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CIRP Annals - Manufacturing Technology 63 (2014) 397–400

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Multi-objective optimization of global manufacturing networks taking intoaccount multi-dimensional uncertainty

Gisela Lanza (2), Raphael Moser *

wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany

1. Motivation

Today, besides huge multinational companies also small andmedium-sized enterprises (SME) operate in globally distributedmanufacturing networks [1,2]. Individual steps of the manufacturingprocess are performed on globally distributed sites. In addition, theproportion of purchased parts is significantly increased by focusing oncore competencies [3]. Overall, globalization is leading manufacturingcompanies into an unpredictable business environment [4,5]. Thecomplexity in the environment makes it impossible to predict futuredevelopments from past ones [6,7]. However, manufacturing and itsglobal network must change and adapt to the new environment [8–11]. On the one hand, the design of global manufacturing networks isof increasing importance for the sustainable competitiveness ofcompanies in the global market and on the other hand, the adaptationprocess is a growing challenge for the management [12]. Currently,teams of experts advise on strategic decisions and mostly intuitivelymake quasi-rational decisions that by far do not include allcorrelations of the global manufacturing network and its environ-ment [13]. As the wave of back-sourcing of formerly outsourcedproduction capacities clearly shows, the original relocation decisionshave not been properly analyzed and evaluated. Thus, besides qualityproblems, the delivery time, growing personnel and transportexpenses, increased coordination efforts and the unavailability ofskilled employees are listed as the most common reasons for back-sourcing [14]. The exclusive consideration of costs based on attractivefactor advantages is unsuitable for a sustainable network planning.

2. State of the art

There are approaches that deal with the planning and desigchangeable production systems in order to prepare early for changethe business environment. For example, Refs. [18,19] focus onevaluation of the adaptability of a production system. However,

[20] aims more strongly at the economic evaluation of

changeability of a production system. Besides, there are reseaapproaches that explicitly focus on the design, evaluation

optimization of manufacturing networks. These approaches, sucRefs. [21,22], often describe holistic methods of designing manufacing networks, while evaluation approaches like Refs. [23,24] provnew methods for the planning of manufacturing networks, often

purely focusing on site evaluations. Optimization approaches sucRefs. [25–27] strongly focus on the product-site-allocation

capacity planning in the manufacturing network. Especially in

field, Refs. [15,28–30] provide concise summaries and analyses.In the field of optimization approaches, many approaches, suc

Refs. [27,31–33] do not pursue a multi-objective target hierarchy

exclusively optimize the network configuration based on cost-orienperspectives. Other works such as Refs. [25,34,35] neglect the objecconsideration of qualitative target dimensions despite the mobjective target hierarchy. However, especially the approach of Ref.

offers considerations for modeling global manufacturing networks

are the basis for this work. The chosen modeling of the manufactusteps and the use of alternative technologies are promising.

integration of a solution method for the identification of a preferen

A R T I C L E I N F O

Keywords:

Manufacturing network

Uncertainty

Optimization

A B S T R A C T

By designing global manufacturing networks still purely cost-based decisions are made, in which object

such as delivery time, quality, flexibility, closeness to customer and coordination effort are neglected.

design and decision-making process becomes more complex, the more influencing factors of the busi

environment are considered. Due to the dynamic and uncertainty of these factors a decision suppo

required which includes all relationships in the network and its environment. This paper presents a dyna

multi-objective optimization model for global manufacturing networks, which evaluates the impac

changes of influencing factors and optimizes the global design of the manufacturing network.

� 2014 C

Contents lists available at ScienceDirect

CIRP Annals - Manufacturing Technology

journal homepage: http: / /ees.elsevier.com/cirp/default .asp

ted,eto-erelti-

Rather, multiple objectives have to be integrated into the evaluation[15–17]. The management task becomes more complex as always themost efficient possible global manufacturing network has to berealized under the given conditions.

lowTheore

* Corresponding author.

http://dx.doi.org/10.1016/j.cirp.2014.03.116

0007-8506/� 2014 CIRP.

orientedoptimalconfigurationofamanufacturingnetworkis neglecthough. The approach proposed by Kohler [25] identifies all paroptimal solutions based on cost and delivery time. Furthermore, thare approaches like Ref. [17] that are based on methods of the muattribute-decision-making class (MADM), although they folmulti-objective target hierarchy including qualitative criteria.

disadvantage of such methods is that eventually better, m

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G. Lanza, R. Moser / CIRP Annals - Manufacturing Technology 63 (2014) 397–400398

mal solutions are not considered due to the pre-selection ofrete alternatives. Generally, none of the mentioned approaches

s the possibility to adequately take the business environment account. Especially the interdependencies between influencingrs in combination with multiple future considerations are notuately appreciated in optimization approaches, as for example

ef. [36]. For this purpose, Ref. [24] provides a first approach.ever, this method considers only isolated sites without

sport connections and cause-effect relationships within theork and is based on a cash-flow evaluation without furthertitative and qualitative target dimensions.

n summary it can be stated, there is no approach whichbines the derived requirements for the strategic planning ofal manufacturing networks as followed:

entify the optimal configuration of the manufacturing network evaluating quantitative and qualitative target criteria in aulti-objective optimization model.nsider the uncertainty in the business environment. Especiallynsider indirect influencing factors which cannot be measuredrectly.entify the point in time and demand for changes in globalanufacturing networks. In particular when continuouslyanges to discrete point in times are required.

pproach

he target of this paper is therefore to take the multi-ensional uncertainty in the business environment into account

to integrate the impact of drivers of change on theufacturing network in a multi-objective, dynamic evaluation.g a multi-objective optimization, the optimal configuration ofmanufacturing network is determined for the discrete timets in the considered planning horizon. Essentially, threeules emerged for the methodology for strategic planning ofal manufacturing networks and the determination of the needtiming for change. These are described as followed.

Optimization module

he optimization module is based on the model that has already presented in Ref. [37]. It was additionally expanded by a time

ponent. The model dynamically identifies the optimal networkgurations Conf at the discrete time t in the considered

ning horizon E, including the preceding and subsequent statee evaluation. First, a manufacturing network model focusingetwork objects and their attributes is created. As objects, theuct P with the required manufacturing process W and thenology T are illustrated. Additional objects are sites S, wherenologies are localized, and customers K who demand products. suppliers L are modeled, who provide the materials M orponents in the form of semi-finished parts w for theufacturing of a product. Suppliers, sites and customers are

ed by transport mode V. A configuration of the productionork is described by the following decision variables:

f ¼

Xts site

Ytst technology

xtpwst production volume

ttls pmv transport volume material supplier

tt transport volume site to site

8>>>>>>>>><>>> (1)

For each of the I objectives cost, delivery time, quality,flexibility, coordination, customer proximity and site qualification,a linear objective function in selected units, such as Euro for costsand days for delivery time, is derived and calculated in therespective unit. These are converted into one common unit in amono-objective replacement problem by means of a transforma-tion. As common unit the benefit u is applied. For this purpose, theupper and lower limits for the objectives are defined. These allowthe normalization of the target dimensions on the interval [0,1].The result is a vector-valued objective function with:

maxu ðConfÞ ¼

ucosts

udelivery time

uquality

uflexibility

ucoordination

ucustomer proximity

usite qualification

0BBBBBBBB@

1CCCCCCCCA

(2)

It is solved with the hybrid scalar method, a combination of thereference point to a distance method after Zeleny and the e-method [38] which delivers the following objective function:

minXI

i¼1

lij1 � uiðConfÞj2" #1=2

(3)

li allows to weight the objectives individually to the preferences ofthe deciders. The solution space of the multi-objective optimiza-tion is limited by constraints. For this reason, nine linear functionshave been formalized. So, the objective function has to beminimized under the following constraints:

� rawmaterial-, semi-finished- and finished product flows,� outsourcing decisions,� capacity restrictions,� minimum activity level for an active site,� matching of technology, product and site,� local-content-requirements,� optional strategic inputs,� non-negativity requirement and� aspiration level per objective.

As a result, the formal, mathematical optimization model for aglobal production network is developed as a mixed-integer problem.

3.2. Uncertainty module

In the uncertainty module, future scenarios for uncertain driversof change are created and the stochastic modeling of uncertain futuredevelopments is derived mathematically. This module requires acollection of drivers of change, which affect the focal company. Withthe help of the influence and relevance analysis, the key drivers aredetermined next. The key drivers are specified further according totheir impact level with regard to the evaluation model. As a result,different global, regional or procedural drivers of change aredetermined. At the local level, for example, labor costs at Germansites differ from those at Chinese sites. At the procedural level,differences in material prices for different production steps can bementioned. As part of a scenario workshop, possible futureprojections are created exclusively for the key drivers and thenlinked to consistent scenarios. This step is performed by a consistencycheck and subsequent clustering into distinct scenarios. Since it is aquantitative approach, future developments of each key driver n aremodeled stochastically and for each scenario a random number with

ss pwv

ttzs pwv transport volume component supplierttsk pv transport volume site customer

>>>>>>:Xt

s represents the binary decision variable for site S in period te Yt

st is for technology t at site S in period t. xtpwst represents the

uction volume for product p and manufacturing step w at site s

technology t in period t. It is necessary to depict not only theting objects in the status quo but also possible future alternatives.

way, the solution space is unfolded in its entirety. The objects ofolution space and their attributes are included into the calculatione objectives as part of the multi-objective optimization.

the corresponding distribution assumption for the expected trend mand the variance s is generated. A distinction is made betweenchange driver events CDE that occur at discrete times during theplanning horizon and continuously changing processes CDP.Changes of local content requirements for instance are discreteevents. The associated driver of change CDEn is modeled with themanifestation h of the event as follows and the time of occurrence tis randomly determined as it is equally distributed with:

CDRnðtÞ ¼ 0 0 � t < tn

hn tn � t � E

�(4)

merongSA.

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G. Lanza, R. Moser / CIRP Annals - Manufacturing Technology 63 (2014) 397–400 399

Daily changing exchange rates are, however, presented as driversof change CDP that change continuously. These ones start at thecurrent value m0 and evolve according to a general Wiener Processwith the expected drift m, the volatility s and the standard WienerProcess Wnt using the following formula:

CDPnðtÞ ¼ ðm � m0Þ � tEþ s � Wnt þ m0 (5)

In addition, there are drivers of change, such as the market entry ofa competitor, which are only indirectly measurable. Therefore,events and processes were linked, so that cause-effect relation-ships between influencing factors based on measurable changedrivers integrated CDI can be evaluated:

CDInðtÞ ¼ CDEnðtÞ þ CDPnðtÞ (6)

3.3. Control module

As part of the control module, random developments of the keydrivers for the planning horizon are simulated based on theuncertainty module. The stochastic simulation is used to simulaterandom developments of drivers of change over the consideredplanning horizon. According to the modeled and parameterizeddevelopment of the random number per drivers of change, a randommanifestation of the driver of change is generated at discrete pointsin time during the planning horizon. In case of application, thesediscrete times correspond to a two-year slice in a ten-year planninghorizon. For the identification of the need and timing of change, theoptimization model of the manufacturing network is solved underthe influence of the random courses of the drivers of change. In fact,the CPLEX solver by IBM is used in the present approach.

4. Case study

The developed method is tested using the example of small andmedium-sized enterprise (SME) from the railway industry. Amanufacturing network for the manufacturing of electronic couplersis analyzed. The pilot network has grown historically and has currentlythree manufacturing sites. The aim of the testing is to derive strategicrecommendations for the proactive design of the manufacturingnetwork. As a starting point, the actual structure of the manufacturingnetworkandthedynamicbusiness environmentneed tobedeterminedand the developments of the drivers of change predicted into the future.

4.1. Solution space

The manufacturing process is defined in a total of 13manufacturing steps (ms). The final stage describes the outgoinggoods and shipment of products to customers. Step 1-5 combinesthe manufacturing processed of single parts which are mainlydriven by investment costs due to utilized machines. Step 6–8comprises the commissioning of parts. Step 9 visualizes theassembly process which is purely labor (wage) driven. Step 10-12concludes the testing process with high investments for the testingfacility again. The technologies are currently located in China (C),Germany (G), Poland (P). Material suppliers are exclusivelyavailable in Europe, mainly in Germany. In addition to existingsites, possible sites are located in India (I), Russia (R), USA (U) andSouth Africa (S) and included in the assessment. For theimplementation of the manufacturing processes, 23 technologies

(26%), quality (12%), flexibility (2%), coordination (9%), custoproximity (16%) and site qualification (0%). As constraints, strlocal content requirements are fixed in the BRIC countries and U

4.2. Future scenario

In the context of an expert workshop with representatives fdevelopment, manufacturing, purchasing and logistics, the folling key drivers were identified: demand, local content requiremenumber of competitors, product innovation of competitors,

change rates, material price, energy price, oil price, political stabisupplier quality. In the scenario workshop, they were then predicand quantitatively parameterized for the future on the three efficlevels: global, regional and process. Particularly significant chanin the demand are expected for India and USA. In addition, lcontent requirements changes in China, India and USA are expecin the planning horizon, along with an increase in labor costs by 3until the end of planning in year 2022.

4.3. Results

The model identified the optimal structure for the manufactunetwork for the discrete points in years 2014, 2016, 2018, 2020

2022. Between the years 2014 and 2022 no changes in the structurthe manufacturing network is shown in the optimization resultfact, only values of production volume, transportation volume

procurement volumes varied over time. This change is necessaryto changing customer demand over planning horizon accordinthe generated and simulated scenarios. Fig. 1 visualizes the strucof Status Quo in year 2012 and the optimal configuration asaverage of 10 optimization runs for year 2014 and at the end ofplanning horizon in the year 2022.

In order to minimize the computing time, sites in Russia and SoAfrica were excluded of the solution space as they were neoccupied with manufacturing steps at the first optimization run.optimization studies point to a multi-stage development of

manufacturing network. In the first time step, the site in Polanclosed. The former cost advantage has clearly shrunk due to

increases in wages in Poland. The difference is 10 s per hour in 2but is not as beneficial as the reduction in delivery time of two ddue to the reduction of two additional transport processes (1 day e

Fig. 1. Structure of the manufacturing network to discrete timepoints inplanning horizon.

are available. These can be located in their entirety at a single siteor duplicated in different sites. Technologies are combinedmachines, work stations and employees. Each technology issuitable for at least one step of manufacturing process. Strategiclimitations of management regarding the protection of know-howare not made. Sufficiently large provisions of capacities for futuredemands are important. In this use case the transport by ship, truckand airfreight is relevant as possible transport modes. They differin speed and cost. The weighting of the objective criteria for theoptimization model results directly from the corporate strategy.The weighting is assumed as follows: costs (35%), delivery time

via tdaysalmomanimpproxsignchancustairfrof mthe

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G. Lanza, R. Moser / CIRP Annals - Manufacturing Technology 63 (2014) 397–400400

ruck). With a total delivery time on average in the network of 4.6, 2 days is a big improvement. At the US site, technologies forst complete manufacturing process are located. Onlyufacturing step 5 has not been localized. One reason is theact of local-content requirements of 37.5%. Moreover, theimity to the market is increased by the local productionificantly by 25%. The delivery times and shipping costs do notge. In fact, one transport between supplier market Germany to

omer market USA is essential. It can be carried out in one day byeight or 34 days by ship’s freight. This can be done before the startanufacturing, meaning at the time of purchasing material, as incase of configuration in 2014, or during the manufacturingess, as in case of Status Quo – year 2012. The situation is similar

the transport costs. Also, import duties do not differ for theus steps of manufacturing for the product under consideration. Ina MS 7 and MS 8 are located. Based on the comparison of the localr wage in China of CNY 50 per hour to the wage in Germany from

per hour, this change is clearly due to the cost. The quality level ofompared to 0.85 for Germany in the interval [0,1] deteriorates theity objective in average slightly. In India the location is opened in 2014 to generate the primary market access through customerimity which increases with 25%. Additionally the site meets the

l content requirements of 20%. In fact, simply manual tasks such asing of the components and cables that are assembled and finallyked at the site China, as well as the dispatch of the finisheducts for distribution to local customer are localized in India.

n the year 2022 site India gets developed as a full assembly andking site. MS 9, 10, 11 and 12 are located in India. On one hand,r costs in China with CNY 175 in comparison to India 2588 INRg so strong that assembly and checking in India compared toa is beneficial from an overall cost perspective view. In addition,ite needs to be extended due to the local-content requirement of

in India. Finally, the technologies for the MS 9, 10, 11 and 12 cantilized sufficiently to 65.8% due to increased demand in India, so

the existing economies of scales through the assembly in Chinapared to the start-up and fixed costs in India are no longerrsely for the decision. The progressive qualification of employ-

in India leads to a qualification level of 0.7, which does notent localization of manufacturing due to poor quality index. Inllel USA gets fully equipped with the missing MS 5.

onclusion

he presented approach was developed at the Institute ofuction Science (wbk) at KIT, Germany. The model is able tolate random developments of drivers of change. In particularions between drivers of change of the uncertain businessronment can be generated. Furthermore effects of the environ-t are evaluated. The optimization module identifies the optimalguration of the manufacturing networks in terms of suppliers,, technologies and logistics at each point in time during thening horizon. The approach was tested in industry and able to findptimal strategy until 2022 according to the dynamic businessronment. The overall approach was realized and implemented in a

are prototype within the research project ‘‘Planning andmization of global changeable production networks (POWer.net)’’.

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