Opportunities of Business Analytics in Lean Manufacturing
Toivo, Mari (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024051311043
https://urn.fi/URN:NBN:fi:amk-2024051311043
Tiivistelmä
In this thesis there were three research questions to be studied in the target organization: what the current state of business analytics in manufacturing is, what the most important use cases are to be developed and what the roadmap for developing the selected use cases could be. The first the key concepts of business analytics, business analytics process, lean manufacturing, use case and roadmap were defined and based on the theoretical frameworks the action research was completed.
In the current state analyses the finding was that there are several descriptive and some diagnostic analytics available. For predictive analytics some pilot projects had been done and no prescriptive analytics was found. For the lean manufacturing there is analytics available to support just in time manufacturing, value stream mapping and kaizen. The challenge is the data availability for the user to build analytics and the data is not integrated to support the analytics of the manufacturing flow.
The developed use cases in the project team were three dashboards to be used in factory level by factory management, team level to be used by the manufacturing team leader and manufacturing cell level to be used by process engineers and operators. Each of the dashboard included key manufacturing analytics like volume, quality, yield, productivity and on time delivery. The development of a roadmap was the most challenging as there were uncertainty of the schedule and resource availability due to corporate wide ERP implementation project.
The future potential for business analytics is in predictive analytics and utilization of artificial intelligence (AI) in it. Also, real time integrated analytics should be used to support the understanding of manufacturing flow. Periodic maturity assessment of data management and business analytics is also recommended.
In the current state analyses the finding was that there are several descriptive and some diagnostic analytics available. For predictive analytics some pilot projects had been done and no prescriptive analytics was found. For the lean manufacturing there is analytics available to support just in time manufacturing, value stream mapping and kaizen. The challenge is the data availability for the user to build analytics and the data is not integrated to support the analytics of the manufacturing flow.
The developed use cases in the project team were three dashboards to be used in factory level by factory management, team level to be used by the manufacturing team leader and manufacturing cell level to be used by process engineers and operators. Each of the dashboard included key manufacturing analytics like volume, quality, yield, productivity and on time delivery. The development of a roadmap was the most challenging as there were uncertainty of the schedule and resource availability due to corporate wide ERP implementation project.
The future potential for business analytics is in predictive analytics and utilization of artificial intelligence (AI) in it. Also, real time integrated analytics should be used to support the understanding of manufacturing flow. Periodic maturity assessment of data management and business analytics is also recommended.