As the supply chain has increasingly become synonymous with just‐in‐time delivery, and as customer demands continue to tighten, the importance of speedy and reliable order fulfillment cannot be underestimated. This is particularly true for organizations with a complex global supply chain. New age enterprise resource planning (ERP) systems and supply chain management (SCM) platforms have becomes the norm in managing everything from planning through to procurement, order fulfillment and logistics.
Business leaders recognize the importance of supply chain digitization. Digital supply chains and logistics automation are the top funding priority for businesses worldwide, with investments in transformation reaching $93 billion in 2018. The worldwide revenue of supply chain management software organizations crossed $12 billion in 2017, with growth running at 13.9% for 2016–2017.
Master data management
In my master data management (MDM) workshops with SCM leaders across industry, I have found that many organizations have not been able to reap the full potential of these SCM platforms, despite spending a fortune. Promises of a reduction in cost-to-serve, of increased agility, and of better business decision-making are far from being realized.
Let’s consider some of the challenges organizations face in running their supply chain functions:
A UK-based consumer packaged goods (CPG) company implemented a leading distributor management system, and started to experience a number of challenges during the pilot phase:
- Retailers and distributors were not able to locate the correct item code while placing an order
- Inconsistent promotions were applied across the same product family
- Blocked orders were coming through the electronic data interchange (EDI).
The root cause of all these challenges was found to be in the material master function, which was not being properly maintained. There was no standard definition for data, nor for business rules for material attributes, leading to inconsistent data, and hence to loss of sales.
Warehousing and distribution
A major US high-tech manufacturer implemented a warehouse management system (WMS) and transportation management system to handle large orders of their products and spare parts across their worldwide distribution centers and repair centers. The put-away plan generated by the WMS could not be used in practice, as it exposed the wrong dimensions maintained in the current material master. The company was facing last-minute expensive shipping because of incorrect product information.
In one of the world’s leading CPG companies, I observed they were keeping high minimum order quantity (MOQ) in the masters. This was greater than the demand generated by the planning optimization engine, and resulted in the chance of higher inventory. Keeping both safety stocks and safety time parameters in their overall planning parameters resulted in excess ordering in material requirement planning. This, in turn, increased excess stock levels.
Further analysis revealed that the lead time between contract and the material master was not in sync, which created a difference in material plan vs. execution. In some cases, this led to excess inventory, and in others there were shortages. Overall, these MDM issues cost the business an excess inventory of over €4 million for the South Africa market alone.
In my second post, I’ll consider the pain points in master data, and how to address them.
Read Capgemini Research Institute’s “The Digital Supply Chain’s Missing Link: Focus ” report to learn more about how organizations across consumer products, manufacturing, and retail understand the digital initiatives they are adopting, the benefits they are deriving, and the way they are transforming their supply chain.
Abhishek Bikram Singh has over 12 years of industry experience (CPG, Manufacturing, Chemical, Retailers) in managing different supply chain functions. He has worked with clients across industries to define their current MDM maturity with respect to people, process, technology, and governance, and to develop their target operating model.