Evaluating demand forecasting analytics solutions can be a confusing and complex process. However, knowing the right questions to ask can greatly reduce the risk of selecting the wrong solution provider.
Over the last decade, advanced analytics has been at or near the top of the list of priorities for many executives and information technology professionals throughout the world. Organizations have made major investments in database technologies including CRM, ERP and other transactional systems that run the operations of a department, division and subsidiaries. As a result, most organizations have access to valuable data and are looking for ways to leverage demand forecasting analytics for a competitive advantage. Transforming raw data into meaningful and useful information enables more effective strategic, tactical, operational insights and decision-making.
A recent survey revealed that organizations that place greater emphasis on the solution provider evaluation process tend to have a more successful project. Not surprisingly, these organizations also consistently outperform their competitors in the marketplace. The true value of a solution for demand forecasting analytics lies in the insight it provides on business operations and the opportunities it creates for better decision making. Understanding the business requirements for an advanced analytics project is crucial to the selection strategy that aims to align with the overall business objectives while addressing the needs of business users. Failure to integrate business requirements into the selection process can result in substantial cost overruns or, even worse, project failure.
With hundreds of advanced analytics tools available in the market, a sound selection strategy that integrates business requirements accurately is more important than ever. Download this white paper, Five Questions to Ask before Selecting an Advanced Analytics Solution Provider, to help you on your journey.
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.
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 is using Logility to reduce costs, deliver to customer expectations and continue to improve performance in a complex supply chain network.
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.
Most companies recognize the importance to improve forecast accuracy and have a repeatable, reliable demand forecasting process. Accurate forecasts help minimize inventory, maximize production efficiency, streamline purchasing, optimize distribution, maximize customer service and ensure confidence in company projections. However, developing accurate product forecasts at all stages of a product’s life cycle can be very challenging. Gartner places demand forecasts at the top of their Hierarchy of Supply Chain Metrics to highlight its impact back through the supply chain. After all, a forecast is not simply a projection of future business; it is a request for product and resources that ultimately impacts almost every business decision the company makes across sales, finance, production management, logistics and marketing.
Typically, a variety of demand forecasting methods are applicable to any particular type of supply chain scenario. Smart supply chain planners use multiple methods tuned to perform well at different phases of the product life cycle, chosen to best exploit the available historical data and degree of market knowledge. The key is to pick the most effective and flexible methods and models, blend their best features, and shift between them as needed to keep forecast accuracy at its peak.
In this updated white paper, 2020 Planning Tip: Eight Methods to Improve Forecast Accuracy, we take a brief look at the three categories of demand forecasting models and the eight methods that have produced superior results for Logility’s many clients in a variety of industries and market conditions around the world. We also discuss how Multi-Variate Demand Signal Management can help you incorporate internal and external demand data to improve forecast quality and uncover insights to make better and faster decisions.
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.
According to Gartner, the typical company provides support services and parts for an average of seven years after an initial product sale, making service parts planning vital. It’s no wonder aftermarket parts and service areas have profit margins as much as 10 times those for initial product sales. Post-sale service is key to securing customer loyalty, fostering the company brand, and maintaining competitive differentiation. All told, aftermarket service and parts account for 20% to 30% of revenues and about 40% of total profits for most manufacturers.
There are multiple components to effective aftermarket service parts planning, including call centers, returns management operations, and promotions and marketing. However, the key driver of effective post-sale support is service parts management. Service parts management is the process of planning and alignment of service parts inventories, resources, and processes to ensure optimal customer service and response with minimal risks and costs.
Common goals for service parts planning include increasing forecast accuracy for service parts; reducing excess spare parts inventory; reducing obsolete spare parts inventory; enhancing scrapping programs; and increasing service levels by increasing fill rates, increasing product availability or up time.
While there are many challenges in achieving the goals stated above, there is one area that stands above the others. How can we do a better job of planning for products that have intermittent demand?
This white paper describes how Service Parts Planning and Optimization (SPP) is the linchpin of any effective service operation, and explains how to plan and align service parts inventories, resources and processes to ensure optimal customer service and response with minimal risk and cost.
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.