Many manufacturing and distribution companies are trying to leverage data in order to solve a variety of business problems. Some companies are focused on improving delays or inaccuracies in order fulfillment. Some are trying to consolidate logistics spend or improve forecast accuracy; while others look to streamline inventory levels and more efficiently store product. Whether you’re tackling one of these problems or attempting to solve all of them, the end goal is to increase efficiency and optimize your supply chain.
One of the biggest barriers in effectively leveraging supply chain data to solve these problems is disparity. Data disparity comes in many forms, including (but not limited to) disparate systems, inconsistent data, and differing data terminology. This conundrum is commonly found in M&D companies because they tend to operate as multiple, separate product lines or business units. It is especially prevalent in organizations that have grown through mergers and acquisitions. A robust, collaborative supply chain analytics solution can overcome disparity through the following:
- Disparate systems: A central data repository, such as an enterprise data warehouse, allows the data from these disparate systems to merge together. Once merged, data can be aggregated across product lines and business units to achieve a holistic view of a supply chain. Since most modern data visualization tools have the flexibility to view aggregated data but also drill down to the details, individual business units can still use an enterprise analytics solution to perform their own independent analysis, but in a more consistent format.
- Inconsistent data: Invest time upfront on correctly mapping different business units’ data into a data warehouse and account for varying business logic to ensure consistency in supply chain reporting and analytics. When the time comes to account for differing pricing structures or handle intra-company procurement spend, this upfront investment will provide end users confidence in the data they consume and aid the adoption of the supply chain analytics solution.
- Differing data terminology: Different business units can have varying definitions for data elements or ways of calculating KPIs. It is crucial to invest time upfront and develop a common data language. Establishing a common definition for what a supplier location is or how to uniformly calculate non-optimal shipments through a common data language, is key to implementing a successful supply chain analytics solution.
When solving these supply chain data challenges, it is important to keep in mind the future growth of the organization. Formulating and documenting a repeatable approach for conquering data disparities will accelerate the process of onboarding additional product lines or business units onto a supply chain analytics solution. If your business is experiencing any of these challenges, formulating and executing a robust supply chain analytics strategy will certainly uncover opportunities to reduce your operating expenses.
For additional insight on conquering data disparity, contact Mark Lewan.