Supply Chain Analytics for Sustainable Operations : a case study on Unilever's GHG emissions
Wang, Han (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024112129284
https://urn.fi/URN:NBN:fi:amk-2024112129284
Tiivistelmä
This thesis explores the application of supply chain analytics in improving companies’ sustainable operations. It provides an example for all companies to understand, predict, and select actions for their sustainability performance. As international requirements for sustainability continue to increase, it is meaningful for enterprises to realize and optimize their sustainability situations with data-driven approaches.
The study uses supply chain analytics, which includes descriptive, diagnostic, predictive, and prescriptive analytics. The supply chain analytics lifecycle is demonstrated through a case study of Unilever's GHG emission reduction. The six-step analytics lifecycle starts from the specific problem identification. Regarding the problem or metrics, relevant data should be collected and then used to describe the past and current situations. Understanding the description is necessary but not adequate because it is more important to explore the root cause behind the information. The next step should be predicting what will happen in the future. Based on the prediction, we can select the correct actions to influence the future. Finally, the action implementation should be checked and standardized for ongoing improvement.
This research illustrates how firms use supply chain analytics to improve sustainability. To make this study applicable to different companies, the challenges in adopting analytics are also discussed, such as data quality, data scalability, time consumption, and data security. Moreover, a detailed review of existing sustainability standards and KPIs is introduced for measuring a company’s sustainability performance. These standards and KPIs guide the direction of a firm’s sustainability strategies. Lastly, the study introduces the nonlinear relationship between profitability and sustainability, which indicates the appropriate strategies for different stages of sustainability and explains the importance and process of stakeholder engagement. It can assist companies in balancing their financial and sustainable performances.
The study uses supply chain analytics, which includes descriptive, diagnostic, predictive, and prescriptive analytics. The supply chain analytics lifecycle is demonstrated through a case study of Unilever's GHG emission reduction. The six-step analytics lifecycle starts from the specific problem identification. Regarding the problem or metrics, relevant data should be collected and then used to describe the past and current situations. Understanding the description is necessary but not adequate because it is more important to explore the root cause behind the information. The next step should be predicting what will happen in the future. Based on the prediction, we can select the correct actions to influence the future. Finally, the action implementation should be checked and standardized for ongoing improvement.
This research illustrates how firms use supply chain analytics to improve sustainability. To make this study applicable to different companies, the challenges in adopting analytics are also discussed, such as data quality, data scalability, time consumption, and data security. Moreover, a detailed review of existing sustainability standards and KPIs is introduced for measuring a company’s sustainability performance. These standards and KPIs guide the direction of a firm’s sustainability strategies. Lastly, the study introduces the nonlinear relationship between profitability and sustainability, which indicates the appropriate strategies for different stages of sustainability and explains the importance and process of stakeholder engagement. It can assist companies in balancing their financial and sustainable performances.