Decision models in global supply chain management

Decision models in global supply chain management

Industrial Marketing Management 33 (2004) 21 – 27 Decision models in global supply chain management Ram Narasimhan*, Santosh Mahapatra Department of ...

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Industrial Marketing Management 33 (2004) 21 – 27

Decision models in global supply chain management Ram Narasimhan*, Santosh Mahapatra Department of Marketing and Supply Chain Management, Eli Broad College of Business, Michigan State University, N370 Business Complex, East Lansing, MI 48824, USA

Abstract Integrative decision making is key to effective supply chain management (SCM). This article examines five illustrative supply chain decision models that demonstrate the importance of integrating the decisions across the supply chain. The models that are discussed illustrate the diversity of analytical approaches and their usefulness in managing global supply chain issues. The paper identifies potential areas of additional research where analytical modeling can generate useful insights. The paper also presents a short categorization of decision models from literature. D 2003 Elsevier Inc. All rights reserved. Keywords: Decision models; Supply chain management; Analytical modeling

1. Introduction The key concept that distinguishes a supply chain from its constituent entities is the integration of operations across the chain. Supply chain management (SCM) goes beyond mere interface coordination across firms in which firms optimize firm-level objectives. SCM explicitly recognizes interdependencies and requires effective relationship management. The challenge in global SCM is the development of decision-making frameworks that accommodate diverse concerns of multiple entities across the supply chain. SCM has been the dominant research paradigm of the last decade. Considerable efforts have been expended in developing decision models for supply chain problems. These have been supported by the integration of these models into decision support systems. These models have adopted conventional techniques, including mathematical programming, simulation, heuristics, and statistical and probability tools. Developing models for complex SCM issues is a challenge. This has motivated researches to continue to develop improved models. Thus, the literature on decision models in SCM is vast, varied, and evolving. Yet, there has not been any systematic examination of the models in SCM research. This paper is an attempt to illustrate the usefulness of SCM * Corresponding author. Tel.: +1-517-353-6381; fax: +1-517-432-1112. E-mail address: [email protected] (R. Narasimhan). 0019-8501/$ – see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2003.08.006

decision models, illustrate their applications in global SCM, and identify areas of potential research.

2. Literature on decision models in SCM The literature dealing with decision models in SCM is extensive. It is difficult to create a typology of models in the limited space that we have available. In the interest of reserving space for highlighting a few interesting models, we organize a selection of papers in Table 1, according to the decision problem considered, i.e., strategic, tactical, and operational SCM decisions. The list is not meant to be exhaustive. Table 1 provides a short description of the decision context and the principal issues studied by the authors.

3. Illustrative decision models Five illustrative models are discussed in this section to demonstrate the usefulness of models in managing buyer –supplier behavior, sourcing, integrated operations, and marketing and logistics in global SCM. We focus our attention on these models because they address the upstream and downstream aspects of SCM and illustrate different modeling approaches. Specifically, the models relate to (a) investment implications of innovation-based competition between buyer and supplier, (b) bidding by a prospective supplier of a product, (c) bid evaluation and

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Table 1 Decision models in SCM Investigations in SCM Problem area Strategic decision making Capacity planning

Global supply chain Supply chain redesign Supply chain configuration Facility location

Supply chain restructuring Locating point of differentiation Service facility location

Tactical aspects of SCM Incentive compatibility in a decentralized supply chain Managing decentralized supply chain Contracts Contracts Buyer – supplier relationship Outsourcing Bidding Bid selection Supplier selection Supplier evaluation Collaborative Planning

Operational aspect of SCM Integrated operations

Integrated distribution

Responsive capacity planning and scheduling Procurement

Authors and year of investigation Issues addressed Robust capacity planning against demand uncertainty by minimizing an augmented objective function that penalizes the sensitivity for various types of uncertainty Modeling for global multistage and multiproduct manufacturing and distribution Quantifying the performance improvements in a PC supply chain due to supply chain redesign Supply base configuring towards quick and accurate response for fashion products Maximizing ROI while considering capacity constraints, multiple products, fixed transportation charge, and spatial interaction among facilities Optimizing plant location and scale of operation for different products Examining relative merits of alternative points of differentiation Solving for optimal location while incorporating both qualitative and quantitative objectives; simultaneously solves for demand allocation across different customer zones

Paraskevopoulos, Karakitsos, and Rustem (1991) Arntzen, Brown, Harrison, and Trafton (1995) Berry and Naim (1996) Fisher and Raman (1996) Revelle and Laporte (1996)

Camm et al. (1997) Garg and Tang (1997) Jayaraman (1999)

Proposing a performance measurement scheme that is effective in aligning the incentives across a supply chain Demonstrating incentive-compatible measurement scheme and communication of accurate customer demand information to the upstream members in a supply chain facilitates coordination Joint optimization of contract parameters and inventory control policies in uncertain demand environments Analysis of the efficacy of supply contract for a single product with demand uncertainty Interfirm incentives between collaborating partners can be effective in strategic management of innovation Exploring the relationship between vendor’s quality cost, the vendor’s input quality, and the imperfections of the manufacturing process Designing effective bids by an unselected bidder on the basis of historical information Selecting an optimal set of bids and proposing effective negotiation strategies for unselected bids in order to make them competitive Supplier selection for strategic and tactical outsourcing Evaluating suppliers while incorporating performance variability measures Assessment of the impact of collaborative forecasting and replenishment in a supply chain with random demand

Development of a comprehensive framework for linking decision and performance throughout the material – production – distribution supply chain using a series of linked, approximate submodels and heuristic optimization procedure Development of an integrated framework that considers the interactions among a firm’s distribution strategy, market share, and distribution costs while designing a profit maximizing distribution networks Allocating capacity, and scheduling shipments for an assortment of products produced by multiple vendors with varied capabilities under demand uncertainty Determination of the optimal purchasing quantities for a multiechelon inventory system

Chen (1999)

Henig, Gerchak, Ernst, and Pyke (1997) Bassok and Anupindi (1997) Nair and Narasimhan (2003) Tagaras and Lee (1996)

Talluri (2002) Narasimhan et al. (2003a, 2003b) Talluri and Narasimhan (2003) Raghunathan (1999) Moinzadeh (2002)

Cohen and Lee (1988)

Robinson and Satterfield (1998)

Agrawal, Smith, and Tsay (2002)

Clark and Scarf (1960)

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Table 1 (continued) Investigations in SCM Problem area Operational aspect of SCM Procurement

Replenishment policy

Competitive inventory policy in supply chain Replenishment policy in vendor managed inventory systems Inventory management Production, planning and scheduling Multistage production system Production planning

Production scheduling Logistics postponement Delivery reliability Lateral shipment Supply chain coordination Information sharing

Authors and year of investigation Issues addressed Assessment of inventory implications of quantity allocation when the raw material or component is dual sourced by the buyer and the supply process is uncertain Determinations of optimal policy parameters for a multiechelon inventory system with an option to replenish their inventory through either a normal or a more expensive emergency resupply channel Game theoretic investigation of the negative effect of competition in a two-stage serial supply chain with stationary stochastic demand Renewal-theoretic optimum replenishment quantity and dispatch frequency in case of Poisson demand in VMI systems

Anupindi and Akella (1993)

Moinzadeh and Aggarwal (1997)

Cachon and Zipkin (1999) Cetinkaya and Lee (2000)

Analysis of the (de)stabilizing effect of inventories in multiechelon manufacturing/distribution supply chains Analysis of the impact of uncertainties of production and demand for the finished product on the production planning, inventory control, quality improvement, and capacity planning Study of requirement planning in multistage production-inventory systems taking into account stability of production, trade-off between capacity and the inventory requirements Production planning for variable production capacity, random yields, and uncertain demand in a periodic review environment to minimize the total discounted expected costs of production, inventory holding, and shortage Evaluation of merit of integrated production schedules for reducing the negative effects of schedule revisions Evaluating the cost of various postponement strategies Analysis of the impact of buyer-specified delivery windows on the supplier’s delivery performance Effect of lateral transshipments and direct deliveries on inventory cost Analysis of efficacy of quantity discount as coordinating mechanism in purchasing and production Analysis of relation between supply-chain profits and information sharing

Bagahana and Cohen (1998) Tang (1990)

Graves, Kletter, and William (1998) Wang and Gerchak (1996)

Lee and Wei (2001) Zinn and Bowersox (1988) Grout (1998) Alfredsson and Verrijdt (1999) Munson and Rosenblatt (2001) Kulp (2002)

supplier selection by a buyer dealing in multiple products, (d) integrated operations in a supply chain, and (e) market integrated distribution. These models encompass various issues of importance in global SCM.

rate, cost structure, and price that impact the buying company’s innovation efforts. The objective functional of supplier S until the buyer B successfully innovates is given by:

3.1. Buyer –supplier behavior

J s ðus Þ ¼ Eusð:Þ

Nair and Narasimhan (2003) investigate the investment behavior of collaborating supply chain partners engaged in product development/innovation based competition. The model has strategic implications in the context of global supply chain when a buyer deals with a highly innovative, nondomestic supplier. The model considers the investment in product development and innovation in a collaborative setting of a supplier firm, with a near monopoly (e.g., Intel) and a buying firm (which invests in a substitute technology to become less dependent on its supplier) as a stochastic differential game. The principal objective in the problem is to evaluate (a) the incentive for each firm to invest in innovation effort, and (b) the strategic interdependence between the firms with respect to supplier’s production

Z

s

ert ½pðus ðtÞÞ  cs us ðtÞdt

ð1Þ

0

where, us(t) denotes the production rate at time t; p(.) denotes the price function of the buyer firm B; and cs denotes the unit cost for production of the supplier S. The objective functional of buyer B is the expected gain from innovation, given by: (Z s

JB ðuB Þ ¼ EuBð:Þ

ert ½rðus ðtÞÞ  cu ðuB ðtÞÞdt

0

) þ ert rðp1 ðcB ÞÞ=r

ð2Þ

where, r(us), is the net gain in demand for firm B’s product; cB denotes the unit production cost of the firm B for achieving

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successful innovation; cU denotes the cost of the R&D effort of firm B; and uBs(t) denotes the innovation rate by firm B. The model provides insights about the incentive for each firm to manage investments in innovation and highlights the significance of coordinated efforts among marketing, R&D, and operations for successful product development activities. 3.2. Sourcing Competitive bidding is one of the most common approaches in sourcing. With the emergence of e-commerce, it is relatively easy for suppliers anywhere in the world to respond to RFQs. Promotion of effective bidding and careful evaluation of bids are essential aspects of competitive bidding-based global sourcing. 3.2.1. Effective bidding by a supplier From the perspective of an international supplier, successful bidding is complex due to competition, growing importance of nonprice factors, and the difficulty in discerning the buyer’s preference structure across attributes. Narasimhan, Talluri, and Mahapatra (2003a) propose multiattribute bidding models for effective bidding strategies by a supplier. These indirectly capture the buyer’s relative preference for various attributes by analyzing the data on historical winning and losing bids, and suggest required levels for the attributes to be successful in the next period. We discuss here the case wherein there is a single winner. The model considers n periods with multiple bidders in each time period, but a single winner in each period. It maximizes the difference between the ‘‘values’’ of the winning and losing bids subject to the restrictions that each of these values cannot exceed a score of 1, which are normalization constraints that bound the problem without changing the fundamental nature of the problem. It then derives buyer’s preference weights for attributes, maximizing the difference between the winning and test bid. The relative values of bids are determined with respect to buyer’s preference weights. Thus, the model identifies what makes the winning bid different from the test bid, which helps modify the test bid to be more competitive in future time periods. The model is shown below: bi 

W v1 QW v1 QLi þ v2 DLi i þ v2 Di  W u1 PiL u1 Pi W v1 Q W i þ v2 Di s:t V1 W u1 P i v1 QLi þ v2 DLi V1 u1 PiL

max

k1 v1 Vv2 Vk2 v1 v1 ; v2 ; u1 z0



g

ð3Þ

W W where, QW i , Di , and Pi are the quality, delivery, and price for the winning bid in ith time period, respectively; QiL, DiL, and PiL are the quality, delivery, and price for the test bid in ith time period, respectively; v1, v2, and u1 are the weights attached to quality, delivery and price, respectively; k1 and k2 are scalars based on broad buyer-based preferences. This model is a nonlinear programming problem that can be linearized by normalizing, i.e., setting the least common multiplicator (LCM) of the denominators in objective function to 1, and rewriting the nonlinear constraints in a linear form. The model is solved n times in determining the optimal attribute preference weights that discriminate between the winning and the test bid in each period. The weights obtained in each time period may be exponentially smoothed in order to estimate the weights and utilized in designing the bid attributes by the bidder in the (n + 1)th period, by comparing those with the winning bid in the nth time period, potentially the most competitive bid in the (n + 1)th period. Alternatively, the bidder may design bid attributes by comparing those against the potentially most competitive (target) bid of (n + 1)th period. Expression (4) is utilized in obtaining the bid attributes of the bidder in question for the (n + 1)th period.

W v1 QTnþ1 þ v2 DTnþ1 v1 QW n þ v2 D n z T u1 PnW u1 Pnþ1

ð4Þ

where QnT + 1, DTn + 1, and PnT+ 1 are the quality, delivery, and price attributes of the test bid in the (n + 1)th time period, W W respectively; QW n , Dn , and Pn are the quality, delivery, and price attributes of the winning bid in the nth time period, respectively; v1 , v2 , and u1 are the smoothed quality, delivery, and price weights, respectively. In expression (4), each of three factors for the test bid are sequentially kept constant and the other two factors are varied in determining the necessary adjustments to make the test bid competitive in the (n + 1)th period. Such analysis provides a range of options for the bidder that are economically and technologically feasible. 3.2.2. Supplier selection Evaluating bids is complex when a firm has to evaluate multiproduct, multiattribute bids for procuring multiple products with varied competitive priorities. Narasimhan, Talluri, and Mahapatra (2003b) propose a model for evaluating competitive bids by a firm, which deals in a portfolio of products with varied competitive priorities and requires that suppliers meet specific supply arrangements. The modeling scenario is the following: a buyer solicits bids for multiple products from domestic and international suppliers with product-specific capacity constraints. Each supplier may bid for a single or multiple products in the same bid, and may submit multiple bids that differ across cost, quality, delivery, and other considerations. The buyer

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has product-specific preference for various attributes. In each period, the buyer incurs variable direct cost and indirect fixed cost of procurement. The buyer seeks to maximize cost, quality, and delivery performance with acceptable ‘‘transaction complexity’’ measured by the total number of bids selected. The model uses multi objective linear programming (MOLP) approach with min– max objective for obtaining Pareto-optimal solution. It identifies strategic and tactical supply arrangements.

3.2.2.4. Transaction complexity minimization (minimize the number of selected bids)

3.2.2.1. Notations used in the mathematical model. C is direct cost of procurement; Q is quality level (above the minimum requirement) of all procurements; D is delivery level (percent on-time delivery above the minimum requirement) of all procurements; X is quantity of product i procured by a bid r from a supplier j; Qirj is quality of product i from supplier j through bid r; QLi is threshold quality level for product i; Dirj is delivery standard of product i from supplier j through bid r; DLi is threshold delivery level for each product i; P is the price of a product; V is the min – max variable; ia[1,N] is the product identifier; rsa[1,Rs] is the bid identifier for bids having a single product; rma[1,Rm] is the bid identifier for bids having multiple products; ja[1,J] is the supplier identifier; birja[0,1], represents allocation of product i to supplier j; via bid r; bja[0,1], represents selection of supplier j; x1i, x2i, x3i, x4, and x5 are the buyer’s preference weights for direct cost, quality, delivery performance, number of bids (transaction complexity), and indirect (coordination) cost; Cirj =variable (direct) cost of sourcing product i from supplier j through bid r; and is given by:

Max Qi ¼

Cirj ¼ Pirj Xirj

bi; br; bj

and f(ACj) = indirect cost (due to ordering, coordinating, and managing the relationship with supplier) due to sourcing products from supplier ( j). 3.2.2.2. Optimization objectives. The model optimizes cost, quality, delivery, transaction complexity, and max – min objectives subject to threshold quality, delivery performance, demand, supply and capacity constraints, and minimum order size. 3.2.2.3. Cost minimization. Two cost-minimization objectives are used to minimize the direct and indirect costs of procurement, respectively.

Min b ¼

Rs X N X J X

birs j þ

i¼1 rs ¼1 j¼1

Rm X N X J X i¼1

rm

ð7Þ

birm j

j¼1

3.2.2.5. Quality and delivery performance maximization Rs X J X ðQirs j QLi Þbirs j rs ¼1 j¼1

þ

Rm X J X ðQirm j  QLi Þbirm j ;

bi

ð8Þ

rm ¼1 j¼1

Max Di ¼

Rm X J X ðDirs j  DLi Þbirs j r¼1 j¼1

þ

Rm X J X ðDirm j  DLi Þbirm j

bi

ð9Þ

rm ¼1 j¼1

The above optimal values are used as target (ideal) values for selecting bids and suppliers while obtaining Paretooptimal solution. 3.2.2.6. Minimizing the maximum difference from ideal level of performance across relevant attributes Min V Subject to Minimax constraints that limit the acceptable percentage deviation from the target (ideal) cost, quality, delivery performance, and transaction complexity levels according to the subjective preference structure of the buyer for the above attributes. For illustration, the Minimax constraints for cost and quality are presented here. x1i ½ðCi  Cimin Þ=Cimin  100 V V ; x2i ½ðQimax  Qi Þ=Qimax  100 V V ;

bi bi

ð10Þ ð11Þ

The model is implemented in two stages in each procurement period. 3.3. Integrated operations

Min Ci ¼

Rs X J X

Cirs j birs j þ

rs ¼1 j¼1

Min ðACÞ ¼

J X j¼1

f ðACj Þbj

Rm X J X

Cirm j birm j

bi

ð5Þ

rm ¼1 j¼1

ð6Þ

Integrated operations aims at synergy across the supply chain composed of domestic and international entities by linking local target performance with overall performance considerations. For example, a leading automotive manufacturer sourced (micro) electric motors from Hong Kong and electronic control panels from a supplier in Japan. The

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supplier from Hong Kong was chosen for its unique design capabilities and ability to supply globally high-quality motors at a low cost. The supplier of control panels in Japan was chosen for reliability and quality. 3.3.1. Strategic analysis of integrated production– distribution systems Cohen and Lee (1998) in a seminal work developed an integrated framework to analyze parallel multistage discrete batch manufacturing operations. The model predicts the performance of a firm with respect to inventory cost, fixed and variable cost of operation, customer service level, and flexibility of operations. The equations in the framework are large in number and a complete discussion of the model is not presented here due to space constraint. The analytic framework considers four linked submodels: (a) material control, (b) production control, (c) finished goods storage, and (d) distribution network control. Since optimizing the whole system is computationally infeasible, they adopt a decomposition approach in which each submodel is solved for optimal operating policy with respect to a set of control parameters. The control parameters set at one submodel serve as linkages to another submodel. The above submodels are implemented in a given sequence with the output of one acting as input to other subproblems for computing the feasible optimal operating policies while satisfying the service levels for individual submodels. 3.4. Market integrated distribution Robinson and Satterfield (1998) propose an integrated planning framework that considers the interactions among a firm’s distribution strategy, market share, and distribution costs while designing the profit maximizing distribution networks. It has three components: (a) a logit model for estimating market share for various demand-influencing parameters such as product price, distribution service, promotion, and lead time, b) a mixed-integer programming model for finding optimal distribution network designs for specific cost and demand parameter values, and (c) sensitivity analysis of the design. The structure of the framework is discussed below. 3.4.1. Assessing market share The market share for a particular distribution facility serving a product family in a market zone is estimated using the following logit model. ajk þ

Pijk ¼

e 1þe

S P

bsjk Xsjk

s¼1

ajk þ

S P

ð12Þ bsjk Xsjk

s¼1

where Pijk = estimated market share in market zone k for product family j when served from facility i; s represents the

performance level on a particular attribute (such as price) that is offered from facility i to customers of product family j in market zone k; bsjk is a coefficient describing the market zone k’s attractiveness to attributes when purchasing product family j from facility i; Xsjk is a binary variable describing the presence or absence of attributes when product j is supplied to market k from facility i; and ajk is the logit model intercept for purchases of product family j from market zone k at facility i. 3.4.2. Optimal network design The optimal network maximizes the total net profit of serving various market zones while satisfying facility, product, and market allocation constraints. Thus, the objective is: Maximize; Z ¼ 

m X

Fi Zi 

m X n X

i¼1

þ

Fij Yij

i¼1 j¼1

q m X n X X ðRjk Cijk ÞPijk djk Xijk

ð13Þ

i¼1 j¼1 k¼1

where n = number of product families; Yij = decision variable for assigning product family to j to facility I; Fij = annual fixed cost for assigning product family j to facility i; Cijk = unit variable cost of supplying market zone k with product family j from facility i that includes all costs for inbound material processing, packaging, and customer delivery; Xijk = decision variable for serving zone k with product family j from facility i; Rjk = unit revenue for product family j in zone; djk = total annual demand for product family j in zone k; Pijk = market share for serving zone k from facility i with product family j; Zi = decision variable for assigning a facility i and Fi = annual fixed cost for establishing facility i. ‘‘Sensitivity’’ and ‘‘what if’’ analyses assess robustness of the proposed design against error in market share estimation, and making allocations decisions on estimated profit.

4. Conclusion We have attempted to illustrate the usefulness of decision support models in the global supply chain context. The models presented are a ‘‘convenience sample’’ representing the work that the authors have recently completed and that of others. Due to space limitation, it is impossible to do justice to the topic. We were guided in the choice of models by a desire to indicate the usefulness of dynamic (e.g., dynamic game theory) and static (e.g., network optimization) as well as single-objective and multiple-objective models. Decision models in global SCM can be developed using well-established, analytical techniques. While operational

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areas in SCM have been investigated in the extant literature, strategic and tactical aspects of global SCM that focus on interorganizational interactions and arrangements have not been investigated adequately. A number of research issues pertinent to global supply chains can be identified: buyer – supplier relationship and information exchange, supply chain agility, and value partitioning and value positioning. In this context, analytical modeling in SCM can benefit from techniques from other disciplines such as auction theory, real-options, and game theory in analyzing the decisions. References Agrawal, N., Smith, S. S., & Tsay, A. A. (2002). Multi-vendor sourcing in a retail supply chain. Production and Operations Management, 11, 157 – 182. Alfredsson, P., & Verrijdt, J. (1999). Modeling emergency supply flexibility in a two-echelon inventory system. Management Science, 45, 1416 – 1431. Anupindi, R., & Akella, R. (1993). Diversification under supply uncertainty. Management Science, 39, 944 – 963. Arntzen, B. C., Brown, G. G., Harrison, T. P., & Trafton, L. (1995). Global supply chain management at Digital Equipment Corporation. Interfaces, 25, 69 – 93. Bagahana, M. P., & Cohen, M. A. (1998). The stabilizing effect of inventory in supply chains. Operations Research, 46, S72 – S83. Bassok, Y., & Anupindi, R. (1997). Analysis of supply contracts with total minimum commitment. IIE Transactions, 29, 373 – 381. Berry, D., & Naim, M. M. (1996). Quantifying the relative improvements of redesign strategies in a PC supply chain. International Journal of Production Economics, 46 – 47, 181 – 196. Cachon, G. P., & Zipkin, P. H. (1999). Competitive and cooperative inventory policies in a two-stage supply chain. Management Science, 45, 936 – 953. Camm, J. D., Chorman, T., Dill, F., Evans, J., Sweeney, D., & Wegryn, G. (1997). Blending OR/MS, judgment, and GIS: Restructuring P&G’s supply chain. Interfaces, 27, 128 – 142. Cetinkaya, S., & Lee, C. (2000). Stock replenishment and shipment scheduling for vendor-managed inventory systems. Management Science, 46, 217 – 232. Chen, F. (1999). Decentralized supply chains subject to information delays. Management Science, 45, 1076 – 1090. Clark, A., & Scarf, H. (1960). Optimal policies for a multi-echelon inventory problem. Management Science, 6, 475 – 490. Cohen, M. A., & Lee, H. L. (1998). Strategic analysis of integrated production and distribution system. Operations Research, 36, 216 – 228. Fisher, M., & Raman, A. (1996). Reducing the cost of demand uncertainty through accurate response to early sales. Operations Research, 44, 87 – 99. Garg, A., & Tang, C. S. (1997). On postponement strategies for product families with multiple points of differentiation. IIE Transactions, 29, 641 – 650. Graves, S. C., Kletter, D. B., & William, H. B. (1998). A dynamic model for requirements planning with application to supply chain optimization. Operations Research, 46, S35 – S49. Grout, J. R. (1998). Influencing a supplier using delivery windows: Its effect on the variance of flow time and on-time delivery. Decision Sciences, 29, 747 – 764.

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Henig, M., Gerchak, Y., Ernst, R., & Pyke, D. F. (1997). An inventory model embedded in designing a supply contract. Management Science, 43, 184 – 197. Jayaraman, V. (1999). A multi-objective logistics model for a capacitated service facility problem. International Journal for Physical Distribution and Logistics Management, 29, 66 – 81. Kulp, S. C. (2002). The effect of information precision and information reliability on manufacturer retailer relationships. The Accounting Review, 77, 653 – 677. Lee, K., & Wei, C. J. (2001). The value of production schedule integration in supply chains. Decision Sciences, 32, 601 – 633. Moinzadeh, K. (2002). A multi-echelon inventory system with information exchange. Management Science, 48, 414 – 426. Moinzadeh, K., & Aggarwal, P. K. (1997). An information based multiechelon inventory system with emergency orders. Operations Research, 45, 694 – 701. Munson, C. L., & Rosenblatt, M. J. (2001). Coordinating a three-level supply chain with quantity discounts. IIE Transactions, 35, 371 – 384. Nair, A., & Narasimhan, R. (2003). Product development and innovationbased competition between collaborating supply chain partners—a differential game based analytical investigation. 14th Annual North American Research Symposium on Purchasing and Supply Management, p. 329. Narasimhan, R., Talluri, S., & Mahapatra, S. (2003). A mathematical model for evaluating multi-factor bids for agile supply base configuration, Working paper. Narasimhan, R., Talluri, S., & Mahapatra, S. (2003). Effective multi-factor bidding strategies: A seller’s perspective, Working paper. Paraskevopoulos, D., Karakitsos, E., & Rustem, B. (1991). Robust capacity planning under uncertainty. Management Science, 37, 787 – 800. Raghunathan, S. (1999). Interorganizational collaborative forecasting and replenishment systems and supply chain implications. Decision Sciences, 30, 1053 – 1071. Revelle, C. S., & Laporte, G. (1996). The plant location problem: New models and research prospects. Operations Research, 44, 864 – 874. Robinson, E. P., & Satterfield, R. K. (1998). Designing distribution systems to support vendor strategies in supply chain management. Decision Sciences, 29, 685 – 706. Tagaras, G., & Lee, H. L. (1996). Economic models for vendor evaluation with quality cost analysis. Management Science, 42, 1531 – 1543. Talluri, S. (2002). A buyer – seller game model for selection and negotiation of purchasing bids. European Journal of Operational Research, 143, 171 – 180. Talluri, S., & Narasimhan, N. (2003). Vendor evaluation with performance variability: A max – min approach. European Journal of Operational Research, 146, 543 – 552. Tang, C. S. (1990). The impact of uncertainty in a production line. Management Science, 36, 1518 – 1531. Wang, Y., & Gerchak, Y. (1996). Periodic review production models with variable capacity, random yield, and uncertain demand. Management Science, 42, 130 – 137. Zinn, W., & Bowersox, D. J. (1988). Planning physical distribution with the principle of postponement. Journal of Business Logistics, 9, 117 – 136. Ram Narasimhan is a University Distinguished Professor in Operations Management in the Department of Marketing and Supply Chain Management at The Eli Broad Graduate School of Management, Michigan State University, USA. Santosh Mahapatra is a PhD candidate in Operations and Sourcing Management area in the Department of Marketing and Supply Chain Management at The Eli Broad Graduate School of Management, Michigan State University, USA.