Measuring the Operational Performance of the Outbound Logistics Process of Brown-Forman Finland
Badji Mansilla, Anna Maye (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023051811818
https://urn.fi/URN:NBN:fi:amk-2023051811818
Tiivistelmä
Companies generate large amounts of data when performing their business activities. While every company manages data in different ways, efficiency in how they gather, process, and analyse this data may result in a variety of possibilities affecting their end goals and financial performance. In the case of Brown-Forman Finland (BFF), data is generated along its supply chain process and forms an extensive data flow. The current company’s data strategy provides various sets of supply chain key performance indicators (KPIs) that managers use to make decisions that will affect Brown-Forman Finland’s financial performance.
This thesis analysed the current state of the outbound logistics process of Finlandia Vodka at Brown-Forman Finland and the measuring of its operational performance. The aim was to find the weaknesses in the business process and the possible ways to optimise them. By doing so, the company can bring the best possible service to their customers around the world, while contributing to improve the company’s financial performance.
The author decided to focus the research on the interpretation of non-numerical data and on the overall data structure of the business process. Primary data was obtained through the author’s experience while working at the company as a Logistics Specialist. Additionally, there were multiple discussions with the Managing Director of the company, Sami Pulkkinen. The secondary data to be analysed consisted of the company’s own data, which was related to the performance results of the outbound delivery process of the company.
As a result of the analysis, two main gaps were found: deficient forecast accuracy and an inefficient data strategy. Indeed, the company’s traditional data strategy was identified as one of the main reasons why there is a low level of forecast accuracy. The report concluded with the recommendation to establish a new modern, digital and performance-driven data strategy, which intends to close those gaps and improve Brown-Forman’s operational performance.
This thesis analysed the current state of the outbound logistics process of Finlandia Vodka at Brown-Forman Finland and the measuring of its operational performance. The aim was to find the weaknesses in the business process and the possible ways to optimise them. By doing so, the company can bring the best possible service to their customers around the world, while contributing to improve the company’s financial performance.
The author decided to focus the research on the interpretation of non-numerical data and on the overall data structure of the business process. Primary data was obtained through the author’s experience while working at the company as a Logistics Specialist. Additionally, there were multiple discussions with the Managing Director of the company, Sami Pulkkinen. The secondary data to be analysed consisted of the company’s own data, which was related to the performance results of the outbound delivery process of the company.
As a result of the analysis, two main gaps were found: deficient forecast accuracy and an inefficient data strategy. Indeed, the company’s traditional data strategy was identified as one of the main reasons why there is a low level of forecast accuracy. The report concluded with the recommendation to establish a new modern, digital and performance-driven data strategy, which intends to close those gaps and improve Brown-Forman’s operational performance.