Optimization of the H. Schlenker ... - Clemson...

Optimization of theworldwide supply chainat Continental Tires:A case study

H. SchlenkerR. KlugeJ. Koehl

Continental Tires faces difficult challenges in its production andsupply chain, both upstream on the production or sourcing side, anddownstream on the market or delivery side. Meeting these challengesinvolves a complex planning process. For example, long-termproduction and supply chain planning, carried out every year,supports mid-term and short-term planning or scheduling. Sincelong-term planning is performed on the worldwide level, it affectsenormous amounts of moneyVbillions of Euros both in terms ofrevenue and costs. The major goals of management and planningteams are: increase sales (e.g., through optimal assignment ofproduction to demands), reduce costs (e.g., through production in thecorrect locations), and balance critical trade-offs (e.g., supply versusdemand). IBM addressed these challenges with the implementationof a new long-term planning solution. The mathematical optimizationapproach, together with a mature software platform, solves thecomplex planning models. The models include aspects such as limitedresources, worldwide supply chain network, multiple productionlocations, many products, detailed material lists, productioncomplexity, capacity limitations, and multiple planning periods.The solution helps supply chain planners and managers create thebest possible plans, and react to unforeseen events and marketchangesVnot only rapidly but also optimally in terms of revenueand costs.

IntroductionIn this paper, we summarize the scientific background of thelong-term planning solution developed at Continental Tires,called FACT (Future Allocation and Capacity Tracking).We discuss how the solution was defined and implemented tomatch the business requirements of Continental Tires, how itworks in Continental’s environment, and what benefit itbrings to the planning process and to Continental’sworldwide supply chain. Continental Tires is the tiresdivision of Continental AG, a major automotive-partsmanufacturer in Germany [1]. We discuss the concept ofoptimization in supply chain planning and its contribution toimproved supply chain performance. Not long ago, in 2010,Qian et al. [2] stated that Bthese [optimization] algorithms

can’t well solve many complex optimization issues[ insupply chain management. Similar statements can also befound in [3, 4]. Even today, production planning at all levelsis still widely done manually (for example, usingspreadsheets, proprietary systems, or Enterprise ResourcePlanning [ERP] systems). It still might be difficult to applystandardized optimization, for example embedded in ERPsystems, to very complex production and supply chainplanning scenarios. However, with the latest improvementsin the runtime performance of optimization solvers (see,e.g., [5]), together with the availability of highly configurableand customizable software solution platforms, we believethat it is now possible to apply optimization to more andmore complex situations. Our solution successfully appliesmathematical optimization and integrates differentproduction areas in manufacturing, supply chainmanagement, and even engineering. Finally, this paper

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H. SCHLENKER ET AL. 11: 1IBM J. RES. & DEV. VOL. 58 NO. 5/6 PAPER 11 SEPTEMBER/NOVEMBER 2014

0018-8646/14 B 2014 IBM

Digital Object Identifier: 10.1147/JRD.2014.2345934

provides insight into the advanced planning process of amajor, global automotive supplier.This paper is structured as follows. In the following

section, we describe the business background of the involvedplanning processes and some of the disadvantages of theformer planning approach, which motivated the need for anew solution. Then, we summarize the current state of the artof optimization for supply chain planning and point out thedifferences between standard packages and configuredplanning and scheduling solutions. After this, we providedetails of the new solution that involves architecture,workflow, and an optimization model. Finally, we summarizethe resulting benefits of the new approach, and we concludethe paper.

Business backgroundContinental Tires is a large tire manufacturer, headquarteredin Germany, but producing and selling around the world [1].It produces passenger and light-truck tires for all sorts of cars,SUVs (sports utility vehicles), vans, light trucks, and evenbicycles and motorcycles. The tires are sold under variousbrands to the two major market segments: an OEM (originalequipment manufacturer) segment, which in the automotiveindustry is a synonym for car manufacturer, and areplacement segment.Continental Tires is a major player in the market,

generating yearly revenue of about 10 billion Euros andemploying more than 40,000 people around the world [1]. In2011, the company launched a project to significantlyimprove the long-term resourcing process of its worldwidetire production. The tires are produced in a global productionnetwork with 18 plants around the world. The products arecurrently produced and marketed in more than 10,000different variations, and every year around 1,000 newproduct types are added.The complex manufacturing process consists of a

combination of chemical and discrete manufacturing tasksand encompasses up to 100 production steps per final product(tire). The process type is make-to-stock: the production istriggered mainly by inventory rules (such as safety-stocklower bounds, combined with the seasonal sales forecast),rather than by booked orders. Manufacturing uses variousdifferent product components, including chemicals, rubber,metal, and fabrics, in order to build the final product. Someraw materials have very long lead times and can only besourced from a very limited number of suppliers worldwide.Other semi-finished products are produced by the companyitselfVsome in the same plant as the final product, some inother plants. Some intermediate products can be stored forlonger time horizons; others have to be processedimmediately due to their limited shelf life. Themanufacturing process is quite asset intensive. Manymachine tools have been designed and built by the companyitself as they are considered as a technology competitive

advantage. Transportation of the final products is donemostly via full truck loads in order to minimize transportcosts. The production facilities are located worldwide, but areclustered in all continents to serve major markets with thelowest supply chain costs. The same products can beproduced in many different plants, enabling rapidreassignment from one plant to another in order to betterserve local market demands with lowest inventory andshipping costs. Production capacity is limited; therefore, fullutilization of the plants is one element of the manufacturingstrategy.On the market side, the company serves many different

markets worldwide (e.g., serving both the OEM market andreplacement customers). The OEM market is especiallychallenging, with many niche products to support andco-develop. In the replacement business, the company servesvery small dealers and garages as well as large wholesalersand retailers or online giants; end customers are servedthrough their own retail chains in selected markets. Thereplacement market is expanding continuously.The product portfolio is diverse: from high-volume,

low-margin products and customer segments to low-volumeand high-margin products and customer segments. Salesfollow strong seasonal curves in some markets, whereasother markets follow a flat sales curve across the year.Forecasting of the product volumes is a complex processwith challenging reliability due to external and internalfactors. Globalization and strong market growth in Asia andSouth America forces the company to invest significantly inemerging markets.

Business problemAt Continental Tires, increasing volatility of customerdemands, as well as serving new customer channels requiresa reliable and strategic long-term production planningprocess. The traditional process did not meet theserequirements. It was very time consuming, highly manual,and did not have consistent underlying data across allinvolved business functions. The planning was mainlycarried out with fragmented spreadsheets across differentbusiness functions. These where loosely connected to theunderlying IT (information technology) infrastructure, basedon SAP and home-grown systems. These systems maintainmost of the master data for the planning.The company manages the global production planning

through a complex planning process, from long-termplanning to mid-term planning down to short-termscheduling and sequencing, with the followingcharacteristics. Long-term planning (carried out yearly ortwice per year) Blooks[ 1 to 3 years ahead, on both the globalnetwork and at the individual plants. In this iterative process,many planning scenarios are created, evaluated, andcompared until the final decision is made. The process is splitinto an allocation part, which plans the allocation of products

11: 2 H. SCHLENKER ET AL. IBM J. RES. & DEV. VOL. 58 NO. 5/6 PAPER 11 SEPTEMBER/NOVEMBER 2014

to the global manufacturing network, and the capacityplanning process, which utilizes these results for moredetailed capacity planning on the plant level. Mid-termplanning, or master planning, (performed weekly) Blooks[12 to 18 months ahead, still considering the entire network,but on a much more detailed level and with higher frequencythan the long-term plan. The master plan defines theproduction requirements on a weekly Bbucket[ level.Scheduling and sequencing (carried out weekly and daily)takes the results of the master plan as input and transforms itinto an executable schedule and sequence. This process takesthe daily and weekly plant constraints into consideration. Theexecution of the schedule and sequence is then finallycontrolled by the Manufacturing Execution System (MES).These processes are closely connected to each other, such

that the long-term and mid-term planning processes arecentralized, and the scheduling and sequencing processes areperformed locally in the plants. We now focus on thelong-term planning process, since this is the planning stepthat has now been improved with the new FACT system.On a long-term basis, the expected production volume is

allocated to the plants, considering the production constraintsand manufacturing capabilities of the plants. The plants thenuse this information to enable manufacturing for theexecution of these plans. They use the allocation plan toderive the detailed requirements on how to execute, usingdetailed capacity models, shift models, machine tools, specialsetups, carriers, transportation capacities, etc. The plantsfurther enhance the already very complex plans withlocalized real capacity information from the shop floor,which includes yield information, losses, maintenancedowntimes, etc. Before FACT, this process was heavilymanual and supported by complex spreadsheets, which oftenbecame inconsistent with each other since there where manydifferent touch points. As the manufacturing network hasgrown through acquisitions across the world, the underlyingmanufacturing technology slightly differs from plant to plant.This results in different capabilities, machine tools, andmaster data among the plants, sometimes even for the sametype of product.The long-term planning process generates decisions

including those involving which product to produce in whichplant, and what products to transport from plants to marketregions. Another outcome of this planning process is therequired extension of resources in a plant. This decisionmight require buying a new machine for a plant, or movingtools from one plant to another. These decisions are taken onthe long-term planning level. However, there are decisionsthat might be revised or overruled on other planning levels;if, for instance, long-term planning determines the volume ofeach product to be produced at each plant, but then due tovolatile monthly demands from customers, the masterplanning process might change the quantities to be produced.Until today, long-term planning has been done once a

year; however, the company has a goal to increase thefrequency in future to better adapt to faster product andmarket changes.The old long-term planning process could no longer meet

today’s challenges for several reasons. Distributed,collaborative planning was not well orchestrated betweencentral functions and the plants. Files had to be exchanged,compared, and merged manually. Furthermore, the planningwas essentially done manually, without the support ofautomated mathematical planning functions. The creation ofmultiple business scenarios for different allocation situationswas virtually not possible, as this took too much time.Finally, underlying data often did not have the required levelof granularity and quality to support high quality decisions.

Related workGenerally speaking, the performance of the supply chaindetermines, to a large degree, the competitiveness of anorganization and therefore its shareholder value. Profit, aswell as capital requirements, is improved by optimizedsupply-chain planning decisions. Revenue is increased bybetter satisfaction of the demand, and cost can be reduced byefficient sourcing, production and distribution processes.Inventory optimization reduces working capital requirementsand fixed capital is used in a more efficient way by improvedsupply chain efficiency. Conflicting goals have to be takeninto account when driving towards the optimal businessvalue. Larger batch sizes can reduce production cost andincrease throughput, but they can also cause increasedinventory and lead times. Producing closer to the customercan increase the capital cost but can also reduce thetransportation cost and lead time. Focusing on the supplychain cost may impact the lead time and productionflexibility, hence impacting customer satisfaction andrevenue. Typically, supply chain planning decisions havebeen based on experience, and spreadsheets are often used toanalyze the impacts of decisions and to compare differentscenarios. However, there often is no way to tell if the overallresult is rigorously optimal in a global sense. In particular, ifdecisions have been made by different groups within theorganization with the goal to optimize their individualperformance measures, the overall result is typically far awayfrom the (company’s) global optimum. Also, the number ofdecisions to be made often exceeds what can be handledin a traditional manual process. The example given by Lapide[6] amounts to 10 million planning elements that need tobe considered, including all the tradeoffs between thedifferent decisions and their impact to the business goals;this example is even on the lower end of the typical rangesof problem sizes we are seeing today.Mathematical optimization is a way to address these

challenges, and it has been noted that Boptimization [grew]out of a discipline called Operations Research[ [7].Sashihara provides a definition: Boptimization is a


decision-making process and a set of related tools thatemploy mathematics, algorithms, and computer software . . .to use that data to make recommendations faster and betterthan humans can[ [7]. He points out that Bwhen effectivelyapplied, optimization can become a competitive gamechanger, as it has for many companies[ [7]. The use ofsupply chain modeling and optimization enables makingdata-driven decisions to determine a plan that optimizes theglobal goals of a company vs. optimizing the oftenconflicting objectives of different organizations.Simchi-Levi et al. [8] point out the challenges to globalsupply chain optimization and present many examples ofactual projects. The annual competition for the FranzEdelman Award for Achievement in Operations Researchand the Management Sciences [9] demonstrates the importantimprovements organizations and society can obtain from theuse of optimization in successful projects. However, thereis still room for a more wide-spread adoption of planning andoptimization processes, as the Supply Chain Digest notes [3],BDespite the fact that the majority of companies say theydo engage in formal sales and operations planning, the realityis that most are still executing those processes at a relativelyimmature level compared with the leaders and what ispossible with a technology-enabled, integrated, anddisciplined approach.[Sashihara [7] gives a good overview of the Bearly

adopters[ of optimization, starting in the 1950s. Animportant driver of the more wide-spread use of optimizationhas been the improvements in the underlying technology forsolving Mixed Integer Programs (MIPs) [4, 10, 11] andConstraint Programs, which are often used in supply chainoptimization applications. According to evaluations done byAchterberg and Wunderling [5], the performance of theoptimization solver engine IBM CPLEX* (see, e.g.,Bixby et al. [11]), increased between 1998 and 2012 suchthat many large MIP models can now be solved about200 times faster than in the past. If we assume an additionalhardware speed-up of 60 during that time, then manyproblems that required a 24 hour run time in 1998 can nowbe solved in 7 seconds, and many problems that could not besolved in a reasonable amount of time are now solvablewith a run time that supports the creation, optimization,evaluation, and comparison of multiple scenarios.As long ago as 1998, Lapide [12] pointed out that while

the market for Advanced Planning Systems (APS) Bhasincreased dramatically over the last decade, only within thelast two to three years has optimization been widelyincorporated into APS systems.[ The improvements in solverspeed increased the successful use of optimization in the yearsfollowing and considerably increased the scope of planningapplications that can benefit from optimization today.There are different ways to use optimization in supply

chain planning. Many standard software packages, such asSAP, Oracle**, and i2 Technologies (see, e.g., Stadtler and

Kilger [13]), use mixed integer programming software likeIBM CPLEX in certain planning modules. The use ofoptimization does improve the quality of the solutionconsiderably, and these packages have increased the use ofoptimization in supply chain planning. A package may,however, not represent the supply chain or productionprocess accurately enough for every company, and often, thepackage can require compromises in the models. Another,less obvious drawback is that the number of distinct featuresthat each package needs to support in order to cover acommercially attractive application space createsconsiderable complexity in the model, which negativelyimpacts run time and memory consumption. Configurablesoftware platforms represent an alternative to standardsoftware packages that can model more precisely the part ofthe supply chain in focus. This approach provides the highestflexibility and performance. The availability of modelinglanguages, including OPL (Optimization ProgrammingLanguage), GAMS (General Algebraic Modelling System) orothers, support efficient model development and make itpossible to adapt the models to fit exactly to the businesssituation at hand, reducing the restrictions given by standardsoftware with hard-coded models. Additionally, softwareplatforms such as the IBM Decision Optimization Center (seenext section) comprise configurable and customizablecomponents such as a planning cockpit to support distributedplanning and scenario comparison.In defining a planning and optimization solution, the

designer must consider carefully the trade-off betweenmodeling details and runtime. The solver performanceimprovements have simplified this task and to a certain extentreduced the modeling effort. Typical modeling considerationsare the length of the time buckets and time horizon,product definitions at SKU (stock-keeping unit) oraggregated level, the level of detail for the supply, productionand transport network, and the associated constraints, andthe costs, which may, for example, include sourcing,production, transportation, and inventory costs.Most applications use some form of hierarchical planning,

sometimes to reduce model complexity, but in many cases toprovide a better fit to the existing planning process andresponsibilities of the planners involved. Bitran et al. [14]show a typical two-step hierarchical production planningapproach favoring Ban aggregate allocation approach atthe higher level of the hierarchical system.[ In manyapplications, the initial strategic planning uses a longer timehorizon and larger time buckets. Another example(Stadtler [15]) uses Baggregation and planning intervalsranging from Faggregated long-term_ to Fdetailed short-term_planning.[ The FACT application also uses different timeBbuckets[ in the two planning steps, allocation and capacityplanning. Stadtler [15] also points out that aggregation andBdisaggregation [of] product types or product families[usually takes place between mid-term and short-term


planning. In contrast, the FACT approach does not aggregateproducts but uses all (about 10,000) products individually inthe planning. However production resources are aggregatedin the production allocation step described below. Inaddition, FACT takes into account production complexityand its impact on production capacity, in a way highlytailored to tire production.Packaged solutions (like the ones mentioned above) did

not meet Continental’s requirements because of the veryspecific allocation and capacity planning process thatContinental developed over many years. It covers complexmanufacturing constraints, like mold setups, green(or semi-finished) tires, or very specific production stepsequences. Continental therefore developed a spreadsheetdriven planning process that required multiple loops ofmanual planning, and which is now replaced by FACT.Figure 1 shows some example planning and optimization

applications developed by IBM, its partners and customers,spanning a wide range of applications from strategic planningdown to production scheduling. In particular, discretemanufacturing, including the automotive industry, has uniquechallenges [3] including complex, multi-level bills ofmaterial, multiple product configuration options, complexproduct lifecycle planning and management environmentsand multi-tier and/or multiple sales channels. Theseapplications address the top business pressures as seen by thesupply chain officers in the discrete industry: BGrowingcomplexity of global operations and rising supply chaincosts[ [16]. The ability to model each industry-specific

aspect of these challenges is a strong point of a tailoredplatform solution. The use of a common modeling platformpermits the execution of multiple planning applications onthe same software and hardware platform. Figure 1 alsooutlines the FACT application scope (shaded area at the topof the figure). This covers more than other productionplanning applications. However, it also coverslessVcompared to sales and operations planning, the FACTapplication focusses on the production and supply side. Thedemand is given as a fixed input and not part of theinteractive planning; financial impacts on revenue and profitbased on different demand scenarios are therefore notevaluated.

Solution details

Core approach and componentsThe following gives a very brief introduction to the basicconcepts. For more details and formal definitions on modelsand solution algorithms, please see, e.g., Chen et al. [17] orBixby et al. [11].As outlined above, optimization uses mathematical

algorithms to solve very difficult decision problems in veryshort time, such that pre-defined goals are maximized orminimized. Technically, this approach uses two corecomponents: an optimization engine that implements themathematical algorithms, and an optimization model thatdescribes the business problem in a way such that the enginecan solve it. The optimization model consists of definitions

Figure 1

Example planning and optimization applications developed by the IBM team and its customers. The shaded area at the top of the figure outlines thescope of the FACT application.


for input data, decision variables or choices, constraints orrequirements, and objectives or goals; the model specifiesmathematically the relationships among all these elements.An optimization model is written (or configured) for eachindividual application, thus reflecting the application’sspecifics as far as possible. The engine uses highlyspecialized mathematical algorithms to solve these models,and it assigns values to all decision variables, such that allpre-defined constraints are met (like production capacitylimitations), and goals (or key performance indicators, KPIs)are optimized (e.g., production costs are minimized).An assignment of values to decision variables is called a

solution. One particular assignment of a value to a decisionvariable represents one particular decision. In the context ofproduction optimization, a decision variable could forexample represent the number of items that a specificmachine will produce within a particular time period, e.g., thefollowing Monday. If this particular decision variable wouldbe assigned the value 100 (in one solution), then in thissolution, the respective machine would produce 100 items,the following Monday. The optimization engine assigned thevalue 100, because it found this value to be (globally)optimal in the given situation (taking into account all givenconstraints, goals, and all other decisions to be made).By generating a solution, the optimization engine

automatically generates an optimal production plan, withrespect to the given model, in a mathematical sense: no bettersolution exists. (Technically, due to the nature of thealgorithms and typical problem sizes, the guarantee is slightlyweaker: the solution falls within a certain, known bound ofoptimality.)

Solution architectureFigure 2 shows the IBM Decision Optimization toolset thatis used in the FACT application. The CPLEX OptimizationStudio provides tools for optimization specialists to writean optimization model (CPLEX is a product name).The model is typically written in the OptimizationProgramming Language (OPL). The model is then deployedfor solution on the CPLEX solver residing on an optimizationserver. A data server manages a Scenario Repositorycontaining the data related to the multiple Bwhat-if[ scenariosunder consideration by the planners. The CPLEX toolscan be integrated into large software systems throughvarious connectors and application programminginterfaces (APIs).The FACT application is built on the IBM Decision

Optimization Center platform. This solution platformprovides a set of software components on top of theunderlying CPLEX optimization technology: theOptimization Server, the Data Server, and a graphical userinterface (GUI) such as the Client. The Client is the front-endapplication for the end user of the application. It serves as aplanning cockpit that provides rich planning functionsincluding data import and export, data editing, analysis,visualization, optimization parameterization, optimizationrun and control, scenario based what-if simulation, etc. TheOptimization Server uses the optimization model and theengine and manages the optimization jobs. The Data Serverstores, using a standard database system, all the planning dataof the application in user-definable scenarios: input data,parameters, error reports, and optimization results. Hence, theFACT application has its own planning data repository. The

Figure 2

The IBM Decision Optimization tools. CPLEX refers to a product name. (IDE: Integrated Development Environment; OPL: OptimizationProgramming Language; API: application programming interface; GUI: graphical user interface.)


planning data is imported from and exported to existingsystems including Enterprise Resource Planning (ERP)systems, data warehouses, reporting systems, and legacy,file-based data sources. During the planning process, theusers import planning data (e.g., master data or forecasts)from the other systems, then analyze this data (checking forconsistency and completeness), prepare the optimization(through parameter settings or input data adaption), and thenrun the optimization. After the computation, the usersanalyze the results. The FACT application provides bothgraphical and tabular data views. These are based on standardviews that the Decision Optimization Center provides and aretailored and extended to fit the needs of the FACT plannerusers. The system also supports team collaborationVmultipleusers, potentially distributed over the worldwide productionnetwork, can access common data workspaces and planningscenarios, reviewing and adjusting each other’s work. TheDecision Optimization Center takes care of personalized dataaccess and consistency.The IBM Decision Optimization Center itself is a generic

solution platform. In an implementation project, the FACTapplication was built on top of this platform throughconfiguration of the optimization model and the userinterface views and through customizations or extensionsthrough application-specific plug-ins. For further informationon the Decision Optimization Center platform and typicalsolution architectures, please refer to [18].

Solution workflowFigure 3 shows the basic workflow of the FACT solution.Initially, a high level allocation scenario is created, based on

expected market volumes, plant restrictions, transport costs,and product specifics (this data is partially re-used fromprevious plannings, and partially imported from external datasources). The allocation scenario is then optimized by theallocation planning. The human planners then sometimesfine-tune the resulting allocation plan, and generate multiplepossible scenarios, using the system’s interactive planningfeatures. The allocation plan is then split into per-plantscenarios, which are then distributed to the individual plants(portfolio per plant). In each plant, a capacity balancingoptimization model simulates the local production, reportsanticipated production bottlenecks, and provides mitigationoptions (such as which resource can be extended and by whatpercentage). The capacity optimization model implementsmuch more detailed constraints and goals than the networkallocation model. In order to create different scenarios, inputparameters can be adjusted for the optimization processes.The capacity balancing process is a Breality-check[ for theallocation plan; its results (validated capacities) are fed backinto the allocation process. In addition, the capacity checkserves as an input for different organizational units includingmanufacturing, supply chain management, global tiresourcing and others. Thus, many organization units atContinental Tires benefit from the FACT solution. Thesolution is currently used by 10 central business users andapproximately 80 users in production locations worldwide;these numbers are planned to increase.

Optimization modelAs stated above, FACT at Continental Tires involvesplanning at two levelsVallocation planning at the worldwide

Figure 3

Solution workflow. The green arrows indicate data flow inside the FACT application. The blue arrows indicate manual parameter settings. Withchanging parameters, the user can simulate different capacity/sourcing scenarios and evaluate the impact. (SCM: Supply Chain Management Division;GTS: Global Tire Sourcing Division.)


level, and capacity planning at the individual plant level. Inthe FACT solution both steps are implemented by tailoredplanning and optimization models. Each optimization modelcomputes plans at capacity level: it assigns productionnumbers to products and resources (such as the plants), andto a few planning periods (monthly or quarterly buckets). Itdoes not produce detailed production schedules, whichwould include, for instance, setup tasks and cleaning times.Computing a detailed schedule for such a big planningproblem would take extremely long, and it would not providesignificant additional value (at this planning level).Nevertheless, the optimization can deal with sophisticatedeffects like production complexity, as we will see.The Allocation Planning essentially assigns production

quantities to the worldwide production locations (about 20).This planning step considers only one planning period, oneyear as a whole. Since the whole worldwide production isplanned, all relevant products must be taken into account,about 10,000 in this application. A few resource types areconsidered, together with their capacity restrictions: plants(e.g., the maximum output of a specific plant for the planningperiod is 10 million tires), certain key machines (togetherwith their availability times during the planning period underconsideration), or product characteristics (e.g., in a certainplant, the maximum production of 19-inch tires is 1 million).Then, for each product and each corresponding resource, aBill-of-Capacity (BOC, [19]) links the product with theresource, and defines a resource usage factor, usually given inminutes of resource usage per unit of final product (the tire).Even production complexity is taken into account: producingmany different product types in one plant requires manysetups, cleaning, and other non-productive tasks in this plant,which reduces the overall production performance in theplant. In the optimization model, this effect is handled asfollows: For every plant, the number of different producttypes is computed. Then, this number is attached to astep-wise function that describes the plant performance (e.g.,100%, 90%, 80% for respective numbers of types). Thisperformance factor adjusts the usable capacity in the resourcecapacity constraints. Thus, production complexityimplications are handled at the capacity level without theneed to compute a detailed production schedule. Finally, theallocation planning takes into account the transportation ofthe products, mainly since this transportation imposessignificant costs that can be avoided through an appropriate,optimal assignment. The result of the allocation planning step(i.e., the worldwide production allocation to the productionlocations) is an important input to the next step: Capacityplanning.Capacity Planning (also called Capacity Balancing) solves

a planning problem that actually has many similarities toproduction allocation planning. One major difference,however, is that capacity planning deals with each plantindividually: one optimization model (or planning team,

respectively) covers only one single production plant,without considering the rest of the network. On the otherhand, capacity planning considers more than one planningperiod, e.g., 12 or 24 distinct months. Since it covers onlyone plant, and takes the input from the preceding planningstep, the capacity planning does not take into account allcompany’s products but only those that have been assignedto this plant in allocation planning. Capacity planningconsiders the detailed Bill-of-Materials (BOM) for eachproductVthe complete list of raw materials and intermediateproducts required in the production process in the plant athand (up to 100 for each final product). It also takes intoaccount all relevant production resources in theplantVmachines that produce intermediate products,machines that produce the final products, and even someworkforces. All resources have a given capacity calendar.Furthermore, the capacity planning model takes into accountthe machine-dependent-time (MDT) for all products and allrespective production resources. The MDT represents theresource consumption for the production of final products.The capacity planning step essentially helps to detectproduction bottlenecks and to find mitigation actions (such aswhich resources to extend). Thus, this step is sometimes alsocalled de-bottlenecking. In contrast to the first planningstepVproduction allocationVcapacity planning is done in adistributed fashion. Each plant does its own capacityplanning. In theory, they can do capacity planningindependently from each other. In practice, however, theymust communicate about possible assignment shifts.Therefore, they need a system that supports them doingcooperative, iterative planning.Technically speaking, both optimization models are mixed

integer programs (MIPs). They contain mainly continuousdecision variables (the number of tires, for example, isrounded after the optimization), and some integral ones forrepresenting piecewise linear functions or certain discretedecisions. The optimization runtime for most instances isbelow ten minutes per scenario.

Capacities, overload, and extensionA major function of the planning system involves theallocation of production to plants and other resources, alwaystaking into account the fact that these resources are limitedand their usage is expensive. Continental Tires often facesthe situation in which the available resources are notsufficient to produce all needed tires in time. Then, theplanners have to decide what not to produce, or whichresources to extend. Very often, the actual productionbottlenecks can be hard to determine: for a complex systemwith thousands of products, utilizing hundreds materials, tobe produced in a complex production chain, it is hard toactually determine why some demands cannot be fulfilled.Sometimes the insufficient availability of very inexpensiveresources might be the root cause for unfulfilled demands and


therefore big sales losses. Thus, it is essential to detect thereal production bottlenecks. In addition, planning incurstrade-offs. For example, in order to avoid transportationcosts, it might seem best to produce all products in the plantclosest to the buying client (or market). However, producingmany different products in one plant is also costly, since itrequires, for instance, keeping all production molds for allproducts in all plants, thus incurring unnecessary andunproductive tasks (like switching machine tools very often)that further limits the available production resources (orproductive time). Therefore, there is a trade-off between localproduction and centralized production, and that trade-offmight look different for different products and differentplants. In this complex reality, it may be very difficult todetermine how to optimally use the resources, where thebottlenecks actually are, what impact they have, and how tosolve production issues. Many other planning applicationsnot based on optimization have to evaluate these trade-offsmanually or with the help of some rule-based heuristics.Mathematical optimization both automates the allocation,and ensures that the planning results are optimal (or at leastclose to optimal).Mathematical optimization helps in multiple ways: it helps

determine what can be produced with the given resourcesand what cannot be produced (products that cannot beproduced, are called overload), it helps identify thebottlenecks, and it helps determine how to extend theresources in order to be able to produce more.In production planning, optimization automatically

respects limited capacities. It shifts production betweenlocations, lines, machines, workforces, and other resources asfar as possible, taking into account factors such as productioncompatibility constraints and also product prices andmarket margins. This is how it automatically determineswhere to produce the products and which products to produceor not to produce, respectively (because, for instance, thecustomer has a low priority, or the product margin is not highenough to buy new resources).Thus, the application allows for analysis of the production

plans: It displays which resources are used at their limits andwhich are not. It helps identify the bottlenecks, theresourcesVmachines, tools, workers, transportation vehicles,etc.Vthat are insufficient and whose limits prevent theproduction of more (or enough) final products.Finally, the FACT solution can automatically compute

resource extensions, as potential actions to solve capacitylimits (by, for example, assigning additional work shifts,buying a new machine, or sometimes even building a newplant). In the optimization model, possible extensions areassigned costsVif the optimizer decides to extend a resource,then it must account for some associated costs. Costs can bereal costs (e.g., C or $), if available, but they can also beestimated or artificial costs, just for use in the goal formulas.Thus, the optimization can determine which (and how)

resources to extend in order to meet certain production goals.It can even balance resource extensions versus possiblygained benefit through sales increase.

ResultsThe FACT software application, together with necessaryprocess changes, has been developed and rolled out globallyby Continental Tires over a period of approximately two years.The rollout started with the central allocation planningwhich is now mainly used by central functions, such asglobal tire sourcing and industrial engineering, which managethe global manufacturing network and tire sourcing process.Based on the allocation plan, the company determines whetherthe global manufacturing capacities match the expectedvolumes or whether and where additional tools andmachinery have to be added. The second, capacity planning,part of the application has also been successfully rolledout into industrial engineering and plant operationsfunctions in all plants worldwide. They use it to orchestratethe optimization of the capacities and constraints withinthe plants.In the implementation project, the following major

challenges had to be solved: user acceptance, data quality,and data size and model complexity. User acceptance of thenew approach and system was accomplished through earlyinvolvement of key business users, who defined therequirements for the new system and took, together with theIT division, the decision to take IBM as the solution provider.Data quality in the new system is assured through amulti-staging data acquisition process: initially the externaldata is loaded verbatim into the loader data stage of theapplication, then certain users check and clean the data in thereview data stage, and finally the data is extracted intoboth the allocation and the capacity planning data stage.Finally, the new system can handle the required data sizeand model complexity through the latest performanceimprovements in the software products and state-of theart optimization modelling tricks.Comparing the process before and after implementing the

new solution, the following benefits have been gained. Thenew FACT solution is based on a consistent data model,which enables the organization to further improve masterdata quality. It has been made much easier to enter andmaintain master data with the new tool, as well as to identifymissing or inconsistent master data. As both the entireallocation and the capacity planning processes are now basedon one single tool, the information flow from centralfunctions to plants and vice versa is much more consistentand a lot simpler due to the elimination of the spreadsheetsand the interfaces between the different business functions.Although the solution still requires data from other sourcesystems, the leading instances for master data have beenclearly defined and missing data from source systems caneasily be identified, highlighted and corrected. It has been


positively attested by the users that data quality has alreadybeen improved significantly during the implementation of thenew solution. The business processes have been harmonizedbetween the central headquarters and local plant functions,especially because now the plants work with a fairlystandardized process setup, even though local peculiaritiesstill need to be considered. The spreadsheets formerly usedhave been replaced by the new solution, so that now standardformulas and algorithms to calculate results or to simulatedifferent business scenarios are available, which finallyresults in better decision making and comparable andtraceable results. The new solution is perceived as a muchmore collaborative planning environment because it is notbased on isolated spreadsheets which had to be somehowmerged together.The new solution achieves a better planning granularity

while enabling creating more business scenarios. Humandecision making has been improved through more visibilityinto capacity bottlenecks, more consistent underlying masterdata, and the creation of what-if scenarios and evaluationcapabilities. Today, the planners focus on key tasks ratherthan number crunching and error debugging. Moredecision proposals can be created and evaluated inshorter time.Overall, the FACT solution improves the planning process

and, hence, the plans. This results in better decision making,therefore ultimately resulting in cost savings and bettercustomer service. FACT provides a good basis for furtherextensionsVmore detailed production and investmentplanning, extension of the planning horizon to three years,consideration of future product and manufacturingtechnologies.

Conclusion and future workThis paper described a case study that applied state-of-the artoptimization technology to a complex planning problem inthe automotive supplier industry. We have provided thebusiness background of the planning process, which wasmanual and heterogeneous before this project. This led to thedefinition and creation of the new solution, FACT. Thesolution assists the various worldwide planning teams in theirregular planning tasks, which we have described, togetherwith the workflow related to how the new solution is actuallybeing used. We have shown that the solution runssuccessfully and helps to manage a large worldwideproduction and supply chain. We have provided a descriptionof the mathematical optimization models that have beenconfigured to match the planner’s requirements. We haveshown how we solved the major challenges in theimplementation project (data size and model complexity, dataquality, user acceptance). Finally, we have presented resultsof the introduction of the new FACT solution, describingwhat benefit it brings to the planning process and to theinvolved company divisions.

As future work, Continental Tires is planning to extend thesolution to other planning tasksVnamely more strategiconesVand to apply recent research results in optimizationunder uncertainty.Many companies see disruptionsVcaused by their

suppliers or carriers, raw materials, and fuel price volatility,as well as environmental catastrophic events, as the mainpressures for supply chain disruptions ([20]). Modelingdifferent risk scenarios and choosing a plan less sensitive toadverse events is one way to deal with risk. Optimizationunder uncertainty will be used to determine plans that arerobust under the unavoidable variation in the conditions,such as demand or cost variation, non-reliable supply leadtime, or exchange rate changes. Here, the solution isdetermined such that the constraints hold for the entireparameter range, and the goal function may either be theexpected value, value at risk, or another stochastic function.

*Trademark, service mark, or registered trademark of InternationalBusiness Machines Corporation in the United States, other countries, orboth.

**Trademark, service mark, or registered trademark of OracleCorporation in the United States, other countries, or both.

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Received January 15, 2014; accepted for publicationFebruary 12, 2014

Hans Schlenker IBM Software Group, Industry Solutions,Hollerithstr. 1, 81829 Muenchen, Germany ([email protected]). Dr. Schlenker is an IBM Technical Sales Specialist for IBMILOG* Decision Optimization products and solutions. He joined ILOGin 2005, and since 2009, worked in different roles in services and salesfor IBM. He sold and delivered projects and solutions for manycompanies in various industries including automotive, industrialproduction, logistics, banking, insurance, and energy. Overall,he has more than 15 years of experience in algorithm development,IT architectures, optimization modeling, and solution design.Dr. Schlenker taught classes at several universities. He currently holdsan IBM research assignment for the Karlsruhe Institute of Technology,where he researches real-world optimization projects. Dr. Schlenkerholds a Master’s degree in computer science from Ludwig MaximiliansUniversitaet, Munich, and a Ph.D. degree in industrial engineering/computer science from Technische Universitaet, Berlin.

Ruediger Kluge Continental AG, Beiersdorfstr. 5, 30165Hannover, Germany ([email protected]). Dr. Kluge is leadingthe Global Supply Chain Planning Solutions Group at ContinentalTires. He studied Mechanical Engineering at Leibniz UniversityHannover and joined IBM Global Services Management Consulting in1996, focusing on supply chain management and operational efficiencyimprovements. In 2001, Dr. Kluge joined a major software vendor inSupply Chain Management, where he led multi-national supply chaintransformation and software solution programs from process definitionto execution. These programs enabled world-class supply chainmanagement and operational efficiencies for business leaders acrossdifferent industries. Dr. Kluge holds a Master’s degree and a Ph.D.degree in mechanical engineering/logistics from Leibniz University,Hannover.

Juergen Koehl IBM Deutschland Research and Development,Schoenaicherstrasse 220, 71032 Boeblingen, Germany ([email protected]). Dr. Koehl is a Distinguished Engineer in the IBM Research andDevelopment lab in Boeblingen, Germany. He studied mathematics inBonn and Paris and developed solutions in chip layout and timingoptimization. He joined IBM in 1989 and applied these optimizationssolutions to the first generations of IBM’s CMOS processors; thiscontribution was recognized by an IBM Corporate Award in 1999.From 2001 to 2003, he was on assignment to IBM Burlington,Vermont, to lead the worldwide ASIC (application-specific integratedcircuit) design turnaround time reduction for IBM’s ASIC Designcenters. In 2009 he joined IBM’s Center for Business Optimization anddeveloped solutions for container logistics, car sharing, pricing, andproduction optimization. From 2011 to 2013, Dr. Koehl led the ILOGoptimization and SCM sales organization in Europe. He holds over40 patents and is a member of the IBM Academy of Technology.


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