Implementing self-service business analytics in support of lean manufacturing initiatives

Abstract

Continuous improvement (CI) programs such as Lean Six Sigma (LSS) are the cornerstones of many high-performance corporate cultures. However, numerous obstacles can arise when comes the time to implement and sustain improvements – high failure rates are reported for CI programs. Leveraging existing information systems (IS) can be an obstacle for lean manufacturing initiatives in environments where data is fragmented across multiple databases of Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES). Self-service business analytics (SSBA) provide the flexibility required to unify fragmented data with minimal turnaround, which makes this class of software ideal for managers piloting lean manufacturing initiatives. SSBA can enable the managers themselves to design and redesign suitable metrics throughout the typical three to six months duration of LSS projects. The main goal of this study is to propose an implementation framework for SSBA supporting lean manufacturing initiatives. This prescriptive framework is designed to guide managers in maximizing results and minimizing delays – making the project successful. To achieve this goal, a Design Science Research (DSR) methodology involving an industrial case study is carried out. First, a systematic literature review is conducted, which establishes a research base and highlights research gaps. Then, an implementation workflow is designed for SSBA. Next, this workflow is applied and evaluated at the case company – the Canadian division of an international steel parts manufacturing company with about 15000 employees worldwide. Lessons learned are then outlined and integrated to yield a generalizable implementation framework backed by empirical evidence in manufacturing. Quantitative evaluation survey results for the implementation case study were above the threshold set. Qualitative observations reveal positive impacts of SSBA supporting lean manufacturing through improved inter-departmental communication leading to better operational decision making.

Date	7 Jan 2019
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Yvan Beauregard (Supervisor)