Pricing optimization in a manufacturing company : leveraging Power BI to focus on the most profitable areas
Harald, Joonas (2026)
2026
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202603265077
https://urn.fi/URN:NBN:fi:amk-202603265077
Tiivistelmä
Pricing is a central driver of profitability in competitive and cyclical markets. The thesis examined how a Finnish manufacturing company can optimize its pricing and how Microsoft Power BI can be used to support pricing decisions and focus on the most profitable areas. The aim was to evaluate the company’s existing pricing framework and to develop a practical tool that models the effects of different pricing scenarios.
The research followed a deductive approach and combined qualitative and quantitative methods. Qualitative data were collected through unstructured interviews with key stakeholders making pricing-related decisions, providing a larger understanding of current frameworks. Quantitative analysis was based on historical sales and cost data extracted from the company’s ERP system and analysed with Power BI. A scenario model was built to simulate price impacts and volume changes on sales, margins and profitability.
The findings show that moderate price increases in selected product categories have a significantly stronger impact on profitability than volume changes. The results also indicate that categories with stronger differentiation and attributes offer greater pricing power and are more suitable for premium pricing initiatives. The Power BI report enables management to test alternative pricing scenarios, compare the effects and support data-driven decision-making. Strengthening pricing capabilities in this way improves long-term profitability and supports the company’s ability to invest in sustainable development.
The research followed a deductive approach and combined qualitative and quantitative methods. Qualitative data were collected through unstructured interviews with key stakeholders making pricing-related decisions, providing a larger understanding of current frameworks. Quantitative analysis was based on historical sales and cost data extracted from the company’s ERP system and analysed with Power BI. A scenario model was built to simulate price impacts and volume changes on sales, margins and profitability.
The findings show that moderate price increases in selected product categories have a significantly stronger impact on profitability than volume changes. The results also indicate that categories with stronger differentiation and attributes offer greater pricing power and are more suitable for premium pricing initiatives. The Power BI report enables management to test alternative pricing scenarios, compare the effects and support data-driven decision-making. Strengthening pricing capabilities in this way improves long-term profitability and supports the company’s ability to invest in sustainable development.