Strategic consensus on manufacturing strategy: operators' and managers' perceptions Nina Edh Mirzaei ([email protected]) Chalmers University of Technology, Technology management and economics, 412 96 Gothenburg, Sweden Björn Lantz Chalmers University of Technology, Technology management and economics, 412 96 Gothenburg, Sweden

Abstract This paper joins the discussion on the need for trade-offs among competitive priorities in manufacturing strategy (MS) and builds on earlier works on strategic consensus on MS by addressing the purpose to examine the level of strategic consensus between different organisational levels regarding the competitive priorities quality, delivery, flexibility and cost. Survey data from 96 employees at one Swedish assembly plant show that the employees rank the priorities significantly different. Further, white collar workers rank six of the 16 studied competitive priority abilities significantly higher than blue collar workers do. Keywords: Manufacturing strategy, strategic consensus

Introduction In this paper, we set out to examine the level of strategic consensus on manufacturing strategy (MS) among individuals at different organisational levels. In an increasingly competitive market, the MS is essential for a manufacturing company to remain competitive. MS concerns a fit between market requirements and operational resources, and provides a link between manufacturing and the company’s corporate strategy (e.g., Miltenburg, 2005; Skinner, 1969; Slack & Lewis, 2011). This paper follows Marucheck et al. (1990, p. 104)’s definition: “Manufacturing strategy is a collective pattern of coordinated decisions that act upon the formulation, reformulation and deployment of manufacturing resources and provide a competitive advantage in support of the overall strategic initiative of the firm.” Manufacturing strategy is commonly operationalised by a distinction between content and process, where content comprises strategic decisions made with respect to competitive priorities (quality, delivery, flexibility and cost), while process entails formulation and implementation (Dangayach & Deshmukh, 2001; Mills et al., 1995; Slack & Lewis, 2011). As almost all individuals within a manufacturing function make operating decisions it is essential for effective decision making and strategic fit that “everyone” has a shared understanding of and commitment to the organisation’s MS (Boyer & McDermott, 1999; 1   

Gagnon et al., 2008; Kellermanns et al., 2005), i.e. that the organisation demonstrates a high level of strategic consensus (Boyer & McDermott, 1999). Strategic consensus, particularly between operators and managers, is an important means for a successful MS process (Kellermanns et al., 2005). Previous studies on strategic consensus have mainly focused on the shared understanding among different managerial levels (Kathuria et al., 1999; Kathuria et al., 2010; Rapert et al., 2002). In this paper we especially focus on the operators’ perceptions and shared understanding of the competitive priorities. To a great extent, we build further on the work by Boyer and McDermott (1999) where both the operators’ and the managers’ perceptions of MS are included and more than one informant per company is used. In our research we divide the operator level into two categories: blue collar workers (BCWs) and white collar workers (WCWs) and thereby open up for a behavioural operations perspective (Croson et al., 2013), where also individuals at the micro-level of the operations function are viewed as individuals as opposite as to constituting a homogenous group. The purpose of this paper is therefore to examine the level of strategic consensus between different organisational levels regarding the competitive priorities quality, delivery, flexibility and cost. The paper also aims to examine the relation between the competitive priorities themselves. Theoretical background/Hypotheses Manufacturing strategy competitive priorities Since Skinner (1969) first emphasised the missing link between operations and the company’s corporate strategy the notion of competitive priorities has become central to the research on MS (Boyer & Lewis, 2002). During the years a “relatively shared framework” of the MS content has developed and concerns “the relative weighting of manufacturing capabilities, including low cost, quality, flexibility, and delivery” (Boyer & Lewis, 2002, p. 9). These competitive priorities, involved in strategic decisions, often encompass:  Costs o including procurement and production expenses (Acur et al., 2003; Kathuria et al., 1999);  Quality o constituting specification quality and conformance quality (Acur et al., 2003; Slack & Lewis, 2011);  Delivery o consisting of production lead time, procurement lead time and ability to fulfil delivery promises (Acur et al., 2003; Boyer & McDermott, 1999; Dangayach & Deshmukh, 2001; Kathuria et al., 1999); and  Flexibility o comprising changes in product, product mix, volume flexibility and capacity adjustments (Acur et al., 2003; Boyer & McDermott, 1999; Dangayach & Deshmukh, 2001; Kathuria et al., 1999). While these competitive priorities are fairly well defined, and their importance for the possibility for successful MS are agreed upon, there are disagreements and debate concerning the relationships among competitive priorities (Boyer & Lewis, 2002; Schroeder et al., 2011). Three perspectives can be identified: the trade-off model, the cumulative model and the integrative model. The trade-off model originates from Skinner (1969) ideas that plants need to focus on one competitive priority at a time since improving one of them will be on the expense of the others (Boyer & Lewis, 2002; Ferdows & De Meyer, 1990; Skinner, 1969). The logic 2   

behind this is that each of the competitive priorities require different operational structures and infrastructures for support (Boyer & Lewis, 2002). The cumulative model came as a response to the trade-off model during the 1980’s as the concept of world class manufacturing emerged. The cumulative model questions the need for trade-offs and emphasise that multiple capabilities can be developed simultaneously (Boyer & Lewis, 2002; Schroeder et al., 2011). Inherent in the model is a sequencing, where one priority enhances another (Ferdows & De Meyer, 1990). Despite extensive studies on the cumulative model, agreement has not been reached on a specific sequence. However, the most renowned is the sand-cone model put forward by Ferdows and De Meyer (1990) where improvements to competitive priorities are accomplished by first focusing on quality, then on quality and dependability, then quality, dependability and flexibility, and finally on all three of them and cost (Ferdows & De Meyer, 1990; Schroeder et al., 2011). The integrative model questions both the trade-off logic and the cumulative model and stress that elements of both can be applicable dependent on the location of the company and its maximum potential performance frontier (Boyer & Lewis, 2002; Schroeder et al., 2011). Based on the reasoning of these three models, this paper sets out to see if there are trade-offs between the competitive priorities or if the sand-cone model does exist in the sequence established by (Ferdows & De Meyer, 1990). Therefore, the first hypothesis is formulated as follows: 

H1: Individuals in the operations function assess the different competitive priorities according to the sand-cone model, that is, the competitive priorities are ranked in the order 1) quality, 2) delivery, 3) flexibility, and 4) cost.

Strategic consensus on manufacturing strategy The strategic consensus concept is an important means to accomplish a successful MS process (Boyer & McDermott, 1999). The concept logically links to the need for the development of a shared vision or mission, which Voss (1995) identifies as helpful in order to “focus the employees of a company and support the achievement of strategic goals” (p. 7). This paper adheres to the definition by Boyer and McDermott (1999): strategic consensus concerns “the level of agreement within an organization regarding the relative importance of cost, quality, delivery and flexibility to the organization’s operational goals, as well as the relationships between these competitive priorities and operational policies”. Through notions of “agreement” and “global understanding”, Boyer and McDermott (1999), as well as Gagnon et al. (2008), emphasised the need to involve all individuals working with operational goals and policies. Strategic consensus focuses on “the degree of agreement within a group […] at a particular point in time” (Kellermanns et al., 2005, p. 721), with the underlying hypothesis that “higher degrees of strategic consensus are associated positively with coordination and cooperation in the implementation of strategy, and hence, with organizational performance” (Kellermanns et al., 2005, p. 722). Earlier research on strategic consensus on MS has to a great extent focused on the shared understanding among different managerial levels (Kathuria et al., 1999; Kathuria et al., 2010; Rapert et al., 2002), in particular at the top management level (Sarmiento et al., 2008). Feger (2014) studied cross-functional strategic consensus among purchasing, production and logistics at the managerial level. Sarmiento et al. (2008) not only stated that strategic consensus had been “under-researched in the field of operations management” (p. 839), but also emphasised the need to explore the topic “at the lower 3   

levels within organisations (e.g., shop-floor employees)” (p. 840). Such exploration was conducted by Boyer and McDermott (1999) who studied intra-plant differences among operators and managers. They found that while high levels of agreement indeed were common, there were “important and systematic differences” regarding the rankings of competitive priorities (Boyer & McDermott, 1999, p. 300). Boyer and Lewis (2002, p. 18) also found differences in how operators and managers within the same organisation perceived manufacturing strategy and suggested that future research need “multiple and varied respondents”. Guided by these earlier studies two conclusions can be made: (1) there are differences between organisational levels’ perceptions of competitive priorities, but these differences are identified on an aggregated level, and (2) there is a need to vary the respondents beyond the groups of operators and managers. Therefore, we set out to see if different abilities of competitive priorities are ranked differently by different organisational groups within the organisational levels. Based on earlier research (primarily Boyer and McDermott (1999) there is an underlying assumption that the “higher” an individual is in the hierarchy, the higher he or she ranks the importance of the competitive priority ability. Therefore, the second hypothesis is formulated as follows: 

H2: White collar workers rank competitive priorities higher that blue collar workers do.

Design/methodology/approach This study is based on a large survey conducted at one of the plants of a large Swedish manufacturing company in the region of Småland. The company was selected based on its willingness to participate in the study, hence, a convenience sample. The questionnaire was primarily developed based on a previous study by Boyer and McDermott (1999). The same competitive priority abilities were also used by Boyer and Lewis (2002). Additional questions in the questionnaire were inspired by previous works by Acur et al. (2003), Bhat and Kumar (2004), Boyer and McDermott (1999), Cagliano and Spina (2002), Edh (2013), Kathuria et al. (1999) and Slack and Lewis (2011). The questionnaire included Likert-type items on the respondents’ views of the importance of cost, quality, delivery, flexibility, structural and infrastructural decisions, and questions related to responsibility. Questions on the respondents’ perceptions of organisational structure and information sharing were also included, along with personal background questions and questions related to the respondent’s personality. After meeting the top management in person it was agreed that the company itself would manage the data collection through distribution of the questionnaire to different departments within the organisation. It was decided to include the following groups in the study:  The top management group  Three departments of white collar workers (WCWs) with close connection to the shop floor o Two departments with production engineers o One planning department (for assembly) o Four assembly lines with blue collar workers (BCWs)  Different products for each line In total the study involved 96 respondents (15 WCWs and 81 BCWs). 11 (1 WCW and 10 BCWs) of the respondents (plus additional three top management members) had management responsibilities but since the samples size was to small the top management 4   

members were excluded from the study and the individuals with management responsibilities were included in the groups BCWs and WCWs, respectively. This study is based on 17 items from the survey. One item was used to classify the respondent’s position as either a BCW or a WCW. The other 16 items were derived directly from Boyer and McDermott (1999) and Boyer and Lewis (2002) and represent abilities related to the four main competitive priorities; cost, quality, delivery and flexibility (see Table 1). The respondents were asked to rate how important they think each ability is for their manufacturing plant on a Likert scale from 1 (not important) to 7 (absolutely critical). The data were analysed with parametric statistical methods. Results Welch’s t-test was used to compare the mean ratings of blue collar workers (BCWs) and white collar workers (WCWs) for the different abilities (see Rasch et al., 2011). The results are displayed in Table 1. BCWs rated 6 of the 16 abilities significantly lower than WCWs did. The other 10 abilities were characterised by insignificant differences. Table 1: Comparisons between BCWs and WCWs Priority

Ability

Position

COST

Reduce inventory

Increase capacity utilisation

Reduce production costs

Increase labour productivity

QUALITY

Provide high performance products

Offer consistent, reliable quality

Improve conformance to design specifications

DELIVERY

Provide fast deliveries

Meet delivery promises

n

Mean

S.D.

BCW

78

3.91

1.43

WCW

15

4.07

1.44

BCW

78

4.29

1.33

WCW

15

5.67

1.18

BCW

77

4.74

1.32

WCW

15

5.93

1.10

BCW

78

4.55

1.51

WCW

14

5.50

1.34

BCW

78

5.55

1.29

WCW

15

6.60

0.83

BCW

78

5.67

1.30

WCW

15

6.33

1.05

BCW

76

4.92

1.52

WCW

15

5.07

1.79

BCW

78

5.23

1.49

WCW

15

5.47

0.92

BCW

78

5.58

1.46

WCW

15

6.07

0.96

5   

t

p

-0.39

0.703

-4.05

0.001

-3.71

0.001

-2.38

0.027

-4.05

< 0.001

-2.17

0.041

-0.29

0.772

-0.81

0.422

-1.64

0.111

Reduce production lead time

FLEXIBILITY

Make rapid design changes

Adjust capacity quickly

Make rapid volume changes

Offer a large number of product features

Offer a large degree of product variety

Adjust product mix

BCW

76

4.63

1.69

WCW

15

5.73

1.03

BCW

75

4.61

1.59

WCW

15

4.13

1.36

BCW

74

4.88

1.50

WCW

15

4.73

1.58

BCW

74

4.46

1.58

WCW

15

4.60

1.55

BCW

75

4.56

1.37

WCW

14

3.79

1.31

BCW

74

4.23

1.56

WCW

15

3.53

1.68

BCW

75

4.57

1.57

WCW

15

4.47

1.41

-3.34

0.002

1.21

0.237

0.33

0.747

-0.32

0.753

2.01

0.059

1.48

0.156

0.26

0.795

The items within each competitive priority exhibited sufficient reliability in terms of Cronbach’s alpha (see Table 2), so the priorities themselves were compared with a repeated measures ANOVA. Mauchly’s test was insignificant (W = 0.91, p = 0.130) indicating that the sphericity assumption was not violated. The results showed that there was a significant difference in the rating of the different prioritites (F = 34.6, p < 0.001). Bonferroni corrected post hoc tests showed that neither the ratings of strategies COST and FLEXIBILITY nor the ratings of strategies QUALITY and DELIVERY differed significantly (p = 0.999 and p = 0.218, respectively), but the ratings of strategies COST and FLEXIBILITY were both significantly lower than the ratings of strategies QUALITY and DELIVERY (p < 0.001 in all cases). Table 2: Comparisons of the competitive priorities Priority

Mean

S.D.

n

Cronbach’s Alpha

COST

4.55

1.05

91

0.746

QUALITY

5.49

1.18

91

0.819

DELIVERY

5.26

1.24

91

0.780

FLEXIBILITY

4.51

1.13

91

0.833

Discussion and conclusion This paper set out to examine the link between individuals at different organizational levels’ perceptions of MS content, hence, the level of strategic consensus on MS. This 6   

purpose was addressed by two research hypotheses. The results show that the first hypothesis was partially confirmed. There are differences in the way the respondents have ranked the importance of the competitive priorities. Quality is considered more important than both flexibility and cost, and delivery is also ranked as more important than flexibility and cost. However, the difference between quality and delivery was insignificant, and so was the difference between flexibility and cost. This implies that the four competitive priorities can be grouped: quality and delivery are ranked as more important than flexibility and cost. This finding is contradictory to previous research which either has used the trade-off model (Skinner, 1969) to explain relations between competitive priorities or the cumulative model (Ferdows & De Meyer, 1990) which emphasises a sequential focus: quality – delivery – flexibility – cost. In six of the 16 cases (i.e., competitive priority abilities) the second hypothesis was confirmed, that is, that WCWs ranke those abilities as more important than BCWs do. This research originally aimed to identify differences between operators’ and managers’ perceptions of competitive priorities. Unfortunately, the data access did not allow for such comparisons due to the small amount of respondents at managerial positions. However, the data instead allowed for comparisons among the respondents without managerial responsibilities: the BCWs and the WCWs. In this study the BCWs were assembly line operators while the WCWs had a variety of responsibilities such as production technicians, production planners and project managers. For cost, there were three abilities that differed significantly between BCWs and WCWs: increase capacity utilisation, reduce production costs, and increase labour productivity. These three abilities can all be viewed as laying close to the BCWs own tasks. In an assembly plant in an industrialised country in particular the abilities related to the reduction of production costs and increase in labour productivity can be directly related to the BCWs’ work tasks. The fact that BCWs rank the importance of these abilities lower than what the WCWs do can therefore be linked to that if the BCWs would rank them higher, they would implicitly say that they themselves do not work efficient enough. For quality there were two abilities that differed significantly between BCWs and WCWs:  provide high performance products, and offer consistent, reliable quality. For these abilities the same logic as above cannot be used to explain the difference. Rather than being related to the BCWs own work tasks and performance, these abilities can be viewed as being on a more aggregated level, clearer linking to the market requirements. In earlier studies, in particular Edh (2013), it has been seen that for BCWs, in particular the ones who work with make-to-stock (MTS) production of large batches (which is the case in this assembly plant) it is more difficult to understand the abilities which are related to the customers, than for the operators who work with make-to-order (MTO) production of smaller batches. Therefore, the differences between the BCWs and the WCWs might be described by the BCWs limited understanding for the end customers’ requirements and needs. For delivery there was one ability where a significant difference could be seen between BCWs and WCWs: reduce production lead time. For this ability both the logic related to the cost abilities, i.e. that it is directly related to the BCWs own work tasks, and the logic related to the quality abilities, i.e. that assembly plant workers who work with MTS have difficulties seeing the impact their work performance has on the customer, might be valid. Reduction of production lead time is often assumed to imply that one should work faster, therefore the BCWs who believe they already work efficiently might relate such ability closer to their own work situation and thereby rank it lower. At the same time the WCWs 7   

might have better chances of seeing the big picture at the whole plant and thereby rank the ability higher. Both the BCWs and the WCWs in this study have formal managers, such as department managers and production managers. Further, there is a formal leadership in place at the shop floor where among the BCWs there are workers who are appointed as team leaders. However, there is also another type of relationship present within the operations function which often is not taken into consideration. That is the relationship between the BCWs and the WCWs. Despite WCWs not having explicit managerial responsibility (such as budget and staff responsibility) their roles in relation to the BCWs resemble that of informal leadership. The task they conduct are often directly affecting the possibilities for the BCWs to perform their work tasks. Earlier interview studies (Edh, 2013) also indicate that the shop floor worker does not always distinguish between the formal and informal leadership/management when defining who their managers are but rather view all WCWs as being “higher” in the organisational hierarchy. This might explain the resemblance between the differences found here between BCWs and WCWs, and the ones found in studies of the operator-manager relationship (e.g. Boyer & McDermott, 1999). To our knowledge, no previous research has looked at the differences at such a detail level within the operations function, but rather both aggregated the abilities and studied them as general competitive priorities and aggregated the workers to large homogenous groups. We have not seen any studies attempting to not only study the micro-level of the operations function (individuals without managerial positions), but also to categories and discover differences within this group. Rather, this group is often looked upon as one entity; one homogenous group which can be addressed as such, rather than as individuals. Our research thereby support the conclusion by Boyer and Lewis (2002, p. 18) that there is a need for plants to “more clearly define and communicate competitive priorities to ensure that daily decision-making supports” the manufacturing strategy. We contribute to the strategic consensus literature by explicating differences among individuals within the operations function. As a complement to Boyer and McDermott (1999)’s study, this paper emphasises the focus on the operational level by also comparing perceptions among different types of workers. This focus on the operators’ perspective is in line with the ideas by Ho (1996). Furthermore, we have added empirical data to the discussion on whether or not trade-offs exists (Boyer & Lewis, 2002; Ferdows & De Meyer, 1990; Schroeder et al., 2011) by identifying two groups, rather than mutually exclusive trade-offs or sequentially cumulative. These groups are: (1 quality and delivery and (2 flexibility and cost, where the first one is ranked as more important than the second. These groups partially coincide with the sand-cone model to the extent that quality and delivery are more important than flexibility and cost. However, the internal sequence within the groups, i.e. quality vs. delivery and flexibility vs. cost as it is explained in the sand-cone model cannot be confirmed. Furthermore, in relation to the study by Boyer and McDermott (1999), this paper focuses on a new country context. Despite Sweden being famous for an organisational culture based on employee involvement and few hierarchical levels, this paper shows that shared understanding and strategic consensus is difficult to achieve even within a group which both in literature and among practitioners is perceived as homogenous. Due to the design of the study, which resembles a single-case study without randomised sample selection, the generalisability of the findings may be questioned. Therefore, future research should focus on larger quantitative studies among workers within the operations function. 8   

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