There are a number of different functional areas that can be readily improved through the use of business intelligence (BI) tools. From logistics and distribution to sourcing, materials management, advanced planning, supplier performance and risk management—all of these serve as top targets for the use of BI tools in manufacturing. And there are also a number of steps that the manufacturing segment of supply chain can take to develop custom BI tools. They can identify preliminary objectives to achieve; classify the data required for analysis; determine where the data exists and collect it; structure the database so it can be readily expanded with additional data fields; and build algorithms to automate the analytical process.
In the case of Jabil Circuit Inc., which provides design, manufacturing and aftermarket product management services to global electronics and technology companies, the company uses a number of best practices for custom BI tools.
Jabil delivers actionable data to its managers that include opportunities for logistics cost reduction (mode changes, service level changes and consolidation) and sourcing improvements (changes in minimum order quantities, better payment terms and moving materials to postponement). Its BI tools automatically prioritize actions by opportunity, difficulty and time to complete.
In addition, the company’s tools automatically check for accuracy through validation rules, followed by a corrective process that ensures data quality and accountability. Numerous validation rules developed depend upon data attributes. For example, part price is checked for percentage change and absolute dollar change between two time periods.
In one case, Jabil analyzed shipping practices—including transport modes, speed and frequency—by site location. A centralized, comprehensive database allowed detailed analysis of all shipments and the BI tool was able to identify small errors and drill down into details due to the richness of the database. This project alone resulted in savings of more than two million dollars.
To show performance trends, Jabil’s toolsets automatically build statistical process control charts in multiple areas of the supply chain. This capability allows Jabil to identify—at a stock-keeping unit (SKU) level—when weekly demand represents a true shift or when changing demand is within the normal variation and doesn’t require action. This type of business decision support enables high service levels at an efficient use of capital without requiring the user of the data to be an expert in statistics. Thus, a higher level of performance is achieved through the scalability of BI tools.
For more information, check out Fred Hartung’s online exclusive article this month, “Best Practices for Business Intelligence.”