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Quality as the interface between manufacturing and marketing: a conceptual model and an empirical study.


The last decade has seen an enormous increase in competitive pressure in almost all product categories due to globalization of markets. Products now have to contend not only with domestic competition but also with a large number of foreign competitors. Moreover, survival of many products depends on their sales in foreign markets. Trade barriers are breaking down all over the world, resulting in changing patterns of competition. Even countries with traditionally protected markets are opening up. India, for example, is now offering a staggeringly large market to the world (Ghose and Mukhopadhyay 1992). Similar scenarios are evolving in China and various East European countries. Marketers now must take strategy decisions appropriate for such changing environments. Manufacturing strategy also must adapt. Such adaptation may include changes in design of the product or incorporation of new manufacturing technology.

Traditionally, manufacturing and marketing departments have tended to operate as relatively independent entities. For example, the manufacturing department designs and produces the products, then the marketing department takes the responsibility for selling them to potential customers. Each department, therefore, tends to be concerned about increasing its own internal efficiency. Such a set of actions is seldom compatible with the firm's overall objective. For instance, manufacturing is interested in having large production runs of a single product leading to economies of scale and therefore lower cost of production. Marketing, on the other hand, would like to have smaller quantities of a variety of products to satisfy the heterogeneity in consumer preferences; this closer matching between products and consumer needs would clearly lead to increases in sales. Such incompatibility of objectives between departments is not beneficial to the firm as a whole, and leads to suboptimality at the company level. Increased intensity of competition due to market globalization is reducing the margin of error that firms can afford to make. Therefore such sub-optimality is becoming increasingly unaffordable.

Now, therefore, an integrated approach is needed where the manufacturing activities of the products are guided by the needs of the consumers in the target market. Recent academic research has highlighted the importance of developing this kind of manufacturing-marketing interface to integrate these two domains (Chakravarty and Ghose 1992).

In this paper, we propose a conceptual model in which a feedback loop is established between the marketing and manufacturing functions of the firm; product quality serves as the interface in this feedback loop. The choice of quality as the interface variable is logical because both manufacturing and marketing emphasize quality, albeit in their own ways. Manufacturing strives to produce products of a specified target quality level while marketing seeks to provide products which match the quality needs of the customers. However, these two functions have different perspectives on product quality.

Manufacturing Aspects of Quality

Traditionally the manufacturing department of an organization is responsible for producing products of a required quality level. From manufacturing's point of view, there are two different concepts of quality: design quality (DQ) and conformance quality (CQ). Fine (1986) refers to design quality as the features, styling, and other product attributes that enhance the fitness for use or "utility" for the consumer. It is manufacturing's responsibility to design the required level of quality into the product.

Conformance quality refers to the degree of the product's conformance to the product specification. It is again manufacturing's responsibility to produce products correctly to specification. The pioneering efforts of researchers such as Juran and Gryna (1974), Crosby (1979), Deming (1982), and Feigenbaum (1983) led to an increasing recognition of the importance of the role of quality and especially of conformance quality. Over the years, Japanese firms have been more active than their U.S. counterparts in implementing the suggestions made by these and other researchers. The result is evident when one considers that U.S. manufacturers have not been able to achieve the high levels of conformance quality attained by Japanese manufacturers (Garvin 1983). The automobile and semiconductor markets are two of the well known areas where U.S. manufacturers have performed below par as far as product quality is concerned (Garvin 1988). This has clearly undermined their competitive position in the global market. In recent years, however, U.S. managers have begun to take corrective action and have started to implement different kinds of quality management programs in their respective organizations (see Reitsperger and Daniel 1991).

In the real world, manufacturing is usually given a target quality level for producing a product. Seldom is there any formalized market feedback system which provides information on the nature of the customer's need for quality. Typically the manufacturing target quality level is static; no allowance is made for the ever-evolving nature of the market. Note that the level of marketing activities like price and promotion are quite dynamic -- i.e., they vary over time and across brands and so on. Therefore, it appears illogical to think that the quality target of manufacturing should remain static when markets are dynamic.

We examine this issue further by discussing theoretical aspects of the marketing concept of quality in the next section.

Marketing Aspects of Quality

The marketing literature identifies two facets of quality: objective quality (OQ) and perceived quality (PQ) (Zeithaml 1988). A product which is high in OQ can be defined as one which is technically excellent. PQ of a product typically refers to the quality level of the product in the judgment of the consumer. Since PQ is a subjective measure, its value even for a given product could vary substantially from one consumer to another. Since OQ is based on actual measurable characteristics of the product, its value should not vary across consumers.

Research studies in marketing have operationalized the concept of OQ by using data published in well known sources such as Consumer Reports (Curry and Faulds 1986; Gerstner 1985; Hjorth-Andersen 1984). The underlying assumption of using such ratings is that these quality rankings are based on actual, objective measurements not affected by any extraneous influences. The latter point can be easily appreciated when one recalls that the publishers of Consumer Reports do not print ads from any companies. Several academic studies have examined whether price is in any way correlated with objective quality as presented by Consumer Reports. These include research done by Sproles (1977), Riesz (1978), and Geistfeld (1982). The general finding is that price is only weakly correlated with OQ. In addition there is a great degree of variability in this correlation across product categories. Such findings have at least one limitation. The price values used are taken from the same source (e.g., Consumer Reports) as the OQ ratings. Prices are, therefore, not necessarily actual transaction level prices paid by consumers across a large number of purchases. Clearly there is a need to empirically evaluate impacts of OQ on the firm's performance in the context of actual transaction level marketing variables such as price and sales promotion. Note that the scanner data used in our "empirical analysis" below utilize actual transaction values.

While OQ is a marketing domain variable, it is in many ways related to the quality concept of the manufacturing domain. As discussed in the previous section of this paper, two primary facets of manufacturing quality refer to design quality (DQ) and conformance quality (CQ). DQ is a direct function of the extent of different attributes present in a manufactured product -- and so is OQ, but the two are not entirely identical. For instance, consider the situation where the manufacturer of a detergent is careful to include a certain exact amount of bleach (as per the planned product design -- i.e., to get the desired DQ). When evaluating the OQ level, the marketing domain is interested in the whitening power of the detergent, but is less interested in knowing the detail of the ingredient causing it. In sum then, OQ evaluation focuses on bottom-line performance while DQ focuses primarily on the physical attributes that should lead to that performance level.

The closeness of the relationship between CQ (of manufacturing) and OQ (of marketing) varies across product types. For example, in many consumer durables such as stereo systems there are strict technical specifications. Manufacturing will try to produce the stereo system so that its performance closely conforms to the specifications -- thus increasing CQ. Evaluations of OQ (such as by personnel from Consumer Reports) not only measure actual performance but at least partially base their reports and OQ rankings by comparing performance with the technical specifications made available by the manufacturers. On the other hand, for many consumer non-durables (e.g., coffee, detergent) such comparisons (in OQ tests) are not made with respect to any given specification of the manufacturers. The operationalization of OQ in such cases is therefore quite unrelated to CQ.

PQ is affected by a large number of market and consumer factors. In theory PQ could and probably does vary from one consumer to another. Next, we will identify some of the primary market forces affecting PQ and examine how PQ might be related to OQ and thus to manufacturing aspects of quality.

PQ fundamentally depends on the perceived attribute levels of the product/brand in question. In the marketing domain, perceived attributes are typically considered to be psychological, rather than physical in nature. An example of such an attribute is the "sportiness" of an automobile. This is an important dimension in many segments of the automobile market -- e.g., the greater the sportiness, the greater the PQ level. However, what exactly is "more sporty" or "less sporty"? It is a psychological dimension whose value varies with the perceptions of different consumers and which must have some physical origins also. Indeed "sportiness" is a function of several physical attributes of the automobile; these include aerodynamic design of the body, a bright red color or shiny wheel covers. These physical features are built into the product by manufacturing. PQ, therefore, is a function of perceived attributes which in turn are influenced by the physical features (and thus design quality) of the product. PQ can also be affected by several other marketing factors such as price, advertising, warranty level and after-sales service.

We can conclude that the level of OQ in a product is affected by the manufacturing built attributes while the PQ level can be affected by a number of marketing variables such as price and promotional activities. There is little, if any, research in the literature about how the effectiveness of these marketing variables might vary for products of different OQ levels. This information is of interest because it could provide useful interactive feedback between manufacturing and marketing domains. For example, if it is found that for a certain low OQ level a substantial amount of promotional effort and expenditure to generate market share is necessary, it may be more cost effective to increase the OQ to a level where less promotional effort may be required. In other words, such information would enable quality to act as an effective interface variable between manufacturing and marketing.

A central point in our paper is that a given marketing effort variable could have different degrees of impact on the market performance of brands of different qualities. Formally our hypothesis can be stated as follows:

|H.sub.1~: The sensitivity of brand market shares to price (or promotion) would be lower for brands having a higher level of objective quality than that for brands possessing a lower level of objective quality.

The above hypothesis is restricted to consumer nondurables. Our logic is that consumers will have an intrinsic preference for higher OQ products leading to substantial brand loyalty; changes in price and promotion will thus not encourage much brand switching. For lower OQ brands, there should be a greater impact of price and promotion changes on market share, since brand loyalty based on quality should be lower.

With frequently purchased products -- i.e., for consumer nondurables -- it is possible to track household level repeat purchases over time to test such hypotheses. With durable products, the length of time between repeat purchases for a given household is so great that various extraneous "noise" variables could bias the results of any findings on this subject. For nondurables, the higher frequency of purchase also enables consumers to get a more accurate idea of what the true OQ level of the brand is; this idea should therefore be close to OQ levels as published in magazines such as Consumer Reports. In this paper, therefore, we evaluate our hypothesis for a consumer non-durable, utilizing quality ratings from Consumer Reports and state-of-the-art scanner data.

Quality as the Interface

The two views of quality presented so far, namely manufacturing's view and marketing's view, serve the purposes of the individual departments adequately. However, serious conflict may occur when they are seen from the point of view of the whole organization. The two departments are naturally concerned with optimizing their own objectives, but the sum of two individually optimized quantities may not be globally optimal for the organization. What we need is an integrated approach. We propose a conceptual model in this section which uses quality as the interface between manufacturing and marketing, thus linking their activities in a synergistic way.

The traditional model of manufacturing and marketing functions is shown in Figure 1. A target level of quality is defined, which is taken by manufacturing as its input to design the product. The main criteria it uses for evaluating the design are the principles of value engineering and design for manufacturability. Production takes the design as input and endeavors to produce the items conforming to the specifications. Thus manufacturing strives to achieve the most efficient production of a product given a target quality level.

From the viewpoint of marketing, the main concern is to use the marketing mix variables to improve the sales performance of the product, given the quality level inherited from manufacturing. The potential for conflict between manufacturing and marketing objectives comes from at least three sources. First, the quality level input to manufacturing may not match the marketing assessment of the market/consumer requirement. In that case, marketing would feel handicapped. Second, the manufacturing target quality level may fail to take into account the dynamic nature of the market in terms of the ever-evolving type of needs among consumers. This may widen the gap between the firm's offering and the market requirement. Third, the traditional model ignores differences in consumers' purchase behavior patterns as an interactive function of the products' OQ levels and marketing mix variables such as price and promotions.

Our proposed conceptual model recognizes these shortcomings of the traditional model and uses a feedback loop to establish a link between manufacturing and marketing. The control system in the feedback loop monitors the marketing decisions and the resulting market reactions and computes the appropriate signals for manufacturing. For example, if marketing decisions have limited impact on market share for a given product quality level, the control system could feed a signal to manufacturing to alter the quality to a level at which market share is much more sensitive to changes in marketing mix variables. In the next section, we use a real data set to evaluate our conceptual model.

Empirical Analysis

In the manufacturing literature, Garvin (1988) has studied the effect of quality (especially conformance quality) on brand performance and image. Several studies in the marketing literature have looked at the correlation or impact of marketing variables (primarily price) on PQ (see Zeithaml 1988, for a review). Other studies (e.g., Philips, Chang, and Buzzell 1983; Carpenter 1987) have used PIMS data to examine the relationship between marketing strategy variables and quality as perceived by managers; note that PIMS data is not at the individual brand level but at an aggregate business level. To the best of our knowledge, there has been no study which evaluates how actual prices and promotion affect brand-level market share for brands identified as having high or low levels of objective quality (OQ).

In this research, we are interested to know if the same type of marketing strategy elicits different market reactions for products of different OQ. If that is the case, we can formulate managerial guidelines suggesting appropriate manufacturing and marketing strategies.

In order to evaluate this issue we employ scanner data for the frequently purchased product category of laundry detergents. Scanner data represents the state-of-the-art in market research and includes voluminous details (i.e., several thousands of observations) about consumer purchase behavior and retail prices and promotion for many brands in many stores.

Specifically, we use the A.C. Nielsen scanner panel data for our analysis. The marketing mix variables used in our analysis are price and in-store display. We obtain our quality data for these brands from Consumer Reports magazine which tested these brands on various attributes and ranked them on the basis of objective quality.


The market share model we will be testing can be described by the following equation:

|S.sub.ij~ = ||Beta~.sub.0~ + ||Beta~.sub.1~ |p.sub.ij~ + ||Beta~.sub.2~ |d.sub.ij~ + ||Beta~.sub.3~ |Q.sub.j~ + Brand specific constants.

where |S.sub.ij~ = household i's share of brand j in the period of study; i = 1,..., I, j = 1,..., J

|p.sub.ij~ = price faced by household i for brand j

|d.sub.ij~ = number of times brand j was seen to be on display by household i in the given period

|Q.sub.j~ = objective quality level of brand j

Similar market share model specifications have been suggested by Cooper and Nakanishi (1988). |S.sub.ij~ is the variable we use to measure the performance of the brand. The assumption here is that a household's decision to buy a particular brand is influenced by its price, and whether or not the brand is on display when the purchase is made. Brand specific constants were included to capture the effect on the brand shares of any factors specific to a particular brand.


Out of 138 weeks of available data (comprising thousands of observations), we chose the first 100 weeks of data for estimation of the parameters of our model and the remaining 38 weeks were held out for predictive validation purposes. We included 7 brands in our analysis which commanded more than 95% of the brand shares in the market. The names of these brands from the A.C. Nielsen scanner data cannot be revealed due to proprietary reasons. Based on the rankings given by Consumer Reports, we categorize these brands into two quality groups: high quality and low quality brands.


We ran our model three different times: once for the pooled data, once for the high quality brands, and once for the low quality brands. Note that |Q.sub.j~ was omitted for the last two runs. Table 1 indicates the estimated parameter values for these three runs separately. The significance level of these parameters are indicated in parentheses.
Table 1. Model Parameters
 Effect on household brand share
 Constant Price Display Quality
Pooled data 0.453 -0.000407 0.04 0.108
p = (0.000) (0.000) (0.002) (0.000)
High quality brands 0.594 -0.000316 0.0336
p = (0.000) (0.047) (0.026)
Low quality brands 0.418 -0.00046 0.082
p = (0.000) (0.004) (0.001)

Effect of price decisions on market share

For the pooled data and for both high and low quality brands, we find that there is a negative effect of price on brand share. The negative sign of the price parameters indicates that brand share decreases with price increase, but the magnitude of this effect is very small as indicated by the low value of the parameters. Consider now the situation relevant for evaluating hypothesis |H.sub.1~. It is evident from the price parameter values in Table 1 that the effect of a unit price change on brand share change is about 46% greater for lower quality brands compared to higher quality brands, other things remaining constant. Such a comparison can be meaningfully made since the data was standardized before estimating the parameters of the market share models.

Also note that the price parameter is more significant for lower quality brands. In sum, the analysis reveals that pricing decisions have relatively less effect on brand share for high quality brands, indicating that customers of high objective quality brands are swayed less by price changes. This supports hypothesis |H.sub.1~.

Effect of display on market share

The results in Table 1 show that display has a positive effect of brand share in the cases of the pooled data and for both high and low quality brands. We also observe that while there is a 3.36% increase in brand share for high quality brands, for a unit increase in display (other things constant), low quality brand share increases much more (i.e., by 8.2%). These comparisons are applicable since standardized data was used for model estimation. Moreover, the low quality display parameter was more strongly significant than that for the high quality brands. In sum, the analysis again shows that households are more sensitive to display changes in the case of lower objective quality brands than for high quality brands. Again this supports hypothesis |H.sub.1~.

Note also from Table 1 that the model parameters estimated from standardized data indicate that the effects of display changes on brand shares are much larger than those of price changes.

Predictive validity of the model

The previous discussions reflect the information obtained on the basis of the parameters estimated for our model. To evaluate the predictive validity of this model, we use the parameters estimated from the calibration sample (100 weeks of data) along with the price and display values in the holdout sample (38 weeks of data) to predict household level brand shares in the holdout sample. These predictions were then compared with actual brand share values. The Mean Absolute Deviation (MAD) and Root Sum Squared Error (RSSE) are standard measures for such comparisons. For high quality brands the MAD and RSSE values were 0.0367 and 0.0528 respectively; for the low quality brands MAD was 0.0331 and RSSE was 0.0607. We also used a chi-square test to test the hypothesis that actual frequency (market share) comes from the same underlying distribution that generated the predicted market share (Note: A similar testing procedure for comparing actual and predicted market shares has been used in Gensch and Ghose, 1992). The null hypothesis of "actual = predicted" could not be rejected at the 0.10 level for high quality as well as low quality brands. These results indicate that the predictive accuracy of our model is quite good.

Managerial Guidelines and Conclusions

Our results show that similar marketing actions with respect to price and display generate different market reactions for different quality level products. For the lower quality brands, price and display changes are more effective in increasing brand shares. This is in accordance with our hypothesis |H.sub.1~.

A managerial guideline developed from this result is that managers of lower quality brands might prefer to resort to price cuts and displays more often because of their relatively larger effect on brand share. On the other hand, managers of higher quality brands should be aware that pricing and display strategies will have a comparatively lesser effect on brand share. Thus, marketing decisions need to be tailored to the objective product quality level -- an output from the manufacturing department. It follows that levels of marketing activities should depend on manufacturing activities as reflected by the quality level.

Let us now consider other aspects of why product quality could be an interface variable that is crucial for the firm as a whole. Observe from the estimation results of our model on the pooled data that when one moves from a low to a high quality brand, the market share increases by about 11%, given that other variables are held constant. Using a similar argument we notice that a unit increase in displays, will increase market share by about 4%. Consider now the scenario shown in our conceptual model. Let us assume that for a low quality brand for certain values of price and display, the brand attains a certain market share. Also assume that the firm is not satisfied with this share level and wants to enhance its value. The firm now has two options. First, it could decide to ask the manufacturing department to increase the quality of the brand. Alternatively it could ask the marketing department to increase the display activities for the brand. What are the ramifications of these options?

From the point of view of increasing market share, it is better to take the first option. However, note that there is a cost to increasing quality as well as there is a cost to increasing promotional display activities. A cost-benefit analysis will have to be done to evaluate which of the two options is better for the firm as a whole. This analysis will be done in the "control system" shown in Figure 2. If quality improvement is then chosen to be the better alternative, feedback (from the control module) will be provided to the manufacturing domain to implement this change in product quality. On the other hand, if the second option is more viable, feedback from the control module will be sent to the marketing department to implement changes in the display activities; the quality will be kept constant. It is obviously possible to change quality and display simultaneously if the firm so chooses.

It is evident then that objective quality can serve as an effective interface variable for manufacturing and marketing. The implications in the context of increased globalization of business are far-reaching. Manufacturing facilities are often situated in a country different from where the brand is being marketed. This may increase the actual cost of coordinating with manufacturing, thus adding to the cost of objective quality enhancement. Thus local management might prefer intensifying the brand marketing activities alone. On the other hand, in some firms (especially those where the parent companies prefer to keep a stronger control on international activities of their subsidiaries), the parent company may prefer to change objective quality of the product at the source in its own country rather than entrust a critical task to the marketing department of the local firm in the foreign country. The utilization of objective quality as an interface variable between manufacturing and marketing, therefore, may be instrumental in managing in a complex global environment.

In this paper, we have presented a conceptual model for using quality as the interface between manufacturing and marketing. The interface needs to capture the market responses as observed and shaped by marketing decisions and convey them to manufacturing for appropriate manufacturing decisions. Moreover, it needs to be dynamic in nature to cope with the ever-changing scenarios in the market place. We empirically demonstrated that consumer responses as reflected by brand market shares are a function of objective product quality, which in turn is an output of the manufacturing domain. In addition, we show that marketing decisions should be predicated on the level of the objective product quality.

In this study, we have concentrated on product quality as a focal variable of the manufacturing-marketing interface only. There is a need to broaden this focus by considering other possible interface variables. There is also a need to empirically analyze data for a variety of physical product categories and also for different kinds of services as the implementation of interface strategies will become increasingly crucial for firms in today's highly competitive global markets.


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Sanjoy Ghose, Assistant Professor and Samar K. Mukhopadhyay, Assistant Professor, University of Wisconsin-Milwaukee, School of Business Administration, Milwaukee, WI, U.S.A.
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Title Annotation:Special Issue: Strategic Quality Management
Author:Ghose, Sanjoy; Mukhopadhyay, Samar K.
Publication:Management International Review
Date:Feb 1, 1993
Previous Article:Quality management in Japanese and American firms operating in the United States: a comparative study of styles and motivational beliefs.
Next Article:Restructuring supplier relationships in U.S. manufacturing for improved quality.

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