There several are data-related pain points across the supply chain, some of which are related to technology and processes, with many others due to subpar data management. From planning to fulfillment and beyond, data plays a crucial role across all main supply chain processes. Without proper management, additional structured and unstructured data inputs cannot be properly and effectively
Supply chain data is the backbone of a proper data strategy, and any move to a more data-driven enterprise model needs to include some level of quality improvement initiatives. Companies often focus on what they perceive to be core supply chain processes and leave out invaluable data benefits. In many of our projects, we work with clients trying to overcome the challenges of maintaining safety stocks and time, which can often lead to excess ordering, increased surplus stock, and slow-moving and obsolete (SLOB) inventory.
To create a successful and agile supply chain, companies need solid data initiatives that deliver accurate, complete, and consistent master data. However, they are often hampered by two factors:
- Lack of visibility and methodology inconsistency – the common lack of an MDM-dedicated process often results in a lack of visibility, as data initiatives and guidelines are heavily fragmented and rarely considered as a whole. This is especially true when a master data item has been created for a while and has undergone inconsistent or illogical updates
- Missing data governance – as data management is commonly not seen as a separate process and thus not given the required resources and attention, it often lacks centralization and data definition varies from one group to the other. This can lead to inconsistency and duplicates that become harder and harder to tackle and negatively influence the entire supply chain.
There are number of proven approaches organizations can leverage to address these data issues:
- Create an integrated solution with dedicated governance and guidelines
- Conduct an end-to-end assessment of your current operating model to assess people, process, and technology, and set realistic objectives that can be achieved in a given timeline
- Achieve clarity in roles and responsibilities
- Establish standards
- Create data quality and process controls to validate change requests
- Establish duplication checks, define SLAs and KPIs for data quality and accuracy, and measure business impact.