Computers & Industrial Engineering 69 (2014) 64–70

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Computers & Industrial Engineering

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An innovative supply chain performance measurement systemincorporating Research and Development (R&D) and marketing policy

0360-8352/$ - see front matter � 2014 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.cie.2013.12.015

⇑ Corresponding author. Tel.: +852 2766 6605.E-mail address: f.chan@polyu.edu.hk (F.T.S. Chan).

Felix T.S. Chan a,⇑, Ashutosh Nayak b, Ratan Raj b, Alain Yee-Loong Chong a, Tiwari Manoj b

a Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kongb Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, India

a r t i c l e i n f o a b s t r a c t

Article history:Received 14 October 2011Received in revised form 31 May 2013Accepted 28 December 2013Available online 7 January 2014

Keywords:Supply chain managementR&DMarketing policyBass diffusion modelPerformance measurement system

Various performance measurement techniques have been developed and applied in their respectivefields, but the existing performance measurement methods have failed to provide significant assistancein the context of marketing strategies and innovation levels of a firm. In this paper, we have consideredan important aspect of marketing policy involving examining the decision of a firm to distribute productsand services to its consumer. The model developed in this paper is an extension to the Bass diffusionmodel which is generalized to incorporate the effects of marketing policy of the firm. In order to examineour model, computer simulation is conducted in order to measure the effect of innovation level and dis-tribution of products and services on the change in the sales of a firm from its previous products and sup-ply chain system. The performance measurement was developed by examining firm’s level of innovationachieved by their Research and Development (R&D) performances, and sales of the products and services.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The concept of supply chain management (SCM) represents themost advanced state in the evolutionary development of purchas-ing, procurement and other supply chain activities (Thomas &Griffin, 1996). The operational level of SCM integrates activitieslike seeking of raw materials and production, marketing and distri-bution of goods. The highest level of integration of these activitiesis achieved through SCM. With the evolution of time, the opera-tional activities have individually become ‘‘virtual business’’ enti-ties and their integration has become more significant makingSCM critical. A supply chain is a network of organizations involvedin different processes and activities producing value in the form ofproducts and services for the ultimate customer (Chen, 2008). SCMappears to treat all organizations within the value chain as aunified ‘virtual business’ entity. It includes activities such as plan-ning, product design and development, sourcing, manufacturing,fabrication, assembly, transportation, warehousing, distribution,and post delivery customer support. In a truly ‘integrated’ supplychain, the final consumers pull the inventory through the valuechain instead of the manufacturer pushing the items to the endusers. Businesses today are facing intense international competi-tions, demanding and sophisticated customers, and diverse trans-forming technological change to succeed in a global market. As aresult, the aim of firms is no longer to develop and produce high

quality products at the right-time, but to use scientific informationto explore the market. While much research attention has beenfocused on understanding how knowledge within firms contrib-utes to performance differences, little is known about the perfor-mance enhancement offered by supply chain knowledge (Min &Zhou, 2002). This is puzzling given the strong focus on the reasonswhy some firms outperform others (Nag, Hambrick, & Chen, 2007),coupled with the increasing importance of the supply chain. Someeven argue that rivalry is becoming more ‘‘supply chain versussupply chain’’ and less ‘‘firm versus firm’’ (Slone, 2004).

The performance of the firms can be improved significantly byunderstanding the information provided by the supply chain. Theprocurement and usage of this information is hindered by manydifferent barriers. These barriers include inability to capture cus-tomer’s feedback, improper maintenance of selective data, poormarket research leading to failure in assessment of market changesand unstandardized performance measurement within a firm. Inthis connection, firms require a performance measurement systemin order to proactively react to these barriers. This calls for theneed to devise a PMS which provides integrated, precise and help-ful information to boost efficiency and to periodically check theperformance of the supply chain in the firm (Ahmed & Abdalla,2002; Bhagwat & Sharma, 2007; Brookes & Backhouse, 1998).Existing research in PMS’s does not have the scope for quantifyingResearch and Development (R&D) in terms of innovation level andin optimizing the marketing policy of the firm, that is the decisionof the firm to sell products and services at their own stores orretailers in a supply chain. R&D, innovation and productivity

Notation

i, j subscripts representing the level of innovation (in time tand t � d respectively)

p coefficient of innovationq coefficient of imitationS cumulative sales of productN potential market of productSr set-up and ordering cost for the retailerDi demand of the product at innovation level iDj demand of the product at innovation level jhs holding cost for the firm/r current sum of set up and other costs for retailer/s current sum of set up and other costs for storehr holding cost for the retailerdr(Si) discount rate to the retailerPi performance factor at innovation level iPj performance factor at innovation level j

Z number of units sold cumulatively by the firm throughits own stores at the present level of innovation.

b deviation coefficient of order by the retailer to the firmprk product selling price at the own store at innovation

level kg(t, t0) units sold in the time period (t, t0)n change in the innovation levela coefficient of performance coefficient factor fairnessb coefficient of consumer feedbackl coefficient of innovation incentivek innovation incentive factorf(t) rate of change of installed base fractionF(t) installed base fractionSa adoption ratex cumulative sales at current innovation levelDx rate of change of cumulative sales

F.T.S. Chan et al. / Computers & Industrial Engineering 69 (2014) 64–70 65

growth are related to each other with true state dependency (Elena& Lourdes, 2011). With time, the technology is becoming complexand it demands R&D in firms to develop new products and bringinnovative (Gassmann, Enkel, & Chesbrough, 2010). R&D developsthe firm’s ability to identify, assimilate, and exploit knowledgefrom the environment while it also generates innovation (Cohen& Levinthal, 1989). An innovation activity may be defined as scien-tific, technological or financial steps which are intended to or leadto implementation of innovation. A firm’s internal R&D is an inno-vation activity complimentary to knowledge acquisition (Cassiman& Veugeleres, 2006). The role of R&D can facilitate an increase inthe innovation level and the role of marketing policy in the contextof SCM. R&D plays an important role in the development of newproducts or new supply chain methods in any firm. In any firm,R&D serves the strategic purposes such as the development ofintellectual property its control. In a way, R&D helps clients insolving their operational problems as clients buy their researchservices. Many successful firms have developed methods tocross-link R&D with marketing, engineering, purchasing and man-ufacturing. R&D is involved at all stages of product developmentstarting from the initial studies, determining the designing attri-butes, engineering the products, transfer to productions and main-tenance and services. These days, businesses are facing intenseinternational competition, demanding and sophisticated custom-ers, and diverse transforming technological change to succeed ina global market. The aim of the firm is no longer to develop andproduce high quality products at the right-time, but to use scien-tific information to explore the market. This can be achievedthrough firms with well equipped R&D. Therefore, they need torenew their products and services by allocating resources to R&D.Firms are constantly pressured to maintain a flow of new productsor new supply chain methods, and R&D is the strategic tool in thisregard.

According to Schumann, Ransley, and Prestwood (1995), R&Dhas four goals: (a) to take advantage of future opportunities inthe market (customers, competition, technology) and avoid orminimize the threats, (b) to meet the needs of the firm’s stakeholders, (c) to utilize the capabilities of the firm, and (d) to fulfilthe desires of the staffs of the firm. Current capabilities, whichinclude projects, resources, and the culture of the organization,should be assessed and evaluated against the future desirablestate. The difference between the current capabilities and thefuture desired state necessary to be known to fulfil the firm’svision, mission and goals, and may be closed through innovation

in R&D. R&D effort has long been viewed in both the popular andacademic literature as a key determinant and indicator of the tech-nological progressiveness of firms, industries, and even nations.American firms, for example, have been criticized for not devotinga greater share of their R &D to the improvement of manufacturingprocesses, for under-emphasizing incremental development ef-forts, and for focusing excessively on short term R&D projects. Jap-anese policy makers, in contrast, have in the past expressedconcern that Japanese manufacturing firms were not conductingenough basic research. These concerns all suggest that the compo-sition of R&D in many national industries may not be socially opti-mal. Before one can evaluate the optimally of any allocation ofR&D, no less than devise appropriate policies, it would be usefulto know what drives it (Cohen & Klepper, 1996). There is an urgentneed to develop and fit the PMS of the supply chain in context tosupport R&D and marketing policy of the firms in the measurementsystem of the supply chain.

Sales and marketing have been described as the key elements inthe value chain. With increasing changes in the modern businessenvironment, firms focus more on sales and marketing systems be-cause managing the outbound systems with correctly adoptedmarketing policy makes it easier to sustain market competition.Sales of products and services can be accomplished through firms’own stores, outside retailers or distribution centres. Outbound sys-tems and sales strategies should be perfectly coordinated to attaincompetitive advantage. Sales through a firm’s own stores assurebetter performance and control over the market share. It has beenwidely acknowledged that firms may increase their competitive-ness and financial performance by being market oriented, as wellas by creating positive bonds with customers (Nordin, 2008). It alsoensures easier access to consumer feedback and hence regulatesquality and variable demand, but is, however, is accompanied byinventory holding costs. Sales through retailers and distributioncentres kill the opportunity of product differentiation in the worldmarket. It also involves a risk of poor consumer satisfactiondepending on the services provided by the retailers. Sale of prod-ucts and services through retailers ensure that the product reachesa wider market through advertising and promotion by the retailers.

Thus a firm has a choice in deciding on its sales strategy andorganizational management with respect to the final distributionof its products to consumers. The firm and retailers share a con-tractual relationship, based on revenue sharing contracts (Taylor,2002). Thus, performance of a firm largely depends on its salesstrategy and performance measurement in terms of sales policy

66 F.T.S. Chan et al. / Computers & Industrial Engineering 69 (2014) 64–70

and is becoming incessantly necessary to enhance a firm’s hold inthe market. Performance measurement in this regard adds manydimensions to traditional performance measurement systems suchquality, time, cost and flexibility the firm should adopt to be intune with the current scenario of changing technology and thegrowing trend of innovation. A PMS developed n this study willalso help decision makers to consider the impact of critical param-eters on multiple performance measures (Longo & Mirabelli, 2008).In this study, a PMS is developed which incorporates R&D and mar-keting policy of a firm.

This paper is organized as follows: after the introduction andliterature review Section 3 describes the developed model. Compu-tational study based on the model is given in Section 4. Section 5explains the interpretation of the numerical model followed bythe result and conclusion in Sections 6 and 7 respectively.

2. Literature review

In today’s highly competitive business environment, productmanufacturers need to provide customized and innovative prod-ucts. To achieve this, they require innovative methods of perfor-mance measurement (Chen, 2008; Hvolby & Thorstenson, 2001).One of the main purposes of performance measurement (PM) isto deliver reliable information to support decision-making (Ukko,Tenhuhen, & Rantanen, 2007). According to the literature, perfor-mance measurement is the process of quantifying action, wheremeasurement is the process of quantification and action leadingto performance (Neely, Gregory, & Platts, 1995). Fitzgerald,Johnston, Brignall, Silvestro, and Voss (1991) suggested that thereare two basic types of performance measure in any organization– those that relate to results (competitiveness, financial perfor-mance), and those that focus on the determinants of the results(quality, flexibility, resource utilization and innovation).

A performance measurement system can be defined as a set ofmetrics used to quantify both the efficiency and effectiveness ofany actions (Neely, 1994). According to Beamon (1999), there arelittle literature available in PMS’s design and performance mea-sures selection for SCM, through various theories and models havebeen proposed and applied in place. Although researchers continueattempts to build new models and measures, they are harassed bytoo many defects with the present requirements of the SCM. It ismuch easier to write the definition of SCM than to actually imple-ment it (Lambert, Cooper, & Pagh, 1998). Only few studies (e.g.Beamon, 1998, 1999; Gunasekaran, Patel, & Tirtiroglu, 2001;Holmberg, 2000; Narasimhan & Jayaram, 1998; Van Hoek, 1998)have covered performance measures and the PMS of the supplychain. However, in most firms, the decision makers suffer fromdata overload. They need to update performance figures on pro-duction, quality, markets, customers, among others, through whichthey can proactively act on controlling several processes to achieveoverall performance targets (Nudurupati, Bititci, Kumar, & Chan,2011). The main reasons behind the absence of such performancemeasurement systems (PMS’s) that would facilitate these impactsare:

� Lack of Management Information System (MIS) infrastructure.This lack of MIS support results in cumbersome and timeconsuming data collection, sorting maintenance and reporting(Marchand & Raymond, 2008; Marr & Neely, 2002; Nudurupati& Bititci, 2005).� Lack of sensitivity and the dynamic nature. They are insensitive

to changes in the internal and external environment of the firm(Kueng, 2001; Marchand & Raymond, 2008; Nudurupati &Bititci, 2000).� Lack of emphasis on marketing policy and R&D.

Although R&D and sales have been linked together before, therehas been no attempt to quantify and use them to measure perfor-mance coefficients of a supply chain. Despite the voluminous liter-ature, theory yields ambiguous predictions about the effects ofproduct market concentration on R &D spending and innovativeperformance. In this paper, we relate market sales of any productwith the level of innovation involved in it.

3. The model

The model in this study assumes a single firm, a single product,a single store owned by the firm and a single outside retailer. Thissituation is a basic frame-work for sales of products and services inmany industries, and is consistent with the notion of maximizingthe profit to the firm while the firm is investing and promotingR&D in order to minimize the market deviations, and hold a strongposition in the market. In industrial sectors where mass customiza-tion is observed, this model is highly relevant. The model devel-oped is an extension to the Bass diffusion model (Ahmed &Abdalla, 2002; Bass, Gordon, Ferguson, & Githens, 2001) which isgeneralized to incorporate the effects of marketing policy of thefirm in terms of sales of products and services and the decisionof the firm on their own stores or retailers (or distributors).

It is assumed in this model that all the factors are deterministic,and the order placed for the products and services are based on thedemand for the product. The performance measurement is done bythe firm based on the level of the innovation achieved by R&D andsale of the products and services through its own stores. It is as-sumed that changes in the innovation level are deterministic, andthe coefficient of innovation and investment in R&D increases withan increase in the level of innovation.

The Bass model describes the process of a new product beingadopted as an interaction between users and potential users. It isformulated as:

f ðtÞ1� FðtÞ ¼ pþ qFðtÞ ð1Þ

Sa ¼ Nf ðtÞ ð2Þ

SaðtÞ ¼ Nðpþ qÞ2

pe�ðpþqÞt=2

1þ qp e�ðpþqÞt

!2

ð3Þ

where Eq. (1) is the conditional probability which is equal to thelikelihood of the product being adopted at time t. Eq. (2) showsthe sales of the firm which is equal to the product of the total poten-tial and the rate of adoption. Eq. (3) reduces the differential intodeterministic form. This model however, does take into accountthe effect of the sales strategy of the firm, and can be generalizedfor incorporating this effect as:

Dx ¼ pðtÞ þ qxN

� �ðN � xÞA ð4Þ

Eq. (4) is the generalized equation, and the multiplication factor isshown in Eq. (5), which includes the sales factor.

A ¼ 1þ Zðpri � hsÞDjpri

b� /s

Dið1þ lÞ

� Disr

x� Zþ ðx� ZÞhr

2� /r

� ��Djbprj þ

ðPi � PjÞPj

a ð5Þ

where Zðpri�hsÞDjpri

b� /sDið1þlÞ is the per unit ratio of the sales profit of

products and services at the current innovation level against thechange in innovation level(n).

F.T.S. Chan et al. / Computers & Industrial Engineering 69 (2014) 64–70 67

ðPi�PjÞPj

is the relative performance coefficient of the firm’s productand services at current innovation level w.r.t. the same at the pre-vious innovation level.

Disrx�Z þ ðx� ZÞ hr

2 � /r� �

=Djbprj is the per unit sales loss to the firmdue to discount of the products and services to the retailer at thecurrent innovation level against the change in innovation level asdeveloped in Munson and Rosenblatt (2000).

On simplification of the differential Eq. (4), with minor changes,the relation can be developed as:

et ¼ pþ qSN

� �l1

� Y � ShDjb

� �l2

� N � Sð Þl3 ð6Þ

where

l1 ¼1

q Y þ NphrqDjb

� �1þ p

q

� � ðiÞ

l2 ¼�Djbprj

h N � YDjbprj

hr

� �pþ qYDjbprj

hr N

� � ðiiÞ

l3 ¼�1

Nðpþ qÞ Y � NhrDjbprj

� � ðiiiÞ

where

Y ¼ 1þ aðPi � PjÞ

Pj�

DisrS�z �

Zhs2 � /r

� �Djbprj

þ ZðSpr � hrÞDjpr

b

� /s

Dið1þ lÞ ðivÞ

The objective of the firm is to maximize the income and thus to reg-ulate and strategize sales. Thus objective is

Maximize zðpr � hrÞb� /s þ ðS� ZÞpr 1�Djsr

ðS�ZÞ þðS�ZÞhr

2 � /r

Djbprj

0@

1A

Subject to constraints:

Z � N ð7Þ

i � j 8i; j 2 ð1;2;3; . . .Þ ð8Þ

Eq. (7) ensures that units sold do not exceed the total potential. Eq.(8) justifies our assumption that innovation level is increasing withtime.

N <1 ð9Þ

@gðt; kÞ@SðtÞ � �1 8k 2 ði; jÞ ð10Þ

Eq. (10) ensures that sales rate does not decrease at a rate greaterthan the sales accumulated.

i ¼ jþ n ð11Þ

Sales during the period from t to t0 is a function of level of innova-tion and time after its launch in the market. It can also be deductedfrom the bass model as shown in Eq. (12)

gðt; t0Þ ¼ pþ qSðt0; jÞNðiÞ

� �ðNðiÞ � Sðt0; jÞ ð12Þ

where cumulative sales g(t, t0) is a function of time and level ofinnovation.

Performance measurement of a firm may be calculated byrealizing the percentage deviation of change in innovation levelfrom optimal change which could have resulted in maximum

change in cumulative sales. Innovation level refers to the innova-tion level of firm’s product or supply chain system or both. Perfor-mance can also be measured on the terms of percentage of netsales accomplished from firm’s own stores and its deviation fromthe optimal percentage, which could have higher increases in salesrate than the current sales policy for product distribution. Themanagers of the firm can decide on the weightage to be given tothese two factors, depending on the current market situation, tomeasure the performance of the firm.

4. Computational studies

This section presents the results obtained from a numericalstudy designed and developed to study the effect of innovationlevel and distribution of products and services on the change inthe sales of a firm from its previous products and supply chain sys-tem. A full factorial experimental design has been developed with apotential market for a new product to be 2000 units and the salesof the new product before the launch of next product variesbetween 1000 units to 1500 units. The value of p and q have beenvaried in the vicinity of their average values of 0.03 and 0.38respectively, that is, p varies from 0.02 to 0.04 and q varies from0.3 to 0.5 in the model. Based on the assumption that the tendencyto move to a new product or supply chain system increases withincrease in the level of innovation, the value of p depends on thecurrent innovation level and has been varied as p = p(1 + 0.1n). Ithas also been assumed that with a higher increase in the innova-tion level, the price of a new product increases at a higher rateand it may cause harm to the sales due the fact that it might notbe able to create a market at that level. The change in sales rateswith varying innovation level and vending strategy has been ob-tained through simulation. The value of b is taken to be varyingbetween 1 and 2 (b = 1 + k) while the values of the holding costsof firm’s own stores and retailer has been assumed to be around10% and 20% of the product selling price. This study has been donewith a change in innovation level from its previous level, rangingfrom 1 to 10. The set-up cost for the firm’s own stores has been di-vided by a factor (1 + l) which takes into account the incentives afirm may expect from government or part of the innovation cost afirm may receive from its vendors of raw materials, hence l is a po-sitive term. It is assumed that a new product is launched before allthe potential consumers have bought the product at the currentinnovation level; hence sales have been kept less than the potentialmarket of 2000 units.

For different changes in level the of innovation, the optimalnumber of units to be sold in the firm’s store has been found fordifferent demands and is compared with different sales policiesthe firm may opt for. Also, for different expected demands forthe new product, the optimal change innovation level is found,which the firm should attain, and the performance is measuredby comparison. Based on the model and the developed simulation,the optimal level of the sales percentage from the firm’s own storesand the change in innovation levels through R&D required for thatoptimal change in sales rate is obtained. The firm can measure itsperformance based on change in innovation level it has undergoneand that required for the optimal results. Performance measuredthus depends not only on the level of innovation but also on thenumber of units sold from the firm’s own stores. The performancecoefficient 1 (PC1) is the change in percentage of the net sales fromthe firm’s own store against the optimal sales policy. Performancecoefficient 2 (PC2) is the percentage deviation in the change ofinnovation level attained by the firm against the optimal changethe company should have attained at the respective demand levelthrough R&D.

Table 1Performance coefficients at various demand and innovation level.

Change in innovation level Sales rate Sale through firm’s own store Units sold through own stores (in %) Performance coefficient1

Performance coefficient2

Demand = 1305 units2 351.912 999 79.792 0 05 323.3794 985 80.936 0.011 0.039 257.826 879 68.564 �0.11 0.07

Demand = 1335 units2 359.1047 999 79.79 0 06 276.7596 868 68.29268 �0.11 0.045 331.1481 985 80.93673 0.012 0.03

Demand = 1432 units7 226.0214 959 78.80033 0 05 311.0223 985 80.93673 0.0115 0.023 177.494 754 50.63801 �0.292 0.04

Demand = 1227 units7 208.4123 959 78.80033 0 05 288.9676 985 80.93673 0.0114 0.026 240.5698 868 68.29268 �0.115 0.01

68 F.T.S. Chan et al. / Computers & Industrial Engineering 69 (2014) 64–70

5. Interpretation of numerical results

The numerical model generates a random variable demandfunction and is based on the demand function, sales rate of thefirm, net sales of the firm, sales from the firm’ own stores and salesfrom the retailer depending on the change in the level of innova-tion attained by the firm. The results of the simulation developedare tabulated in Tables 1 and 2. Table 1 shows the two performancecoefficients of the firm, one based on the sales from its own storesand the other is based on the deviation from optimal change oflevel of innovation. Table 1 also shows how the sales rate and perfor-mance coefficient of a product or supply chain system change withchange in net sales, sales from the firm’s own stores and the levelof innovation when the demand is kept constant. With the increasein the percentage of sales from own stores, the performance coef-ficient factor 1 generally increases and the performance coefficientfactor 2 increases with decrease in deviation from the optimal levelof innovation. Thus the performance index may be equal to w1-

(1 + PC1) + w2(1 + PC2) where w1 and w2 are the weightage givento the performance coefficient 1 (PC1) and performance coefficientfactor 2 (PC2). This weightage is given by the managers of the firm,depending on the current level of innovation and the scope forincrease in the level of innovation. Table 2 shows same statisticsbut when the level of innovation attained by the firm is kept fixedand the demand of new product changes. The performance coeffi-cient factor obtained is based on the percentage of sales obtained

Table 2Performance coefficient at different innovation level.

Demand Sales rate Sale through firm’s own store

Change in innovation level = 91294 310.4573 9551176 189.2101 7371452 187.498 763

Change in innovation level = 81276 292.1334 8811444 273.9527 9141356 266.8028 869

Change in innovation level = 61294 317.785 9551390 176.0785 7381452 192.8839 763

Change in innovation level = 11294 323.6042 9551289 211.7664 8651406 242.3849 772

from the firm’s own stores. Fig. 1 shows how the rate of changein sales varies with net sales and the number of units sold fromfirm’s own stores. The surface is enclosed within maximum andminimum rates of change of sales of the product.

6. Results

From the model developed and the simulation results, it canobserved that level of innovation attained achieved by a firmthrough R&D and marketing strategy does play a major role inchange in the rate of sales of a product and thus maximizing theopportunity to hedge against liquidity of the present competitivemodel. It is observed that the overall performance of a firm doesdepend on R&D and vending the products through own stores. Itcan be observed that the rate of sales can be related to theweighted sum of PC1 and PC2. A firm must measure the perfor-mance of its management and processes with the performancecoefficient factors developed in this study. It allows the firm to planfor investment in R&D and in estimating the perfect mix of numberof units to be sold through the retailers and its own stores. The sig-nificant contribution of this paper is that it provides insights intothe importance of the extreme ends of the supply chain – planningon investment and final dispatch of products to the consumers.Depending on the innovation level a firm can achieve, the vendingstrategy can be planned and the best fitting investment decisionson R&D under given conditions can be realized. Furthermore, this

Units sold through own stores (in %) Performance coefficient

78.27869 054.31098 �0.23957.67196 �0.20606

71.50974 061.79851 �0.097167.94371 �0.03565

63.93443 060.38492 �0.035551.17158 �0.12763

78.27869 071.07642 �0.0720256.59824 �0.2168

Fig. 1. Sales rate versus net sales and the number of units sold from firm’s own stores.

F.T.S. Chan et al. / Computers & Industrial Engineering 69 (2014) 64–70 69

paper also discusses on the dependency of the rate of sales on theperformance of a new product in the market which may or may notmeet consumer satisfaction levels. It is recommended that the fol-lowing performance measurement system should be implementedby the firms to actually measure the effectiveness and the effi-ciency of investment and state of the product as to whether itcan be sustained in the market or whether a new product isrequired to be launched by the firm to increase the sales rate. Ina supply chain with a moderate number of products, resources,own stores and outside retailers, let the following guidelines befollowed, that is, (1) a firm should try to minimize the number ofoutside vendors and outside retailers; (2) the firm should explorethe market to see the apparent need and exploit the market byimproving the product platform by R&D, thus going for an appro-priate level of innovation that may attract consumers and avoidsuperfluous products; (3) depending on the quality of feedbackfrom the consumers, the innovation level can be modified throughR&D depending on the robustness of the supply chain. For a supplychain providing services, innovation should be incorporated in theservice strategies and supply channels of the firm to expand thesale of services.

7. Conclusion

This paper reviews important issues in the current state ofperformance measurement, and the focus has been on the devel-opment of a comprehensive performance measurement systemwhich measures the performance of a firm with respect to itsinnovation policy and marketing strategy. A deterministicapproach has been proposed, with the objective of identifyingthe important effects of performance coefficients. This perfor-mance measurement system can incorporate many advantagessuch as predicting the perfect launch time of a product andproduct life. A general performance measurement term ‘perfor-mance coefficient (PC)’ is developed, and it has been proven ana-

lytically that a firm with higher values of PC fares better in thecompetitive market. A simulation technique is applied based onthe model developed to show that higher level of innovationalways may not always lead to higher changing sales rate asthe market may not always be ready for a drastic change. An-other focus is on the sales policy adopted by the firm in respectof its innovation strategy. It is beneficiary for a firm to increasesales through its own stores as there is only a marginal loss ofdiscounts and it endeavours to have better consumer feedbackand relations. A data set and plot has been identified and pre-sented. After analyzing the data set, an insight is that in measur-ing the performance of a firm with this model will determinethe firm’s position in the technology savvy market with increas-ing levels of innovation. Due to modularized performancedimensions used in the model, managers can compare the per-formance between different firms with similar products.

8. Scope for further research

In this paper, it has been assumed that all stages in the supplychain of the firm can respond to the change in innovation level, andthe demand for a new product is assumed to be deterministic,assumptions which require further research. Performance mea-surement system developed here is not developed collaborativelybut on an individual pattern which may be expensive for a firm.Also, in this paper, no backtracking has been assumed and productsleft unsold after the launch of new product have been neglected.More research is needed to incorporate these cases in the perfor-mance measurement system.

Acknowledgement

The authors would like to thank The Hong Kong PolytechnicUniversity Research Committee for financial and technical supportthrough an internal Grant (Project No. G-YL47).

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