Dynamic adapt to changes in real-time to ensure that accurate decisions are made as it eliminates guessing at safety stocks levels and reduces wasted raw materials. MRP uses specific technologies and strategies that require huge amounts of data. These dynamic behaviors and forecasting allow for more sustainable operations and reduce the impact on operations affected by shorter lead times and fragmented customer demand.
Understanding dynamic MRP
To face daily challenges and meet the increasing demands of evolving customer behaviors, organizations are using features in predictive analytics and dynamic MRP. But what is dynamic MRP and how does it work with predictive analytics to help supply chains?
MRP is defined as manufacturing resource planning. It was originally derived from materials requirement planning. The methodology determines effective planning of resources in the supply chain, which may include capital, supplies, or employees. When integrated into operations, it helps with decision-making, cost control, and inventory management.
One area where MRP may be used is with producing products and finished goods. When this type of dependent demand is used daily, it helps expedite parts a supply chain needs to produce. MRP with predictive analytics involves operations planning, financial planning, and address “what if” scenarios that are needed to ensure operational success.
How to use MRP
For example, say a business wants to ensure they have adequate materials in inventory for production. MRP helps with creating and altering production plans to meet the anticipated demand. It’s timely access to raw materials and product components. A bill of material (BOM) is generated for each end product, detailing all that’s needed to manufacture it. MRP then looks at the BOM, estimated demand, and lead time to initiate a master production schedule. The schedule details assembly and production to meet estimated demand.
Consumer Demand Changes
Consumers want discounted shipping and fast deliveries (see . For businesses to keep up, they need predictive analytics and forecasting models to anticipate and meet customers’ growing and evolving demands. Companies can no longer “go with their gut” or “guess” at optimal inventory levels because it can cost them wasted capital and resources as demand changes.
Predictive forecasting and dynamic MRP address various factors that affect operations. These include logistics requirements for shipping from warehouses, for example, to help determine capacity.
Demand planning and forecasting software include predictive forecasting features that help improve inventory optimization and determine future inventory replenishments and SKU demand. Additionally, helps the supply chain target appropriate inventory levels for each location and a reorder schedule.
How predictive analytics and dynamic MRP help
As there are millions of data points that supply chains must interpret, companies need accurate demand planning and forecasting. They need a supply chain planning platform that sets specific rules to help forecast future demand balanced with constraints.
While no forecasting model can ensure 100% accuracy, having forecasting models that help determine future behaviors assists supply chains in monitoring and lowering costs. Dynamic MRP and predictive forecasting provide organizations with rules to improve operations and increase their flexibility for better and more accurate decision-making to reach their bottom line.
When supply chains turn to dynamic MRP and predictive analytics, they’re better able to create material plans and production schedules needed to meet future demand. MRP alone isn’t enough. Predictive supply chain planning software is also crucial to help ensure the accuracy of optimized inventory levels.