Editor’s Note: Last year ToolsGroup became Microsoft’s partner for Multi-echelon Inventory Optimization (MEIO) in Industrial accounts. Microsoft’s Bill Moffett and I collaborated on an introductory blog on MEIO for manufacturing businesses. You can see Bill’s version here on the Microsoft website and my slightly abridged version below.
Imagine a worker sitting in a convenience store, reading a book, at 3:00 AM without having seen a customer in hours. Paying for the labor on the off chance that a customer needs a soda, is wasteful and inefficient. This store is sacrificing resources to maximize customer satisfaction and sales – but without a payoff.
On a much larger scale, manufacturers can forfeit working capital by keeping large amounts of inventory on hand to help satisfy wide-ranging customer demand. And the cost of producing, moving, and maintaining this inventory, from factories to distribution centers, wholesalers, and beyond, is much greater than staffing a single employee through the night.
Successfully managing inventory is increasingly difficult
Many manufacturers’ supply chains struggle to face the modern challenges of volatile demand. Inventory planners and supply chain managers are constantly battling to balance maintaining desired customer service levels while keeping inventories in check. But rough approximations, gut instinct, and one-size-fits-all logic often fall far short of effective inventory management, leading to stock-outs, customer service issues, and missed opportunities.
The increasing desire from consumers for customized and complex products has led to far more unique stock keeping units (SKUs), adding to the challenge of modern inventory management. This expansion of SKU levels has triggered a rise in long tail demand: with manufacturers seeing more sales coming from a larger number of niche products, instead of a narrower product offering.
Simpler inventory segmentation approaches generally group these SKUs into arbitrary categories and then apply a one-size-fits-all logic. Unfortunately, this approach does not adequately account for variations in ordering practices and often leads to poor recommendations. For example, a sales pattern of one order of four tires every four months may generate a forecast of one tire per month and recommend keeping just one tire in stock, without accounting for customers replacing all four tires at once.
Also, the globalization of manufacturing has led to long, complex supply chains requiring organizations to interact with vastly more raw materials suppliers, distribution centers, and other partners than ever before. Additional echelons increase the difficulty of maintaining an agile and efficient supply chain that effectively reacts to fluctuating demand. Additionally, each individual player in the supply chain often optimizes inventory independently from the other echelons. This leads to the “bullwhip” effect, where upstream businesses overemphasize downstream demand predictions – that can lead to drastic overestimations of true customer demand.
These manufacturing challenges create the need for a more modern approach to inventory optimization.
Intelligent inventory optimization increases service levels and decreases costs
The main goal of inventory optimization is to provide customers with the right product, at the right location, at the right time. A business can accomplish this objective by maintaining excessive inventory levels to ensure they never run out of stock, even in peak demand. This approach, however, consumes an organization’s working capital, is exceedingly expensive, and can cripple a manufacturer’s ability to compete in an increasingly tight-margin marketplace. It also won’t work with goods that are perishable or that can become obsolete.
An intelligent inventory optimization solution automatically adjusts inventory levels to accurately meet changing customer demands, thereby improving service levels, freeing up working capital, increasing inventory turnover, and decreasing operational costs.
Multi-Echelon Inventory Optimization (MEIO) enables manufacturers to use their existing sales data to dramatically increase service levels and decrease costs. Unlike inventory systems using gross approximations to predict demand, it utilizes proprietary algorithms to optimize inventory even for slow-moving “long tail” items. It can manage even the most complex supply chains effectively, across multiple production and logistical echelons. Plus, MEIO is a future-focused solution that can be enhanced to further optimize inventory based on early demand signals such as IoT, weather forecasts, and macroeconomic trend data.
The net effect is a large return on investment for a low total cost of ownership, improving service levels while simultaneously increasing inventory turns.