Enhancing Production Line Efficiency in Assemble-to-Order Product Environment
Nieminen, Aura (2022)
Nieminen, Aura
2022
Tuotantotalouden DI-ohjelma - Master's Programme in Industrial Engineering and Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2022-05-30
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202205195105
https://urn.fi/URN:NBN:fi:tuni-202205195105
Tiivistelmä
The efficiency of production lines is one of the key factors to remain competitive in ever-tightening market conditions. The importance of production efficiency is further emphasized on production lines that focus on manual work in high-wage countries such as Finland. The target company for this thesis is General Electric (GE) Healthcare, a health technology manufacturer which operations found on lean philosophy. The targets of this research are two assemble-to-order production lines that manufacture hospital monitor Carescape One and its by-product F0 dock station.
In lean production, efficiency improvements are achieved by simultaneously optimizing production flow and resource efficiency. To achieve high flow and resource efficiency, production work must be standardized, with work sequencing, WIP, and takt time always tailored to situational specs. The precondition for standardization eliminating variability and waste caused by variation. The aim of this work was to achieve sustainable efficiency improvements for the target lines by eliminating waste using the single/double loop learning -framework and by creating an optimal standard work for the production lines.
The efficiency waste was identified based on already existing and observed, qualitative and quantitative data. Root cause analyzes were performed for the most complex, double loop learning -issues. The solutions for the identified root causes and for the simpler, single loop problems were evaluated based on the solution durability and how well the solution serves the entire plant. The target was to achieve measurable efficiency improvements already during the research, so the simplicity of the solution and its short-term feasibility affected choosing best solutions. The best solutions were then implemented, their success was monitored, and the changes were iterated based on single/double loop learning.
The research successfully eliminated several types of waste from waiting to unnecessary walking and overprocessing. The resource efficiency of the line was improved by 11 %, the throughput time of a device was reduced by 93 % and the walking distances within the standard work were reduced by 54 %. As a by-product of the throughput improvements, a faster feedback loop was achieved for quality issues, as well as significant space savings as the Work-In-Process (WIP) of the subassembly was reduced from 100 subassemblies to 5 and the WIP of untested devices was reduced from 50 devices to 5.
In addition to the actual efficiency improvements, the takeaways of the research are the key factors of lasting change. The most important factors contributing to the change were the active involvement of employees through the change process and the perseverance of the management in implementing the change. Employees were involved in problem definition, root cause analysis, solution generation and further development of the change. Full-time focus on the improvement projects in this research enabled the change agent to become sufficiently familiar with the problem and carry out the necessary follow-up. However, the research situation is rare for the research target company, where many improvement projects are carried out, but with very limited schedules. The biggest obstacle for the target company in achieving lasting improvements is the lack of follow-up. The change process is therefore perceived as a linear, momentary event instead of a learning loop where change is iterated and properly rooted.
In lean production, efficiency improvements are achieved by simultaneously optimizing production flow and resource efficiency. To achieve high flow and resource efficiency, production work must be standardized, with work sequencing, WIP, and takt time always tailored to situational specs. The precondition for standardization eliminating variability and waste caused by variation. The aim of this work was to achieve sustainable efficiency improvements for the target lines by eliminating waste using the single/double loop learning -framework and by creating an optimal standard work for the production lines.
The efficiency waste was identified based on already existing and observed, qualitative and quantitative data. Root cause analyzes were performed for the most complex, double loop learning -issues. The solutions for the identified root causes and for the simpler, single loop problems were evaluated based on the solution durability and how well the solution serves the entire plant. The target was to achieve measurable efficiency improvements already during the research, so the simplicity of the solution and its short-term feasibility affected choosing best solutions. The best solutions were then implemented, their success was monitored, and the changes were iterated based on single/double loop learning.
The research successfully eliminated several types of waste from waiting to unnecessary walking and overprocessing. The resource efficiency of the line was improved by 11 %, the throughput time of a device was reduced by 93 % and the walking distances within the standard work were reduced by 54 %. As a by-product of the throughput improvements, a faster feedback loop was achieved for quality issues, as well as significant space savings as the Work-In-Process (WIP) of the subassembly was reduced from 100 subassemblies to 5 and the WIP of untested devices was reduced from 50 devices to 5.
In addition to the actual efficiency improvements, the takeaways of the research are the key factors of lasting change. The most important factors contributing to the change were the active involvement of employees through the change process and the perseverance of the management in implementing the change. Employees were involved in problem definition, root cause analysis, solution generation and further development of the change. Full-time focus on the improvement projects in this research enabled the change agent to become sufficiently familiar with the problem and carry out the necessary follow-up. However, the research situation is rare for the research target company, where many improvement projects are carried out, but with very limited schedules. The biggest obstacle for the target company in achieving lasting improvements is the lack of follow-up. The change process is therefore perceived as a linear, momentary event instead of a learning loop where change is iterated and properly rooted.