Extending business analytics to supply chain management
Though a supply chain management team doesn’t directly influence sales, cost-to-serve factors such as transportation and palletization can have a significant influence on profitability when delivering hundreds of millions of dollars’ worth of chocolate.
Unfortunately, Lindt’s supply chain management team had been under-serviced. Left to their own devices, they had resorted to using legacy reporting tools such as Excel that required manual gathering, slicing and dicing of data. Consequently, this data was siloed, unshareable, hard to use, lacked quality and governance controls, and could not be used in automated processes.
By incorporating new data feeds from transportation providers and warehouses and aggregating these to the master dataset, Lindt developed a cost-to-serve dashboard in Cognos Analytics. Now Lindt could ask new questions and draw new insights: Why do we spend more shipping to certain retailers? How can we drill into the data to identify underlying factors and get a better outcome?
Advancing strategic data analytics capabilities
The solution helped Lindt’s supply chain management team reduce their reliance on Excel, and reduced time and effort. The data ingestion process improved data quality and governance; automation also improved data quality by eliminating manual merge and preparation of calculations. A consolidated view of data is now available through the enterprise data warehouse and through Cognos Analytics.
Overall, the solution has increased the speed-to-insight and ability of Lindt’s supply chain team to share and visualize high-level KPIs from their own dashboards and data sets. It has also freed up the executive team’s time to focus on more strategic activities.
Next steps for Lindt: building a dynamic cube in Cognos Analytics and exploring how the company can use IBM Planning Analytics to improve forecasting and data-driven decisions for competitive advantage.