Performance trade-offs in manufacturing plants
If manufacturing organisations are to remain competitive they must continuously improve their levels of operating performance. In order to do this, operations managers must understand which are the key drivers that are most effective at creating performance improvements and how the various measures of operating performance interact. The research addresses both of these issues. First it attempts to identify the key drivers that seem most effective in achieving increases in overall operating performance. Then it explores the relationship between the levels of performance for different operating measures in the same manufacturing plant. The basis of the research was a database of 953 UK manufacturing plants. These plants had all participated in the UK Best Factory Awards database during the years 1993- 1996. The plants were grouped into 6 industrial categories. The plants in each industrial category were then ranked for each performance measure and divided into three equal-sized groups of high, medium and low performers. The groups of high and low performers were then compared in order to identify characteristics that were statistically different for the two groups. The high performers were found to put a greater emphasis on continuous improvement, involving a higher proportion of the workforce in this activity. The workforce was also more flexible in terms of the range of tasks that they were competent to carry out. The high performers exhibited much less variability in their processes with greater adherence to schedule, more consistent processing times, lower scrap rates and more reliable supplier deliveries. Using the results of this analysis in combination with an analysis of the literature on the characteristics of high performing plants a tentative model was constructed attempting to show how these characteristics would impact on operating performance. The model suggested that improvements in unit manufacturing cost, quality consistency, speed of delivery and delivery reliability would be positively correlated. The model also suggested that the size of the product range would be negatively correlated with unit manufacturing cost, quality consistency, speed of delivery and delivery reliability. The database was used to test for statistical correlations between measures of these aspects of performance and the results provided general support for both of these propositions. Six of the plants in the database were visited and staff responsible for planning, purchasing and production were interviewed. The objective was to test whether the conclusions reached on the basis of statistical analysis could also be validated at individual plants. There was general support for the differences in the characteristics of high and low performing plants. There was also general support for the propositions that plants achieve similar performance on unit manufacturing cost, quality consistency, speed of delivery and delivery reliability relative to plants in the same industrial sector and that increasing the size of the product range adversely affects unit manufacturing cost, quality consistency, speed of delivery and delivery reliability.