SUPPLY CHAIN INTEGRATION AND PERFORMANCE: THE IMPACT OF BUSINESS CONDITIONS Taco van der Vaart1, Cristina Giménez2 and Dirk Pieter van Donk1 1
University of Groningen, Faculty of Management and Organisation, Groningen, The Netherlands,
[email protected],
[email protected]
2
ESADE Business School - Universitat Ramon Llull, Barcelona, Spain,
[email protected]
ABSTRACT Over the past decade, one of the main themes in the Supply Chain Management literature has been the impact of integration on performance. Many authors agree that integrative practices have a positive impact on corporate and supply chain performance. However, very few researchers have considered the different underlying constructs of supply chain integration and different business conditions. The main objectives of this paper are: to analyze the different dimensions under the integration construct and to analyze integration under different business conditions (high and low demand and technology uncertainty). We present the results of a survey conducted among Dutch and Spanish companies. The results show that there are different dimensions under the integration construct (practices, patterns and attitudes) and that these are correlated. The results also show that under environments characterized by high demand and technology uncertainty higher levels of integration lead to improvements in performance, while under low uncertainty environments very few integration practices lead to performance improvements. Thus, the main conclusion is that supply chain integration needs a more tailored approach in order to be successful. Keywords: survey, supply chain integration, supplier, business conditions
INTRODUCTION Over the past decade, one of the main themes in the supply chain management literature has been integration as a key factor in achieving improvements (e.g. Tan et al., 1999; Romano, 2003). Many authors agree that integrative practices and a high level of integration have a positive impact on corporate and supply chain performance. Recent empirical work (Frohlich & Westbrook, 2001; Vickery et al., 2003; Childerhouse & Towill, 2003; Gimenez & Ventura, 2005) shows convincing empirical evidence for the relationship between integration and performance. Whereas the empirical evidence seems to be overwhelming, a part of the literature doubts the results and approach taken in supply chain integration research. Firstly, starting form the well-known and often cited article of Fisher (1997) an increasing number of researchers have realized that supply chain integration might need a more tailored approach in order to be successful. One possible way to further explore that is to include context (Ho et al., 2002) or business conditions (Van Donk & Van der Vaart, 2004,2005; Van der Vaart & Van Donk, 2006). A first example is the research of Ramdas & Spekman (2000). Secondly, others emphasize the need for sound constructs and methodologies to better understand the relationship between supply chain integration and performance. Tan (2001) and Croom et al. (2001) review the literature and state that the variety of supply chain management and integration definitions is large. The same can be concluded with respect to the constructs and measurement scales that are used in survey research in supply chain management (Chen & Paulraj, 2004). All in all, the consistency of measures and constructs is still limited, according to Ho et al. (2002). One point of concern is that different aspects of integration are measured, without explicitly addressing such choices. E.g. the papers of Johnston et al. (2004) and Frohlich & Westbrook (2001) both address integration, but the first one measures patterns of behaviour, while to second one focuses on operational practices. The number of items used to measure a specific aspect of integration seems to be small, in some research. 473
And, thirdly, a last point of concern relates to the level of analysis. Some studies measure integration as an organisational variable and only a few (Johnston et al, 2004; Gimenez and Ventura, 2005) consider single links and relationships. Related to this point is what a measurement of performance actually means from a conceptual or theoretical perspective, e.g. the relationship between the level of integration with one single supplier and the buying firm’s financial performance is hard to understand. Based upon the above considerations and remarks this paper seeks to serve two different but related goals. The first aim of the paper is to develop a framework for measuring the relationship between integration and performance, that incorporates different aspects of integration and explicitly takes into account the influence of business conditions. This part of the paper builds on a recent paper by Van Donk & Van der Vaart (2005) that reviews survey-based research in this area. The second aim of the paper is to empirically investigate the above relationship by conducting a survey among suppliers. Based upon the previous part, we develop a questionnaire that uses to a large extent items and questions derived from earlier work. Here, we analyze integration under different business conditions: Our basic assumption is that a high level of integration is only needed in case of complex business conditions (e.g. high level of uncertainty in demand, complex operations, high variety, small batches) and only under these circumstances integration results in better supply chain performance. On the other hand, we expect that in simple business conditions (e.g. low variety, low uncertainty, large batches) a low level of integrative practices will be sufficient and profitable. The paper is structured as follows. The next section will present the main findings from the review of the literature and will develop the main theoretical constructs and their relationships. Then, we will explain the methodological approach and the development of the questionnaire. The third section will present the provisional findings from our ongoing research. The subsequent section will discuss these findings and relate them to our theoretical background. The last section will present the main conclusions along with suggestions for further research.
LITERATURE REVIEW The introduction presented a number of questions and remarks related to the survey-based research. In this section we present the findings of a review of 33 papers from high-level Operations Management and Logistics journals published from 2000 onwards. Further details of the review can be found in Van Donk & Van der Vaart (2005) and in an extended working paper by the same authors. There are three main conclusions that are elaborated below. Recent survey based research can be criticized for: (1) measuring integration in a limited way without distinguishing different dimensions or differences between practices, attitudes and patterns; (2) measuring integration across all links as a variable at the organizational level and not at the level of a specific link or dyad; (3) ignoring business conditions (such as uncertainty in demand, variety, market structure, and product and process characteristics) and power.
SCM factors A first idea in analyzing the measurement of supply chain integration in the above papers, is that factors or variables used to measure integration can be compared or grouped into recognizable groups. This sensible idea has not resulted in anything as it turns out that different items and constructs are used to measure the same or closely related SCM factors. As an example we consider the factors SC integration (Vickery et al, 2003) and external integration (Giménez & Ventura, 2005). Vickery et al operationalize SC integration with aggregated items like supplier partnering, closer customer relationships, and crossfunctional teams. Giménez & Ventura use concrete items like informal teamwork, shared information, and joint development of logistics processes. Another issue here is that SCM factors are measured using a non homogeneous set of items. E.g. Carr & Pearson (1999) measure buyer-supplier relationships with items varying from loyalty and frequent face-to-face communication to direct computer links with suppliers. Based upon the above findings an alternative way of analyzing and categorizing the papers is considering the items used to measure integration, labelling these items as attitudes, patterns or practices. SC practices are concrete activities or technologies that play an important role in the collaboration of a focal firm with its suppliers and/or customers. Examples are the use of EDI, integrated production planning, packaging congruence, Vendor Managed Inventories (VMI), and deliveries synchronization (see for instance De Toni & Nassimbeni, 1999; Frohlich & Westbrook, 2001; Kulp et al, 2004). Related to these practices are the SC patterns or interaction patterns between the focal firm and its suppliers 474
and/or customers. Examples are regularly visits to the supplier’s facility, frequent face-to-face communication, high corporate level communication on important issues with key suppliers, and formal, periodic written evaluation of suppliers (see for instance Bagchi & Skjoett-Larsen, 2005; Carr & Pearson, 1999; Chen et al, 2004; Duffy & Fearne, 2004; Stanley & Wisner, 2001). The last category includes items that measure the attitude of buyers and/or suppliers towards each other or towards supply chain management in general. Examples used in the questionnaires are “we expect our relationship with key suppliers to last a long time”, “we view our suppliers as an extension of our company”, “problems that arise in the course of this relationship are treated as joint rather than individual responsibilities”, and “the responsibility for making sure that the relationship works for both the other party and us is shared jointly” (see for instance Chen et al, 2004; Johnston et al, 2004).
Performance measurement The review of the literature confirms that many surveys measure output performance of the focal firm on an aggregate level, as was indicated in the introduction. We doubt the value of such an approach, specifically if only measures as market share or ROI are used. If we assume that integration means investing in a buyer-supplier relationship, it would make sense to measure performance in terms of the aims of these efforts with respect to this particular relationship. Possible aims are to reduce reaction times and/or stocks, but also to increase the visibility in the chain or to attain a more effective and efficient way of communication. Measuring on the level of relationship directly as some papers do (e.g. Benton & Maloni, 2005; Duffy & Fearne, 2004; Humphreys et al, 2004; Johnston at al, 2004; Giménez & Ventura, 2003, 2005), can also help in dealing with another measurement issue. A large amount of the current papers uses subjective measurements of performance relative to the past or relative to competitors, that are hard to validate. Directly measuring the performance of the relationship could be relatively easy: e.g. reduction in inventory turns, improved service, and shorter lead time.
Business conditions Although the paper of Fisher (1997) is widely cited, it has taken some time to influence the survey based research, with a clear exception of Ramdas & Spekman (2000) and Maloni & Benton (2000). Only recently, some more studies (Benton & Maloni, 2005; Fynes et al., 2005; Kaufmann & Carter, to appear) have considered the role of business conditions further. Earlier case-study based work (Van Donk & Van der Vaart, 2004; Van der Vaart & Van Donk, 2006) clearly shows that the assumption that higher levels of integration improve supply chain performance needs revision. One of the main determining factors for the type and level of integrative practices is uncertainty related to demand (volume, mix, specification) that has been considered by others as well (e.g. Childerhouse & Towill, 2002). So far, the survey based research has not addressed all relevant issues and findings are not all clear, yet. In fact, the statement of Frohlich & Westbrook (2001, p.185): “Our knowledge is relatively weak concerning which forms of integration manufacturers use to link up with suppliers and customers” still seems to hold.
RESEARCH FRAMEWORK Based on the concerns discussed above we propose the research model as depicted in Figure 1. Future research should focus more on measuring explicitly attitudes, patterns and practices instead of mixing these three into one factor, as is often done in current research. Next, we aim at understanding the possible interactions between attitudes, patterns and practices. On the one hand it seems that (positive) attitudes are a first step in developing a relationship and improving integration. On the other hand, intensive daily contact and the development of SC patterns might influence attitudes. Another interesting subject is the interaction between patterns and practices. The framework also stresses the importance of the moderating role of business conditions in the relationship between integration (patterns and practices) and performance. In general, we assume that the type and level of integrative practices and patterns should “fit” with the type of business conditions.
475
SC patterns Performance buyer-supplier relationship
SC attitudes SC practices
Business conditions
Figure 1 – Research framework Business conditions relate to the type of uncertainty in demand (volume, mix, specification), de variety of demand and the main order-winners and the competitiveness in general. Other business conditions relate to the type of process technology (e.g. batch size) and complexity of the product and process (e.g. number of different operations). We assume that a high level of integration is only needed in case of complex business conditions (e.g. high level of uncertainty in demand, mix or specification of demand, complex operations, high variety, small batches) and only under these circumstances integration will result in better supply chain performance. More specifically, we expect that certain patterns and practices will not be standardized to accommodate intense cooperation and communication on relatively ambiguous information. Of course, it might be expected that also high-level ICT solutions will be used for the standard communication in those cases. On the other hand, we expect that in simple business conditions (e.g. low variety, low uncertainty, large batches) a low level of integrative practices will be sufficient. Here, the focus will be on standardization and formalization of the integrative practices as little uncertainty is present in the relationship. A last remark relates to the measurement of performance. We aim at measuring the performance in the buyer-supplier relationship such as lead time, customer service, etc.
METHODOLOGY Two main issues are dealt within this section. Firstly, how we elaborated the content of the questionnaire and, secondly, how data was gathered.
Constructing the questionnaire The paper uses a questionnaire that was developed from various sources. Based upon the above considerations from our literature review, we did not use factors from previous surveys, but we did use many of their items. The measurement of business conditions is based on Van Donk & Van der Vaart (2004) and Van der Vaart & Van Donk (2006). Integration is measured using items of practices, patterns and attitudes. In the measurement of practices, we distinguish between three main types of practices: practices of integration in the physical flow, practices related to the type of information exchanged and practices related to the areas in which companies are working together with buyers. Frohlich & Westbrook (2000) and DeToni & Nassimbeni (1999) are used for the items on physical integration. And, Gimenez & Ventura (2005) provide items related to the type of information exchanged and areas in which companies are working together. Additional items of coordination (areas on which companies work together) are adopted from Chen & Paulraj (2004) and Carr & Pearson (1999). The measurement of patterns is mainly based upon the work of Carr & Pearson (1999). And, Chen et al. (2004) and Johnston et al. (2004) provide the majority of questions/items for measuring attitudes. Finally, regarding performance measurement we use items developed by Gimenez & Ventura (2005). Questions were changed to make them appropriate for suppliers in their relationship and integrative efforts with their key-buyer. The questionnaire was pre-tested in several ways. After a large number of revisions by the authors, colleagues were asked to assess the questions and the way questions were asked. Next, a pre-test was conducted with professionals attending an Operations course of one of the authors to check clarity and to assure the time needed for completing the questionnaire. 476
Data gathering Our starting population is the companies in NACE Rev 1.1 business codes 24, 25 and 27 -35 in Spain and The Netherlands. In contrast to earlier work we seek to investigate the relationship between a supplier and its main customer/buyer (in a business to business relation) from the supplier’s perspective. As a result, our target population is not fully known as we do not know which companies are the suppliers beforehand. Internet is used to assess if the company is a supplier to another manufacturer or not or a phone call is made to have this information. Next step is to contact the appropriate person in the supplying company and ask for cooperation. Depending upon the size of the company this can be the general manager, production manager or logistics manager. If a person of the company is willing to participate, an e-mail is sent that has a link to an Internet page that enables automatic filling out the questionnaire. In case of slow response, another e-mail contact and/or phone call is sought to improve response to the survey. Once the questionnaire is completed, it can be automatically uploaded to a database. Companies are randomly taken from the NACE list. Equal sizes of respondents are sought in Spain and The Netherlands. Data collection is from January to June 2006. By the time this paper is written (beginning of April 2006) we have sent 128 questionnaires in the Netherlands and 221 in Spain. We have received 22 valid responses in The Netherlands and 32 in Spain, which represents a response rate of 17.2% in the Netherlands and 14.5% in Spain.
DATA ANALYSIS Factor analysis Factor analysis was carried out to reduce the business conditions, integration practices and patterns, and attitudes to a smaller number of underlying factors. Principal components analysis with varimax rotation was used. In the interest of convergent and discriminant validity, we only considered items that had high a factor loading and did not have important cross-loads (items with a loading in excess of 0.3 on a second factor were omitted for further analysis). The business conditions scale yielded two factors, demand uncertainty and technology uncertainty (see Table 1). These factors explained 81.25% of total variance. Four factors were obtained in the integration and patterns scale: Physical integration, Planning information, Joint improvement and Communication patterns (see Table 2). The four factors explained 68.9 % of total variance. Ten items had a loading in excess of 0.3 on a second factor and thus were omitted. Two attitude factors were obtained: Cooperative behaviour and Long term relationship (see Table 3). The two factors explained 75.6%. Five items had a loading in excess of 0.3 on a second factor and thus were omitted. In addition, the reliability of each scale was satisfactory: Cronbach α values of at least 0,7 were achieved in all scales, except for Demand uncertainty, which was 0.63. For further analysis, additive scales were calculated for each factor.
Factor Demand uncertainty
Technology uncertainty
Table 1 Factor analysis: Supply chain uncertainty Scale items The uncertainty of the demand of the key buyer with respect to mix/specification. The uncertainty in the demand of the key buyer with respect to volume. The changes in the technology used to manufacture the products delivered to the key buyer. The changes in the technology of the products delivered to the key buyer.
477
Factor loading .877 .791 .951 .897
Factor Physical integration
Planning information Joint improvement
Communication patterns
Factor Long term relationship Cooperative behaviour
Table 2 Factor analysis: Integration practices and patterns Scale items We deliver to our key buyer on a short notice. We deliver to our key buyer frequently. The products delivered to the key buyer can be automatically identified (e.g. bar coding). Containers and packaging instruments of outgoing materials are adapted to the precise requirements of the key buyer. We manage the stocks of our key buyer (e.g. VMI). We receive information about the production plans. We receive information about changes in the production plans of our key buyer at once. We work together with key buyer to improve operations and logistics processes. We work together with our key buyer in order to reduce costs. We schedule deliveries together with our key buyer. We communicate with our key buyer by phone, videoconference and/or chat. We have face-to-face communication with our key buyer. We communicate with our key buyer by e-mail. We have high corporate level communication on important issues with our key buyer.
Table 3 Factor analysis: Attitudes Scale items We see our relationship as a long term alliance. We value a long-term relationship with our key buyer. When some unexpected situation arises the parties would rather work out a new deal than to hold each other to the original terms. It is expected that the parties will be open to modifying their agreement if unexpected events occur. We are willing to work with our key buyer to improve our processes in the long run. We believe that the key buyer is willing to work with us to improve our processes in the long run. We view our key buyer as an extension of our firm.
Factor loading .900 .874 .742 .676 .489 .911 .858 .872 .767 .764 .842 .809 .736 .708
Factor loading .932 .924
.879 .873 .841 .795 .767
Correlation analysis Bivariate correlation analysis was carried out to identify which integration constructs (Physical integration, Planning information, Joint improvement and Communication patterns) and factor attitudes (Long term relationships and Cooperative behaviour) correlate with each other (see Table 4) and which integration factors correlate with measures of cost and service performance. In order to take the business conditions into consideration, correlations between integration factors and performance measures were measured under four different environments: low demand uncertainty, low technology uncertainty, high demand uncertainty and high technology uncertainty (see Table 5). The 54 valid responses were considered to perform the correlation between integration and attitudes factors; while for the correlation between integration factors and performance the sample was divided into 478
two sub-samples: high and low uncertainty. As the sample size was not big enough to consider the combination of both business conditions simultaneously, the sample was divided taking into account demand and technology uncertainty independently. First, the sample was divided taking into account demand uncertainty (low and high) and correlations were calculated for each sub-sample. Then, the 54 responses were again divided into two sub-samples, but now taking into account the level of technology uncertainty. The cut points for classifying the companies under the low and high uncertainty environments were calculated with the aim of obtaining two equivalent sub-samples in terms of size. The cut point for demand uncertainty was 4.5. This means that companies with a level of demand uncertainty lower than 4,5 were classified under the low demand uncertainty group and companies with a level of demand uncertainty equal to or higher than 4.5 were classified into the group of high demand uncertainty. The cut point for technology uncertainty was 3.5.
Physical integration
Table 4 Correlation analysis: Integration and attitudes Physical Planning Joint Communication Long term integration information improvement patterns relationship .346* .248 .136 -.188
Planning information
.320*
Joint improvement
Cooperative behaviour .360**
.325*
.117
.269*
.347*
.187
.560**
.134
.294*
Communication patterns Long term relationship
.280*
Cooperative behaviour * Denotes significant at α = 0.05; ** Denotes significant at α = 0.01
Table 5 Correlation analysis: Integration and performance under different business conditions Demand
Low uncertainty Technology
Demand
Physical Integration
High uncertainty Technology
Product mix (.562**) Special req. (.439*) Quant. ord. (.551**)
Planning Information
Product mix (.662**) Special req. (.594**) Quant. ord. (.482*) Special req. (.409*)
Joint Improvement
Product mix (.445*) Product mix (.422*)
Transp. Costs (-.422*)
Stock outs (-.459*) Early Notifications (.435*)
Communication patterns
Prod. Costs (-.525**)
Adm. Costs (-.512*)
Prod. Costs (-.413*) Adm. Costs (-.422*) Stock outs (-.465*) Early Notifications (.435**)
* Denotes significant at α = 0.05; ** Denotes significant at α = 0.01
479
DISCUSSION Based upon a literature study, we proposed to separate attitudes, patterns and practices as distinct elements in supply chain integration. The empirical evidence clearly shows that each of these elements can be detected. Table 2 and Table 3 show that within each category factors can be measured. In our research model we did not distinguish between different types of attitudes. Factor analysis finds two aspects that can be easily labelled and understood. We will shortly discuss the relationships between the different attitudes, patterns and practices. Interestingly, the attitudes Long-term relationship and Cooperative behaviour are correlated, but only the second one is related to patterns and practices. The correlation can be understood as a mutual reinforcement process in which striving for a long-term relationship can lead to a positive attitude for cooperative behaviour, while it is also well understandable that a more positive attitude in cooperative behaviour will positively influence the attitude towards the long-term relationship. The absence of a relationship between Long-term relationship and practices and patterns can be interpreted as an indication that a positive attitude towards the long-term is a prerequisite for a cooperative behaviour, but that the attitude Cooperative behaviour is directly related to the development of integrative patterns and practices. As said above, the factor analysis over all items related to patterns and practices finds four distinct factors, that can be identified and labelled as Joint Improvement, Planning Information and Physical Integration as practices and Communication Patterns. The three practices are close to the four areas or dimension as distinguished in Van Donk & Van der Vaart (2004): Physical Integration, Planning & Control, Information, and Organisation/Relation. Based on our sample, it seems that the dimensions Planning & Control and Information can be measured in one practice: Planning Information, as it is labelled here. The other two practices (Physical integration and Joint improvement) can easily be associated with the remaining two dimensions. Another interesting remark is that Physical Integration is measured using the items from the IMSS database as used in Frohlich & Westbrook (2001) to measure integration. Our findings provide support for our belief that supply chain integration is a broader concept than just being related to operational and physical aspects, as used in that article.
communication pattern long-term relationship
cooperative behavior joint improvement
planning information
physical integration
Figure 2 – Adapted research framework A closer look at the correlations between practices and patterns shows that that Planning Information, Joint Improvement and Communication Practices are all related to each other. This is not surprising if we consider the underlying items like working together, sharing information and communication. Physical Integration is only related to Planning Information. Explanation for this relationship can also be found in the underlying items. It is conceivable that items like delivery on short notice, frequent deliveries and VMI on the hand and information about (changes) in the production planning on the other hand go hand in hand. Based upon these ideas our research model can be slightly adapted to reflect the empirical findings (see figure 2, leaving out performance for sake of clarity). With respect to the role of business conditions, the results presented in the previous section support our initial ideas. Table 5 shows sufficient evidence that integrative practices specifically help in achieving a better performance in the supplier-buyer relationship if the uncertainty in demand or technology is high, while for the companies that face low uncertainty we hardly found a relationship with integration. We do not only find a relationship with costs, but our results also show that integrative practices and patterns relate to improvements in logistical performance measures as stock outs, product mix and early 480
notifications (about late deliveries or stock-outs). Based on the results, we submit that business conditions have a moderating effect on the relationship between integration and performance.
CONCLUSION The aim of this paper was to explore different aspects of supply chain integration (attitudes, patterns and practices) and their relationship with performance under different business conditions. Although the current paper is based upon a limited sample, a number of interesting findings can be detected. The results show clearly that communication between partners and integrative practices are important factors to achieve improvement in the performance of supply chains. More importantly, the results support the idea that the relationship between integration and supply chain is moderated by uncertainty in demand and/or technology. The conclusion is that integration clearly has more impact on the performance improvement in supply relations characterized by high levels of uncertainty compared with the relations characterized by low levels of uncertainty. Another contribution of this paper is that we found support for the idea that it is useful to distinguish between attitudes, patterns and practices in supply chain integration research. The results show that there are different dimensions under the integration construct (practices, patterns and attitudes) and that these are partly correlated. Further research should focus on the interaction between the different constructs. There are a number of limitations associated with this study. First, the data collection is not completed yet and the results are based on the first 54 responses. More principal are the limitations associated with the use of single respondents and the reliance on supplier perceptions of SC relationship. Multiple respondents and objective measures have important advantages, but also complicate research design and influence response. A good alternative is to combine survey research with thorough case studies. After the data collection has completed, more advanced techniques will be used to analyze the data. A larger number of responses provides opportunities to analyze structural models and to get more support for the moderating effect of business conditions. In further analysis we will also look at other business conditions like market complexity, order winners, volume-variety characteristics, and the position of the order decoupling point.
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