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The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
Pergamon PII: S0166-4972(98)00031-5
Technovation, 18(10) (1998) 627–638 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0166-4972/98 $19.00 + 0.00
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives? D. R. Snaddon Division of Industrial Engineering, University of the Witwatersrand, Postbag 3, WITS 2050, South Africa
Abstract This work measures links between tasks and manufacturing objectives in Strategic Management Consultants and compares them with previous work in Fast Moving Consumer Goods manufacturers. As these are different industries, managers may use different tasks to accomplish the same objectives. There is some difference but managers use and invest in similar tasks for similar objectives. This suggests managers in different industries can focus firms on specific objectives by choosing to invest in tasks. Alternatively managers in different industries use similar tasks for pursuing similar objectives. Links between tasks and objectives display stability across these industries. Research finds links in general agreement with the sandcone model when all tasks are combined. It further points to the inadequacy of costs as an economic measure. 1998 Elsevier Science Ltd. All rights reserved
1. INTRODUCTION TO THE PROBLEM After considering the need for a study, manufacturing mix objectives are explained and selected, manufacturing mix theories outlined, then tasks and consulting are discussed before the research is presented.
1.1 Need for study The ultimate objective of a firm is to survive, and hopefully to thrive. A firm wishing to survive over the longer term is involved in strategy in the processes
and decisions managers undertake. Behaviour of the firm is central in influencing market share, profit and survival, so managers monitor, analyse and react. One important aspect according to Porter (1980, p. 75) is: “Recognising and accurately reading the market signals, then, is of major significance for developing competitive strategy”. Competitive strategy formulates and implements plans over the longer term. Threats to a company’s profits are, according to Porter (1980), potential entrants, suppliers, substitutes, buyers and industry competitors. Strategy formulation divides into two categories, customers, that is buyers,
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and competitors consisting of potential entrants, suppliers, substitutes, buyers and industry competitors. Slack sums up neatly the two fold emphases on customers and competitors respectively. He says: “(c)ustomers and competitors are both central to a competitive manufacturing operation because they define its aims succinctly: to satisfy one and be better than the other” (Slack, 1991, p. 7). To maintain a competitive position Slack (1991, p. 1) points to the importance of manufacturing stating that “the competitive environment for most companies requires both strategic wit and manufacturing muscle”. Skinner points to the specific need saying that “the greatest research, writing, and consulting need and opportunity in the field” of manufacturing in a corporate strategy is to “provide more links between tasks, objectives and specific manufacturing policies...” (Skinner, 1992, p. 22). This attempts to link tasks to manufacturing mix objectives.
1.2 Manufacturing mix Slack (1991, p. 17) defines manufacturing strategy as “the set of co-ordinated tasks and decisions which need to be taken in order to achieve the company’s required competitive performance objectives”. This is not restricted to making and assembling goods only. Many authors (Slack, 1991; Schonberger and Knod, 1994; Miller and Roth, 1992) agree that manufacturing strategy should support corporate objectives providing the firm with a competitive advantage. As Miller and Roth (1992, p. 3) say: “..(s)uperior performance will follow for those firms that develop congruence between their business and manufacturing strategies.” Some test speculation that alignment between manufacturing and strategy improves performance. Richardson et al. (1985) show that improving focus in the corporate mission and manufacturing tasks, and increasing congruence between the corporate and manufacturing objectives, produces better corporate performance. Swamidass and Newell (1987) show that, when manufacturing managers play a larger role in strategic decision making, the company shows better performance. Such research shows that ties between manufacturing and corporate strategy affect the firm’s overall performance. This evidence points to linking manufacturing and business strategy to improve a firm’s performance and cases of these links have been documented (Marucheck et al., 1992, pp. 95–104). In addition empirical links between manufacturing strategy and a business unit’s dominant orientation have been established. (Williams et al., 1995)
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According to Platts and Gregory, this link is achieved by “using market requirements to establish the performance variables against which manufacturing should be measured”. Platts and Gregory (1992, p. 33) and Nilsson and Nordhal (1995, p. 8) provide frameworks of the link between variables (objectives) and the working system. In these frameworks customers’ needs define objectives which manufacturing operations should strive to achieve. For example customers may want quality and speed. The firm’s aim would then be to give customers high quality goods and services at high speed. Objectives provide insight into how individual firms compete successfully. Firms can focus on objectives to establish a competitive advantage. The set of information objectives that are consistent with profit, clear in meaning, limited in number, hopefully independent, and suitable for operations is called the manufacturing mix (Snaddon, 1996, p. 387). There is evidence that manufacturing mix objectives have been accepted and used. Using criteria for objectives Charissis and Stephens say that “(i)dentification of... criteria in each of a manufacturer’s selected customers must be matched by the ability of the organisations as a whole to provide and meet those.... criteria” (Charissis and Stephens, 1993, p. 8). They claim that South African manufacturing companies rate quality and customer service as much more important than other objectives1 and that “continual improvement in these areas has become the rule...” (Charissis and Stephens, 1993, p. 6). Which manufacturing mix objectives should be used? This study uses Slack’s five objectives, namely cost, quality, speed, dependability and flexibility. These objectives are discussed in the literature (Snaddon, 1996, pp. 387–388) and defined for Strategic Management Consulting (S.M.C.) in Table 1. Definitions of the three objectives used in the study on Fast Moving Consumer Goods (F.M.C.G.) manufacturers, reported elsewhere (Snaddon, 1996, p. 388), are also in Table 1. While the S.M.C. study uses all objectives the F.M.C.G. study used only cost, quality and speed. Quality in the F.M.C.G. study included dependability and the F.M.C.G. study did not use flexibility.
1.3 Manufacturing mix theories Mefford says managers weigh different manufacturing mix objectives equally (Mefford, 1986, p. 98). Schmenner says that the order depends upon the pro1 Statistical reliability, definitions and experimental procedure are questioned in this work.
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
TABLE 1. Definitions of manufacturing mix objectives used Objective Cost Quality Speed
F.M.C.G.
S.M.C.
Is to do the task inexpensively
Means making products or providing services at a lower cost than competitors Means making or doing things properly the first time. This means providing error free products/services to design specifications Involves making or doing things faster than competitors. It brings customer requests and delivery closer together Means making or doing things on time, i.e. keeping the delivery promise Involves being able to change or adapt the operation either for customer needs or due to changes in the environment. A company needs to be flexible to cope with internal variety and long and sort term uncertainty
Is to do the task “right” including correctly, when promised, accurately and safely Is to do the task “fast”
Dependability
(See quality above)
Flexibility
(Not used)
duction process (Schmenner, 1993, pp. 11–13). Ferdows and De Meyer (1990) argue for a particular ordering of objectives in their sandcone model. Ferdows and De Meyer (1990, p. 169), using principles as a synonym for objectives, say: “that excellence in manufacturing is perhaps built on a common set of fundamental principles which are easier to get in place starting with one particular type of activity, and then pursuing other activities that expand and enrich this set of principles. The sequence is important because it is the combination of organisational priorities which form the best vehicle for enhancing the appropriate foundation principles”. Their sequence for objectives, puts quality at the base, followed by dependability, then speed, and finally cost. Slack (1991, p. 115) adds flexibility between speed and cost. This ordering is placed in the form of a sandcone. This means that the sandcone model orders objectives as follows Quality → Dependability → Speed → Flexibility → Cost (The symbol → means is followed by but still includes the former) Mefford’s, Schmenner’s and the sandcone theory conflict. They form the conundrum: how do managers weigh objectives? Snaddon (1996) answered this for the F.M.C.G. industry. This paper initially seeks to find the relative importance of manufacturing mix objectives for a similar range of tasks in S.M.C. firms.
1.4 Tasks2 The task forms a basic unit of analysis. A task is one side of a transaction that takes place across “tech2 I thank Mr P.J. Huang for drawing attention to the psychological definition of a task.
nologically separable interfaces” (Williamson, 1981). Here the transaction occurs between sections within consulting practices. This study deals with tasks undertaken regularly. In the psychological literature there is some consensus on the definition of a task. Hackman (1969) and Weick (1965) agree that a task is beyond a pure physical property as it includes stimuli, prescriptions and/or objectives or goals. Brown et al. (1969) and O’Brien and Owens (1969) empirically show that clear-cut tasks affect work performance. Naylor and Dickinson (1969) concur showing that more structured tasks yield better performance. This provides grounds for linking tasks to manufacturing objectives in the same way as overall performance associates with focus. A task may be linked to objectives. Links can be measured in absolute terms. Alternatively tasks may be important, as, when marginal resources are applied, changes on objectives are large. Marginal effects can also be measured. Both absolutes and marginals are investigated. This research considers a range of tasks. Objectives are analysed for these tasks in consulting.
1.5 The consulting industry In SA consulting consists of many diverse firms. Firms sampled range in size between 20 and 400 full time employees. (Smaller firms between the one and 19 employees are excluded, as the sample frame of such firms is unstable). Competition between consultancies is intense, but, at times, alliances are formed between firms. An example of this, is reengineering of Spoornet where Gemini and Andersen Consulting formed an alliance. At times clients may need a standardised service
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but clients often build up a long term relationship with the consultant or consulting firm. The consultant becomes familiar with the client, gaining insight into the client’s needs and operations. Zimbler points to the idiosyncratic nature of this relationship saying that: “...the change initiatives and strategies most likely to succeed are those which are consistent with the unique culture and circumstances of each organisation” (Zimbler, 1994, p. 60). The choice of a consultant is important, especially for complex long-term projects. Strategic management consultants offer a large variety of services to general management including strategic services/planning; change management (helping people adapt); systems integration (often managing information in technology); business process management (placing management emphasis on the core business); and organisational effectiveness (analysing a business and its environment to improve company effectiveness). Services vary between consultancies. Consultancies usually have few levels separating top management from the work force and consultants often spend much time unsupervised at distant locations. Operating instructions for consultants tend to be elementary (unless the consultancy is selling a proprietary product). Little is written within these organisations although this may change at large consultancies. Consulting is dependent upon consultants’ skills. Consultants perceive that they need to give services with effective results producing measurable benefits for their clients. A typical S.M.C. employs people with advanced training and education. S.M.C. is service orientated and in stark contrast to F.M.C.G. manufacturing which is goods orientated. S.M.C. firms can be compared with F.M.C.G. manufacturers on a variety of parameters. Some parameters are compared in Table 2. Fast moving consumer goods manufacturers differ from strategic management consulting firms on all parameters in Table 2. They differ in size, relation-
ships with customers, methods of operation and products offered. The research undertaken on strategic management consultants is next presented.
2. RESEARCH With the problem introduced the hypothesis tested is set, the research approach outlined, results reported for S.M.C. before comparing results with the F.M.C.G. study.
2.1 Hypotheses to be tested The starting point is defining the problem. The null hypothesis is that managers in strategic management consulting weigh objectives equally for all tasks whether for absolute totals or marginal changes. This was tested previously where evidence pointed to the sandcone model using a restricted set of objectives, namely cost, quality and speed (Snaddon, 1996, p. 388). If the null hypothesis is rejected then the association between this and the previous experiment (Snaddon, 1996) is sought to show if similar tasks have similar objective values between these industries. This partly uses Snaddon’s research method for the industry, an industry different from the S.M.C. firms.
2.2 Empirical research3 Flynn et al. gives approaches to empirical research in this field. Within these approaches this study verifies [sic] theory using survey research design. It uses a questionnaire that follows the F.M.C.G. industry study, but the questionnaire is reduced to 17 tasks and expanded to five objectives (Flynn et al., 1990, p. 254). Consultants and managers checked the questionnaire. A list of tasks and objectives used are in 3 I thank Miss T.C.A. Palmos for collecting and analyzing the Strategic Management Consulting data.
TABLE 2. F.M.C.G. vs S.M.C. Parameter Size Relationships with customers Operations
Products
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F.M.C.G.
S.M.C.
Average size 3500 people. (Largest 1.5% of food manufacturers 20–400 full time people. in SA). Market based. Dependant upon goods supplied. Idiosyncratic people based. Dependent on consultants’ skills. Factory-based operations mainly high volume line flow Jobbing work at clients’ premises. Usually few levels separating techniques and distribution networks. People work under close top management from the work force. Consultants spend much supervision. time unsupervised at distant locations. Well-defined goods orientated, e.g. groceries. Large variety of services (see text).
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
Table 4 and Table 5. Like the F.M.C.G. study it is a cross-sectional, or one-time, study. The sample frame used for the S.M.C. industry came from an association directory (Murphy, 1994). The study uses equal unit probability sampling where the consultancy is the element or unit. It is stratified as consultancies with less than 20 persons are eliminated. Beyond this, consultancies are randomly selected. Such an approach allows precision to be estimated about the characteristic measured. Within each unit, available managers are interviewed based upon the judgement of the initial respondent e.g. telephone operator. These were mainly managing directors or partners. They were from the top tier of management. It is therefore a two-stage technique. Personal and, when costs per interview were large, telephone interviews are used. (Telephonic interviews were only used in a few cases.) This differs from the F.M.C.G. study that used mail questionnaires. Research involves finding the relative importance of tasks and objectives. This is done in absolute and marginal terms. The null hypothesis is that the average manager weighs objectives equally for the same tasks. In measuring the absolute importance of objectives the F.M.C.G. study uses a 7-point, where the S.M.C. study uses a 5-point scale. Meanings attached to the values are given in Table 3. The S.M.C. scale average is one point less than the F.M.C.G. average. Both studies ask respondents to measure marginal values by ticking which objectives would be most improved with extra resources for each task.
2.3 Results Twenty-five S.M.C.s of the 44 listed gave results using measures in Table 4. Averages of responses are calculated for each of the five objectives, and for each task. Table 4 gives the average results for the absolute totals of each criterion. The S.M.C. results are in the left-hand side. The F.M.C.G. results are in the righthand side of cells from the previous study.
S.M.C. results range from a low of 2.04 for the criterion “speed”, and the task “interpersonal relations”, to a high of 5 on the criterion “quality”, for the task “selection”. This implies that the quality of selection is the most important association in consulting. Overall averages for the five objectives are: quality 4.25, dependability 3.66, flexibility 3.32, speed 3.11 and cost 3.08. Except for cost and speed, significant4 differences are found between the averages of all objectives. In absolute terms, managers weigh all objectives, excepting cost and speed, differently. Mefford’s (or the null) hypothesis that managers weigh objectives equally, is rejected. Table 5 shows results for marginal changes, as percentages. Results range from a low of 3% for “speed” in “interpersonal relations” and “cost” in “credit control”, to a high of 59% for “quality” of the task “public relations”. Overall averages of all tasks for the objectives are: quality 39%, speed 18%, dependability 17%, flexibility 13% and cost 13%. For marginal changes, significant differences are among all objectives except “speed and dependability” and “flexibility and cost”. In marginal terms, the hypothesis of equality between objectives can be rejected but for “speed and dependability” and “flexibility and cost”. Table 6 shows averages for absolute and marginal importance of the five objectives converted to percentages. If tasks selected in this research reflect all tasks undertaken by S.M.C. firms, then such firms consider quality as the most important objective in both marginal and absolute terms. Cost in both marginal and absolute terms, rates as the least important objective. These results contradict Mefford’s (1986, p. 98) assertion that managers weigh objectives equally. There is a significant difference for many objectives.
4
Significance is claimed at the 95% level.
TABLE 3. Scales used in studying the absolute importance of objectives Value/study F.M.C.G. S.M.C.
1
2
3
4
5
6
7
Not applicable Irrelevant
Poor Little importance
Fair Average
Average Important
Good Very
Best –
The only –
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TABLE 4. Average results of absolute totals Tasks
Objectives
Bookkeeping Credit control Disbursements expenditure Selection Employee training Interpersonal relations Benefits Termination of employment Contracts Quality assurance Scheduling/assignment Advertising Public relations Sales Data processing General management Research and development Overall average
Cost
Quality
Speed
Dependability
Flexibility
2.60 3.60 2.48 3.54 3.24 3.86
4.52 6.15 4.04 5.50 3.64 5.32
3.52 4.69 3.80 4.59 2.48 4.30
4.4 4.16 3.4
2.56 2.32 2.4
2.72 2.84 2.20 3.56 3.08
3.32 4.00 3.19 4.50 3.30
5.00 4.60 4.20 3.96 3.72
6.00 5.58 5.73 5.47 4.83
2.88 2.48 2.04 2.12 3.56
2.88 3.86 4.00 3.18 4.00
4.04 3.44 3.6 3.04 2.68
3.08 3.76 4.16 3.32 2.68
3.48 2.68 3.08 3.40 2.92 3.40 3.08 2.84 2.84 3.08
3.57 3.74 3.85 5.08 4.26 4.46 3.79 3.30 3.71 4.12
4.68 4.52 4.44 4.20 4.24 4.56 4.60 4.80 4.32 4.25
5.72 5.88 5.23 5.60 5.76 5.88 5.70 6.13 5.43 5.58
3.20 2.68 3.88 3.64 2.76 3.60 3.72 3.16 2.96 3.11
4.26 4.35 5.20 4.22 4.35 5.20 5.62 4.54 4.18 4.49
4.2 4.12 3.88 3 3.4 4.2 3.96 4.2 3.52 3.66
3.4 3.04 4 2.76 3.24 3.88 3.6 4.32 3.8 3.32
TABLE 5. Average results of marginal changes (in percentages) Tasks
Objectives Cost
Bookkeeping Credit control Disbursements expenditure Selection Employee training Interpersonal relations Benefits Termination of employment Contracts Quality assurance Scheduling/assignment Advertising Public relations Sales Data processing General management Research and development Overall average
632
Quality
Speed
Dependability
Flexibility
5 3 19
0 4 19
31 30 27
59 58 43
29 42 27
41 38 38
26 18 12
9 7 15
6 14
3 6
50 44
66 74
19 11
31 20
19 19
6 12
4
8
48
80
3
12
21
24
33 24
38 18
33 24
56 53
4 28
6 29
10 5
20 19
8 12
24 9
46 38
52 67
23 17
24 24
20 21
3 12
8 22 11 11 12 12
7 19 7 22 12 4
33 43 59 40 24 43
32 73 80 56 33 91
22 7 8 19 27 10
62 7 14 22 55 4
17 14 11 16 20 21
20 14 11 14 17 14
12
14
49
55
12
32
15
12
13
15
39
54
18
30
17
13
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The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
TABLE 6. Importance of objectives (in percentages) Objective
TABLE 7. Comparison of absolute figures (in percentages)
Absolute (%)
Marginal (%)
24 21 19 18 18 100
39 17 13 18 13 100
Quality Dependability Flexibility Speed Cost Total
Research study F.M.C.G. — all tasks F.M.C.G. - same tasks as S.M.C. study S.M.C.
Cost
Quality
Speed
29 28 29
39 41 42
32 31 29
Tasks are next compared against the F.M.C.G. study. Comparing the overall averages in Table 4 and Table 5 between jobbing (S.M.C.s) and flow line (for F.M.C.G.s) manufacturing processes checks Schmenner’s theory. While manufacturing mix definitions and industries vary Schmenner’s theory is not supported. Within the accuracy of the assumptions and results, absolute values for all tasks can be measured on the performance scale: Overall Score = 0.24*Quality + 0.21*Dependability + 0.19*Flexibility + 0.18*Speed + 0.18*Cost or Quality → Dependability → Flexibility → Speed ⬇ Cost (The symbol → means is followed by but still includes the former) The evidence places flexibility before speed where Slack placed flexibility after speed. Alternatively the same ordering is found except speed and flexibility. These results support Ferdows and de Meyer’s sandcone model. For marginal analysis, the following comparative overall orders are: Marginal:Quality → Speed ⬇ Dependability → Flexibility ⬇ Cost Slack:Quality →Dependability → Speed → Flexibility → Cost As “cost and flexibility” and “dependability and speed” are not significantly different this test also lends credence to the sandcone model5.
2.4 Comparisons with F.M.C.G. study Compare results between the F.M.C.G. and S.M.C. study by eliminating the dependability and flexibility objectives. Absolute and marginal results are in Table 7 and Table 8. Results are from more than 30 F.M.C.G. and 25 S.M.C. firms. This checks if the average manager in the F.M.C.G. and S.M.C. firms associate tasks and objectives similarly. To check association between the F.M.C.G. and S.M.C. study, data are given in Table 4 and Table 5. Table 4 has absolute, while Table 5 has marginal, averages. Associations between the F.M.C.G. and S.M.C. study can be checked using linear regression on the seventeen similar tasks in absolute and marginal form. The F.M.C.G. values are the dependent (y) variable, and this study’s values are the independent (x) variable. Results using the data in Table 4 and Table 5 are in Table 9 and Table 10 respectively. Absolute results for cost, quality, speed and totals are in Table 9. (Note that the quality objective of the F.M.C.G.’s study incorporates dependability. Table 9 and Table 10 give results for no dependability in quality and then equal weighting for quality and dependability in a combined quality objective). Marginal results for cost, quality and cost and quality combined are in Table 10. (As speed makes cost, quality and speed add to 100% in the F.M.C.G. marginal study, speed is not independent). All linear equations have significant association between the F.M.C.G. and S.M.C. studies. (Significant variations between studies, expected by chance, include tasks of “termination of employment” and “contracts” on “cost”, and “interpersonal TABLE 8. Comparison of marginal figures (in percentages)
Results support the sandcone model in overall terms.
5 The results do not agree with the survey by Charissis and Stephens (1993).
Research study F.M.C.G.— all tasks F.M.C.G.— same tasks as S.M.C. study S.M.C.
Cost
Quality
Speed
15 13 18
54 60 56
30 27 26
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TABLE 9. Association between S.M.C. and F.M.C.G. results for absolute totals Associated variables Cost Quality Quality + dependability Speed All objectives All objectives + dependability
Linear association and standard errors
Other statistics
y = 1.2861 + 0.85566x S.E. 0.4042 0.2678 y = 2.7300 + 0.6686x S.E. 0.2351 0.1594 y = 3.003 + 1.6937x
R2 = 0.4055 n = 17; d.f. = 15 R2 = 0.5397 n = 17; d.f. = 15 R2 = 0.8794
S.E. 0.5108 0.1242 y = 2.1955 + 0.7044x S.E. 0.4270 0.1798 y = 1.0339 + 1.03154x S.E. 0.4233 0.0770 y = 0.6972 + 1.1641x
n = 17; d.f. = 15 R2 = 0.5056 n = 17; d.f. = 15 R2 = 0.7854 n = 51; d.f. = 49 R2 = 0.7350
S.E. 0.3423 0.0999
n = 51; d.f. = 49
y is the average value Snaddon (1996, p. 392) finds; x is the average value in this study; R2 is the coefficient of correlation squared; S.E. s the standard error of the values in the equation in the cell above; n is the number of paired observations; d.f. is the number of degrees of freedom.
TABLE 10. Association between S.M.C. and F.M.C.G. with results for marginal changes Associated variables Cost Quality Quality + dependability Cost and quality Cost, speed and quality + dependability
Linear association and standard errors
Other statistics
y = 0.7339 + 0.9330x S.E. 6.5894 0.2091 y = 19.4507 + 0.9330x S.E. 12.6115 0.2091 y = 13.0327 + 1.7032x
R2 = 0.5704 n = 17; d.f. = 15 R2 = 0.4299 n = 17; d.f. = 15 R2 = 0.3902
S.E. 15.6346 0.5497 y = − 4.0813 + 1.5726x S.E. 11.5291 0.1253 y = − 4.1765 + 1.9188x
n = 17; d.f. = 15 R2 = 0.5704 n = 34; d.f. = 32 R2 = 0.6448
S.E. 4.4918 0.2034
n = 51; d.f. = 49
y is the average value Snaddon (1996, p. 393) finds; x is the average value in this study; R2 is the coefficient of correlation squared; S.E. is the standard error of the values in the equation in the cell above; n is the number of paired observations; d.f. is the number of degrees of freedom.
relations” for “speed” in the absolute study. A significant variation between studies on the marginal association is “general management” on “quality”. All variations are expected with limits used). There is significant association between results for F.M.C.G. and S.M.C. This may be partly ascribed to similar methodology but the variation between industries is large. With the association found an estimate can be given for the combined studies. Table 11 and Table 12 list average values of absolutes and marginals respectively in decreasing order. These tables can be read as follows. The first entry in Table 11 is “quality” and “employee selection”. This means that the most important aspect of all tasks
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sampled in a firm in these two industries combined is the association between employee selection and quality. No causality can be given to this. The research may either be interpreted that quality affects, or is affected by, employee selection. The most important association for “speed” is “data processing” and for “cost” it is “advertising”. The first entry in the marginal association in Table 12 is the association between “quality” and “public relations”. This means that marginal resources added to the task “public relations” is most associated with “quality”. Again no causality is implied. The greatest marginal association for “speed” is “manufacturing scheduling” and “cost” is “employee benefits”.
3. CONCLUSIONS, MANAGERIAL IMPLICATIONS AND FURTHER RESEARCH 3.1 Conclusions This shows that the sandcone model proposed by Ferdows and de Meyer may apply to the average strategic management consultant at the level of the firm rejecting both Mefford’s and Schmenner’s theories. Within the firm tasks may not follow the sandcone model. S.M.C. is far removed from F.M.C.G. manufacturing, yet both industries show that the average manager links tasks with manufacturing objectives similarly. While there is variation, there is underlying similarity. This indicates an underlying web of information that exists building up the product/services in organisations, presently in partial and often unrelated systems. Integrating such information could yield stronger management information systems than presently exist. Absolute analysis is useful when managers wish to classify important objectives. A manager wishing to pursue “quality” associates this with “employee selection”. Marginal analysis shows where managers do focus by investing in the firm. A manager wishing to pursue “quality” may, for example, invest in tasks where “quality” dominates, such as “public relations”. (This may say that public relations can improve quality by telling the market or it may say that public relations is the conduit that makes the firm more aware of quality). Marginal analysis identifies tasks where extra resources associate with a selected criterion.
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
TABLE 11. Absolute importance of tasks and objectives in decreasing order Objective
Task
Quality Quality Quality Quality Quality Quality Quality Quality Quality Quality
Employee Other Accounting Marketing Manufacturing Legal Other Employee Marketing Employee
Quality Quality Quality Quality Quality Speed Speed Quality Speed Quality
Marketing Other Manufacturing Accounting Employee Other Manufacturing Accounting Marketing Employee
Cost Speed Speed Cost Speed Cost Speed Speed
Marketing Accounting Accounting Employee Marketing Marketing Other Employee
Speed Cost Speed Speed Cost Cost Speed Cost Cost Cost Speed Speed Cost Cost Cost
Legal Marketing Other Marketing Accounting Legal Manufacturing Manufacturing Other Employee Accounting Employee Other Manufacturing Employee
Speed Cost Cost Speed
Employee Accounting Other Employee
Cost Cost Cost
Employee Accounting Employee
Speed
Employee
F.M.C.G. Selection General management Bookkeeping Sales Quality assurance Contracts Data processing Training Public relations Operating and interpersonal relations Advertising Research and development Scheduling Credit control Benefits Data processing Scheduling Disbursements/expenditure Sales Termination of employment Advertising Credit control Bookkeeping Benefits Advertising Sales General management Termination of employment Contracts Public relations Research and development Public relations Disbursements/expenditure Contracts Quality assurance Scheduling Data processing Training Disbursements/expenditure Selection Research and development Quality assurance Termination of employment Training Bookkeeping General management Operating and interpersonal relations Selection Credit control Operating and interpersonal relations Benefits
Absolute and marginal ways of dealing with objectives are complementary. They extract different information both important to the surviving firm. There is some stability across the F.M.C.G. and
S.M.C.
Average
6 6.13 6.15 5.88 5.88 5.72 5.7 5.58 5.76 5.73
5 4.8 4.52 4.56 4.52 4.68 4.6 4.6 4.24 4.2
5.5 5.465 5.335 5.22 5.2 5.2 5.15 5.09 5 4.965
5.6 5.43 5.23 5.5 5.47 5.62 5.2 5.32 5.2 4.83
4.2 4.32 4.44 4.04 3.96 3.72 3.88 3.64 3.6 3.72
4.9 4.875 4.835 4.77 4.715 4.67 4.54 4.48 4.4 4.275
5.08 4.59 4.69 4.5 4.22 4.46 4.54 4
3.4 3.8 3.52 3.56 3.64 3.4 3.16 3.56
4.24 4.195 4.105 4.03 3.93 3.93 3.85 3.78
4.26 4.26 4.18 4.35 3.86 3.57 4.35 3.85 3.79 4 4.3 3.75 3.71 3.74 3.3
3.2 2.92 2.96 2.76 3.24 3.48 2.68 3.08 3.08 2.84 2.48 2.88 2.84 2.68 3.08
3.73 3.59 3.57 3.555 3.55 3.525 3.515 3.465 3.435 3.42 3.39 3.315 3.275 3.21 3.19
3.86 3.6 3.3 4
2.48 2.6 2.84 2.04
3.17 3.1 3.07 3.02
3.32 3.54 3.19
2.72 2.48 2.2
3.02 3.01 2.695
3.18 4.61
2.12 3.47
2.65 4.04
S.M.C. as for associations between tasks and objectives. Do similar tasks have similar objectives? Alternatively do managers, wishing to pursue specific objectives, chose to undertake specific tasks rather than modifying tasks to pursue objectives?
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TABLE 12. Marginal importance of tasks and objectives in decreasing order Objective
Task
Quality Quality Quality
Marketing Other Employee
Quality Quality Quality Quality Quality Quality Quality Quality Quality Quality Speed Speed Speed Quality
Employee Marketing Employee Manufacturing Other Legal Marketing Accounting Employee Accounting Manufacturing Other Accounting Employee
Cost Quality Speed Speed Quality Quality Speed
Employee Accounting Accounting Accounting Manufacturing Other Employee
Speed Speed Speed Cost
Employee Legal Other Employee
Speed Speed Cost Cost Cost Cost Speed Cost Cost Speed Cost Cost Cost Cost Speed
Manufacturing Marketing Marketing Accounting Marketing Legal Employee Other Other Marketing Manufacturing Employee Marketing Other Employee
Cost Speed Speed Cost
Manufacturing Marketing Other Employee
Speed Cost Cost Cost
Employee Employee Accounting Accounting
F.M.C.G. Public relations General management Operating and interpersonal relations Training Advertising Selection Quality assurance Research and development Contracts Sales Bookkeeping Benefits Credit control Scheduling Data processing Credit control Termination of employment Benefits Disbursements/expenditure Bookkeeping Disbursements/expenditure Scheduling Data processing Termination of employment Selection Contracts Research and development Termination of employment Quality assurance Sales Advertising Disbursements/expenditure Sales Contracts Training Research and development Data processing Public relations Quality assurance Training Public relations General management Operating and interpersonal relations Scheduling Advertising General management Operating and interpersonal relations Benefits Selection Credit control Bookkeeping
3.2 Managerial implications Results from these studies have managerial implications. Obvious benchmarks of tasks and objectives exist in the Tables. Being from very different industries they add evidence that:
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S.M.C.
Average
80 91 80
59 43 48
69.5 67 64
74 73 66 67 55 52 56 59 56 58 62 55 38 53
44 43 50 38 49 46 40 31 33 30 22 27 42 24
59 58 58 52.5 52 49 48 45 44.5 44 42 41 40 38.5
38 43 41 38 32 33 29
33 27 29 27 33 24 28
35.5 35 35 32.5 32.5 28.5 28.5
31 24 32 18
19 3 12 24
25 23.5 22 21
24 22 19 19 22 24 20 14 12 14 9 6 7 4 12
17 19 22 19 11 8 11 12 12 8 12 14 11 12 3
20.5 20.5 20.5 19 16.5 16 15.5 13 12 11 10.5 10 9 8 7.5
7 7 4 8
8 7 10 4
7.5 7 7 6
6 3 4 0 33.35
4 6 3 5 23.25
5 4.5 3.5 2.5 28.30
쐌 reengineering firms in terms of core competence is hazardous. Changing tasks in and out of businesses may have unforeseen difficulties. For example changing credit control to save costs may seriously affect quality!; 쐌 employees with skills in one task e.g. legal con-
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
tracts, have an orientation which eases their transfer between industries. (This may be a major reason for the popularity of professional institutes, examinations and education); 쐌 emphasis placed upon costs as a source of management decision seems excessive. A reason is that legislation, especially tax legislation, forces firms to keep costs in particular ways. As management derives this information at low marginal cost, they use it. Such legislated costs may differ from economic costs as items such as quality, dependability and speed objectives need careful monitoring, especially by owners; and 쐌 the manufacturing mix objective quality is most important in both industries whether in absolute or marginal form.
3.3 Further work The finding that managers in the F.M.C.G. and the consulting industry weigh manufacturing mix objectives similarly opens areas of further research. This intimates the need to map the information web that yields the product/service mix offered by the firm. Comparisons in other industries are needed. A point assumed in this and previous work (Snaddon, 1996) is that selected tasks comprise the firm and that each task is equally important especially for the marginal analysis. This needs closer investigation. This does not mean that research on taxonomies of the manufacturing mix is complete. Flexibility is a criterion not considered by some authors as independent (e.g. Ferdows and De Meyer, 1990 and Snaddon, 1993). Analysis can provide insight into how effectively companies service their customers’ needs. Such information could be valuable to model competing firms. Where specific objectives dominate, it may be possible to associate inputs and outputs by a method previously outlined (Snaddon, 1990).
REFERENCES Brown, I. D., Tickner, A. H. and Simmonds, D. C. V. (1969) Interference between concurrent tasks of driving and telephoning. Journal of Applied Psychology 53, 419–424. Charissis, N. and Stephens, K. (1993) Building global manufacturing competitiveness. In Annual International Manufacturing Survey — South African Results, Deloitte Touche Tohmatsu International. Ferdows, K. and De Meyer, A. (1990) Lasting
improvements in manufacturing performance: in search of a new theory. Journal of Operations Management 9, 168–184. Flynn, B. B., Sakakibara, S., Schroeder, R. G., Bates, K. A. and Flynn, E. J. (1990) Empirical research methods in operations management. Journal of Operations Management 9, 250–284. Hackman, J. R. (1969) Toward an understanding of the role of tasks in behavioural research. Acta Psychologia 31, 97–128. Marucheck, A., Pannesi, R. and Anderson, C.(1992) An exploratory study of the manufacturing strategy in practice. In Manufacturing Strategy — Process and Content, ed. C. A. Voss, pp. 89–120. Chapman and Hall, London. Mefford, R. N. (1986) Introducing management into the production function. Review of Economics and Statistics 68, 96–104. Miller, J. G. and Roth, A. V. (1992) A taxonomy of manufacturing strategies. Fuqua School of Business Working Paper, July. Murphy, N. (ed.) (1994) Management Report — Consultants Directory. Systems Publication, Craighall. Naylor, J. C. and Dickinson, T. L. (1969) Task structure, work structure, and team performance. Journal of Applied Psychology 53, 167–178. Nilsson, C. -H. and Nordhal, H. (1995) Making manufacturing flexibility operational — part: a framework. Integrated Manufacturing Systems 6, 5–11. O’Brien, G. E. and Owens, A. G. (1969) Effects of organizational structure on correlations between member additives and group productivity. Journal of Applied Psychology 53, 525–530. Platts, K. W. and Gregory, M. J. (1992) A manufacturing audit approach to strategy formulation. In Manufacturing Strategy — Process and Content, ed. C. A. Voss, pp. 29–56. Chapman and Hall, London. Porter, M. E. (1980) Competitive Strategy — Techniques for Analyzing Industries and Competitors. The Free Press, New York. Richardson, P. R., Taylor, A. J. and Gordon, J. R. M. (1985) A strategic approach to evaluating manufacturing performance. Interfaces 15, 15–27. Schmenner, R. W. (1993) Production/Operations Management, 5th edition. Macmillan, New York. Schonberger, R. J. and Knod, E. M. (1994) Operations Management: Continuous Improvement, 5th edition. Irwin, USA. Skinner, W. (1992) Missing the links in manufacturing strategy. In Manufacturing Strategy: Process and Content, Chap. 2, ed. C. Voss, pp. 13–25. Chapman and Hall, London. Slack, N. D. C. (1991) The Manufacturing Advantage. Mercury, London.
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Snaddon, D. R. (1990) Productivity within sections of firms — I the design of a productivity measure for a section of a firm. Technovation 10, 235–251. Snaddon, D. R. (1993) The goals of manufacturing strategy, P. O.M. techniques and perceptions — a pilot study. South African Journal of Business Management 24, 147–153. Snaddon, D. R. (1996) The manufacturing mix. Technovation 16, 385–396. Swamidass, P. M. and Newell, W. T. (1987) Manufacturing strategy, environmental uncertainty, and performance: a path analytic model. Management Science 33, 509–524. Weick, K. E. (1965) Laboratory experimentation with organisations. In Handbook of Organisations, ed. J. G. March, pp. 194–261. Rand McNally, Chicago. Williams, F. P., D’Souza, D. E., Rosenfelt, M. E. and Kassaee, M. (1995) Manufacturing strategy, business strategy and firm performance in a mature
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industry. Journal of Operations Management 13, 19–33. Williamson, O. E. (1981) The modern corporation: origins, evolution, attributes. Journal of Economic Literature 19, 1537–1568. Zimbler, A. (1994) A strategic approach ensures return on your consulting investment. Management Report — Consultants Directory, p. 60. Systems Publishers, Johannesburg. D. Roy Snaddon is an Associate Professor in Industrial Engineering at the University of the Witwatersrand, South Africa. Before joining the university he worked in industry as an engineer, financial and managing director. He has published over 25 research papers in this and other journals and currently teaches Production and Operations Management and Engineering Economics. He also consults. This paper carries the dedication: “For any good in this to God be the glory” His e-mail address is [email protected]
Technovation, 18(10) (1998) 627–638 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0166-4972/98 $19.00 + 0.00
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives? D. R. Snaddon Division of Industrial Engineering, University of the Witwatersrand, Postbag 3, WITS 2050, South Africa
Abstract This work measures links between tasks and manufacturing objectives in Strategic Management Consultants and compares them with previous work in Fast Moving Consumer Goods manufacturers. As these are different industries, managers may use different tasks to accomplish the same objectives. There is some difference but managers use and invest in similar tasks for similar objectives. This suggests managers in different industries can focus firms on specific objectives by choosing to invest in tasks. Alternatively managers in different industries use similar tasks for pursuing similar objectives. Links between tasks and objectives display stability across these industries. Research finds links in general agreement with the sandcone model when all tasks are combined. It further points to the inadequacy of costs as an economic measure. 1998 Elsevier Science Ltd. All rights reserved
1. INTRODUCTION TO THE PROBLEM After considering the need for a study, manufacturing mix objectives are explained and selected, manufacturing mix theories outlined, then tasks and consulting are discussed before the research is presented.
1.1 Need for study The ultimate objective of a firm is to survive, and hopefully to thrive. A firm wishing to survive over the longer term is involved in strategy in the processes
and decisions managers undertake. Behaviour of the firm is central in influencing market share, profit and survival, so managers monitor, analyse and react. One important aspect according to Porter (1980, p. 75) is: “Recognising and accurately reading the market signals, then, is of major significance for developing competitive strategy”. Competitive strategy formulates and implements plans over the longer term. Threats to a company’s profits are, according to Porter (1980), potential entrants, suppliers, substitutes, buyers and industry competitors. Strategy formulation divides into two categories, customers, that is buyers,
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and competitors consisting of potential entrants, suppliers, substitutes, buyers and industry competitors. Slack sums up neatly the two fold emphases on customers and competitors respectively. He says: “(c)ustomers and competitors are both central to a competitive manufacturing operation because they define its aims succinctly: to satisfy one and be better than the other” (Slack, 1991, p. 7). To maintain a competitive position Slack (1991, p. 1) points to the importance of manufacturing stating that “the competitive environment for most companies requires both strategic wit and manufacturing muscle”. Skinner points to the specific need saying that “the greatest research, writing, and consulting need and opportunity in the field” of manufacturing in a corporate strategy is to “provide more links between tasks, objectives and specific manufacturing policies...” (Skinner, 1992, p. 22). This attempts to link tasks to manufacturing mix objectives.
1.2 Manufacturing mix Slack (1991, p. 17) defines manufacturing strategy as “the set of co-ordinated tasks and decisions which need to be taken in order to achieve the company’s required competitive performance objectives”. This is not restricted to making and assembling goods only. Many authors (Slack, 1991; Schonberger and Knod, 1994; Miller and Roth, 1992) agree that manufacturing strategy should support corporate objectives providing the firm with a competitive advantage. As Miller and Roth (1992, p. 3) say: “..(s)uperior performance will follow for those firms that develop congruence between their business and manufacturing strategies.” Some test speculation that alignment between manufacturing and strategy improves performance. Richardson et al. (1985) show that improving focus in the corporate mission and manufacturing tasks, and increasing congruence between the corporate and manufacturing objectives, produces better corporate performance. Swamidass and Newell (1987) show that, when manufacturing managers play a larger role in strategic decision making, the company shows better performance. Such research shows that ties between manufacturing and corporate strategy affect the firm’s overall performance. This evidence points to linking manufacturing and business strategy to improve a firm’s performance and cases of these links have been documented (Marucheck et al., 1992, pp. 95–104). In addition empirical links between manufacturing strategy and a business unit’s dominant orientation have been established. (Williams et al., 1995)
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According to Platts and Gregory, this link is achieved by “using market requirements to establish the performance variables against which manufacturing should be measured”. Platts and Gregory (1992, p. 33) and Nilsson and Nordhal (1995, p. 8) provide frameworks of the link between variables (objectives) and the working system. In these frameworks customers’ needs define objectives which manufacturing operations should strive to achieve. For example customers may want quality and speed. The firm’s aim would then be to give customers high quality goods and services at high speed. Objectives provide insight into how individual firms compete successfully. Firms can focus on objectives to establish a competitive advantage. The set of information objectives that are consistent with profit, clear in meaning, limited in number, hopefully independent, and suitable for operations is called the manufacturing mix (Snaddon, 1996, p. 387). There is evidence that manufacturing mix objectives have been accepted and used. Using criteria for objectives Charissis and Stephens say that “(i)dentification of... criteria in each of a manufacturer’s selected customers must be matched by the ability of the organisations as a whole to provide and meet those.... criteria” (Charissis and Stephens, 1993, p. 8). They claim that South African manufacturing companies rate quality and customer service as much more important than other objectives1 and that “continual improvement in these areas has become the rule...” (Charissis and Stephens, 1993, p. 6). Which manufacturing mix objectives should be used? This study uses Slack’s five objectives, namely cost, quality, speed, dependability and flexibility. These objectives are discussed in the literature (Snaddon, 1996, pp. 387–388) and defined for Strategic Management Consulting (S.M.C.) in Table 1. Definitions of the three objectives used in the study on Fast Moving Consumer Goods (F.M.C.G.) manufacturers, reported elsewhere (Snaddon, 1996, p. 388), are also in Table 1. While the S.M.C. study uses all objectives the F.M.C.G. study used only cost, quality and speed. Quality in the F.M.C.G. study included dependability and the F.M.C.G. study did not use flexibility.
1.3 Manufacturing mix theories Mefford says managers weigh different manufacturing mix objectives equally (Mefford, 1986, p. 98). Schmenner says that the order depends upon the pro1 Statistical reliability, definitions and experimental procedure are questioned in this work.
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
TABLE 1. Definitions of manufacturing mix objectives used Objective Cost Quality Speed
F.M.C.G.
S.M.C.
Is to do the task inexpensively
Means making products or providing services at a lower cost than competitors Means making or doing things properly the first time. This means providing error free products/services to design specifications Involves making or doing things faster than competitors. It brings customer requests and delivery closer together Means making or doing things on time, i.e. keeping the delivery promise Involves being able to change or adapt the operation either for customer needs or due to changes in the environment. A company needs to be flexible to cope with internal variety and long and sort term uncertainty
Is to do the task “right” including correctly, when promised, accurately and safely Is to do the task “fast”
Dependability
(See quality above)
Flexibility
(Not used)
duction process (Schmenner, 1993, pp. 11–13). Ferdows and De Meyer (1990) argue for a particular ordering of objectives in their sandcone model. Ferdows and De Meyer (1990, p. 169), using principles as a synonym for objectives, say: “that excellence in manufacturing is perhaps built on a common set of fundamental principles which are easier to get in place starting with one particular type of activity, and then pursuing other activities that expand and enrich this set of principles. The sequence is important because it is the combination of organisational priorities which form the best vehicle for enhancing the appropriate foundation principles”. Their sequence for objectives, puts quality at the base, followed by dependability, then speed, and finally cost. Slack (1991, p. 115) adds flexibility between speed and cost. This ordering is placed in the form of a sandcone. This means that the sandcone model orders objectives as follows Quality → Dependability → Speed → Flexibility → Cost (The symbol → means is followed by but still includes the former) Mefford’s, Schmenner’s and the sandcone theory conflict. They form the conundrum: how do managers weigh objectives? Snaddon (1996) answered this for the F.M.C.G. industry. This paper initially seeks to find the relative importance of manufacturing mix objectives for a similar range of tasks in S.M.C. firms.
1.4 Tasks2 The task forms a basic unit of analysis. A task is one side of a transaction that takes place across “tech2 I thank Mr P.J. Huang for drawing attention to the psychological definition of a task.
nologically separable interfaces” (Williamson, 1981). Here the transaction occurs between sections within consulting practices. This study deals with tasks undertaken regularly. In the psychological literature there is some consensus on the definition of a task. Hackman (1969) and Weick (1965) agree that a task is beyond a pure physical property as it includes stimuli, prescriptions and/or objectives or goals. Brown et al. (1969) and O’Brien and Owens (1969) empirically show that clear-cut tasks affect work performance. Naylor and Dickinson (1969) concur showing that more structured tasks yield better performance. This provides grounds for linking tasks to manufacturing objectives in the same way as overall performance associates with focus. A task may be linked to objectives. Links can be measured in absolute terms. Alternatively tasks may be important, as, when marginal resources are applied, changes on objectives are large. Marginal effects can also be measured. Both absolutes and marginals are investigated. This research considers a range of tasks. Objectives are analysed for these tasks in consulting.
1.5 The consulting industry In SA consulting consists of many diverse firms. Firms sampled range in size between 20 and 400 full time employees. (Smaller firms between the one and 19 employees are excluded, as the sample frame of such firms is unstable). Competition between consultancies is intense, but, at times, alliances are formed between firms. An example of this, is reengineering of Spoornet where Gemini and Andersen Consulting formed an alliance. At times clients may need a standardised service
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but clients often build up a long term relationship with the consultant or consulting firm. The consultant becomes familiar with the client, gaining insight into the client’s needs and operations. Zimbler points to the idiosyncratic nature of this relationship saying that: “...the change initiatives and strategies most likely to succeed are those which are consistent with the unique culture and circumstances of each organisation” (Zimbler, 1994, p. 60). The choice of a consultant is important, especially for complex long-term projects. Strategic management consultants offer a large variety of services to general management including strategic services/planning; change management (helping people adapt); systems integration (often managing information in technology); business process management (placing management emphasis on the core business); and organisational effectiveness (analysing a business and its environment to improve company effectiveness). Services vary between consultancies. Consultancies usually have few levels separating top management from the work force and consultants often spend much time unsupervised at distant locations. Operating instructions for consultants tend to be elementary (unless the consultancy is selling a proprietary product). Little is written within these organisations although this may change at large consultancies. Consulting is dependent upon consultants’ skills. Consultants perceive that they need to give services with effective results producing measurable benefits for their clients. A typical S.M.C. employs people with advanced training and education. S.M.C. is service orientated and in stark contrast to F.M.C.G. manufacturing which is goods orientated. S.M.C. firms can be compared with F.M.C.G. manufacturers on a variety of parameters. Some parameters are compared in Table 2. Fast moving consumer goods manufacturers differ from strategic management consulting firms on all parameters in Table 2. They differ in size, relation-
ships with customers, methods of operation and products offered. The research undertaken on strategic management consultants is next presented.
2. RESEARCH With the problem introduced the hypothesis tested is set, the research approach outlined, results reported for S.M.C. before comparing results with the F.M.C.G. study.
2.1 Hypotheses to be tested The starting point is defining the problem. The null hypothesis is that managers in strategic management consulting weigh objectives equally for all tasks whether for absolute totals or marginal changes. This was tested previously where evidence pointed to the sandcone model using a restricted set of objectives, namely cost, quality and speed (Snaddon, 1996, p. 388). If the null hypothesis is rejected then the association between this and the previous experiment (Snaddon, 1996) is sought to show if similar tasks have similar objective values between these industries. This partly uses Snaddon’s research method for the industry, an industry different from the S.M.C. firms.
2.2 Empirical research3 Flynn et al. gives approaches to empirical research in this field. Within these approaches this study verifies [sic] theory using survey research design. It uses a questionnaire that follows the F.M.C.G. industry study, but the questionnaire is reduced to 17 tasks and expanded to five objectives (Flynn et al., 1990, p. 254). Consultants and managers checked the questionnaire. A list of tasks and objectives used are in 3 I thank Miss T.C.A. Palmos for collecting and analyzing the Strategic Management Consulting data.
TABLE 2. F.M.C.G. vs S.M.C. Parameter Size Relationships with customers Operations
Products
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F.M.C.G.
S.M.C.
Average size 3500 people. (Largest 1.5% of food manufacturers 20–400 full time people. in SA). Market based. Dependant upon goods supplied. Idiosyncratic people based. Dependent on consultants’ skills. Factory-based operations mainly high volume line flow Jobbing work at clients’ premises. Usually few levels separating techniques and distribution networks. People work under close top management from the work force. Consultants spend much supervision. time unsupervised at distant locations. Well-defined goods orientated, e.g. groceries. Large variety of services (see text).
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
Table 4 and Table 5. Like the F.M.C.G. study it is a cross-sectional, or one-time, study. The sample frame used for the S.M.C. industry came from an association directory (Murphy, 1994). The study uses equal unit probability sampling where the consultancy is the element or unit. It is stratified as consultancies with less than 20 persons are eliminated. Beyond this, consultancies are randomly selected. Such an approach allows precision to be estimated about the characteristic measured. Within each unit, available managers are interviewed based upon the judgement of the initial respondent e.g. telephone operator. These were mainly managing directors or partners. They were from the top tier of management. It is therefore a two-stage technique. Personal and, when costs per interview were large, telephone interviews are used. (Telephonic interviews were only used in a few cases.) This differs from the F.M.C.G. study that used mail questionnaires. Research involves finding the relative importance of tasks and objectives. This is done in absolute and marginal terms. The null hypothesis is that the average manager weighs objectives equally for the same tasks. In measuring the absolute importance of objectives the F.M.C.G. study uses a 7-point, where the S.M.C. study uses a 5-point scale. Meanings attached to the values are given in Table 3. The S.M.C. scale average is one point less than the F.M.C.G. average. Both studies ask respondents to measure marginal values by ticking which objectives would be most improved with extra resources for each task.
2.3 Results Twenty-five S.M.C.s of the 44 listed gave results using measures in Table 4. Averages of responses are calculated for each of the five objectives, and for each task. Table 4 gives the average results for the absolute totals of each criterion. The S.M.C. results are in the left-hand side. The F.M.C.G. results are in the righthand side of cells from the previous study.
S.M.C. results range from a low of 2.04 for the criterion “speed”, and the task “interpersonal relations”, to a high of 5 on the criterion “quality”, for the task “selection”. This implies that the quality of selection is the most important association in consulting. Overall averages for the five objectives are: quality 4.25, dependability 3.66, flexibility 3.32, speed 3.11 and cost 3.08. Except for cost and speed, significant4 differences are found between the averages of all objectives. In absolute terms, managers weigh all objectives, excepting cost and speed, differently. Mefford’s (or the null) hypothesis that managers weigh objectives equally, is rejected. Table 5 shows results for marginal changes, as percentages. Results range from a low of 3% for “speed” in “interpersonal relations” and “cost” in “credit control”, to a high of 59% for “quality” of the task “public relations”. Overall averages of all tasks for the objectives are: quality 39%, speed 18%, dependability 17%, flexibility 13% and cost 13%. For marginal changes, significant differences are among all objectives except “speed and dependability” and “flexibility and cost”. In marginal terms, the hypothesis of equality between objectives can be rejected but for “speed and dependability” and “flexibility and cost”. Table 6 shows averages for absolute and marginal importance of the five objectives converted to percentages. If tasks selected in this research reflect all tasks undertaken by S.M.C. firms, then such firms consider quality as the most important objective in both marginal and absolute terms. Cost in both marginal and absolute terms, rates as the least important objective. These results contradict Mefford’s (1986, p. 98) assertion that managers weigh objectives equally. There is a significant difference for many objectives.
4
Significance is claimed at the 95% level.
TABLE 3. Scales used in studying the absolute importance of objectives Value/study F.M.C.G. S.M.C.
1
2
3
4
5
6
7
Not applicable Irrelevant
Poor Little importance
Fair Average
Average Important
Good Very
Best –
The only –
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D. R. Snaddon
TABLE 4. Average results of absolute totals Tasks
Objectives
Bookkeeping Credit control Disbursements expenditure Selection Employee training Interpersonal relations Benefits Termination of employment Contracts Quality assurance Scheduling/assignment Advertising Public relations Sales Data processing General management Research and development Overall average
Cost
Quality
Speed
Dependability
Flexibility
2.60 3.60 2.48 3.54 3.24 3.86
4.52 6.15 4.04 5.50 3.64 5.32
3.52 4.69 3.80 4.59 2.48 4.30
4.4 4.16 3.4
2.56 2.32 2.4
2.72 2.84 2.20 3.56 3.08
3.32 4.00 3.19 4.50 3.30
5.00 4.60 4.20 3.96 3.72
6.00 5.58 5.73 5.47 4.83
2.88 2.48 2.04 2.12 3.56
2.88 3.86 4.00 3.18 4.00
4.04 3.44 3.6 3.04 2.68
3.08 3.76 4.16 3.32 2.68
3.48 2.68 3.08 3.40 2.92 3.40 3.08 2.84 2.84 3.08
3.57 3.74 3.85 5.08 4.26 4.46 3.79 3.30 3.71 4.12
4.68 4.52 4.44 4.20 4.24 4.56 4.60 4.80 4.32 4.25
5.72 5.88 5.23 5.60 5.76 5.88 5.70 6.13 5.43 5.58
3.20 2.68 3.88 3.64 2.76 3.60 3.72 3.16 2.96 3.11
4.26 4.35 5.20 4.22 4.35 5.20 5.62 4.54 4.18 4.49
4.2 4.12 3.88 3 3.4 4.2 3.96 4.2 3.52 3.66
3.4 3.04 4 2.76 3.24 3.88 3.6 4.32 3.8 3.32
TABLE 5. Average results of marginal changes (in percentages) Tasks
Objectives Cost
Bookkeeping Credit control Disbursements expenditure Selection Employee training Interpersonal relations Benefits Termination of employment Contracts Quality assurance Scheduling/assignment Advertising Public relations Sales Data processing General management Research and development Overall average
632
Quality
Speed
Dependability
Flexibility
5 3 19
0 4 19
31 30 27
59 58 43
29 42 27
41 38 38
26 18 12
9 7 15
6 14
3 6
50 44
66 74
19 11
31 20
19 19
6 12
4
8
48
80
3
12
21
24
33 24
38 18
33 24
56 53
4 28
6 29
10 5
20 19
8 12
24 9
46 38
52 67
23 17
24 24
20 21
3 12
8 22 11 11 12 12
7 19 7 22 12 4
33 43 59 40 24 43
32 73 80 56 33 91
22 7 8 19 27 10
62 7 14 22 55 4
17 14 11 16 20 21
20 14 11 14 17 14
12
14
49
55
12
32
15
12
13
15
39
54
18
30
17
13
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The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
TABLE 6. Importance of objectives (in percentages) Objective
TABLE 7. Comparison of absolute figures (in percentages)
Absolute (%)
Marginal (%)
24 21 19 18 18 100
39 17 13 18 13 100
Quality Dependability Flexibility Speed Cost Total
Research study F.M.C.G. — all tasks F.M.C.G. - same tasks as S.M.C. study S.M.C.
Cost
Quality
Speed
29 28 29
39 41 42
32 31 29
Tasks are next compared against the F.M.C.G. study. Comparing the overall averages in Table 4 and Table 5 between jobbing (S.M.C.s) and flow line (for F.M.C.G.s) manufacturing processes checks Schmenner’s theory. While manufacturing mix definitions and industries vary Schmenner’s theory is not supported. Within the accuracy of the assumptions and results, absolute values for all tasks can be measured on the performance scale: Overall Score = 0.24*Quality + 0.21*Dependability + 0.19*Flexibility + 0.18*Speed + 0.18*Cost or Quality → Dependability → Flexibility → Speed ⬇ Cost (The symbol → means is followed by but still includes the former) The evidence places flexibility before speed where Slack placed flexibility after speed. Alternatively the same ordering is found except speed and flexibility. These results support Ferdows and de Meyer’s sandcone model. For marginal analysis, the following comparative overall orders are: Marginal:Quality → Speed ⬇ Dependability → Flexibility ⬇ Cost Slack:Quality →Dependability → Speed → Flexibility → Cost As “cost and flexibility” and “dependability and speed” are not significantly different this test also lends credence to the sandcone model5.
2.4 Comparisons with F.M.C.G. study Compare results between the F.M.C.G. and S.M.C. study by eliminating the dependability and flexibility objectives. Absolute and marginal results are in Table 7 and Table 8. Results are from more than 30 F.M.C.G. and 25 S.M.C. firms. This checks if the average manager in the F.M.C.G. and S.M.C. firms associate tasks and objectives similarly. To check association between the F.M.C.G. and S.M.C. study, data are given in Table 4 and Table 5. Table 4 has absolute, while Table 5 has marginal, averages. Associations between the F.M.C.G. and S.M.C. study can be checked using linear regression on the seventeen similar tasks in absolute and marginal form. The F.M.C.G. values are the dependent (y) variable, and this study’s values are the independent (x) variable. Results using the data in Table 4 and Table 5 are in Table 9 and Table 10 respectively. Absolute results for cost, quality, speed and totals are in Table 9. (Note that the quality objective of the F.M.C.G.’s study incorporates dependability. Table 9 and Table 10 give results for no dependability in quality and then equal weighting for quality and dependability in a combined quality objective). Marginal results for cost, quality and cost and quality combined are in Table 10. (As speed makes cost, quality and speed add to 100% in the F.M.C.G. marginal study, speed is not independent). All linear equations have significant association between the F.M.C.G. and S.M.C. studies. (Significant variations between studies, expected by chance, include tasks of “termination of employment” and “contracts” on “cost”, and “interpersonal TABLE 8. Comparison of marginal figures (in percentages)
Results support the sandcone model in overall terms.
5 The results do not agree with the survey by Charissis and Stephens (1993).
Research study F.M.C.G.— all tasks F.M.C.G.— same tasks as S.M.C. study S.M.C.
Cost
Quality
Speed
15 13 18
54 60 56
30 27 26
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TABLE 9. Association between S.M.C. and F.M.C.G. results for absolute totals Associated variables Cost Quality Quality + dependability Speed All objectives All objectives + dependability
Linear association and standard errors
Other statistics
y = 1.2861 + 0.85566x S.E. 0.4042 0.2678 y = 2.7300 + 0.6686x S.E. 0.2351 0.1594 y = 3.003 + 1.6937x
R2 = 0.4055 n = 17; d.f. = 15 R2 = 0.5397 n = 17; d.f. = 15 R2 = 0.8794
S.E. 0.5108 0.1242 y = 2.1955 + 0.7044x S.E. 0.4270 0.1798 y = 1.0339 + 1.03154x S.E. 0.4233 0.0770 y = 0.6972 + 1.1641x
n = 17; d.f. = 15 R2 = 0.5056 n = 17; d.f. = 15 R2 = 0.7854 n = 51; d.f. = 49 R2 = 0.7350
S.E. 0.3423 0.0999
n = 51; d.f. = 49
y is the average value Snaddon (1996, p. 392) finds; x is the average value in this study; R2 is the coefficient of correlation squared; S.E. s the standard error of the values in the equation in the cell above; n is the number of paired observations; d.f. is the number of degrees of freedom.
TABLE 10. Association between S.M.C. and F.M.C.G. with results for marginal changes Associated variables Cost Quality Quality + dependability Cost and quality Cost, speed and quality + dependability
Linear association and standard errors
Other statistics
y = 0.7339 + 0.9330x S.E. 6.5894 0.2091 y = 19.4507 + 0.9330x S.E. 12.6115 0.2091 y = 13.0327 + 1.7032x
R2 = 0.5704 n = 17; d.f. = 15 R2 = 0.4299 n = 17; d.f. = 15 R2 = 0.3902
S.E. 15.6346 0.5497 y = − 4.0813 + 1.5726x S.E. 11.5291 0.1253 y = − 4.1765 + 1.9188x
n = 17; d.f. = 15 R2 = 0.5704 n = 34; d.f. = 32 R2 = 0.6448
S.E. 4.4918 0.2034
n = 51; d.f. = 49
y is the average value Snaddon (1996, p. 393) finds; x is the average value in this study; R2 is the coefficient of correlation squared; S.E. is the standard error of the values in the equation in the cell above; n is the number of paired observations; d.f. is the number of degrees of freedom.
relations” for “speed” in the absolute study. A significant variation between studies on the marginal association is “general management” on “quality”. All variations are expected with limits used). There is significant association between results for F.M.C.G. and S.M.C. This may be partly ascribed to similar methodology but the variation between industries is large. With the association found an estimate can be given for the combined studies. Table 11 and Table 12 list average values of absolutes and marginals respectively in decreasing order. These tables can be read as follows. The first entry in Table 11 is “quality” and “employee selection”. This means that the most important aspect of all tasks
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sampled in a firm in these two industries combined is the association between employee selection and quality. No causality can be given to this. The research may either be interpreted that quality affects, or is affected by, employee selection. The most important association for “speed” is “data processing” and for “cost” it is “advertising”. The first entry in the marginal association in Table 12 is the association between “quality” and “public relations”. This means that marginal resources added to the task “public relations” is most associated with “quality”. Again no causality is implied. The greatest marginal association for “speed” is “manufacturing scheduling” and “cost” is “employee benefits”.
3. CONCLUSIONS, MANAGERIAL IMPLICATIONS AND FURTHER RESEARCH 3.1 Conclusions This shows that the sandcone model proposed by Ferdows and de Meyer may apply to the average strategic management consultant at the level of the firm rejecting both Mefford’s and Schmenner’s theories. Within the firm tasks may not follow the sandcone model. S.M.C. is far removed from F.M.C.G. manufacturing, yet both industries show that the average manager links tasks with manufacturing objectives similarly. While there is variation, there is underlying similarity. This indicates an underlying web of information that exists building up the product/services in organisations, presently in partial and often unrelated systems. Integrating such information could yield stronger management information systems than presently exist. Absolute analysis is useful when managers wish to classify important objectives. A manager wishing to pursue “quality” associates this with “employee selection”. Marginal analysis shows where managers do focus by investing in the firm. A manager wishing to pursue “quality” may, for example, invest in tasks where “quality” dominates, such as “public relations”. (This may say that public relations can improve quality by telling the market or it may say that public relations is the conduit that makes the firm more aware of quality). Marginal analysis identifies tasks where extra resources associate with a selected criterion.
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
TABLE 11. Absolute importance of tasks and objectives in decreasing order Objective
Task
Quality Quality Quality Quality Quality Quality Quality Quality Quality Quality
Employee Other Accounting Marketing Manufacturing Legal Other Employee Marketing Employee
Quality Quality Quality Quality Quality Speed Speed Quality Speed Quality
Marketing Other Manufacturing Accounting Employee Other Manufacturing Accounting Marketing Employee
Cost Speed Speed Cost Speed Cost Speed Speed
Marketing Accounting Accounting Employee Marketing Marketing Other Employee
Speed Cost Speed Speed Cost Cost Speed Cost Cost Cost Speed Speed Cost Cost Cost
Legal Marketing Other Marketing Accounting Legal Manufacturing Manufacturing Other Employee Accounting Employee Other Manufacturing Employee
Speed Cost Cost Speed
Employee Accounting Other Employee
Cost Cost Cost
Employee Accounting Employee
Speed
Employee
F.M.C.G. Selection General management Bookkeeping Sales Quality assurance Contracts Data processing Training Public relations Operating and interpersonal relations Advertising Research and development Scheduling Credit control Benefits Data processing Scheduling Disbursements/expenditure Sales Termination of employment Advertising Credit control Bookkeeping Benefits Advertising Sales General management Termination of employment Contracts Public relations Research and development Public relations Disbursements/expenditure Contracts Quality assurance Scheduling Data processing Training Disbursements/expenditure Selection Research and development Quality assurance Termination of employment Training Bookkeeping General management Operating and interpersonal relations Selection Credit control Operating and interpersonal relations Benefits
Absolute and marginal ways of dealing with objectives are complementary. They extract different information both important to the surviving firm. There is some stability across the F.M.C.G. and
S.M.C.
Average
6 6.13 6.15 5.88 5.88 5.72 5.7 5.58 5.76 5.73
5 4.8 4.52 4.56 4.52 4.68 4.6 4.6 4.24 4.2
5.5 5.465 5.335 5.22 5.2 5.2 5.15 5.09 5 4.965
5.6 5.43 5.23 5.5 5.47 5.62 5.2 5.32 5.2 4.83
4.2 4.32 4.44 4.04 3.96 3.72 3.88 3.64 3.6 3.72
4.9 4.875 4.835 4.77 4.715 4.67 4.54 4.48 4.4 4.275
5.08 4.59 4.69 4.5 4.22 4.46 4.54 4
3.4 3.8 3.52 3.56 3.64 3.4 3.16 3.56
4.24 4.195 4.105 4.03 3.93 3.93 3.85 3.78
4.26 4.26 4.18 4.35 3.86 3.57 4.35 3.85 3.79 4 4.3 3.75 3.71 3.74 3.3
3.2 2.92 2.96 2.76 3.24 3.48 2.68 3.08 3.08 2.84 2.48 2.88 2.84 2.68 3.08
3.73 3.59 3.57 3.555 3.55 3.525 3.515 3.465 3.435 3.42 3.39 3.315 3.275 3.21 3.19
3.86 3.6 3.3 4
2.48 2.6 2.84 2.04
3.17 3.1 3.07 3.02
3.32 3.54 3.19
2.72 2.48 2.2
3.02 3.01 2.695
3.18 4.61
2.12 3.47
2.65 4.04
S.M.C. as for associations between tasks and objectives. Do similar tasks have similar objectives? Alternatively do managers, wishing to pursue specific objectives, chose to undertake specific tasks rather than modifying tasks to pursue objectives?
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TABLE 12. Marginal importance of tasks and objectives in decreasing order Objective
Task
Quality Quality Quality
Marketing Other Employee
Quality Quality Quality Quality Quality Quality Quality Quality Quality Quality Speed Speed Speed Quality
Employee Marketing Employee Manufacturing Other Legal Marketing Accounting Employee Accounting Manufacturing Other Accounting Employee
Cost Quality Speed Speed Quality Quality Speed
Employee Accounting Accounting Accounting Manufacturing Other Employee
Speed Speed Speed Cost
Employee Legal Other Employee
Speed Speed Cost Cost Cost Cost Speed Cost Cost Speed Cost Cost Cost Cost Speed
Manufacturing Marketing Marketing Accounting Marketing Legal Employee Other Other Marketing Manufacturing Employee Marketing Other Employee
Cost Speed Speed Cost
Manufacturing Marketing Other Employee
Speed Cost Cost Cost
Employee Employee Accounting Accounting
F.M.C.G. Public relations General management Operating and interpersonal relations Training Advertising Selection Quality assurance Research and development Contracts Sales Bookkeeping Benefits Credit control Scheduling Data processing Credit control Termination of employment Benefits Disbursements/expenditure Bookkeeping Disbursements/expenditure Scheduling Data processing Termination of employment Selection Contracts Research and development Termination of employment Quality assurance Sales Advertising Disbursements/expenditure Sales Contracts Training Research and development Data processing Public relations Quality assurance Training Public relations General management Operating and interpersonal relations Scheduling Advertising General management Operating and interpersonal relations Benefits Selection Credit control Bookkeeping
3.2 Managerial implications Results from these studies have managerial implications. Obvious benchmarks of tasks and objectives exist in the Tables. Being from very different industries they add evidence that:
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S.M.C.
Average
80 91 80
59 43 48
69.5 67 64
74 73 66 67 55 52 56 59 56 58 62 55 38 53
44 43 50 38 49 46 40 31 33 30 22 27 42 24
59 58 58 52.5 52 49 48 45 44.5 44 42 41 40 38.5
38 43 41 38 32 33 29
33 27 29 27 33 24 28
35.5 35 35 32.5 32.5 28.5 28.5
31 24 32 18
19 3 12 24
25 23.5 22 21
24 22 19 19 22 24 20 14 12 14 9 6 7 4 12
17 19 22 19 11 8 11 12 12 8 12 14 11 12 3
20.5 20.5 20.5 19 16.5 16 15.5 13 12 11 10.5 10 9 8 7.5
7 7 4 8
8 7 10 4
7.5 7 7 6
6 3 4 0 33.35
4 6 3 5 23.25
5 4.5 3.5 2.5 28.30
쐌 reengineering firms in terms of core competence is hazardous. Changing tasks in and out of businesses may have unforeseen difficulties. For example changing credit control to save costs may seriously affect quality!; 쐌 employees with skills in one task e.g. legal con-
The manufacturing mix and consulting firms — how different are they in associating tasks with objectives?
tracts, have an orientation which eases their transfer between industries. (This may be a major reason for the popularity of professional institutes, examinations and education); 쐌 emphasis placed upon costs as a source of management decision seems excessive. A reason is that legislation, especially tax legislation, forces firms to keep costs in particular ways. As management derives this information at low marginal cost, they use it. Such legislated costs may differ from economic costs as items such as quality, dependability and speed objectives need careful monitoring, especially by owners; and 쐌 the manufacturing mix objective quality is most important in both industries whether in absolute or marginal form.
3.3 Further work The finding that managers in the F.M.C.G. and the consulting industry weigh manufacturing mix objectives similarly opens areas of further research. This intimates the need to map the information web that yields the product/service mix offered by the firm. Comparisons in other industries are needed. A point assumed in this and previous work (Snaddon, 1996) is that selected tasks comprise the firm and that each task is equally important especially for the marginal analysis. This needs closer investigation. This does not mean that research on taxonomies of the manufacturing mix is complete. Flexibility is a criterion not considered by some authors as independent (e.g. Ferdows and De Meyer, 1990 and Snaddon, 1993). Analysis can provide insight into how effectively companies service their customers’ needs. Such information could be valuable to model competing firms. Where specific objectives dominate, it may be possible to associate inputs and outputs by a method previously outlined (Snaddon, 1990).
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Snaddon, D. R. (1990) Productivity within sections of firms — I the design of a productivity measure for a section of a firm. Technovation 10, 235–251. Snaddon, D. R. (1993) The goals of manufacturing strategy, P. O.M. techniques and perceptions — a pilot study. South African Journal of Business Management 24, 147–153. Snaddon, D. R. (1996) The manufacturing mix. Technovation 16, 385–396. Swamidass, P. M. and Newell, W. T. (1987) Manufacturing strategy, environmental uncertainty, and performance: a path analytic model. Management Science 33, 509–524. Weick, K. E. (1965) Laboratory experimentation with organisations. In Handbook of Organisations, ed. J. G. March, pp. 194–261. Rand McNally, Chicago. Williams, F. P., D’Souza, D. E., Rosenfelt, M. E. and Kassaee, M. (1995) Manufacturing strategy, business strategy and firm performance in a mature
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