research paper series - Stanford Graduate School of Business

RESEARCH PAPER NO. 1103

Making Manufacturing Market Driven

Warren H. Hausman David B. Montgomery October 1990

RESEARCH PAPER SERIES

GRADUATE SCHOOL OF BUSINESS STANFORD UNIVERSITY

MAKING MANUFACTURING MARKET DRIVEN Warren H. Hausman and David B. Montgomery October 17,1990 Research Paper # 1103

HAUSMAN AND MONTGOMERY

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MARKET DRIVEN MANUFACTURING

MAKING MANUFACTURING MARKET DRIVEN Warren H. Hausman and David B Montgomery OCTOBER 17, 1990 Research Paper # 1103

The authors are Professor and Chairman of Indus~ia1Engineering and Engmeering Management, School of Engineering and Robert A Magowan Professor ofMarketing, Graduate Schooi of Business, Stanford University,respectively Research support from the LEK Partnership, the Stanford Business School, and the Stanford School ofEngineenng is gratefully acknowledged Dr Sara Beckman ofHewlett-Packard provided valuable conceptual suggestions dunng the formative stages of this project Timothy Hough and Timothy Scholes ofLEK furnished valuable suggestions at a later stage of this project The authors are indebted to K J Smgh, doctoral student m Industrial Engineering and Engineering Management for his able assistance in this research


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MAKING MANUFACTURING MARKET DRIVEN ABSTRACT World-Class Manufacturing has received a great deal ofattention in the past few years, drawing largely on successful Japanese manufacturing practices. There has also been a separate explosion in the use ofconjoint analysis to determine customer preferences formarketing purposes. In this paper these two developments are used together to determine the most advantageous manufacturing priorities for a given competitive situation. For illustration purposes we use customer preference data from two produ~tclasses (automobiles and office equipment); these preferences are segmented (both a priori and statistically) and we then show how the various segments link to differing sets of manufacturing priorities. Finally, we examine how strategic manufacturing priorities can be translated into specific action-oriented improvementprograms~


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1. Introduction “In 1958, John Kenneth Galbraith wrote that the United States had solved the production problem. Nothing has proved to be further from the truth.” (Beckman et al, 1989, p. 53) World-Class Manufacturing (WCM) has received growing attention in the past several years Seminar after seminar, book after book, speaker after speaker chant the litany ofconcepts and techniques which comprise WCM But how is a firm to prioritize among all the elements ofWCM and find those which are most relevant to the firm’S competitive situation” One answer is provided by the methodology described in this paper Making Manufacturing Market Driven This paper combines methodologies from manufacturingstrategy and marketing strategy to create a unified approach to the marketing/manufacturing interface We propose using conjoint analysis to assess consumer preferences by product segment, and then to trace the manufacturing implications of those preferences by a general linkage between manufacturingpriorities and marketplace competitive dimensions Two product classes (automobiles and office equipment) will be used to illustrate the concepts The practical result of such an approach is that it produces unified rather than competing marketing and manufacturing strategies, which we believe is a significant competitive advantage -

Background In the past half-dozen years numerous factors have begun to impact marketing strategy shortened product life cycles and time based competition, greater reliance on new products, greater difficulty in forecasting demand, greater segmentation and variety, heightened awareness ofrisk reduction, overwhelming emphasis on quality, attention to value in use and costs, and globalization of competition Similarly, manufacturing has had to cope with many new emphases global competition, rapidly changing technology (both product and process), shorter product life cycles, use ofquality as a strategy, shorter time to market for new products, demands forincreased production flexibility, ‘bundling” ofoptions (variety in sets), design for manufacturability/assembly/quahty, Taguchi experimental design methods, group technology and cellular layout, new approaches to workforce teamworkand motivation (including self-directed teams), computerized dynamic scheduling systems, and the various elements of computer-integrated manufacturing (CIM) We propose a unifiedrather than independent approach to cope with these major challenges to marketing and manufacturing strategy We next turn to a discussion ofmanufacturing priorities and marketplace competitive dimensions


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Manufacturing Priorities and Marketplace Competitive Dimensions The now-classic manufacturing strategy literature (e.g., Hayes and Wheelwright, 1984) typically deals with the dimensions of manufacturing priorities listed in Table 1. While none of these priorities can be ignored, usually some are more cnucal than others for a given competitive environment, indeed, many academic cases in manufacturing strategy courses require the reader to discern some rank ordering ofthese priorities Table 2 lists six dimensions on which customers evaluate products and services relative to the competition Specific product-market segments may have additional competitive dimensions, e g , computer software or hardware may have an ease-of-use dimension which could be classified as a “feature’ but which should perhaps be evaluated separately The dimensions in Table 2 should answer the question, Why would a customer buy from us” Similar to the manufacturing priorities, the marketplace competitive dimensions allow a business unit to determine relative priorities among the various dimensions An interesting exercise is to select product or service offerings which focus on each ofthe six marketplace competitive dimensions (e g , Volkswagen for low-cost transportation, Maytag for high reliability, Federal Express for rapid dehvery, Burger King for customized burgers, Sony for product features, and Caterpillar for post-sales service) Figure 1 illustrates the critical linkages between the marketplace competitive dimensions and manufacturing priorities While the linkages between manufacturing and marketing in Figure 1 are straightforward, it is necessary to keep them in mind when asking the fundamental question, why do customers buy from us rather than the competition” In particular, without a manufacturing focus, it is too easy to perform conjoint analyses which may do an excellent job ofdiscerning which collection of product features may be most suitable, but which may completely miss the opportunity to measure the impact ofdependable delivery/availability or post-sales service 2 CALIBRATING MARKET PREFERENCES USING CON TOINT ANALYSIS The formulation ofmarket driven strategies entails understanding three majorentities the company itself, customers, and competitors as well as the environment in which they interact Ohmae (1982) has termed these three entities the strategic triangle A schematic is presented in Figure 2 In this section we discuss a methodology for calibrating the value to customers ofvarious combinations of product or service attributes, be they from the company or competitorsi We seek to -

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Although the focus of this paper is the customer value interface and its relation to company manufacturing strategy, the ~uategictriangle does suggest that the strategist must also seek to understand and predict competitor reactions and the environment. Porter (1980) has suggested several frameworks and 1


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accomplish this calibration in a valid and readily implementable form so that customer choice may be predicted among a competitivecollection of product or service alternatives and so insight may be

obtained into the relativevalue to customers ofalternative product attributes, product formulations, and marketing support programs In a later section we tie these market calibrated preferences to manufacturing pnonties2 The methodology we use to calibrate customer preferences is termed conjoint analysis3 Conjoint analysis is designed to decompose a multiattribute situation into constituent attributes Each attribute may take on one of several levels The method presents a respondent with a series ofrank order choices and then seeks to construct a set of part worth utilities which will best reproduce the respondent’s rank order choices These part worth utilities may then be used to assess the relative importance to the respondent ofthese attributes and to predict how a respondent might chose among a set ofalternative combinations oflevels of these attributes For example, suppose we consider studying a consumer s purchase ofphoto processing Important attributes ofsuch a purchase would most likely be processing time, price, quality of prints, likelthood oflosing the film, and convenience oflocation Each of these attributes ofa photo processing purchase would itself have two or more relevant levels Consider processing time and price The levels of processing time ofinterest in such a study might be 1 hour and 2 days, while price levels might be $7 and $10 (In an actual study there would probably be more levels ofboth price and processing time ) Naturally, levels of interest would have to be identified for the other attributes noted above In our research we used a two attribute at a time tradeoff analysis task to collect rank order preferences from the respondents4 To illustrate the task in the photo processing purchase context

methods for the assessment of likely competitor response and the likely evolution of competitive forces within an industry Opportunities and methods for strategic intelligence systems designed to monitor competitors and other vital facets of the environment are discussed in Montgomery and Weinberg (1979) The cost interface between the company and competitors has been extensively discussed since the Boston Consulting Group introduced the notion of an experience curve and its implications for strategy Day and Montgomery (1983) discuss application issues in using the logic of experience curves as well as summarizing evidence as to their form and existence 2 The cost interface between a company and competitors will be a function of both the manufacturing and marketing strategies of the firms and will impact the prices various competitors may charge and thereby the total value customers receive for a given attribute bundle 3 Although it had its origins in psychometrics conjoint analysis was judged the most significant development in marketing methods in the post war era (Meyers Greyser and Massy 1979) ~ Other procedures are used as well such as givmg respondents several profiles which are each composed of one level of each attribute The respondent is then asked to place these profiles in a complete rank order While it has been argued that this is more realistic than asking respondents to evaluate or trade-off attributes two at a tune (because in the real world alternatives occur as complete profiles) one author has had experience with both the full profile and the two attribute ata tune trade off method in which the respondents given the two attribute at a time task were more likely to respond, took only two thirds the time on average to respond, were more confident of their answers and the resulting partworths better predicted their actual real world choices ex ante Although none of these advantages for two ata time trade


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consider Table 3 where we have asked the respondent to indicate her first, second, etc.choice, all other things equal, for the fourdifferent combinations ofprice and processing time. In just her four answers, this consumer has given us some information about her relative preference for processing speed and price Naturally she prefers the shortest processing time at the cheapest price first and the longest processing time at the highest price least. But the fact that she would rather pay an additional $3 and get her prints back in an hour tells us that for her, at these levels ofprice and processing time, processing tune is more valuable to her than price By the time a consumer has completed several of such two attribute at a time trade-off tables a substantial picture of her preferences emerges Each respondent’s rank order preferences are analyzed using an algorithm which attempts to identify or estimate a part worth utility for each level of each attribute In our illustrative photo processing example, the respondent in Table 3 might have the following part worths (illustrative only) a price of $ 7 has utility of 04 while $10 has a utility of-U 4, and 1 Hour delivery has utility of 0 9 while 2 day delivery has utility of -0 9 to this respondent This reflects the preference of this respondent for speed of service and her willingness to pay, if necessary, to achieve it One must use several such trade off tables in order to capture a fairly clear picture of an individual s preferences In the next section we describe in detail the attributes and their associated levels which were used in this study The actual part worth utilities for each level of each attribute for each respondent were calculated using Snmvasan and Shocker’s Linmap IV program (1981) One useful measure of the relative importance of attributes to a respondent is to compute the of each attribute s utilities acro~sall le’~7el~i e stibtra~tthe lowest u~ilitylevel for th~.ta~thbüte

range

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from the highest level In the example for the respondent above, her importance weight or utility range for levels of processing time is 18 =

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0 9 (-0 9) and her importance weight for price is just 0 8 -

0 4 (-0 4) This importance weight measure indicates the incremental value to a given customer s -

utility of having an attribute improved from its least preferred level to its most preferred level In our example, improving processing time from two days to one hour is more than twice as important as reducing the price by $3 Lmmap rescales these attribute importance weights so that the weights are bounded by zero and one and sum to one5

off were statistically significant due to limited sample size it is notable that by all four of these cnteria the two attribute at a time trade-offs outperformed the supposedly more realistic full profile methoct. 5For further discussion of the trade off methodology and evidence of its predictive validity in contexts as diverse as MBA job choice STOL aircraft travel demand in Canada North Atlantic air fares supermarket acceptance of new products and consumer brand choice see Montgomery (1985) Its increasingly widespread use in marketing has been documented in the surveys by Cattin and Wittink (1982) and Witnnk and Catnn (1989)


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Respondents and Response Rate

Conjoint surveys were mailed in advance to executives attending a total offourone- and twoweek executive programs conducted at the Stanford Engineering School and the Graduate School of Business In addition, the surveys were distributed during a one week seminar at a high technology company6 In all cases, there was a pedagogical purpose in addition to the obvious research objectives The announced pedagogical purpose was to familiarize all atttendees with the respondent’s task in a conjomt analysis and therebyenable the attendees to be more informed customers of the results of such analyses In all five programs, subsequent to their completion ofthe conjoint surveys, attendees were given a lecture/discussion on the potential use ofconjoint analysis to address strategic issues in light of measured market preferences The linkage of the research instrument with the pedagogical purposes of the programs led to an excellent response rate of 274 out of 346 or 79%

Research Instrument Automobiles and office automation were chosen as two productswith which the executive respondents would likely be familiar and thereby give reasonably informed answers to the conjoint questions The next task was to identify a set of attributes and to define the levels we wished to assess This identification was aided by several colleagues who were knowledgable in the product areas, an auto executive, and the active involvement of both authors as customers in both the automobile market and the computer market in the period preceding the formulation ofthe study The resulting attributes and levels are shown in Table 4 for automobiles and Table 5 for office automation The scenarios respondents were asked to use in answering were the following AUTOMOBILES

You are buying a car for your personal use You

will be the principal driver

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OFFICE AUTOMATION

Please respond to this questionnaire in terms of a

purchase of office automation such as a personal computer and related software The use of this equipment will be at your office in the company where you are employed If every attribute were to be directly compared to each other attribute, a respondent would need to complete twenty-eight trade-off tables for each product. This would no doubt have impacted 6The programs were 1) the two week Strategic Marketing Seminar for senior marketing executives 2) the two week Stanford- American Electronics Association Executive Institute for senior managers in the electronics industry 3) the one week Effective Management of Production Operations” formiddle managers in manufacturing and operations 4) the one week Manufacturing Strategy for Competitive Advantage for semor manufacturing executives and 5) an in company marketing seminar for upper middle managers from all functional areas bf a high technology company. The response rates were 95%, 88%, 55% 65% and 74% respectively

HAUSMAN ~ND MONTGOMERY

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negatively upon response time and respondent fatigue and thereby the response rate and accuracy.

Consequently, foreach product, a respondent was asked to complete only sixteen trade-off tables. The design ofthe trade-offquestionnaire is given in Figure 3 where the arrows indicate attribute pairs which were directly compared in the trade-off analysis The questionnaire was constructed so that if two attributes were not directly compared, they would both be directly compared to four other shared attributes Although heuristic, this design rule should build redundancy into the assessment of indirectly compared attributes, therebyenhancing the likelihood of valid part worth estimates

3 AGGREGATE MARKET CALIBRATION RESULTS Automobiles A total of two hundred seventy-four questionnaires were returned Of these, eighty-eight percent (242) provided complete data and presented trade-off tables which exceeded our cut-off level for consistency in the Linmap solution7. The average scaled importance weights for automobiles are presented in the left portion of Table 6 The confidence and completeness averages are comparable to those found in the MBA job

choice conjoint validation studies (Montgomery, 1985) The attributes in Table 6 are arrayed in order oftheir average importance weights Price is clearly in a class by itself as the most important attribute Less than half as important as price are features, safety, and performance, somewhat less important are reliability and warranty Fuel economy and flexibility of feature selection are the least important attributes, far behind price in attribute importance In fact, they are less than twenty-five percent as important. The wide range in price levels ( $10,000 to $ 35,000) used in this study may account in part for the dominating importance ofprice

It is interesting to exanune the market behaviorofdiffere~automobile companies in light of these customer preference results At about the time this study was conducted, General Motors was -

offering to send consumers a diskette for either an Apple or an IBM compatible personal computer so that a consumer could price and subsequently order a Chevy truck with whatever individually selected

options that consumer desired This seemed the penultimate in providing consumer flexibility to specify features In contrast, Toyota, to some extent out ofnecessity due to its distance from the US market, was offering a very restricted variety of options best described as “variety in sets” Thus if a Toyota consumer wanted a sunroof, she might be requiredto also take the upgraded radio, or viceversa Toyota’s variety in sets enables it efficiently to produce in Japan and ship cars a great distance into the US market This efficient move, of course, enables Toyota to address the consumer’s number

one priority, price In contrast, GM developed a sophisticated, flexible system to assist consumers on ~Linmap computes the average Kendall rank order correlation between the actual ranks ofthe cells in a table and those computed from the Linmap estimatesof the part worth utilities (Srinivasan and Shocker 1981) We utilized the customary Kendall tau greater than 0 85 critenon as the threshold for including a respondent in subsequentanalysis


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a low-priority attribute. Toyota’s strategy enabled itto give up a bit on a low priority attribute while enabling it to be more responsive on the major automobile preference attribute because of the enhanced manufacturing efficiency of “variety in sets”8. The standard deviations of the importance weights in Table 6 are of the same order of magnitude as the average importance weights themselves This suggests that there are substantial differences among respondents in the importance they attach to various attributes Consequently, the

potential exists to identify different preference segments i e groups ofrespondents who attach similar importance to the attributes We will subsequently explore both a priori and empirically the -

issue of the existence and nature of preference segments Office Automation The right hand side of Table 6 presents the average importance weight results for office automation Somewhat more respondents, two hundred sixty-five, completed this portion ofthe questionraire and satisfied the tau > 0 85 consistency criterion Again, the confidence and

completeness averages are comparable to previous studies The confidence average is slightly lower than for the automobile portion ofthe questionnaire, while the completeness ofthe attributes is substantially and significantly larger The contrasting confidence results may reflect the fact that virtually all respondents had purchased automobiles and made regular use of them, whereas one

would expect office automation use experience to be more varied,with a consequent decline in average confidence (By the same token, the greater experience with automobiles, as well as perhaps the automobile’s greater dimensionality as a product, could suggest additional, somewhat idiosynchratic attributes to consumers concerning automobiles, thereby leading to a lower completeness score)

By far the two most important attributes here are software availabthty and ease of use/learning In the second tier ofattribute importances are reliability, maintenance & repan, and customer training All three would seem to reflect a need for hassle-free functioning of the office

automation product in performing its anticipated service Price is much less important for office automation than it was for automobiles This no doubt reflects the lower level of price, its narrower range, and the fact that office automation is more often purchased with the company’s money, whereas automobiles are more likely to be a personal expense Each of these factors would be expected to reduce price sensitivity foroffice automation in contrast to automobiles in this study Once again, it is interesting to note the relation ofthese average importance weights in relation to company strategy IBM and IBM compatible machines have myriads of software available, which 8 The cost implications of total flexibility are readily seen by noting that ten options each available at two levels creates 210 = 1024 distinct combinations of options The Cadillac Division of General Motors has the potential to produce 2 X io17 unique automobiles This complexity and variety are extremely likely to create inefficiencies in the production and distribution process


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reflects a strategy well targeted to the most important attribute. However, Apple’s strategy of ease of use/learning is consistent with the other leading attribute. Naturally, a company which succeeds in meeting both ofthese top attributes should have the opportunity to reap considerable competitivq advantage. Again, the standard deviation across respondents is of the same order ofmagnitude as the attribute importances, suggesting that attempts at market segmentation based upon preferences should prove fruitful.

Pooling Results The preceding aggregate results have pooled the data from all five ofthe executive programs discussed earlier. This pooling is legitimate provided the groups themselves do not differ significantly in the importances they attach to the attributes. The difference between means for each pair of groups was tested for significance foreach of the eight attributes plus the confidence and completeness scales. This yields one hundred pairwise comparisons for automobiles, and a similar number for

office automation. Only one such comparison was significant at the 0.01 level forautomobiles, which is exactly the “type one” expected error at the .01 level ofsignificance. For office automation only one ofthe one hundred was significant at the 0.05 level, well below the “type one” expected error for the test at the .05 level. We conclude that, although respondents seem to differ in their attribute preferences, it does not appear to result from systematic differences between the marketing, high technology, and manufacturing executives included in the study from the-five different programs. 4. A PRIORI SEGMENTATION The previous section treated the market foreach product ( automobiles and office automation) as monolithic. In fact, each of these markets is large enough to segment into different market segments with different importance weights for the various attributes. These differences should be incorporated into corresponding manufacturing priorities- for-each segment. Before running a statistical cluster analysis on attribute importances, we first specified our expectations as to which attributes would tend to be at high levels foreach cluster. A high level for an attributein a clusteris indicated by the attributehaving an above average importance weight for that cluster. Our “priors”, while not based upon specific theories, are reflective ofthe authors’ multiple experiences as customers in both ofthe product categories. Wefelt it was imperative to write down our priors in order to guard against excessive post hoc rationalization which is so seductive when one examines the data first. We invite the readerto test her prowess by writing down explicit expectations about what the preference clusters will look like before proceeding to our discussion of our priórs and their subsequent test. Recall that the attributes were: -


AUTOMOBILES:

1. 2. 3. 4.

RELIABILITY

5. 6. 7. 8.

PRICE

WARRANTY PERFORMANCE SAFETY

FEATURES FLEXIBILITY FUEL ECONOMY

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OFFICE AUTOMATION: 1. PRICE 2.SALESFORCE EXPERIENCE

3. MAINTENENCE AND REPAIR 4. CUSTOMER TRAINING & SUPPORT 5. DELIVERY TIME 6 RELIABILITY 7. SOFFWARE AVAIlABILITY 8. EASE OF USE/ LEARNING

(Please continue once you have written down your pnors or have given up) Our expectations fora priori segments are given in Table 7 In specifying our pnors we associated each attribute with one and only one segment This seems in retrospect to have been a bit too restrictive, although it has the advantage ofproviding very focused prior hypotheses Automobiles For automobiles we anticipated five segments The Price Segment was expected to attach high importance to price and fuel economy, reflecting concern with both initial cost and operating costs The Safety Segment was expected to consider safety of key importance This segment was anticipated

based upon the successful safety positioning of some- automobile conipames such as Volvo which have featured ads showing Volvos crashing into brick walls with the dummy passengers “surviving”, Volvos dropping off high rise parking garages, and even a Volvo station wagon with a large Volvo truck sitting on top of it. All of these ads appeal to consumers who are largely concerned with reducing risk to themselves and their families in automobile travel The High Performance segment was expected to consider performance (e g acceleration,

passing, han4hng, and braking) and product features to be ofkey importance With mcreasing numbers of two career families, which implies higher incomes and less time for taking corrective action when products fail, we anticipated a Hassle Free segment which would rate reliability and warranty high in importance Finally, we anticipated a segment which would find flexibility of choice to be ofimportance The people in this segment would prefer to order the exact car of their choice rather than purchasing a car from available dealer inventory We did not quantify any prior expectations as to the relative size ofthese expected segments, but the empirical results should be suggestive of their relative numerical importance among executive

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target markets. Naturally, given the nature of the respondents, these results are not reflective of different groups of consumers such as consumers at large. The results for automobile a priori segments are presented in Table 8. The respondents were clustered using theirrelative importance weights for each attribute as the measure of similarity. The BMDP K-Means clustering algorithm was used to develop the five clusters which we anticipated. The table presents the mean importance -weight for consumers falling into each cluster as well as the ranking of the mean importance weight forthe attribute across the five clusters. The @ flags our a priori hypotheses for the segments. The Price, Safety, High Performance, and Hassle Free segments emerged from the analysis. Only the Flexibility segment failed to occur as anticipated. To be sure, the fifth segment did indeed have the highest average importance forFlexibility ofany of the five segments. What was not anticipated a priori was that this fifth, and largest segment (60.3%), also had the highest level of importance for features, warranty, and fuel economy and the second highest importance for price. Consequently, this largest segment should be viewed as one in which there is a reasonable balance among the importance weights ofthese five attributes. For the other four segments, the major attribute expected to define the segment was empirically confirmed. Whenever our prior hypotheses were not strictly fulfilled in the case ofa somewhat secondary attribute, the average attribute importance was second across the segments (fuel economy in the Price segment and features in the High Performance segment) or a close third (warranty in the Hassle Free segment). Since we formed our a priori hypotheses with one variable per segment, we did not anticipate the importance of reliability in the Safety segment nor the importance ofwarranty in the price segment, although both ofthese results seem very reasonable. Reliability certainly contributes to .~safety,while an excellent wafranty can lessen the risk from future operating and repair costs. The key role ofprice may be seen from the fact that price is the single most important attribute on average for both the price segment and the more balanced fifth segment. These two segments represent nearly eight-five percent (22.4%+ 60.3%= 84.7%) ofthe executives in this study. When one considers the upscaleeconomic profile of the respondents, it underscores the vital role of efficiency in the automobile market. It is interesting to note that the Safety segment ( 6.6%) is slightly larger than the High Performance segment (5.4%). This would be consistent with the recent emphasis on safety features, even from performance-oriented companies such as Mercedes Benz. ..~

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Office

Automation Our priors for office automation were that six segments would emerge, but again we had no expectations as to the size of these segments. Rather, we allowed the empirical results to suggest the .

relativemagnitude of each segment.

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Once againwe anticipated a Price segment which would have relatively high importance for price, although we expected price to be less important for office automation than for automobiles given two factors: 1) the price level for office automation was at maximum $ 5000 whereas for automobiles the range was.$ 10,000 to $ 35,000 and 2) the respondents were more likely to spend company money for office automation and personal money for automobiles, thereby lowering the price elasticity of officeautomation relative to automobiles Respondents were also expected to divide into two segments for which either software availability or userfnendlmess would be ofdominating importance The final three a priori segments were Support Sensitive, Downtime! Repair Sensitive, and Delivery Sensitive, with relatively high levelsof attribute importance for the attributes indicated in Table 7 The results are presented in Table 9 and were obtained in the same manner as those for automobiles with the exception that we developed six clusters in the case ofoffice automation The Job Application! Scope, User Friendly, Support sensitive and Downtime/Repair segments emerged largely as anticipated Again the key a priori variable played a dominating role in each of these segments In the case ofmore secondary variables the importance ofsalesforce expertise had its second highest level in the Support sensitive segment while maintenance and repair only had its third highest level in the Downtime! Repair segment Consequently, these four segments were substantially as anticipated a priori The Delivery sensitive segment did not emerge Ratherin its place there occurred a Maintenance sensitive segment comprising 4 2% ofthe sample and reflecting a very high importance for maintenance (0728) Although delivery did have its second highest average importance weight for this segment, it was less than threepercent of the importance ofmaintenance and repair Consequently, a better post hoc label for the sixth segment would be Maintenance rather than Delivery The low average importance formaintenance and repairin the Downtime!Repair segment (only 0 065) coupled with the above result in the sixth segment, suggests that customers see reliability in a substantially different light from maintenance Nearly twice as many executives focus upon reliability as do on maintenance (7 9% vs 42%) Evidently reliability for office automation also implies hassle-free use just as for autos This depends upon manufacturing and design quality The smaller group is more concerned with getting it fixed once there is an actual problem This, of course, relates to the after sale provision of service Finally, the Price segment did indeed have the highest average importance for price across the six segments, as expected However, for this segment price was only the fifth most important attribute Consequently, in retrospect this segment (66 4%) is again a segment which shows balance between the attributes in theirimportances In fact, every attribute except rehabthty has its first or second highest attribute importance for this segment, reflecting this balance For this largest segment software is the most important attribute, followed closely by ease ofuse! learning


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For office automation price had a much lesser importance than was true for automobiles. It has a relatively low importance (0.097) even in the segmentin which it was most important, and it was only the fifth most important attribute in that segment. For autos price was the most important attribute for two major segments comprising 847% ofthe respondents For office automation the key attributes of software availability and ease of use!learning were both of very high importance to two major segments compnsing 747% and 76 6% of the respondents, respectively As a younger, less mature industry it is not surprising that office automation is by far less sensitive to price-and far more sensitive to other attributes than are automobiles

Summary for A Priori Segments The prior expectations regarding preference segments based upon attribute importance weights were largely borne out, but more so for automobiles than for office automation Neither was perfect In both cases a majorpreference segment emerged which seeks substantial balance-in the attributes This suggests that companies must weigh the advantages of seeking a broad and substantial market via performing reasonably well at everything or selecting a target submarket which has its own particular preferences which may enable a company to focus its manufacturing priorities The a priori segments reinforce the dominating importance ofpnce in automobiles and the lesser importance of

price in office automation The a pi~ionresults also indicate that the IBM software availability strategy and the Apple ease of use strategy each have somewhat protected niches However, the software availability and ease of use attributes are nearlyequal in importance for the largest segment comprising over 66% ofthe respondents, thereby indicating substantial overlap in preferences for these two vital attributes in terms ofcustomer preferences This large group which highly values both of these attributes seems likely to stimulate more direct confrontation between Apple and IBM and the compatibles The recent launch of Microsoft windows is an early salvo in what could prove to be a protracted contest

S STATISTICAL PREFERENCE SEGMENTS The previous section examined the results for our a priori expectation for preference segments based upon clustering respondents according to the similarity of the importance weights they attach to each attribute of a product In this section, we conduct the K-means cluster analysis in a similar fashion, however, we now do so iteratively for different numbers of prespecified clusters The final number of clusters to retain was determined by judgment cors’denrg the incremental Improvement m the maximum clusteraverage distance from any respondent in a cluster to the centroid of that cluster and the size and practical significance of the resulting clusters We then seek tointerpret the resulting


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clusters and to examine the implications of their size and preferences for the product attributes we consider. These results will then be related to manufacturing priorities in the next• section. Automobile Statistical Segments For automobiles we-elected to retain eight clusters. Although the maximum average distance in a cluster ( distance is the Euclidian distance between the attribute importance weights ofa respondent and the cluster ceniroid) was not reduced as we move from seven to eight clusters, the eighth cluster effectively subdivided the largest cluster in the seven cluster-solution into two new clusters containing 22.7% and 36.4% ofthe sample respondents. Consequently, the eighth cluster seemed to add importantly to the segmentclustering. In contrast, adding a ninth clusterprovided little incremental interpretation. The nine cluster solution only grouped three respondents together into the ninth cluster, representing just over 1% of the total sample. Based upon these results it seemed reasonable to elect to retaineight clusters. The eight-cluster solution is presented in Table 10. The table entries are the average attribute importance weights for the respondents in each respective cluster. The first clusteris one in which the importance ofprice-is dominant ( average importance (Al) of 0.664) and contains nearly 20% of the respondents, making it the third largest automobile segment. The second segment is a small one (3.3% ofthe respondents) in which performance plays an overwhelmingly dominant role (Al = 0.828). For the small number of respondents in this cluster, performance is virtually the only purchase criterion which matters. The third segment (6.2%) is strongly focused on safety (Al = 0.563) and has an above average (Al = 0.139) concern for reliability. As in the a priori segmentation, this segmentappears to be a safety concerned segment where the reliability factoris related to safety concerns. Consistent with the existence of this segment some automobile companies have-explicitly developed safety related reliability appeals, especially to women who drive at night. Cluster four (3.3%) suggests that the other aspect of reliability is the “hassle free” issue discussed earlier with reliability having a dominant Al of 0.7 12. The fifth cluster, which did not emerge in the a priori analysis, reflects a small (2.5%) cluster with a very high concern for warranty (0.764). This may be thought ofas the “risk averse” cluster, the ones who want to minimize buyer’s regret, at least financially. The sixth cluster (5.8%) has high Al’s forfeatures (0.388) and performance (0.309). Although this segment has the highest Al for flexibility (0.086) ofany segment, this attribute is only the fourth most important in this segment. It seems reasonable for a segment heavily weighting and balancing features and performance, to also have relatively the highest appreciation for flexibility, which enhances their ability to get just the right combination of features and performance. This segment and the cluster two performance segment adds a richer view of the automobile segmentation than did the a priori analysis. -

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The seventh cluster is the second largest ( 22.7%). Price (AI= 0.340) is the most important

attributeto this segment, with an Al more than twice that ofthe second most important attribute. Nevertheless, features (AI= 0.154) and safety (AI= 0.132) are also ofsubstantial importance. Among all the segments, this one is most concerned with fuel economy, consistent with its high sensitivity to price.The eighth and final cluster is the largest (36.4%) and exhibits once again a substantial balance in the importance ofthe attributes. This segment is above average in Al for all attributes except price, which nevertheless is the second most important attribute in the segment. In contrast to the a priori segmentation, the statistical segmentation has allowed several new aspects of automobile segmentation to emerge. In the first place, it has shown that the largest segment in the a priori analysis may be usefully divided into-a very balanced group on attribute weighting (cluster 8) and one more responsive to price, features, and safety (cluster 7). The two resulting segments are the two largest in the market. Kiiowing they have distinctive preferences should be helpful in targeting product and marketing activities toward these consumers. This in turn, ofcourse, has implications forthe design, manufacturing, and delivery system. Second, the statistical segmentation revealed the performance dominated segment (cluster 2). This then led to segment six in which features and performance are nearly coequals. The combined share of segments two and six is 9.1%, whereas -the more aggregated a priori analysis suggested that the high performance segment contained only 5.4% of the respondents. Hence the statistical analysis nearly doubles the percent of the market seen as sensitive to performance, while slightly increasing those sensitive to features. Finally, the statistical segmentation revealed the small, risk adverse segment (cluster 5). It is also interesting to explore the implications of the statistical segmentation for the various attributes. Price is again confirmed as the overwhelmingly important factor in the automobile market. ------it- is- dominant in segment one, of number one importance in segmentseven and is a clcse second inimportance in segment eight. These three segments have a combined total of 78.9% of the respondents. Note that this dominating importance of price occurs even in this high income, upscale executive population. How much more vital it must be in the general population. Features are ofnumber one importance in segments six and eight and second in importance in segment seven. Thus features are of substantial importance to 64.6% of the market. Safety is dominant in seginent three but is also of considerable importance in the large segments (7 and 8).

-

-

-

Consequently, safety is a key attribute for 59.1% of the market. Reliability is dominant in segment four, number two in segment three and is fifth (but with an Al of 0.13) in segment eight. Thus reliability is an important attribute for 45.9% of the market, as estimated from these results.

Performance plays a dominant role in cluster two, is number two in segment six and is quite important in segment eight (AI= 0.142). Thus performance is ofsubstantial importance to 45.5% of the market. While warranty is of dominating importance for only the smallest segment ( cluster five), is has an Al above 0.1 for cluster eight. Thus although warranty is ofoverwhelming importance for


17


only a miniscule 2.5% ofthe market, it is a substantial choice factor for fully 38.9% ofthe market. Hence design, production, and aftersale service systems cannot afford to neglect this attribute. Fuel economy and flexibility were not particularly important for any segment. These data illustrate how conjoint market calibration may b~used to assess consumer preferences and then use them to establish manufacturing priorities. The reader should be cautioned about two aspects, however. It has been mentioned that the data were gathered from executives who were upscale in both income and education, to say nothing ofproven management achievement. Consequently, the specific conclrisions cannot be extrapolated to other populations. Of equal importance is the fact that consumer preferences are dynamic. Importance weights are likely to shift over time and, indeed, have. One needs only note our finding ofthe importance ofthe safety segments in our results. In the mid 1950’s Ford was badly beaten by GM when Ford emphasized safety while GM escalated the horsepower race. Today, even Chrysler is challenging -the other automakers by advertising how air bags are widely available on Chrysler cars, but only available on limited models from the Japanese imports. This suggests that companies need to continually seek to identify changing customer preferences and integrate this with planning future manufacturing operations. -

-

-

Office Automation Statistical Segments Similar to automobiles, eight clusters were chosen for office automation. Going from seven to eight clusters reduced the maximum average distance in the clusters and once again split the largest a priori group into two -meaningful subgroups.The maximum average distance in a cluster was no longer reduced when a -ninth cluster was added and further, the ninth clustercontained only one re~pOndenwhbhãd an u7tiUsuallyh ghvariie for dèli~éry(A10~839).Con~e~u~u1~i,eightc1usters seemed to be the best solution foroffice automation as well. The office automation statistical segment tion results are presented in Table 11. Segment one (7.9%) is dominated by the importance of software availability (A! = 0.744). Ease of use dominates cluster two (Al = 0.708) and is somewhat larger at 9.1%. Segment three (5.3%) is dominated by reliability (Al = 0.633) although at a bit lower level of importance than the dominating attributes for clusters one and two. Cluster four ( 4.2%) reveals a maintenance (Al = 0.728) dominated segment. Segment five (3.0%) is very sensitive to training (A! = 0.719). Segment six (3.4%) has price as its most important attribute (AT = 0.438) while software (A! =0.144) and reliability (Al = 0.115) are respectively the second and third most important attributes in this segment.Thus for office automation the second smallest segment is the price segment and price does not dominate this segment in importance in the manner in which other attributes~ dominate segments one throtigh five. The seventh segment ( 27.2%) is the second largest and reflects substantial importance for software (A! = 0.323), ease of use (Al = 0.203), and reliability -

-


18


0.177). The final cluster is also the largest at 40.8% of the respondents. As in autos, this final segment reflects relatively balanced importances forthe attributes; with only delivery being relatively low in importance (A! = 0.064). Even so, this segmenthas the highest AT fordelivery of any (Al

=

segment. In contrast to the a priori results, the statistical segmentation has shown that the largest a priori

segment may be usefully broken into two segments, cluster eight indicating balanced preferences while cluster seven substantially preferring software, ease of use, and reliability. The small price segment was also revealed by the statistical segmentation, although the importance ofprice is dramatically less than for automobiles, as expected. The separate segments emphasizing reliability and maintenance suggested by the a priori analysis was confirmed, albeit at a slightly r~ducedfraction of the respondent sample. These results also again confirm the discussion of the a priori results that preferences for software and ease ofuse share a substantial overlapin the consumers attracted to these attributes. This will no doubt force competitive convergence over time on these attributes, thus bringing the Apple strategy ever closer into direct competition with IBM and- the compatibles. The implication of these results for office automation attributes is informative. Using the somewhat arbitrary, but reasonable, criterion that an A! of more than 0.10 is required for an attribute to be ofsubstantial importance to a segment, software, ease ofuse and reliability are all important to the vast majority of the respondents. The respective percentages being 78.6%, 77.1%, and 76.7%. Maintenance and training are of major importance to substantial minorities in the respondent group with 45% and 43.5%, respectively. Perhaps most suprising, given ourearlier results, is that salesforce expertise is of substantial importance to over forty percent ofthe respondents. Thus salesforce expertise seems to be important to a considerable minortity ofthe respondents, a fact that was not revealed in the aggregate or the a priori segmentationr~sults~ This suggests that it would behoove both dealers and manufacturers to emphasize dealer salesforce training and updating. Price met the Al >0.10 criterion for only 3.4% ofthe respondents. It proved to be marginally important (AI= 0.94) for an additional 40.8%. Thus price is clearly of secondary importance in this -

- -

-

market. But it is not completely irrelevant,just of lesser importance than other attributes. Finally, delivery never rose above A! of0.07, suggesting that at least at this time for these executives the delivery aspect of time based competition would not yield much competitive advantage. This should not be mistaken for the aspects oftime based competition which relate to time to market and pioneering advantages which may reflect themselves in many ofthe other attributes9.

~See Lieberman and Montgomery (1988) for a review ofresearch on first mover advantages and Lieberman and Montgomery (199 F) fora more managerial discussion of the time based aspects of pioneering.


19


9. Implications for Manufacturing Strategies and Tactics Strategic Considerations In this section we will discuss the implications of the statistical segmentation of the conjoint analysis results çl’ables 10 and 11) for manufacturing strategies and tactics. For the automobile market segmentation, we have observed that Price is of high importance for the Price segment (19.8%), the Price/Features/Safety segment (22.7%), and the Balanced segment (36.4%); together these segments represent 78.9% of thetotal. Hence, for firms competing in these large segments of the automobile market~,a primary manufacturing strategy emphasis must be placed on cost (based on the linkages shown in Figure 1). Features are ofhigh importance in the Features/Performance, Balanced, and Price/Features/Safety segments, totalling 64.6% of the total sample. Here the manufacturing strategymust emphasize rapid innovation (see Figure 1) so that features desired by the marketplace are available. Safety is an important attribute in the Safety, Price/Features/Safety, and Balanced segments, totalling 59.1% of the market. Safety as such is not a manufacturing priority; it is a set offeatures “broken out” as a separate bundle to be analyzed by conjoint analysis. Thus, if a firm wishes to compete in these three segments its manufacturing strategy should again emphasize innovation so that new safety features can be rapidlyput into the marketplace. Reliability is important to the Safety, Hassle Free, and Balanced segments, totalling 45.9% ofthe market. Product reliability is linked tothe manufacturing strategy priority ofQuality (see Figure 1). Performanceis dominant In the PerformanCe; Feat~es/Perforrnancc, ~ndBalan~ed segments. One should not identify Performance with Quality; the conjoint analysis has measured these dimensions separately. Hence, Performance is again a bundle offeatures “-broken out” by our conjoint analysis and shown statistically to have major impact on 45.5% of the market. Competing on Performance again requires rapid innovation as a manufacturingpriority. Warranty emphasis is ofcritical importance to the Warranty segment, which is very small (2.5%); but its importance weight is above 0.10 forthe large Balanced segment (36.4%), so it is of some importance to 38.9% ofthe market. Offering excellent warranty coverage makes economic sense only when a firm’s product is of sufficiently high reliability that the firm avoids excessive warranty costs; hence a firm wishing to compete on this dimension should emphasize Quality. Note, however, that this -factor is apparently less important than the others discussed so far. The last two attributes, Fuel Economy and Flexibility of choice on options, are not particularly important; in no case does any importance weight exceed 0.10. However, it is instructive to consider the manufacturing strategies which- would be consistent with these attributes. —--

-

-

-

- -

-

-

-

-

- - -

-

- -


20


Fuel Economy is againa “bundling” ofa set offeatures affecting miles per gallon, and engineers designing powerplants would state a fundamental tradeoff between fuel economy and performance (implying rapid acceleration among otherthings). In a static technological environment this is true; however, technological change may allow one to gain on both of these dimensions simultaneously (e.g. using multivaive engines). Again, there would be a need for rapid innovation. Regarding Flexibility of choice, it is indeed fortunate for the Japanese automakers that this attribute is oflow importance, for the international shipping of automobiles from Japan to the U.S. market precludes individual “tailoring” of a Japanese automobile. In the U.S. we have been told that well over 80% of domestic automobiles are ordered by dealers rather than final purchasers; this is consistent with the low weight placed on flexibility of choice. If, however, this attribute carried a much larger weight (as it might, for example, with your ability to order your own preferred meal at a fine restaurant) then one would need to design a manufacturing and delivery system to respond to that need (i.e., a kitchen, chef and waiter arrangement which creates “custom” meals out of “standard” -

components). Let us re-examine the eight segments in Table 10 to study their total implications for -

-

-

manufacturing strategy. Major auto firms competing in high-volume segments cannot ignore segments 8, 7 and 1; thus they must emphasize Cost and maintain a balanced approach to many of the other attributes. They need not pay much attention to flexibility ofchoice (assuming the varietyin-sets concept ofassembling various options in a given automobile is carried out in a reasonable manner), and given consumer preferences alone, they could afford to pay little attention to fuel economy (the CAFE government-mandated fuel efficiency standards and penalties force auto companies to be more concerned with fuel economy than our sample would prefer). Warranty can he give~le enphaais than thCr amlbutes.’0 Suppose, however, that we consider a firm emphasizinghigh-performance, expensive automobiles. The Performance and Features/Performance segments would be the target segments for this producer, and the manufacturing priorities should emphasizerapid innovation. Furthermore, what, manufacturing priorities are less important for this firm? Cost, Short-term Flexibility, Dependability (here meaning Availability without delay) and even Quality are of significantly less importance to this market! Turning to the Office Automation results (Table 11), here we have the opportunity to broaden the manufacturing concept to one of Operations. Purchasing an office automation piece of equipment is not simply acquisition ofhardware;\it also commits one to that “platform” for existing -

-

-

-

-

‘0An October 1990 report (CNN) indicated that a multimilion dollar automobile refund would be made by US automobile companies, some Japanese manufacturers, and Volvo relative to an alleged violation ofNew York law which held that these companies did not have sufficient warranties. Thus it appears that the government is stepping in to enforce performance on attributes which appearto -be oflesser significance to consumers, at least in this executive sample. -

-



21

and emerging software. We first consider those attributes related to the hardware itself; the most important ofthese is Reliability, and the least important is Price and Delivery Availability. Hence the manufacturing portion of a firm competing in this market should have a manufacturing strategy emphasizing Quality (linking to Reliability) and generally not giving as high priority to Cost and Dependability (here meaning Delivexy Availability). Software availability is very important for a large fraction of this market. A computer without software is an expensive doorstop; the user needs software as well as hardware. Even though it has become traditional for different parties to provide hardware and software, from the customer’s viewpoint the total package (hardware and separately-purchased software) is what is being evaluated and selected. Software availability is only indirectly affected by traditional manufacturing priorities; it is driven by software writers’ assessment ofthe potential market for the software being contemplated on the particular computer platform (or system platform) under consideration. Thus the manufacturerofhardware would want to have his/her hardware hold a large market share, and presumably the importance weights in Table 11 would show how to best achieve that goal. Ease of use is another highly important attribute in this market. This attribute relates partially to the features of the hardware and partially to the features of the softwarerunning on the hardware. This attribute describes a moving target; initial spreadsheet programs on early PC’s were interpreted as easy to use by computer hackers accustomed to higher-level languages such asFORTRAN and BASIC, but were likely viewed initially as unfriendly by clerical personnel trained on electric typewriters. Along came the Macintosh, which created a new definition ofease ofuse; and undoubtedly the future will againredefine what we mean by ease ofuse. What are the rtiãriüfãtitiuiiii~and ó~ itl~Iii 1ifo~of this b~tWs~or~iz~ce? Product design for ease of use might be called a non-manufacturing implication if mAflI~fa~nmn g were interpreted narrowly; in any event, the implications are for product design (both for the underlying computer platform and software using it) rather than fabrication, assembly, and testing of the hardware- itself. Note the pattern emerging here. With the exception of Quality, the levers of traditional narrowly-defined manufacturing priorities (of Figure 1) are not particularly important-in the market for office automation. However, product design (hardware and platform for programming) is highly important. -

-

- -

Additional Information from Conjoint Analysis Conjoint analysis programs use-ranges of values called “part worth utilities” in order to calculate-the importance weights shown in Tables 10 and 11. The range ofutility values from the lowest level of any attribute to its highest level is interpreted as its importance weight (after normalizing). There are situations in which a more detailed observation of the actual part worth -

-

-

-

-

-

-


22


utilities can provide important information regarding customer preferences. We illustrate one such situation arising in the automobile preference data)’ Focusing on statistical cluster 2 (the Performance group) in Table 10, Figure 4 below illustrates the average part worth utility for each of the three levels of Performance described in the original questionnaire (see Table 4): Level 1: Excellent acceleration/passing/handling/braking Level 2: Very Good acceleration/passingThandling/braldng Level 3: Average acceleration/passing/handling/braking For -this Performance cluster, the importance weight for Performance is very large, indicating that for this segmentof the automobile market, Performance is indeed a critical attribute. However, Figure 4 demonstrates that virtually all the customerpreference is associated with Level 2 relative to Level 3 of this attribute; a product with the higher Level 1 Performance has only marginal consumer preference, even for this segment. This detailed information would be extremely valuable in positioning a product, and similar detailed calibration ofcustomer evaluations ofdelivery time, quality, and price would be helpful in reassessing a company’s manufacturing and operations strategy. -

Other Industrial Applications The automobile and office automation applications discussed -above were developed from conjoint analysis of executives’ preferences from executive seminar attendees, without regard to a specific company’s manufacturing and marketing strategies. Here we describe two industrial situations in which conjoint analysis has explicitly helped to reshape the manufacturing and operations strategy of a US and a European company. -

-

-

-

-

-

Example 1 Remodeling Materials. A conjoint analysis was carried out for a manufacturer ofbuilding components used primarily in remodeling. Among the attributes evaluated were the following: a) Breadth/Customization ofProduct Line, b) Delivery Time,- c) Quality, and d) Price --

~ An additional illustration adds insight into the dominating importance of price for automobiles. The acrossrespondents average part worth utilities for each level ofprice forautomobiles was found to be: $ 10,000 utility of 27.6 -

-

utility of 19.8 utility of 9.0 $ 25,000 utility of 10.3 $ 35,000+ utility of 44.3 The importance of price (before normalization of importances across all attributes) then is just 27.6 (-44.3) = 71.9. But note that 34171.9 = 47% of the importance ofprice is driven by disutility for the highest level of price relative to the second highest level. This suggests that price will play a somewhat less dominant role within a narrower price range. -

-

$ 15,000 S 20,000

-

-

-

-

-

-

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23

(fourprice levels). The conjoint analysis results quantified the customers’ sensitivities to delivery times, and the company’s manufacturing/operations strategy was altered to place greater emphasis on delivery times. This improvement in an attribute shown to be important in the marketplace also allowed the company to alter its pricing policy to improve profitability the conjoint analysis also demonstrated the sensitivity of customers to various price levels and calibf~atedthe delivery/price tradeoff. When customers were segmented by value of the property being remodeled, the conjoint analysis clearly showed that sensitivity to price dropped markedly with higher property value, while sensitivity to quality increased. Qualitatively, the general direction of these results is to be expected; conjoint analysis, however, has added a method to explicitly quantify these quality and price sensitivities ofcustomers. -

-

-

Example 2 Industrial Supply Product. A company producing an industrial supply product performed a conjoint analysis to determine customer preferences. In order of preference, the major attributes were: a) Delivery Speed, b) Price, c) Product Source (machine manufacturer vs. other), d) Labelling (machine manufacturer, private label, or generic), and e) Breadth of Product Line. speed relates to inventory and production resupply considerations; pricing relates to costs; and labelling relates to value-added by downstream integration (i.e., adding the capability of private labelling to the operations system). The conjoint analysis spelled out specifically the benefit to the --

Delivery

customer of one week delivery vs. two weeks, three weeks, et cetera; it also showed the effects of

list price vs. list price less 20%. A surprising and unexpected result occurred regarding private labelling; purchasers valued highly the opportunity to have theirprivate label placed on the product ~-~(priorto theirreselling-it-). The company used the results and modified their manufacturing and packaging so that sample turnarounds could be achieved using a laser printer, enabling salespeople to offer an “own label” product within 24 hours ofreceiving a trial order.- It is difficult to imagine how this change would have comeabout without the convincing support of the conjoint analysis illustrating the unexpected value ofthis modification in operations. -

-

-

-

Tactical Options Strategic manufacturing priorities must be translated into tactical manufacturing actions, plans and policies (cf. Beckman et al, 1989). Table 12 below lists a series of world-class manufacturing concepts and practices together with our estimates oftheirprimary strategic impacts. -

After conjoint analysis and segmentation have clarified appropriate manufacturing strategy priorities, the elements ofTable 12 can be used to create meaningful action plans for manufacturing which are consistent with marketing and business strategy for any given market segment.


24.


Consider an automobile manufacturer competing in the high-performance market segment. Note that from Table 12, emphasis on the popular Just-in-Time concepts would not contribute to the required manufacturing strategy priority ofinnovation. Thus, for competitors in this environment, just-in-time concepts should take a back seat to other tactical manufacturing efforts which emphasize innovation suchas Concurrent Engineering and Design For Manufacturability/ Assemblylrest. In contrast, for the very large price-sensitive segments, Just-In-Time methods and SPC/Quality Awareness programs focus on both cost and quality; these goals match the price-. -

-

-

sensitivity found for this market segment by conjoint analysis. Furthermore, while Hard Automation provides a cost payoff, it does not necessarily provide a quality payoff, and hence may

be less suitable than .111’- and SPCT1’aguchi Methods for this market segment. Beckman et al (1989) emphasize that not only performance measurement systems but capital investment justification systems need to be changed to reflect the specific manufacturingpriorities selected for each business segment. Most capital investment analyses are still based on a return-oninvestment CR01) calculation which focuses on cost and are not well-suited for evaluating other manufacturing priorities. Beckman et al argue that if a project is undertaken for quality improvement purposes, then it should be measured on its contributions to quality; and similarly for other manufacturing priorities. -

-

10. Conclusions How should firms approach world-class manufacturing? There is no universal answer it depends on a firm’s priorities. One useful way to determine relevant manufacturing priorities is to use the tool ofconjoint analysis to determine, for each market segment, those customer priorities which are most important in the marketplace. Then one can trace linkages from marketplace competitive dimensions to strategic manufacturing priorities, and from the latter to specifLc tactical manufacturing programs designed to meet the required strategic goals. How does this conjoint analysis methodology differ from Quality Function Deployment or : QFD (see Hauser and Clausing, 1988)? QFD focuses on many details of product attributes for a specific product; e.g., the design parameters and fabrication/assembly tolerances for an automobile door gasket which will achieve desired customer satisfactioii in closing the car door, keepingrain out, et cetera.- Conjoint analysis is necessarily much less detailedbut at the same time can cover a much broader set ofcompetitive dimensions (beyond product attributes themselves) which have: been shown to be important in the marketplace, such as delivery time, warranty coverage, orspeed of customization. Furthermore, as illustrated above, conjoint analysis can provide a firm with critical information for its desired manufacturing strategy. Then the translation from manufacturing strategy to manufacturing tactics (via Table 12) offers concrete guidance on how to select among all -

-

-

•


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25


elements of world-class manufacturing those which will provide a real competitive advantage in the

market in which the firm competes. Thus, conjoint analysis can contribute significantly to making world-class manufacturing market-driven.


26


References Beckman, Sara L. Boller, William A., Hamilton, Stephen A., and Monroe, John W. (1989), “Using Manufacturing as a Competitive Weapon: The Development ofa Manufacturing Strategy”, Chapter 3 of Strategic Manufacturing: Dynamic New Directions for the 1990’s, Patricia E. Moody, (Ed.) Dow Jones-Irwin, APICS Series in Production Management, November 1989. Cattin, Philipe and Wittink, Dick (1982),”Commercial Use of Conjoint Analysis: A

Survey,” J. ofMarketing, Vol.46 (Summer). Day, George S. and Montgomery, David B. (1983) “Diagnosing the Experience Curve,” J. of Marketing, (Winter).

-

Hauser, John and Clausing, Donald (1988), “The House of Quality,” Harvard Business Review, (May/June).

Hausman, Warren H. and Montgomery, David B. (1985),” Managing the Marketing/Manufacturing Interface,” Issues (Autumn) and Gestion 2000 (Vol .2,No.5, 1986). Hayes, Robert H., and Wheelwright, Stephen C.( 1984), -Restoring Our Competitive Edge~ Competing Through Manufacturing. John Wiley & Sons, 1984. Lieberman, Marvin B. and -Montgomery, David B., (1988),” First Mover Advantages,’ Strategic Management Journal. Lieberman, Marvin B. and Montgomery, David B. (1991), “To Pioneer or Follow?: Strategy of Entry Order,” in Glass, H. (Ed.), Handbook of Business Strategy (2nd Ed.), (Warren, Gorham, and Lamont: New York). -

-

-

Meyers, John, Greyser, Stephen A. and Massy, William F. (1979), “The Effectiveness of

Markeiing’s ‘~&D’fór~ (January).

Ati A~sessriient,’~ J~of Mark~fing,var. 43 -

Montgomery, David B. (1985), “Conjoint Calibration ofthe Customer/Competitor Interface in Industrial Markets,” in Backhaus, Klaus and Wilson, David,(Eds.), New Developments in Industrial Marketing, (Springer Verlag). Montgomery, David B. and Charles B. Weinberg (1979), Toward Strategic Intelligence Systems,” J. of Marketing, (Fall). Ohmae, Kenichi (1982), The Mind of the Strategist. (McGraw Hill: New York). Porter, Michael (1980), Competitive Analysis. (The. Free Press: Glencoe,fll.). Srinivasan, V. and Shocker, Allan D. (1981), LINMAP VERSION IV --USER’S MANUAL: Linear Programming Techniques forMultidimensional Analysis of Preference Judgments, (Graduate School of Business, Stanford University). Wittink, Dick R. and Cattin, Philippe ( 1989),”Comrnercial Use of Conjoint Analysis: An Update,” J. of Marketing, Vol. 53 (July). -

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-TABLE 1 MANUFACTURING PRIORITIES Cost (Lowest total cost)

-

-

Quality (Conformance to Specifications) Dependability (Meeting delivery/availability commitments) Short-Term Flexibility (adapting to changing product volume and mix requirements) Innovations (rapid introduction of new products and features) -

TABLE 2 MARKETPLACE COMPETITIVE DIMENSION-S -

-

Price Quality (reliability, durability; not features) Availability (response time mean and variance) Variety (width ofproduct line; options, customization) Features (Quality residual; vs. competition; first to market) Post-Sales Service (including spare parts availability TABLE 3 PHOTO PROCESSING TRADE-OFF EXAMPLE PROCESSING TIME 1 Hour

P

$7.00

-

2 Days

-

1

3•

2

4_

I C

$10.00

E (1

=

most preferred,..., 4

=

least-preferred)

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28


TABLE 4 AUTOMOBILE ATTRIBUTES AND LEVELS 1. Predicted Reliability (based on an ‘independentconsumer magazine): Much better than average

Better than average Average 2. Warranty Coverage: 1 year! 12,000 miles 3 years! 36,000 miles 6 years! 60,000 miles 3. Performance:

-

-

-

-

Excellent acceleration/passing/handling/braking

Very good acceleration/passing/handling/braking Average acceleration/passing/handling/braking

4. Safety (protection in a collision): Much better than average Better than average Average 5. Price (including destination charges, taxes, and license fees): $ 10,000 $ 15,000 $ 20;000 $ 25,000 $ 35,000÷ 6. Levels of Features LOW: No power steering or power brakes; AM radio only; no air -

-

-

-

-

MIDDLE:

HIGH:

-

conditioning All the above plus: powersteering/brakes; air conditioning; AM/FM stereo radio; bucket seats; tinted glass; tilt steering wheel; cruise control All the above plus: alloy wheels; digital AM/FM stereo radio with cassette and graphic equalizer, automatic climate control; sunroof; 4-wheel disk brakes with anti-locking braldng system; central locking; anti-theft systen{


29

MARKET DRIVEN MANUFACTUR.ING

7. Flexibility ofFeature Choices: a) Complete Flexibility. Each individual feature (e.g. sunroof) can be included or excluded as you wish. You pay only for those features selected. b)

Choice Among Four Sets. You must select one out of four sets of features,

from a basic set through a luxury set you cannot combine features from different sets or groupings For example, you cannot select a radio from one feature set and seat covenngs from another you must select one of the four sets of features in its entirety c)

Choice Among Two Sets You must select either the set of manufacturerprovided features which come with the car ( at no extra cost) of a smgle

“Luxury” set which mcludes additional features at extrs cost 8. Fuel Economy ( average miles per gallon) 18 mpg

22 mpg 26 mpg 30 mpg

H


30


TABLE 5 OFFICE AUTOMATION ATTRIBUTES AND LEVELS 1. Unit Price: Level 1:

List price equals suggested retail price = $ 5,000 for a hypothetical computer system Level 2 List price minus 10 percent, or $ 4,500 Level 3 List pnce minus 20 persent, or $ 4,000 Level 4 List price minus 30 percent, or $ 3,500 2 Salesforce Expertise HIGH Very knowlegeable Means the sales person can tell the customer exactly what types ofhardware/software configurations will resolve the customer’s need(s), and be able to address concisely the pros and cons of each configuration Salespeople ofthis caliber could also provide solutions for costeffective office automation MIDRANGE KNOWLEDGE Means that the salesperson is quite knowledgeable about what types ofhardware/software configurations will resolve the customer’s needs and can deal effectively with non-standard questions of midrange difficulty BASIC Means that the salesperson has only a basic knowledge ofhardware and software, and some understanding ofhow popular software packages work and what they can do for the customer 3 Maintenance and Repair EXCELLENT Manufacturer offers fast turn-around time (1-3- days) which is performed on your premises THIRD-PARTY Service performed by third party arranged by purchaser Onpremises service with turn-around time of2-4 days BASIC Service performed at service centers (e g , carry-in) with turn-around time averaging between 4 days and 2 weeks 4 Customer Trainmg and Support HIGH Comprehensive classes on hardware/software use, including thorough treatment ofspecialized software Typically up to 2-day classes Access to 800-number telephone “hotline” where user questions will be addressed immediately

—

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31


MEDIIJM: One or two 2-hour classes on hardware/software use, with focus on popular software applications. Telephone requests for assistance by users will usually be answered within 24 hours. BASIC: Salesperson provides limited assistance on installation. Userquestions may be addressed to salesperson and may be answered in 2-3 days. 5. Delivery Time: IMMEDIATE Takewith you or same day delivery 2 7 days 2-4 weeks 6. Reliability: An independent, reliable computer magazine reports the following average number ofmonths oftrouble-free operation a usermay expect: EXCELLENT: 36 months oftrouble-free operation VERYGOOD: 24 months oftrouble-free operation GCOD: 12 months oftrouble-free operation 7. Software Availability: LIMiTED: Basic word processing; basic spread sheets SUBSTANTIAL: Advanced word processing; spread sheets with MACROS; limited data systems; BASIC/FORTRAN HIGH: Wide variety; advanced data base systems; advanced statistical programs; optimizationpackages; artificial intelligence; PASCAL; dynamic storage allocation; plus all the applications noted above. 8. Ease ofUse/Learning STANDARD: Command-driven only, no menus; limited documentation ENHANCED: Choice ofmenu driven and command driven; better documentation EXCELLENT: Extensive and clear documentation; full “HELP” commands; windows; mouse, icons -

-

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32



TABLE 6 Attribute Importance Weights Total Sample Office Automation

Automobile Attribute

Importance Wt.

--Price

Importance Wt.

.28 (.22)

SoftwareAvailability

.22 (.19)

.14 (.11)

Ease ofUse/Learning

.20 (.19)

--

.13 (.15)

Maintenance & Repair

.14 (.15)

Performance

13 (.16)

Reliability

.14 (.15)

Reliability

.11 (.13)

Customer Training

.11 (.13)

Warranty

.09 (.12)

Price

.08 (.09)

Fuel Economy

.06 (.06)

Salesforce Experience

.07 (.07)

Flexibility ofFeatures

.06 (.07)

Delivery Time

.04 (.07)

Features Safety

Confidence

-

Attribute

.

5.40 (0.95)

Confidence

5.30 (0.90)

-

Completeness of Attributes 4.84 (1.18)

Completeness ofAttributes 5.14 (1.08)

Sample Size

Sample Size

242

-

265

Attributes are in rank order of theirimportance weight in the total sample. The numbers in parentheses are the standard deviations of the importance weights across respondents.

33

HAUSMAN AND MONTGOMERY -


TABLE 7 A PRIORI SEGMENTS AUTOMOBILES (5 a priori segments) 1. Price Segment

Price and Fuel Economy importances expected to be high. 2. High Performance Segment ==> Performance and Features importance high. 3. Hassle Free Segment => Reliability and Warranty importancehigh. 4. Safety Segment ==~. Safety importance high. 5. Flexibility ofChoice Segment => Flexibility importance high. ==>

OFFICE AUTOMATION (6 a priori segments) 1. Price Segment ==> Price importance high. 2. Support Sensitive Segment ==> Customer Training & Support and Salesforce Expertise importances high. 3. Downtime/ Repair Sensitive Segment => Reliability and Maintenance & Repair importances high. 4. Job Application/ Scope Segment ==> Software Availability importance high. 5. User Friendly Segment ==> Ease ofUse/ Learning importance high. 6. Delivery Sensitive Segment => Delivery Time importance high.


34


TABLE 8 AUTOMOBILES A PRIORI SEGMENT AVERAGE ATTRIBUTE IMPORTANCE SEGMENTS# ATFRIBI.TFE

HASSLE FREE

REUABILITY

@ .712

WARRANIY

@

PRICE

HIGH PERFORM.

#1

SAFETY .133 #2

.049 #2

.047 #3

.119 #1

@ .685 #1

PERmRMANCE

@ .552 #1

SAFELY

@

PRICE

.200 #2

.622 #1

@

FEATURES

.183 #1

.144 #2

@ .075 #1

FWaBIIJrY

@

ECONOMY Sample size 242 (100%)

FLEXIBL1TY.

8 (3.3%)

.053 #2

59 (24.4%)

= a priori expectation of high average importance weight. = segment rank in avg. importance weight for attribute

.074 #1 13

i6

146

(5.4%)

(6,6%)

(60.3%)



35

TABLE 9 OFFICE AUTOMATION A PRIORI SEGMENT AVERAGE ATTRIBUTE IMPORTANCE SEGMENTs~~ A1ThIBLTIE

PRICE

PRICE

@ .097

SUPPORT

DELIVERY

FRIENDLY

#1

SALESFORCE EXPERTISE

.087 #1

MAINT.& REPAIR

.141 #2

CUST.TRAIN & SUPPORT

.122 #2

DELIVERYTIME

.052 #1

RELLABILrFY

@

.048 #2

@ .065 @

.199 #2

EASEOFUSEI LEARNING

.171

#3

.728 #1

.719 #1

@ -

SOFTWARE

Sample Size 265 (100%)

USER

DOW~~MEI JOB APPL./ REPAIR SCOPE

@ .550 #1 @

.709

#1

@

#2

176 (66.4%)

21 (3.0%)

.022 #2

(7.9%)

= a priori expectation of high average importance weight # = segment rank in avg. importance weight for attribute

22 (8.3%)

.679 #1

27 (10.2%)

11 (4.2%)

36


MARKET DRIVEN MANUFACTURiNG

TABLE 10 AUTOMOBILE STATISTICAL SEGMENTS CLUSTER ATTRIBUTE

1.

Price

2.

Perform.

2.

4.

Safety

Hassle Free

L

2.

t

Warranty

Features! Perform. -

Price! Features/ Safety

&

MEAN

Balanced

RELLABII.IFY

.053

.031

.139*

.712*

.021

.033

.080

.130*

.111

WARRANrY

.052

.018

.029

.047

.764*

.039

.061

.108*

.089

PER~RMANCE

.041

.828*

.103

.022

.015

.309*

.099

.142*

.135

SAFETY

.058

.013

.563*

.066

.026

.024

.132*

.132*

.129

PRICE

.664*

.036

.057

.040

.067

.092

.340*

.164

.282

FEATURES

.059

.032

.068

.077

.038

.388*

.154*

.168*

.139

FLEXIBILITY

.028

.023

.025

.021

.017

.086*

.048

.084*

.055

ECONOMY

.045

.019

.019

.015

.053

.031

.087*

.075*

.061

48

8

15

8

6

14

55

88

242

2.

&

MEAN

#INCLUSTER =

Average importance in segment exceeds total sample average importance.

TABLE 11 OFFICE AUTOMATION STATISTICAL SEGMENTS CLUSTER

1

2.

PRICE

.043

.025

.055

.032

.022

.438*

.049

.094*

.077

SALESFORCE

.028

.041

.039

.027

.048

.027

.036

.117*

.069

MANTENANCE

.035

.045

.075

.728*

.040

.082

.098

.163*

.138

TRAINING

.037

.046

.036

.028

.719*

.065

.085

.141*

.114

DEUVERY

.012

.019

.021

.022

.011

.047*

.029

.064*

.041

RELIABILITY

.040

.048

.633k

.036

.044

.115

.177*

.118

.143

SOF~WARE

~744*

.070

.069

.040

.056

.144

.323*

.147

.219

EASEOF USE

.057

.708*

.071

.087

.060

.081

.203*

.156

.199

14

11

8

9

72

108

265

ATTRIBUTE.

#IN CLUSTER *

=

19

24

4.

Average importance in segment exceeds total sample average importance.


37


Table 12. Linking Manufacturing Tactics and Strategy xxx = Major Impact; xx = Intermediate Impact; x = Some Impact -

Tactical Program Cost Awareness Statistical Process Control Taguchi Experimental Design Quality Function Deployment (QFD) Just-in-Time Design for Manufacturability/Assembly/ Test Concurrent Engineering Sophisticated Inventory Control Hard Automation Flexible Automation Workcells Self-Directed Teams (broader span of control, less hierarchy) Dynamic Scheduling Systems Activity-Driven Costing Performance Measurement Quality

New Capital Justification Techniques Reducing Mfg Cycle Time Time Based Competition (concept to customer cycle, order fulfillment cycle) Bundling of options (variety in sets) Information and Database Management CA~D/CAM CII~

xx xx

x x xx xx xx xx xxx xx x

Strategic Goal: Quality Depend- Flexi- Innovaability bility tion xxx xxx x x xxx x x x xx xx xx x xx xx xx

x

xxx xxx

xx xx xx xx xx

x xx

x

x -

xx

x xxx xx xx (Contributes to the specific priorities being measured) x xx x x xx xx

x x xx x

x x x

xx x xx x x

xx xx x x x

xxx

xx xx

FIGURE 1

MANUFACTURING/MARKETING INTERFACE CRITICAL LINKAGES MANUFACTURING PRIORITIES

MARKETING PRIORITIES

FIGURE 2 THE STRATEGIC TRIANGLE

ENVIRONMENT

CUSTOMERS

VALUE

VALUE

COMPANY

COMPETITORS COST

FIGURE 3 DIRECT ATTRIBUTE TRADEOFFS AUTOMOBILE

OFFICE AUTOMATION

1. Reliability 2 Warranty 3. Performance 4 Safety 5.-Price 6 Levels of Features 7 Flexibihty of Feature Choice Fuel Economy

1. Unit Price 2. Salesforce Expertise 3 Maintenance & Repair 4 Customer Training & Support 5 Delivery Time 6 Reliability 7. Software Availability 8. Ease of Use! Learning

-

8.

L

V

#7

~5y H.

#3j

40 30

20 10 UTILITY OF

PERFORMANCE -(CLUSTER2)

0

.LEV ~L1 -Iv

LEVEL2

20 -30’ -40’ -50 -60 -70

FIGURE 4 MEAN PART WORTH UTILITIES FOR THREE LEVELS OF PERFORMANCE ATTRIBUTE ( CLUSTER 2) -