Clarios, formerly Johnson Controls Power Solutions, is the world leader in smart energy storage technologies for the automotive industry – globally, one in three vehicles is powered by its batteries. Hear how Clarios integrated a complex ERP landscape to improve the quality of its supply chain data for demand and supply planning using Logility.
Industry: Service Parts
Machine learning is a type of supervised or unsupervised artificial intelligence where software has the ability to learn without being explicitly programmed. For more than a decade, companies have used the power of machine learning to improve supply chain planning efficiencies and develop optimized supply chain decisions. Automatic model switching to improve forecast accuracy is just one of many examples of the early use of machine learning to continually tune the digital supply chain and optimally leverage physical supply chain network performance.
Early results are driving the hype of machine learning applications to a fever pitch and there’s no question that machine learning is a topic that supply chain practitioners should be actively investigating. The real question is, “Are we, as a profession, ready to embrace machine learning in an unsupervised fashion”? If so, what does that mean and how do we get there?
Three areas where you can start with machine learning in your supply chain planning efforts are:
- Forecasting: Forecast accuracy is a top challenge for many companies and a quick win application of machine learning could be the automated adoption of “Best-Fit” algorithms across your portfolio.
- Supply Chain Optimization: Another high value opportunity of machine learning is gained by continually analyzing the state of your digital supply chain and automatically tuning planning parameters to meet customer requirements while maximizing company objectives.
- Multi-Echelon Inventory Optimization (MEIO): Using the latest demand and supply information, machine learning can enable a continuous improvement in your company’s ability to meet a desired customer service level at the lowest inventory investment.
This eBook defines machine learning in greater details, explores its use as part of an overall supply chain planning strategy, and further recommends where and how to get started.
Wholesale distributors sit in a precarious location when it comes to supply chain risk management. They “fill the gap” between manufacturers and retail/service customers by providing access to a wide assortment of products, industry knowledge and value-added services. In this role, wholesale distributors face many of the same supply chain risk management challenges as manufacturers and retailers, including:
- Product availability and time-definite delivery — having the right products when and where the customer needs them, and at the right price
- Reliably supplying a broad and deep product line of complementary items, alternatives, assortments, variations and lots, nearly always consolidated from multiple, potentially competing suppliers
- Consolidation and one-stop shopping
- Delivery and installation
- Managing substitutions for comparable or better fit, form, function, or lower price
- Managing succession where technology, regulation, competition, or fashion drive rapid product life cycles
Faced with growing costs, shrinking margins, new competitors and demanding customers, wholesale distributors must turn to advanced supply chain risk management capabilities to separate themselves from the pack including:
Advanced modeling capabilities to create a valid, forward-looking demand plan by product, customer, channel and geography, with more accurate forecasts throughout the product life cycle.
Inventory optimization capabilities to examine stock positions in each stocking location and to model interrelationships between stocking locations to reduce overall inventory levels while meeting customer service requirements.
Enhanced long-range planning capabilities to enable more accurate predictions of market changes, identify and mitigate potential risks, and empower a more predictable and repeatable integrated planning process.
This eBook, Building a Profitable Wholesale Distribution Supply Chain, presents several demand-driven supply chain planning techniques to help wholesale distributors get control of costly overstocks, stockouts and expediting charges and compete from a more profitable position.
Increasingly companies are recognizing the value of aftermarket parts and services as a line of business. Learn how Rheem Manufacturing is using Logility Voyager Solutions™ to simplify its service parts business by mapping stocking levels to customer demand.