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Gaining greater insight into P&L across the organization with centralized analytics
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Consumer Goods
Use Cases
- Demand Planning & Forecasting
- Predictive Quality Analytics
Services
- Data Science Services
The Challenge
WD-40 Company, a global marketing organization, faced a significant challenge in analyzing its profit and loss (P&L) due to data being held in different systems. This lack of a clear view of P&L by customer, region, or stock-keeping unit (SKU) was hindering decision-making. The complexity of the company's internal financial landscape, with multiple international subsidiaries, and customer and SKU data stored at different levels of granularity, made it extremely challenging to analyze P&L in different parts of the business. Gaining an overview of the profitability of a particular customer, region, or SKU was a laborious, manual process – and in most cases, it was simply too time-consuming to perform this kind of analysis on a regular basis.
About The Customer
WD-40 Company is a global marketing organization headquartered in San Diego. The company is dedicated to creating positive lasting memories by developing and selling products that solve problems in workshops, factories, and homes in more than 176 countries around the world. As a supplier of leading industrial and consumer product brands to retailers across the globe, WD-40 Company has a complex internal financial landscape, with multiple international subsidiaries, and customer and SKU data stored at different levels of granularity.
The Solution
To solve the challenge, WD-40 Company engaged Aviana, an IBM Premier Business Partner, to help develop a solution. The Aviana team built a data mart that centralized the P&L data into a single source of truth, enabling rapid analysis and reporting using IBM Cognos Business Intelligence. This solution helped WD-40 Company’s finance teams understand business performance at a much deeper level than was previously possible – providing insight into profitability by SKU and customer, as well as by region and sales groups, and at different levels of aggregation. It also enabled users to analyze gross margin, contribution, and cost of goods sold (COGS).
Operational Impact
Quantitative Benefit
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