Factor analysis was carried out to reduce integration practices, patterns and attitudes to a smaller number of underlying factors. The solution explains 73.361% of the variance. The results suggest an eight factor structure.
Factor 1 is mainly comprised of items addressing joint improvement, such as “working together to improve operations and logistics processes”, and “working together to synchronize operations and logistics processes”. Factor 2 includes items addressing cooperative behaviour, such as “the parties would rather work out a new deal than to hold each other to the original terms when some unexpected situation arises”, and “it is expected that the parties will be open to
modifying their agreement if unexpected events occur”. Factor 3 includes items regarding planning information (“we receive information about production plans, changes in the production plans, and sales forecasts”). Factors 4 and 5 are related to the way both companies exchange information. Factor 4 is related with structured communication (direct computer-to-computer link, online access to the planning systems of the key buyer, and the use of formal communication channels) while factor 5 with non-structured communication (face-to-face communication, high corporate level of communication and the use of phones and videoconferences to communicate). Factor 6 is related with the concept of long-term relationship and includes the following items: “we value a long –term relationship with our key buyer”, and “we see our relationship as a long-term alliance”. Finally, the last two factors are related with physical integration: packaging and delivery integration. Packaging integration refers to the idea of adapting the packaging materials (pallets, containers, etc.) to the needs of the key buyer, while delivery integration refers to the synchronisation of the delivery activities.
Factor analysis was also carried out to reduce the number of uncertainty variables into a number of underlying dimensions. Principal components analysis with varimax rotation was used. The results suggest a two factor structure. Factor 1 can be labelled technology uncertainty, as it comprises items related with changes in technology, while factor 2 can be labelled demand uncertainty, as it includes items related with demand uncertainty (volume and mix).
Correlation analysis
Bivariate correlation analysis was carried out to identify 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 within four different groups of companies: low demand uncertainty, low technology uncertainty, high demand uncertainty and high technology uncertainty.
For both demand uncertainty and technology uncertainty two sub-samples were obtained: one with high uncertainty and another with low uncertainty. The cut off points used to classify the companies were calculated with the aim of obtaining two equivalent sub-samples in terms of size and both tried to be one third of the sample. The cut off points for demand uncertainty were 3 and 4.5. This means that companies with a level of demand uncertainty lower than or equal to 3 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 off points for technology uncertainty were 2 and 4 (companies with a level of technology uncertainty lower than or equal to 2 were classified under the low technology uncertainty group and companies with a level of technology uncertainty equal to or higher than 4 were classified into the group of high technology uncertainty).
Table 1 presents the results of the correlation analysis, that is, the significant correlations between the SCM factors and the performance items for each of the four constructed groups. Most significant correlations indicate positive impact of integration on performance improvements. Positive means lower costs or improved service delivery. There are five exceptions, that is, that integration has a negative impact on performance improvements for instance an increase in the administrative costs.
Table 1. Correlation analysis: Integration and performance improvements Low uncertainty High uncertainty
Demand Technology Demand Technology Packaging integration Agreed date (-.409*) Production costs (.297*)
Special requirements (.319*)
Early notifications (.485**)
Delivery integration Special requirements (.415**)
Early notifications (.315*) Short delivery LT (.610**)
Short delivery LT (.574**) Product mix (.310*) Quantities ordered (.312*) Transportation costs (.314*) Product mix (.298*) Quantities ordered (.354*) Early notifications (.425**) Short delivery LT (.482**)
Long term relationship Administrative costs (- .344*)
Administrative costs (-.309*) Transportation costs (.332*) Administrative costs (.334*) Stock outs (.292*) Early notifications (.351*) Early notifications (.480**) Non-structured communication Product mix (.334*) Quantities ordered (.404**) Product mix (.382**) Early notifications (.391**) Product mix (.311*) Early notifications (.506**)
Structured communication Short delivery LT (.313*) Cost-to-serve (-.295*) Short delivery LT (.313*)
Planning information Special requirements (.361**)
Agreed date (.293*)
Special requirements (.403**)
Early notifications (.329*)
Cooperative behaviour Stock outs (.398**) Administrative costs (.297*)
Early notifications (.407**)
Joint improvement Cost-to-serve (-.356*)
Quantities ordered (.340*)
** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
INTERPRETATION AND DISCUSSION
What to do with the results of the factor analysis? Do we discuss them as extensively as in the other paper, or do we focus on performance here.
No, only focus on uncertainty related to performance issue
An overall assessment of the results presented in table 1 seems to support the propositions. The different supply chain practices, patterns and attitudes have a higher impact on performance improvements if uncertainty is high. This is especially true for technology uncertainty. For the group of companies with low technology uncertainty table 1 reveals only one positive correlation between integration and performance. This is in clear contrast with the 15 positive correlations for the group with high technology uncertainty. This provides strong support for the proposition that high levels of integration are only necessary within buyer-supplier links with a high level of technology uncertainty.
The same is to some extent true for demand uncertainty. However the contrast between low and high is less clear (7 versus 12 positive correlations). That means that contrary to our initial expectations also buyer-supplier links with low levels of demand uncertainty benefit from supply chain integration. A more detailed consideration of the results indicates that in these links specific practices or patterns might be beneficial. It is plausible that also in situations with low demand uncertainty frequent deliveries and EDI connections can contribute to improvements in the delivery performance. This is especially true under circumstances that allows for a regular flow of materials (stable demand, high volume, low variety).
A list of references along with additional tables is available from the authors upon request.
LOGISTICS AND SUPPLY MANAGEMENT IN SERVICE INDUSTRIES