The complete case study has been published as a Woxénreport (see: Grünberg et al, 2002). Results concerning the managers’ and employees’ views of pro- ductivity have also been presented in a conference paper (see: Tangen, 2002c).
9.2.1 Background
This case study was conducted at ABB Robotics in Västerås the year 2001. At the time of the study ABB Robotics employed approximately 650 employees and had turnover around 300 million dollars per year. Further, ABB Robotics was one of the world’s leading robot manufacturers, and had since 1974 sold and installed more than 90.000 robots across the world. The manufacturing activities at ABB Robotics were heavily dominated by manual assembly opera- tions. Their robots came in a number of variants and were all customer ordered. When the study was performed, ABB Robotics was working with development of its production system to be able to improve the service to their customers. One part of this was to improve the productivity in the company’s processes.
9.2.2 Objective and method
The main objective with this case study was to generally explore the productiv- ity improvement work that was conducted at ABB Robotics as well as to iden- tify and suggest new areas for potential productivity improvement. This in- cluded an investigation of how the company and its employees defined produc- tivity and other terms related to performance as well as an evaluation of the performance measures that were used within the company. In addition, another objective connected to this case study was to start to formulate a theoretical base (for the productivity project) of how to generally improve productivity in manufacturing companies.
The case study involved 20-minutes long structured interviews (see: West- lander, 2000) of 27 employees from top management to bottom line operators. Several internal documents were also studied in which the productivity im- provement work at ABB Robotics was explained. Direct observations in the plant were also made during a visit of 2 days.
9.2.3 Results
Strategic objectives and operational performance factors
From an overall point of view, ABB Robotics had specified four major strate- gic objectives (in figures) to be reached within the following years:
• Profitability – reduce prices and keep material costs on a constant level • Dependability – ability to handle variations and increases in volume • Quality – deliver products to the right quality (zero defects)
• Flexibility – ability to handle a large range of variants
In summary, the following two factors were identified during the study to have a major negative effect on productivity from an operational perspective:
• Low supplier performance – some of the more important suppliers of- ten delivered raw material with low quality as well as they could not deliver according to schedule.
• Low labour productivity – the value adding time of the operators in the assembly was at an unacceptable level compared to total time.
Terms and concepts
The case study showed that there was little consensus among the interviewees of meaning of productivity as well as other related terms. The lack of produc- tivity definition had unfortunately resulted in that the employees did not fully understand the management’s goals for improvement, even though the goals had been specified in figures. Many different opinions of what characterize productivity were emphasized during the interviews, including “making money”, “efficient use of labour” or just “good performance”, however, the term was seldom linked to the relation between output and input. Despite it was agreed that a clear definition would be very useful, allowing people to focus and “speak the same language”, the management did not show any interest in this issue. A major reason for not defining the term was the difficulties in actu- ally agreeing on what productivity meant.
Used performance measures
Many different performance measures were found at ABB Robotics. For ex- ample, the following three were more frequently used to analyse and improve productivity in the manufacturing and assembly area:
products produced of Number ime operator t Reported ty productivi ime Assembly t = (9.1)
Sales stock in Capital ty productivi Inventory = (9.2) products produced of Number cost ing Manufactur cost production Unit = (9.3)
The performance of the suppliers was measured both in quality (parts per mil- lion) and in delivery (time and quantity). This information was collected in a web-based and easily accessible database called “Suppliers web”, which in turn classified the suppliers according to their performance (red, yellow and green). Finally, several performance measures were used in order to monitor the per- formance towards the customers, including: customer delivery performance and volume flexibility (short-term, medium-term, long-term).
Performance measurement evaluation29
The PMS at ABB Robotics could be classified as a 2nd class system since it, for
example, had a multi-dimensional view on performance and considered both internal and external needs. However, there were still several issues to be solved before it could be possible to progress to the 1st class. The used per-
formance measures supported all four stated strategic objectives to a high de- gree. The company had also included performance measures that dealt with the two operational problem areas: supplier performance and labour productivity. One could therefore say that the limitations of the PMS were not consequences of the way that ABB Robotics had answered the question what to measure. They were, on the contrary, mostly caused by how ABB Robotics had decided to measure.
First, the main problem was that many of the performance measures were not measured to a proper level of detail and in frequency. For example, one of the more centrally used performance measures was unit production cost. However, this measure covers not only the manufacturing cost but also the overhead costs of management, process development, logistics and purchasing. This makes it hard to identify the cause of a fluctuation. The other two productivity measures were also very broad as well as that they were mainly used on an aggregated level. In other words, these measures could indicate symptoms of various prob- lems in the manufacturing system but they did not give enough information to make a diagnosis. Such measures can easily lead to false interpretations and
create misunderstandings. Their value is also limited when it comes to im- provement activities.
Second, another vital problem was that the employees were not able to affect the outcome of some performance measures. For example, the assembly time productivity measure was, of course, affected by the performance of the as- semblers. However, this measure was also highly influenced by the perform- ance of the suppliers since it was not designed to take into account waiting time caused by lack of material or quality defects in material (which were two very common disturbances). Consequently, the assemblers could therefore be blamed for disturbances that had been caused by the suppliers. This problem did also affect other performance measures that were used later in the supply chain, such as the customer delivery measures.
Third, the number of used performance measures was rather high from an over- all point of view. It was also not clear what performance measures that should be considered as the most important ones. This can increase the risk of confu- sion when analysing the measures as well as it can make it difficult to agree on what actions to take when trying to improve the performance of the company.
9.2.4 Conclusions
As earlier mentioned in chapter 2, the result from this case study has mainly been used from an explorative point of view when designing the performance measurement progression map that was described in section 8.1. Several impor- tant issues to consider when evaluating performance measures where identified during the study. For instance, the study showed that it is not just important that the PMS consists of proper performance measures (i.e. reflecting the stra- tegic objectives of the company). It is also vital that each performance measure is measured in properly. Otherwise the PMS will not function in the way that it is intended. In conclusion, the “what to measure” and the “how to measure” are highly connected to each other and it is therefore necessary that both these questions are considered when evaluating a PMS.
The study also illustrated several important properties that performance meas- ures should fulfil, such as: a measure must be measured to an appropriate fre- quency and precision, one should be able to affect the results of the measure, and that it is needed to make specifications. These properties have in turn been used when specifying the measure property form (see Table XII Measure property form).