Abstract
PurposeThe purpose of this paper is to identify factors influencing implementation of control charts on key performance indicators (KPIs).Design/methodology/approachFactors driving organizational change described in literature are analyzed inspired by the affinity-interrelationship method. A holistic multiple-case design is used to conduct six workshops to affect the usage of control charts on KPIs at a global company in the automotive industry. The theoretical factors are compared with the result from the case study.FindingsThe important factors for implementation success differ to some extent between the theoretical and empirical studies. High-level commitment and a clear definition of the goal of change could be most important when creating a motivation for change. Thereafter, having a dedicated change agent, choosing an important KPI and being able to describe the gain in financial terms becomes more important.Practical implicationsBy using control charts on KPIs, the organization in the case study has become more proactive, addressing the right issues upstream in the process, in the right way, cross-functionally.Originality/valueFactors affecting the implementation of already available solutions in the industry are highlighted. This potentially provides a basis for improved decision making, which has a significant value.
Highlights
It is surprising how many good ideas never make it to the shop floor
Themes influencing change and implementation The resulting 20 levels 1 heading from using the affinity-interrelationship method (AIM) can be considered as themes important for successful implementation; they are shown below
To making the changes needed, other factors can become more important such as having a dedicated change agent, choosing an important key performance indicators (KPIs) and being able to describe the gain in financial terms
Summary
It is surprising how many good ideas never make it to the shop floor. No matter how extraordinary the research is, as long as it is not implemented, the value to the company is limited: We are looking at new technologies, hoping they will solve our problems. The potential of being able to implement just a few of them is very big (Technical manager at an international automotive company) One of these solutions, just waiting to be generally implemented for operational data, is the control chart. Caulcutt (1996) on the other hand states that statistical process control (SPC), which includes the control chart, is not a collection of tools but a way of thinking about variability. He continues that any manager who does not understand the most fundamental concept of SPC will very likely cause a waste of resources (when overreacting on random variation).
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