Machine learning is hot. Solution providers in supply chain planning (SCP) tell me customers want to know how these technologies will be used in future SCP solutions. But machine learning is just one form of intelligence that can be embedded in SCP applications. The growing intelligence of these solutions ranges from better integration frameworks all the way up to fully automated planning.
Better Integration Frameworks
Integration frameworks allow data from multiple sources and networks to be pulled into planning solutions much more easily. Logility’s Karin Bursa, an executive vice president, points out that “many companies have multiple ERP systems.” She sees faster integration with better certainty and master data management, as a key differentiator for Logility. The master data logic understands the range of data that is appropriate for a particular field and can track and highlight when inappropriate data gets entered. Logility’s solution also uses net change logic. In other words, their system only looks at data elements that have been updated or changed. This makes same day or inter-day data updates more efficient.
Robust Role-based Views
This is not a new area of investment; it has been going on for several years. Many suppliers have invested in easier to use interfaces, particularly excel style interfaces. These interfaces have workflows that allow planners to tackle the most important planning problems in order of importance. Demand planners may want to view forecasts in units by week at ship to locations. Financial planners may want to see monthly views of revenues by business unit. Many suppliers offer integrated business planning (IBP) modules, sometimes called supply chain control towers or cockpits, that allow for a variety of views by the wide variety of actors in a corporation involved in balancing supply with demand in ways that maximize the company’s strategic objectives. Those objectives might differ by product or customer and can include things like profit maximization, achieving revenue targets, gaining market share, and other things as well.
Bigger, Better Solves
There are always new problems to solve. Omnichannel is the best current example of that. Manhattan Associate’s Scott Fenwick, director of product strategy, points out that when a new flow is supported, like order online but pick-up-in store, inventory allocation decisions need to change. But picking up that shift in the demand signal can be difficult. They are using machine learning to help solve this true demand problem.
Read more at Supply Chain Planning Systems Become Increasingly Intelligent
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