Mohammed, Abdul Rehan Khan (2020) A holistic approach for selecting appropriate manufacturing shop floor KPIs. PhD thesis, University of Warwick.

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Abstract

In the era of globalization, manufacturing industries need to monitor their manufacturing operations acutely in order to remain competitive. Manufacturers seek to engineer highly flexible, robust, and efficient manufacturing processes enabling the production of high-quality goods at competitive costs while always addressing and adapting to evolving challenges. As a result, manufacturing industries in the present time have realized the significance of shop floor data analysis. They are implementing performance measurement systems to continually assess and improve the operational state of their manufacturing operations. These systems comprise a set of Key Performance Indicators (KPIs), which can enumerate the effectiveness, competence, efficiency, and proficiency of manufacturing processes. There is a lack of KPI understanding by the manufacturers and no framework or methodology available in the literature to select KPIs systematically, methodically, and/or scientifically for a manufacturing facility. This deficiency typically leads to failures in reporting and monitoring critical performance measures, with resultant losses to achieve key business objectives.

Viewing the current industrial needs and limitations highlighted in the literature, this research presents a holistic approach that enables manufacturers to systematically understand, analyze, and select appropriate KPIs for their shop floor operations assessment. The approach is mainly centered on the premise that KPIs can be chosen based on a set of measures that are theoretically grounded.

First, a manufacturing shop floor exploration model is developed to 1) recognize the key business objectives, 2) identify the bottlenecks in the manufacturing shop floor facility that negatively impacts the throughput, 3) point out the problems and challenges, and 4) list the KPIs used for monitoring shop floor performance. The model uses a set of questionnaires and structured interviews to collect the required data (i.e., data related to manufacturing shop floor performance) along with the real-time data extracted from the manufacturing shop floor.

Second, a novel KPI guideline is developed to systematically guide the manufactures to understand, analyze, and select appropriate KPIs. These guidelines consist of five stages: information stage, discernment stage, scheming stage, the origin of the data stage, and assisting technology to capture the data stage. Every stage consists of a set of measures and their corresponding elements dedicated to providing vital information to help manufacturers better monitor their shop floor operations and improve decision-making capabilities. Last, to streamline the decision-making by prioritizing key business objectives and KPIs, the SMART criteria technique is prudently selected. The practicality of the proposed approach is demonstrated through its application to an automotive seat manufacturing company.

It is sensible to indicate that the complete methodology of selecting appropriate KPIs and reviewing the manufacturing shop floor performance is a continuous process. After suggesting and implementing the KPIs, the manufacturers should evaluate the performance regularly since, in the current complex manufacturing environment, both internal and external business factors change over time. Hence it is necessary to incorporate these changes and provide continuous improvement, evaluating the shop floor performance on a regular basis.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	H Social Sciences > HD Industries. Land use. Labor T Technology > TS Manufactures
Library of Congress Subject Headings (LCSH):	Manufacturing industries, Production management, Manufacturing processes, Workflow
Official Date:	September 2020
Dates:	Date Event September 2020 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Warwick Manufacturing Group
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Harrison, Robert (Professor) ; Ahmad, Bilal
Sponsors:	University of Warwick ; Great Britain. Government
Format of File:	pdf
Extent:	193 leaves : illustrations (some colour)
Language:	eng

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