U8-dq

Supply chain design through QFD-based optimization

K.G. Durga Prasad Department of Mechanical Engineering, Wellfare Institute of Science,

Technology & Management, Visakhapatnam, India

K. Venkata Subbaiah Department of Mechanical Engineering, Andhra University,

Visakhapatnam, India, and

K. Narayana Rao Department of Mechanical Engineering,

Government Model Residential Polytechnic, Visakhapatnam, India

Abstract

Purpose – The purpose of this paper is to demonstrate a methodology to design a supply chain with a view to achieve a strategic fit between competitive and supply chain strategies. Design/methodology/approach – Quality function deployment (QFD)-based optimization methodology is employed to design a supply chain for a product through aligning the competitive and supply chain strategies. Normal boundary intersection (NBI) method is adopted to obtain optimal weights of the supply chain design objectives. Weighted additive model is developed for multi-objective optimization. Utility-based attribute function, which structure the relationship between the elements of competitive and supply chain strategies is established. The utility functions and the information contained in the House of Quality (HOQ) of QFD are used to define the supply chain performance (SCP). Findings – SCP index is computed using the set of supply chain design objectives obtained by solving the weighted additive model. On the basis of SCP index, the supply chain activities are planned accordingly. An illustrative example is presented in this paper to describe the QFD-based optimization methodology for designing a supply chain. Originality/value – QFD-based optimization is a novel approach to design a supply chain with a focus on aligning competitive and supply chain strategies.

Keywords Competitive strategy, Supply chain strategy, Quality function deployment, Supply chain performance index, Utility function, Weighted Additive Model

Paper type Research paper

1. Introduction Supply chain is a network of facilities that performs the functions of procurement of raw materials, transformation of raw materials into finished products and the distribution of the finished products to the customers. Supply chain management (SCM) is a set of synchronized decisions and activities utilized to efficiently integrate suppliers, manufacturers, warehouses, transporters, retailers and customers so that the right product or service is distributed at the right quantities, to the right time in order to minimize system-wide costs while satisfying customer service-level requirements (Zhang et al., 2011). SCM has emerged into practice during the 1990s and continues to be a focal point for making organizations more competitive in the global market. The primary objective of SCM is to fulfill customer needs through the efficient use of resources and facilities including transportation, distribution, inventory and work force. When the resources and capabilities of a firm are matching with the demands of the customers, a competitive strategy will be successful.

The current issue and full text archive of this journal is available at www.emeraldinsight.com/1741-038X.htm

Received 6 March 2012 Revised 18 December 2012 21 December 2012 22 February 2013 Accepted 6 March 2013

Journal of Manufacturing Technology Management Vol. 25 No. 5, 2014 pp. 712-733 r Emerald Group Publishing Limited 1741-038X DOI 10.1108/JMTM-03-2012-0030

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Therefore, supply chain should support the competitive strategy of a firm. Supply chain design includes decisions regarding transportation, inventory, operating facilities, information flow in the supply chain to maximize the overall value generated. The value generated by a supply chain is the difference between what the final product is worth to the customer and the effort the supply chain expends in filling the customer’s request. In the current competitive business environment, every firm needs to carry out the supply chain activities toward satisfying their customers. A supply chain design can be initiated only after the competitive strategy is decided. Supply chain design begins with the strategy that determine the nature of procurement of raw materials, transportation of materials to and from the company, manufacture of the product or operation to provide the service and distribution of the product to the customer along with any follow-up service (Chopra and Meindl, 2003). The supply chain has to be designed to support the strategic objectives of the firm. Therefore, it is essential for the firms to focus on aligning competitive and supply chain strategies.

Quality function deployment (QFD) is one of the quantitative tools and techniques of total quality management that could be used to translate customer requirements into appropriate technical or service requirements (Deros et al., 2009). The House of Quality (HOQ) chart is the principle tool of QFD methodology. The HOQ chart provides the priorities of the design requirements to reflect the customer needs (Durga Prasad et al., 2011). QFD has been adopted in various fields such as product development, process selection, service sector, concurrent engineering, etc. In the present days, the scope of QFD application is extended to the field of SCM. Holmen and Kristensen (1998) presented a case study to illustrate how QFD can be used in the pre-interactive stage of a single product development project, and how the identified correlations and non-correlations between the characteristics of the planned product can be used by a customer as a practical approach for discrimination between the suppliers. Different techniques and concepts have been applied to improve the supply chain performance (SCP). A few methods are available to explore supply chain inter-relationships, detect process key problems and co-ordinate planning processes in different supply chain partners. Li et al. (2001) proposed QFD approach to support web-based co-ordinate planning process which improves supply chain co-operation. Sohn and Choi (2001) adopted QFD approach to systematically relate customers’ needs with design variables in each supply chain of product development process. Kuei et al. (2002) developed a two-stage frame work that involves empirical assessment of strategic supply chain quality and technology variables, and then using QFD to deploy them to improve the competitiveness of the supply chain. They emphasized the importance of the deployment process in achieving supply chain excellence. Tang et al. (2005) focussed on the synthesis, evaluation and selection of part design scheme in a part deployment process with part-supplier involvement. They developed a zero-one integer programming model by combining the information of HOQ and evaluation results of the part design scheme and taking into account the design budget for selection of parts combinatorial scheme in supplier involved part deployment process. Bevilacqua et al. (2006) suggested a method that transfers the HOQ approach typical of QFD problems to the supplier selection process. Gunasekaran et al. (2006) proposed a fuzzy multi-criteria decision-making approach, which provides decision making with an optimal solution less imprecise in a QFD-based collaborative product design environment. The approach helps to find a set of optimal solution with respect to the performance of each supplier. Bottani and Rizzi (2006) addressed the issue of how to

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deploy the HOQ to effectively and efficiently improve logistics processes and it leads to customer satisfaction. Nukala and Gupta (2006) employed Fuzzy QFD (FQFD) and method of total preferences to evaluate the marketing strategy of a closed – loop supply chain with respect to the drivers of public participation in the network. Baramichai et al. (2007) introduced agile supply chain transformation matrix with the support of QFD. Ni et al. (2007) developed a supplier selection methodology based on extended QFD and data-mining (DM) techniques. Ahmed and Haque (2007) applied QFD to improve supply chain logistics planning. Zokaei and Hines (2007) proposed “Supply Chain Kano-QFD technique” which helps drive effectiveness by focussing on how the various supply chain members might jointly develop innovative solutions to create unique, individualized sources of consumer value. The conventional value chain improvement methodologies lack the rigor for improving the effectiveness of the supply chains. The proposed technique will allow the improvement team to make informed decisions taking into account supply chain effectiveness as well as supply chain efficiency. Zhang et al. (2008) accentuated that the application of QFD in logistics services planning would contribute to make the customer needs and market development more clear, and enhance enterprise competitiveness. Kasapoglu and Lorcu (2008) presented a case study on solving supplier selection problem in the pneumatic valve industry using QFD technique. Pochampally et al. (2009) addressed the metrics that help to evaluate the performance of a reverse/closed-loop supply chain and also a mathematical model is developed using QFD and Linear Physical Programming to measure the satisfaction level of the supply chain with respect to each of the metrics. Bhattacharya et al. (2010) addressed the relationship among the criteria for supplier selection decision making and devised an integrated hierarchical QFD methodology which combines both cardinal as well as ordinal preferences of the selection decision trading off among the criteria and sub-criteria. Tabrizi and Moghaddam (2010) presented an integrated frame work using QFD for the strategic and tactical supply management including supplier selection and network design. Buyukozkan and Cifci (2010) investigated the requirements of a sustainable supply chain management (SSCM) structure by the aid of HOQ which underlies the QFD technique. They proposed a methodology which is compatible with the requirements of the various stakeholders, suppliers, manufacturers and clients, involved in the supply chain. Bhattacharya et al. (2010) ranked the suppliers through a concurrent engineering approach integrating analytic hierarchy process (AHP) with QFD in combination with cost factor measure. Raut et al. (2010) adopted an integrated model for supplier selection by using QFD-fuzzy and fuzzy AHP techniques. Leina et al. (2010) applied QFD technique to transform the fourth party logistics requirements in to the supply chain process design requirements. Zarei et al. (2011) used QFD to identify viable lean enablers to be practically implemented with a view to increase the leanness of the food supply chain. Ho et al. (2011) developed an integrated analytical approach combining QFD and AHP for supplier selection and measuring the performance of the supplier. Vinodh et al. (2011) reported a case study in which QFD technique has been used for supplier selection in an Indian electronics switches manufacturing company. Kumar et al. (2011) proposed an integrated model of FQFD and Multiple Objective Linear Programming for supplier selection and order allocation in global context.

Most of the researchers focussed their attention toward the use of QFD technique with other decision support tools for supplier selection under multi-criteria decision-making environment. In this paper, QFD-based optimization approach is

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employed to reflect competitive strategy in making a trade-off between the supply chain design objectives. Normal boundary intersection (NBI) method is adopted to obtain optimal weights of the design objectives and then weighted additive model is developed for multi-objective optimization. SCP is computed using the optimal set of supply chain design objectives which is obtained by solving the weighted additive model. On the basis of SCP index, the supply chain activities are planned accordingly. An illustrative example is presented in this paper to describe the QFD-based optimization methodology. The rest of the paper is structured as follows. In the Section 2, the proposed methodology is presented. The application of the methodology is described with an illustrative example in Section 3. Finally conclusions are presented in Section 4.

2. QFD-based optimization methodology for supply chain design In order to maximize the prospective value generated by a supply chain, it is essential to understand the relationship between competitive and supply chain strategies (Durga Prasad et al., 2012). The proposed QFD-based optimization methodology for supply chain design is shown in Figure 1, provides a path for aligning the competitive strategy with supply chain strategy. Different authors employed different terminology

Yes No

Start

Establish Competitive strategy

Establish Supply chain Design Objectives (Supply chain strategy)

Obtain relative importance values of Competitive strategy

Determine the minimum and maximum values of Supply chain design objectives (SDOs)

Establish Inter-relationship matrix of HOQ using swing method

Establish a set of utility functions

Compute supply chain performance index (SCP) with the existing set of SDOs

Obtain the weight structure of SDOs using NBI method

Develop weighted additive model and solve

Compute SCP with the values of the SDOs obtained by solving

Weighted additive model

Does the SCP with the optimal set of SDOs or less than that with the

existing set of SDOs?

Establish supply chain planning decisions with the Optimum set of SDOs

End

Establish supply chain planning decisions corresponding to the set of SDOs that give

higher value of SCP Figure 1.

Flow chart for methodology of

QFD-based optimization

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for customer requirements and engineering characteristics in QFD. In this paper competitive strategy and supply chain strategy (supply chain design objectives) are used instead of customer requirements and engineering characteristics, respectively. Swing method ( Kim, 1997) is employed to obtain the relationship coefficients between the elements of competitive and supply chain strategies. This method assess the decision-makers’ options on the relationship coefficients accurately and systematically than the conventional relationship rating scale used in HOQ (Kovach and Cho, 2008). Locasio and Thurston (1994) have demonstrated the application of multi-attribute utility theory to HOQ for generating quantitative values that can be used in design. In utility analysis, the designer is asked to express preferences between attributes and among values of the attributes independently. This information is then used to determine the worth of an alternative as a combination of performance attributes. Utility theory allows the designer to emphasize preferences in certain attributes (Kirschman and Fadel, 1997). Utility analysis helps a decision maker in making real valued function that encodes the decision maker’s preference for all possible outcomes (Scott, 2004). Though there are different forms of utility functions namely linear, quadratic, logarithmic, power, exponential, but exponential form of utility function is widely considered (Yang et al., 2003). A set of single utility functions of exponential form are established in this paper for mapping from the levels of supply chain design objectives into the satisfaction level of the elements of competitive strategy. The satisfaction level of the each element of competitive strategy and the inter-relationship values in HOQ are used to define SCP index. Since the supply chain design is a multi-objective optimization problem, a weighted-additive model is developed to obtain the optimum values of the supply chain design objectives. The method of formation of tree structure of weights in NBI technique is applied to generate weight structure, which is used in establishing weighted-additive model. Finally, supply chain activities are planned corresponding to higher value of SCP index.

The step by step methodology of QFD-based optimization is discussed below:

(1) Step 1: establishment of competitive strategy. Every firm competing in an industry has a competitive strategy which may be established explicitly through a planning process or may be evolved implicitly through the various activities of the functional departments of the firm (Porter, 1980). Market research includes systematic gathering, recording and analyzing the information about the customers and markets helps to set the competitive strategy of the firm.

(2) Step 2: establishment of supply chain strategy (supply chain design objectives). After the competitive strategy is decided, the appropriate supply chain strategy is established by the managers of the firm.

(3) Step 3: obtaining the relative importance values of the competitive strategy. Assign a relative value of 100 to the most significant element of the competitive strategy and rate the others on a 0-100 scale depending on their priority.

(4) Step 4: determine the minimum and maximum values of the supply chain design objectives. The minimum and maximum values of the design objectives are obtained by optimizing the objectives individually. The outcome of the optimization helps to set the best and worst values of the objectives.

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(5) Step 5: prepare inter-relationship matrix of HOQ using swing method. The relationship coefficients between the elements of competitive and supply chain strategies are obtained by using swing method (Kim, 1997). To prepare inter-relationship matrix, the following procedure is adopted:

. First alter the levels of the elements of the supply chain strategy to improve the elements of competitive strategy. For each element of competitive strategy, let the decision maker choose the design objective that would improve the worst alternative if its level changes from the worst to the best.

. Next rate the supply chain design objectives. Adopt 0-100 scale to rate the supply chain design objectives in proportion to its relative influence to each element of the competitive strategy.

. Normalize the ratings of the design objectives so that they add up to one. The normalized ratings can be used as the inter-relationship values in the HOQ chart.

(6) Step 6: establish a set of utility functions. The utility function plays the role of mapping from the level of a design objective into the satisfaction level of the elements of competitive strategy. To establish utility functions, the following procedure is employed:

. Choose the exponential form based on the nature of the utility function. If the nature of utility function is decreasing, then the exponential utility function is:

UðxÞ ¼ a 1� ecxþb � �

ð1Þ

If the nature of utility function is increasing, then the exponential utility function is:

UðxÞ ¼ a 1� e�cxþb � �

ð2Þ

In the Equations (1) and (2), c is the risk aversion coefficient and a, b are constants. The risk aversion coefficient (c) indicates the degree of risk aversion. The reciprocal of risk aversion coefficient is known as risk tolerance (r) (Abbas and Matheson, 2005). When the risk aversion coefficient goes to zero (r), the decision maker values deal at their expected value and is said to be risk neutral. As the magnitude of the risk tolerance increases, the utility function becomes more linear. Generally, risk tolerance (r) is of the order of magnitude of the range from low to high. Specifically, if ro0.1 � (high value�low value), then the utility function displays highly risk averse behavior. If r410 � (high value�low value), then the utility function is essentially linear (Kirkwood, 1997). . Estimate the risk tolerance (r).

The risk tolerance is assessed by determining the best and worst values in the data and using the certainty equivalent (CE). A value of CE has to be chosen by the decision maker. The risk tolerance is estimated by using the following equation:

r ¼ RðHigh value� Low valueÞ ð3Þ In the Equation (3), the value of R is directly read from the tables (Kirkwood, 1997) corresponding to Z0.5 and the value of Z0.5 is calculated by using the

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following equations based on the nature of utility function:

If UðxÞ is decreasing function ; z0:5 ¼ High value � CE

High value� Low value ð4Þ

If UðxÞ is increasing function ; z0:5 ¼ CE� low value

High value� Low value ð5Þ

Obtain the value of R and find the value of r and then determine the value of c. . Set up utility functions.

The utility function is established by evaluating the constants a and b by substituting the value of c. Similarly set up all the utility functions.

(7) Step 7: compute the SCP index with each existing set of supply chain design objectives. On the basis of HOQ chart, the SCP can be expressed as:

SCP ¼ X

wi Sið Þ ð6Þ

where wi is the relative importance of the ith element of competitive strategy, Si the individual satisfaction level of the ith element of competitive strategy Si ¼

P rij Uij xj

� �� � , rij the relationship coefficient between the ith element of

competitive strategy and the jth supply chain design objective, Uij(x j) the utility function representing the relationship between ith element of competitive strategy and the jth supply chain design objective.

(8) Step 8: determine the optimum supply chain design objectives under multi- objective optimization by solving weighted additive model. The general formulation of the weighted additive model is given below:

Minimize Z ¼ Xn i¼1

bi Zi ð7Þ

where i is the number of objectives and bi the weight of the ith objective. Instead of assigning the weights intuitively in the weighted additive model for the objectives, the weight structure is generated using the procedure of developing weight structure in NBI technique (Das, 1997). Obtain optimum values of the supply chain design objectives by solving the weighted additive problem with different weights.

(9) Step 9: computation of the SCP index with the optimal supply chain design objectives. Compute SCP for the optimal set of supply chain design objectives. If SCP with the optimal set of objectives coincides or less than that with the existing sets of design objectives, proceed for establishing supply chain planning decisions according to the set of design objectives whose SCP is high. Otherwise, carry out the supply chain planning decisions in accordance with the optimal set of design objectives to ensure greater customer satisfaction and to deliver prospective value to the shareholders. The methodology is demonstrated with numerical illustration in the next section.

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3. Illustrative example In the present days refrigerators are becoming the common item for house hold use, vendor shops, hotels, motels, offices, laboratories, hospitals, chemists and druggists’ shops, etc. In India, refrigerators have the highest aspirational value of all consumer durables, with the exception of televisions. This accounts for the high growth rate of the refrigerator market. Domestic refrigerators are manufactured in different sizes to meet the needs of various groups of people. The supply chain network is structured to supply various parts of the domestic refrigerators of different models considered in this example shown in Table I for assembly and transport the refrigerators to the different customer zones.

To illustrate the methodology, multi-echelon supply chain network model shown in Figure 2 is considered. The supply chain network includes three suppliers, three manufacturers, three distribution centers (DCs) and four customer zones.

A supply chain can be designed for these products with a view to achieve the satisfaction of the concerned customer group.

3.1 Establishment of competitive and supply chain strategies It is the fact that customers are very much price sensitive and also pricing affects the behavior of the buyer of the good or service, thus affecting SCP. Therefore, price parity will be one of the elements of competitive strategy. Firms usually contemplating strategies to increase their responsiveness to customer needs by offering product variety with quick response, minimum lead-time and minimum inventory. The general elements of competitive strategy (Ayers and Odegaard, 2008) and the corresponding supply chain design objectives are considered and are shown in Table II.

Sl. no. Part Refrigerator model I Refrigerator model II Refrigerator model III

1 Hermetic compressor

1/3 hp 1/8 hp 1/6 hp

2 Wire -on-tube condenser

3/8 in. dia., 30 ft length 1/4 in. dia., 12 ft length 1/4 in. dia., 20 ft length

3 Roll-bond evaporator

550 mm � 2,000 mm and 1.2 mm plate thickness (aluminum)

300 mm � 1,000 mm and 1.2 mm plate thickness (aluminum)

400 mm � 1,500 mm and 1.6 mm plate thickness (aluminum)

4 Capillary tube Diameter 0.036 in. and 14.6 ft length

Diameter 0.030 in. and 10.6 ft length

Diameter 0.036 in. and 13.6 ft length

5 Overload protector with PTC relay

Open type Box type Box type

6 Refrigerator cabinet

PUF insulation make Glass wool insulation make

Glass wool insulation make

7 Automatic defrost mechanism

Electronic sensors type Manual type Manual type

8 Thermostat control

Electronic sensors type Mechanical type Mechanical type

9 Refrigerator compartments

Three large size compartments

Two medium size compartments

Two small size compartments

Table I. Different models of

domestic refrigerators with their parts features

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3.2 Relative importance values of the competitive strategy The priority ratings of the elements of competitive strategy affect the SCP. The SCP index will vary greatly with the variation in the priorities of the competitive strategies. The elements of the competitive strategy are rated by adopting 0-100 scale and the normalized ratings are as follows:

{PP, QR, MI, MLT}¼ {75, 100, 50, and 25}¼ {0.3, 0.4, 0.2, and 0.1}

Therefore, the relative importance values of the elements of competitive strategy are shown in Table III.

3.3 A mathematical model to obtain pay-off values of the design objectives A mathematical model is developed to obtain pay-off values of the design objectives. The following notations are adopted in the mathematical model.

Notations: s index for suppliers, sAS c index for customers, cAC d index for distribution centers, dAD p index for manufacturing plants, pAP i index for the product, i¼ 1,2,3 j index for part of the refrigerator, jA J

Supplier 1

Supplier 2

Supplier 3

Plant 2

Customer Zone 1

Distribution Center 1Plant 1

Plant 3

Distribution Center 2

Distribution Center 3

Customer Zone 2

Customer Zone 3

Customer Zone 4

Figure 2. Supply chain network model

Competitive strategy Supply chain strategy

Price parity (PP) Minimize supply chain network cost (Min. SCN cost) Quick response (QR) Minimize inventory cost (Min. INV cost) Min. inventory (MI) Maximize volume flexibility (Max. VF) Min. lead time (MLT)

Table II. Competitive and supply chain strategies of a firm

Competitive strategy PP QR MI MLT

Relative importance values (wi) 0.3 0.4 0.2 0.1

Table III. Relative importance values of the elements of competitive strategy

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tjsp unit transportation cost for the part j from supplier s to the plant p (INR/unit) ajsp quantity of part j shipped from the supplier s to the plant p (units/period) ljs unit cost of the part j at supplier s (INR/unit) ( fc) p fixed cost of establishing the plant p (INR/period) Aip unit cost of assembly of the product i at the plant p (INR/unit) Xip number of products i to be produced at the plant p (units/period) (fc)d fixed cost of establishing the distribution center d (INR/period) tipd unit transportation cost for the product i from the plant p to DC d (INR/unit) tji utilization rate of part j for the product i ad minimum throughput at the distribution center d (units/period) bd maximum throughput at the distribution center d (units/period) bipd quantity of the product i shipped from the plant p to DC d (units/period) tidc unit transportation cost for the product i from DC d to the customer zone c

(INR/unit) bidc quantity of the product i shipped from DC d to the customer zone c (units/period) Dic demand of the product i at customer zone c (units/period) Zip lower bound of capacity of the product i at plant p xip upper bound of capacity of the product i at plant p W p capacity of the plant p (units/period) dip equivalent units at plant p per unit of product i Wd distribution capacity at DC d (units/period) did equivalent units at DC d per unit of product i Wjs capacity of the supplier s for the part j (units/period) hid unit holding cost of product i at DC d (INR/period/unit) Qid ordering quantity of the product i at DC d sd standard deviation of lead time demand at DC d nd safety factor ordering quantity at DC d Sid order setup cost of product i at DC d (INR) Dic demand of the product i at customer zone c (units/period) pid penalty cost at DC d (INR/unit) (eso)d expected stock out at DC d

The objective functions and the constraints of the supply chain elements in the example are presented below.

Objective functions:

(1) Minimize supply chain network cost (Z1). Z1 transportation cost for part from supplier to plantþ transportation cost for product from plant to distribution centerþ transportation cost for product from distribution center to customer zoneþ cost of parts for supplierþ assembly cost for the products at plantþ fixed cost of establishing the plantþ fixed cost of establishing the distribution center:

Min: Z1 ¼ X jsp

tjsp � �

ajsp

" # þ

X ipd

tipd bipd

" # þ

X idc

tidcDic ydc

" # þ

X jsp

ðljsÞajsp

" #

þ X

ip

AipXip

" # þ

X p

fcð Þpqp

" # þ

X d

fcð Þdqd

" #

ð8Þ

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(2) Minimize inventory cost (Z2). Z2¼ holding costþ reorder costþ back order cost:

Min:Z2 ¼ X

d

qd hid Qid 2 þ ndsd

� � þ Sid

Qid

X c

ydc Dic þ pidsd esoð Þd

" # ð9Þ

(3) Maximize volume flexibility (Z3). Z3¼ plant volume flexibilityþ distribution center volume flexibility:

Max:Z3 ¼ X

p

qpWp � X

p

dip Xip

!" # þ

X p

qd bd � X

p

did Dic ydc

!" # ð10Þ

Constraints:

(1) Supply of parts. Part j supplied from supplier s to the plant ppcapacity of the supplier s for the part j : X

ajsppWjs ð11Þ

(2) Parts requirement for production:

tji xip � �

pajsp ð12Þ

(3) Plant production capacity. Total production should not exceed the plant capacity:X

dip xip � �

pWp qtð Þp ð13Þ

(4) Bounds on production capacities of plants. Production quantity should be within the bounds:

Zip qtð Þpp xip � �

pxip ð14Þ

(5) Bounds on throughput capacities of DCs. Throughput quantity should be within the bounds:

ad qthð Þdpdid Dicð Þydcpbd qthð Þd ð15Þ

(6) Assignment of customer zone. Each customer zone must be assigned to exactly one DC:X

ydc ¼ 1 8 c ð16Þ

(7) Availability of the product at the plant:

Xip ¼ X

bipd ð17Þ

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(8) Demand satisfaction of the customer zone:X bipd ¼

X Dic ð18Þ

(9) Demand requirement at distribution center:X bipd ¼

X ydcDic 8 i; d ð19Þ

(10) qp; qd; ydc ¼ 0 or 1 ð20Þ (11) ajsp; bipd; bidc;Xip are intergers ð21Þ

The pay-off values of the objectives are obtained by optimizing the objectives individually using LINGO 8.0 are presented in the Table IV.

3.4 Inter-relationship matrix of HOQ chart The decision maker has two alternatives. The worst possible alternative is at the worst level on each of the design objectives, and the best possible alternative is at the best level. The best and worst sets of values of {SCN cost, INV cost, VF} are given below.

best¼ {6,663,505, 13,361, 8,270} and worst¼ {7,034,375, 37,688, 1,670}. In order to satisfy the competitive strategy, the decision maker has to change the

levels of the design objectives from worst to best. For instance, to improve price parity the SCN cost and INV cost have to be improved.

Price parity: (7,034,375, 37,688, 1,670) x(6,663,505, 13,361, 1,670). Similarly to improve quick response, the volume flexibility has to be improved. Quick response: (7,034,375, 37,688, 1,670) x(7,034,375, 37,688, 8,270) and to improve minimum inventory, the inventory cost has to be improved. Minimum inventory: (7,034,375, 37,688, 1,670) x(7,034,375, 13,361, 1,670) and to improve minimum lead time the Inventory cost and volume flexibility have to be improved. Minimum lead time: (7,034,375, 37,688, 1,670) x(7,034,375, 13,361, 8,270). Adopt 0-100 scale to rate the supply chain design objectives in proportion to its

relative influence to each parameter of the competitive strategy. The inter-relationship matrix of HOQ is obtained by adopting the procedure

discussed in step 5 of the Section 2 and is shown in Table V.

3.5 Establishment of utility functions Utility functions can be used to represent the preferences of decision makers. The utility function plays the role of mapping from the level of a design objective into

Objectives Supply chain network cost

(SCN cost) INR Inventory cost (INV cost) INR

Volume flexibility (VF) units

Z1 6,663,505 18,226 2,670 Z2 7,034,375 13,361 1,670 Z3 7,033,640 37,688 8,270

Table IV. Pay-off values of the supply chain design

objectives

Price parity SCN cost-100 Quick response SCN cost-100 VF-75 Minimum inventory INV cost-100 Minimum lead time INV cost-100 and VF-75

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the satisfaction level of the parameters of the competitive strategy. The procedure discussed in the step 6 of Section 2 is employed to setup the exponential utility functions.

Utility function for SCN Cost in view of satisfying price parity U11(x) is established as follows:

. Minimum value of SCN cost: INR 6,663,505

. Maximum value of SCN cost: INR 7,034,375

. Preference of the decision maker is the increase of utility by decreasing the SCN cost; therefore choose exponential utility function as U11(x)¼ a(1�ebþ cx)

. When x¼ 6,663,505, U11(x)¼ 1

. When x¼ 7,034,375, U11(x)¼ 0 Assume certainty equivalent (CE)¼ 6,950,000:

z0:5 ¼ 7;034; 375� 6; 95;0000 7; 034; 375� 6; 66; 3505 ¼ 0:23

The value of R corresponding to z0.5¼ 0.23 is 0.36. The risk tolerance r¼ 0.36 (7,034,375�6,663,505)¼ 133,513.2. The risk aversion coefficient c ¼ 1=133; 513:2 ¼ 0:000007489 and the values of b and a are – 52.68 and 1.066, respectively. The resulting utility function is U11(x)¼ 1.066(1�e�52.68þ 0.000007489x). In the same way the remaining utility functions are established on the basis of the nature of decreasing or increasing. The utility functions are presented in the Table VI.

Supply chain strategy Competitive strategy Priority SCN cost INV cost VF

Price parity 0.3 1 0 0 Quick response 0.4 0.57 0 0.43 Minimum inventory 0.2 0 1 0 Minimum lead time 0.1 0 0.57 0.43

Table V. Inter-relationship matrix of HOQ chart

Supply chain design objective Preference of the decision maker Utility function

SCN cost in view of satisfying price parity

Increase of utility by decreasing the SCN cost U11(x)¼ 1.066(1�e�52.68þ 0.000007489x )

SCN cost in view of satisfying quick response

Increase of utility by decreasing the SCN cost U21(x)¼ 1.066(1�e�52.68þ 0.000007489x )

VF in view of satisfying quick response

Increase of utility by increasing VF U23(x)¼ 1.011(1�e�0.000689xþ 1.15)

INV cost in view of satisfying minimum inventory

Increase of utility by decreasing the INV cost U32(x)¼�0.037(1�e5.164�0.000137x )

INV cost in view of satisfying minimum lead time

Increase of utility by decreasing the INV cost U42(x)¼�0.037(1�e5.164�0.000137x )

VF in view of satisfying minimum lead time

Increase of utility by increasing VF U43(x)¼ 1.011(1�e�0.000689xþ 1.15)

Table VI. Utility functions

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The utility functions of SCN cost, INV cost and VF with a view to satisfy the competitive strategy are represented in Figure 3.

3.6 Computation of SCP index The SCP index can be computed as follows:

For the set of values of the supply chain design objectives presented in the first row of the Table IV, the individual satisfaction of each element of the competitive strategy is calculated and then SCP is determined using the procedure discussed in step 7 in the Section 2:

Si ¼ X

rij Uij xj � �� �

S1 ¼ r11� U11ð6; 663; 505Þ½ � ¼ 1 S2 ¼ r21� U21 6; 663; 505ð Þ½ � þ r23� U23 2; 670ð Þ½ � ¼ 0:787 S3 ¼ r32� U32 xð Þ½ � ¼ 0:496 S4 ¼ r42� U42 xð Þ½ � þ r43� U43 xð Þ½ � ¼ 0:5

SCPð Þ1 ¼ w1 S1ð Þ þ w2 S2ð Þ þ w3 S3ð Þ þ w4 S4ð Þ ¼ 0:3 1ð Þ þ 0:4 0:787ð Þ þ 0:2 0:496ð Þ þ 0:1 0:5ð Þ ¼ 0:764

Similarly, the values of SCP for the remaining rows of the Table IV are computed.

3.7 Weighted additive model with the supply chain design objectives For multi-objective optimization, weighted additive model is formulated as shown below:

Minimize Z ¼ b1Z1 þ b2Z2 þ b3Z3 Subject to constraints from (11) to (21)

The weights b1, b2 and b3 are obtained from NBI technique (Das, 1997). The model is solved by using LINGO 8.0 solver and values of design objectives obtained are shown in Table VII.

From the Table VII it is observed that the optimal set of supply chain design objectives includes supply chain network cost as INR 66,63,755; inventory cost as INR 21,914 and volume flexibility as 8,270 units. The SCP is computed with the optimal set of objectives to take decision on supply chain activities.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

13,361 23,361 33,361

U til

ity

INV cost

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

6,663,505 6,863,505

U til

ity

SCN Cost

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

1,670 6,670

U til

ity

V F

Figure 3. Representation of Utility

functions for SCN cost, INV cost and VF

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3.8 Computation of SCP index with the optimal set of supply chain design objectives SCP index with the optimal set of values is computed as follows:

S1 ¼ r11� U11ð6;663;755Þ½ � ¼ 0:999 S2 ¼ r21� U21 6;663;755ð Þ½ � þ r23� U23 8;270ð Þ½ � ¼ 0:99943 S3 ¼ 1� U32 21;914ð Þ½ � ¼ 0:284 S4 ¼ r42� U42 21;914ð Þ½ � þ r43� U43 8;270ð Þ½ � ¼ 0:592 SCPð Þ ¼ 0:3 0:999ð Þ þ 0:4 0:99943ð Þ þ 0:2 0:284ð Þ þ 0:1 0:592ð Þ ¼ 0:815

3.9 SCP indices The SCP indices for the existing set and optimal set of supply chain design objectives are presented in the Table VIII.

Weights Values of the objectives

Problem no. b1 b2 b3

Supply chain network cost (INR)

Inventory cost (INR)

Volume flexibility (units)

1 0 0 1 7,036,260 21,571.69 8,270 2 0 0.2 0.8 7,037,835 17,420.94 7,270 3 0 0.4 0.6 7,030,605 14,468.18 5,670 4 0 0.6 0.4 7,037,208 13,360.71 1,670 5 0 0.8 0.2 7,029,890 14,468.18 5,670 6 0 1 0 7,030,985 13,360.71 1,670 7 0.2 0 0.8 6,663,755 21,914.00 8,270 8 0.2 0.2 0.6 6,663,755 21,914.00 8,270 9 0.2 0.4 0.4 6,667,655 14,468.18 5,670

10 0.2 0.6 0.2 6,667,655 14,468.18 5,670 11 0.2 0.8 0 6,669,055 13,360.71 1,670 12 0.4 0 0.6 6,663,755 21,914.14 8,270 13 0.4 0.2 0.4 6,664,905 17,976.50 7,270 14 0.4 0.4 0.2 6,664,910 17,976.50 7,270 15 0.4 0.6 0 6,667,655 14,468.18 5,670 16 0.6 0 0.4 6,663,755 21,979.16 8,270 17 0.6 0.2 0.2 6,663,755 21,914.00 8,270 18 0.6 0.2 0.2 6,663,705 17,777.48 2,670 19 0.8 0 0.2 6,663,755 22,792.76 8,270 20 0.8 0.2 0 6,663,505 17,777.36 2,670 21 1 0 0 6,663,505 18,225.74 2,670

Table VII. Solution of the weighted additive model with different weights

Supply chain design objectives Supply chain performance index (SCP)Criteria

SCN cost (INR)

INV cost (INR)

VF (units)

Single objective optimization SCN cost 6,663,505 18,226 2,670 0.764 INV cost 7,034,375 13,361 1,670 0.257 VF 7,033,640 37,688 8,270 0.218

Multi-objective optimization 6,663,755 21,914 8,270 0.815

Table VIII. Supply chain performance indices

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From the Table VIII, it is observed that the SCP index for the optimal set of design objectives presented in the last row of the Table VIII is high compared to that with the other sets of design objectives.

3.10 Supply chain planning decisions The supply chain planning decisions for the optimal solution are discussed below:

(1) Parts procurement plan. Table IX shows the parts procurement plan which indicates the quantity of parts shipped from particular supplier to the specific plant.

(2) Production plan. Production plan is shown in Table X indicates that all the plants are involved in producing three models of refrigerators. The Table XI shows the demand fulfillment of the products.

Plants Part Specifications Suppliers 1 2 3

Hermetic compressor 1/3 hp 1 0 0 0 2 0 0 0 3 95 20 30

1/6 hp 1 0 0 0 2 0 0 0 3 200 100 195

1/8 hp 1 0 0 0 2 40 50 150 3 0 0 0

Wire-on-tube condenser 3/8 in. dia., 30 ft. length 1 0 0 0 2 95 20 30 3 0 0 0

1/4 in dia., 20 ft. length 1 0 0 0 2 200 100 195 3 0 0 0

1/4 in dia., 12 ft. length 1 0 0 0 2 0 0 0 3 40 50 150

Roll-Bond Evaporator 550 mm � 2,000 mm 1.8 mm thick plate (Aluminium)

1 0 0 0 2 95 20 30 3 0 0 0

400 mm � 1,500 mm 1.8 mm thick plate (aluminium)

1 200 100 195

2 0 0 0 3 0 0 0

300 mm � 1,000 mm 1.2 mm thick plate (aluminium)

1 0 0 0 2 0 0 0 3 40 50 150

Automatic defrost mechanism Electronic sensors type 1 0 0 0 2 95 20 30 3 0 0 0

Manual type 1 0 0 0 2 0 0 0 3 240 150 345

(continued)

Table IX. Parts procurement plan

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(3) Distribution plan. Distribution plan is shown in Table XII indicates the assignment of distribution centers to customer zones. Distribution center 1 is assigned to customer zone four. Distribution center 2 is assigned to the customer zones 1 and 3. The third distribution center is assigned to second customer zone. The distribution of finished products from plant to distribution centers is shown in Table XIII.

(4) Inventory plan. Inventory plan decides the inventory variables (order quantity, reorder point and customer service level) of products. The Table XIV shows the inventory variables of the products.

Plants Part Specifications Suppliers 1 2 3

Thermostat control Electronic sensors type 1 0 0 0 2 95 20 30 3 0 0 0

Mechanical type 1 0 150 0 2 240 0 345 3 0 0 0

Capillary tube 0.036 in. dia., 14.6 ft length 1 0 0 0 2 95 20 30 3 0 0 0

0.036 in. dia., 13.6 ft length 1 0 0 0 2 200 0 195 3 0 100 0

0.036 in dia., 10.6 ft. length 1 0 0 150 2 0 50 0 3 40 0 0

Overload protector with PTC relay

Open type 1 0 0 0 2 0 0 0 3 95 20 30

Box type 1 0 0 0 2 240 150 345 3 0 0 0

Leak proof refrigerator cabinet PUF insulation make 1 0 0 0 2 0 0 0 3 95 20 30

Glass wool insulation make 1 0 0 0 2 240 150 345 3 0 0 0

Multi-purpose compartment Compartments 3 Nos 1 0 0 0 2 0 0 0 3 95 20 30

Compartments 2 Nos (medium) 1 0 0 0 2 200 100 195 3 0 0 0

Compartments 2 Nos (small) 1 0 0 0 2 40 50 150 3 0 0 0Table IX.

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All the three suppliers are involved in supplying different parts of the refrigerators. The plants 1 and 3 meet the maximum demand satisfaction for the refrigerator model-I (65.5 percent) and refrigerator model-II (62.5 percent), respectively. The plant 1 is also fulfilling the maximum demand of the refrigerator model-III (40.4 percent). The pattern of distribution of finished refrigerators from the three plants to the distribution centers 1, 2 and 3 is shown in Table XIII. The average customer service level of finished products at distribution-center echelon is 0.789. The supply chain planning decisions help the management to achieve competitive advantage in the refrigerator market.

4. Conclusions In the present competitive market scenario, the success of any product depends on the effective integration of firms’ competitive strategy with the supply chain strategy. In order to achieve strategic fit, a firm has to understand the customer needs and that helps to set competitive strategy. The competitive strategy examines the way in which a firm can compete more effectively to reinforce its market position. Then the supply chain strategy has to be designed in aligning with the competitive strategy to meet the highest satisfaction of the customer. In this paper QFD-based optimization method is employed to design a supply chain with a view to achieve a strategic fit between competitive strategy and supply chain strategy. The QFD-based optimization helps to make trade-offs between multiple objectives from customers’ perspective.

Plant Product 1 2 3

Refrigerator model-I 95 20 30 Refrigerator model-II 40 50 150 Refrigerator model-III 200 100 195

Table X. Production plan

Demand fulfillment of the products at plants (%) Product

Plant Refrigerator model-I Refrigerator model-II Refrigerator model-III

1 65.5 16.7 40.4 2 13.8 20.8 20.2 3 20.7 62.5 39.4

Table XI. Demand fulfillment

of the products

Customer zones Distribution centers 1 2 3 4

1 0 0 0 1 2 1 0 1 0 3 0 1 0 0

Table XII. Assignment of

distribution centers to customer zones

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The methodology finds optimal supply chain design objective values, which would maximize SCP. It provides an approach to reflect customers’ preferences and designers’ thoughts in to the supply chain planning decisions. From the management perspective, the research paves the way for the managers to establish a supply chain with customer focus to achieve good competitive advantage. The performance of a supply chain is an essential element for effective planning and decision making, which can be viewed in terms of qualitative and quantitative measures such as customer satisfaction, flexibility, supply chain network cost, inventory cost, etc. In the present work, the SCP index is computed using QFD and utility-based optimization. The SCP index provides a concise means for the managers to take the decision on choosing the optimum set of supply chain design objectives to satisfy the competitive strategy. Once competitive strategy is defined, a firm must focus on preservation of its competitive advantages through delivering the best value to its customers. Conjoint analysis may be carried out as further research to estimate the customer preferences for setting up the competitive strategy.

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Corresponding author Dr K.G. Durga Prasad can be contacted at: [email protected]

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