central car pools, accessible for several retailers, are afirst step in this direction. The choice of adequate aggregation levels, allowing topostpone decisions as long as possible, is crucial.

In other lines of business it has been shown that LP and MIP models can supportplanning tasks like budget, master production and allocation planning. Wahl [54]has proven that this would also be true for (at least the sales side of) automotiveindustries. The reasons, why the proposals of Wahl have not been put into practice,should have diminished or even vanished in the meantime. Information technologyhas improved dramatically in recent years and there seems to be a higher willingnessto make use of OR tools. APS, for example, are a comfortable and user friendly wayto apply LP and MIP methods in practice. In addition, simultaneous optimizationcovering several departments like production, procurement and sales in a singlemodel could exploit further potentials and – at least simulatively – support andaccelerate the lengthy coordination rounds (see Sect. 3.3).

Regarding the traditional order-driven planning it has been shown that ORsupport for the planning tasks plant assignment and line assignment & model mixplanning was very poor. However, these tasks will change their character anywaywhen online ordering and the CTP scenario are installed. On the other hand, there isrich literature on assembly line sequencing and research in this field is an ongoingprocess. Recent OR-related papers tend to pursue several objectives simultaneously,thus becoming more attractive for practical application in the automotive industry.However, scalability of sophisticated methods is still a problem and should be atopic of future research.

As we have seen, the measures to move from BTS to BTO also have signifi-cant impact on planning. The consequences for forecast-driven planning have beensketched above. Further challenges can be identified for the future order-drivenplanning. Due to late order assignment the close coupling of body, paint and as-sembly shops has been decreased now. Thus there remains supplementary freedomfor paint shop sequencing and batching of paints of the same color. However, be-cause of still limited buffer sizes, OR models have to take care that paint shopsequences may not deviate too far from assembly sequences.

Online ordering is most challenging in the CTP scenario when incoming ordershave to be booked directly into a (capacitated) production plan of a plant. In this case,online order promising takes over functionalities of the traditional plant assignmentand the traditional line assignment & model mix planning. The three most crucialproblems are


– how to model quotas and model mix restrictions as constraints for the onlineorder promising (within the netting procedure, respecting the results of theprevious master production and allocation planning),

– which fast algorithms or search rules to use for allocating free QATP, MATPand CTP (within online order promising) and

– how to revise the resulting preliminary production plans in case of still uncon-sidered constraints and unforeseen short-term events (new line assignment &model mix planning, respecting the already promised due dates).

Research has to be done on both OR models/methods for the different planning tasksinvolved and – since responsibilities change – also on the (hierarchical) interrelationof these planning tasks within the overall planning framework. If, above all, carmanufacturers think about customized sales prices, which may vary according to thedelivery times desired by final customers, the relationship to revenue management(see e.g. [34,48]), as common in airline industries, has to be further investigated.

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