Using Operations Metrics for Data-driven Decision-making
Lankinen, Teemu (2025)
2025
All rights reserved. This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025111728348
https://urn.fi/URN:NBN:fi:amk-2025111728348
Tiivistelmä
The objective of this master’s thesis is to create a proposal for data-driven decision-making process to effectively drive customer value for a genetic testing company that operates on a global market. It was identified by the company management that having transitioned from a start-up to scale-up the company was lacking ability to effectively use operational data in a forward-looking manner to drive decisions.
The current state of data utilization and decision-making was investigated by using qualitative methods. The qualitative research consisted of document analysis, six formal and two open interviews with managers of operational units, operations director, business controller and the manager of the data science team. The interviews uncovered four areas for improvement pertaining to effectiveness of turning data to action, measuring true capacity, availability of forward-looking metrics, and interdepartmental transparency. Available knowledge was used to identify best practices for solving the issues identified in the current state analysis.
The outcome of this thesis is a proposal for a data-driven decision-making process that enables a systematic approach to planning and managing operations by using one set of numbers. The proposal included targets against which the company can better measure its ability to improve the flow of supply and execute tactical planning. More specifically, based on the above this study proposed improvements to selection and validation of KPIs to better understand what is it that is being measured and does it help the organization to drive the operations the right way at the optimal velocity. The proposal also included improvements to the process that leads to decision and how the action plans can be effectively implemented. Finally, there were development ideas on how to prepare for recurring monthly meetings, how to act during those meetings and what to do after them.
By implementing the proposed development ideas to metrics and processes the case company can improve its performance through heightened focus on measuring what matters and promptly acting on discovered insights and in the long run increase its data maturity and build an even more robust processes for balancing supply and demand. The proposed improvements were approved by the case company and are going to be implemented in the near future. At the time of publishing this master’s thesis the implementation has already commenced.
The current state of data utilization and decision-making was investigated by using qualitative methods. The qualitative research consisted of document analysis, six formal and two open interviews with managers of operational units, operations director, business controller and the manager of the data science team. The interviews uncovered four areas for improvement pertaining to effectiveness of turning data to action, measuring true capacity, availability of forward-looking metrics, and interdepartmental transparency. Available knowledge was used to identify best practices for solving the issues identified in the current state analysis.
The outcome of this thesis is a proposal for a data-driven decision-making process that enables a systematic approach to planning and managing operations by using one set of numbers. The proposal included targets against which the company can better measure its ability to improve the flow of supply and execute tactical planning. More specifically, based on the above this study proposed improvements to selection and validation of KPIs to better understand what is it that is being measured and does it help the organization to drive the operations the right way at the optimal velocity. The proposal also included improvements to the process that leads to decision and how the action plans can be effectively implemented. Finally, there were development ideas on how to prepare for recurring monthly meetings, how to act during those meetings and what to do after them.
By implementing the proposed development ideas to metrics and processes the case company can improve its performance through heightened focus on measuring what matters and promptly acting on discovered insights and in the long run increase its data maturity and build an even more robust processes for balancing supply and demand. The proposed improvements were approved by the case company and are going to be implemented in the near future. At the time of publishing this master’s thesis the implementation has already commenced.