Identifying Improvement Areas in Production Planning Meetings by Assessing Organisation and Information Systems at a Small Production Company

Paper in proceeding, 2016

The increased mass-customisation of production requires operators to manage an increasing number of complex work tasks. From a social sustainability perspective, better sharing and dissemination of production information supports operators cognitively to manage and understand their work tasks, which in turn improves quality of work. So, the focus of this paper is to study how production planning meetings can be improved. Previous research suggests that the MEET model can be used as a framework for improving meetings and information sharing by studying 10 different areas within a company’s Organisation System (OS) and Information System (IS) whilst considering the time and place prerequisites and aims for these meetings. In this paper, the applicability of the MEET model and its 10 areas are tested at a small production company by applying two different approaches. First, a questionnaire was presented to and filled out by a manager, the results of the questionnaire identifies the improvement potential of each of the 10 areas. Second, a comprehensive current-state analysis based on observations on the shop-floor and interviews with operators were carried out with regards to the 10 areas. The results from these two approaches were compared and the comparison showed that both approaches point towards similar areas for potential improvements. This paper concludes that the MEET model can be used as a general framework to inspire change by suggesting areas with potential improvement in information sharing. While the selfassessment questionnaire can identify a direction, additional information and involvement of other stakeholders are recommended for actual implementations of change. For future research, the methods based on the MEET model will be further developed to improve accuracy and the suggestions provided to the case company in this paper will be tested as a validation of the model.