This article is an excerpt from the reportThe Right Stuff — Managing Inventory to Enable Agility for Manufacturing and Distribution Companies. A copy of the full report can bedownloaded here.
In Part Two of this series, we explored how to achieve accurate inventory visibility. Here we talk about how to take that visibility and use inventory intelligence to implement smarter tradeoffs.
Inventory Management Strategies
A company’s inventory management strategy impacts its customer service and satisfaction, cash flow and cash availability, logistics strategy, size and location of distribution centers, third-party partnerships, and more. There is a core tradeoff between service levels and inventory levels — in general, the more inventory a company has, the higher the service level. However, if it is the wrong inventory at the wrong location, that correlation falls apart. Adding inventory intelligence into the equation can allow lowering of inventory levels while simultaneously improving service levels. Inventory intelligence starts with accurate inventory visibility and an understanding of actual demand patterns. Building on that, more sophisticated inventory optimization may be implemented.
To Be Lean or Not to Be Lean —
Companies face a tradeoff between service levels and inventory levels. Some companies will choose a strategy to hold a lot of inventory in order to ‘never run out of stock,’ in particular when the consequences of running out are high. For example, hospitals want to ensure that needed supplies are always available for critical or life-threatening situations. Automotive parts providers want to ensure that they always have the needed part when a dealership calls — with the car up on the lift, demanding immediate delivery so they can finish the repair. In contrast, other companies, such as those producing commoditized items on low margin, with reasonably predictable demand, may take aggressive steps to minimize inventory levels.
Improving the ServiceâInventory Tradeoff
Regardless of where a company falls on the spectrum of service-levelâinventory-level tradeoffs, there are almost always opportunities to improve that tradeoff – i.e. to increase service levels without adding inventory, or lower inventory levels without lowering service levels, or improve both simultaneously. This can be accomplished through some combination of the following capabilities:
- Accurate SKU-location inventory data – s discussed above, the foundation is having accurate SKU-level inventory data for each location. This provides the underlying raw data needed for better forecasts, optimization, and process improvements.
- Improved forecast accuracy — When forecast accuracy is improved, the amount of safety stock can be reduced1 without sacrificing service levels.
- More accurate lead times — Inaccurate lead times are a perennial problem. Replenishment decisions and algorithms depend on the lead time being accurate. The most reliable way to improve lead time accuracy is with a system that monitors actual lead times and reports when they deviate too much from the lead time recorded in the system that is used for replenishment decisions/optimization. Such an approach can also capture lead-time variability, which is also needed for proper replenishment decisions.
- Shorter lead times — Inventory levels can be reduced by shortening lead times and by reducing lead-time variability. Companies may choose to source locally to reduce the transportation portion of lead times. They can work with their strategic vendors on reducing production lead times, as well as improve their own internal processes to reduce internal lead times (such as in-house production cycle times and fulfillment lead times) and make lead times more reliable and consistent (less variability in lead times).
- Kanban/Just-in-Time — Just-in-Time lean techniques, such as kanban replenishment, can be used to reduce the amount of inventory needed. Modern kanban typically uses barcode scanning and EDI (the latter for sending external replenishment orders).
- Inventory optimization – s a firm’s operations become larger and more sophisticated, it may use inventory optimization algorithms to create more optimal levels2 of inventory across its network, while maintaining or improving service levels.
Spare Parts and Multi-echelon Networks
Spare parts often present a multi-echelon inventory challenge, spanning the parts manufacturing plant, central DC, regional DCs, repair depots, parts retailers, dealerships, and trunk stock. Spare parts are also often a multi-enterprise inventory challenge involving inventory being produced and stocked by suppliers and/or third-party parts manufacturers, and carried by distributors, dealer networks, retailers, and third-party service providers.
Spare parts inventory is subject to fundamentally different demand patterns3 and geographic distribution than primary product inventory. Therefore, spare parts inventory requires its own set of algorithms and strategy to ensure that required service levels are met without having to carry an enormous amount of extra inventory, spread out across the multi-echelon service network. This unique functionality includes:
- Algorithms to help decide how much to hold at each echelon; how much to pool at the center vs. keep at the edges, including in the trunk stock of repairpersons;
- Dealing with extremely slow-moving parts (sometimes less than one consumed per year for a given location);
- Consideration of the consequences of a stockout, such as service level agreements that promise delivery within a specific time window and/or the loss of lifetime value of a customer, whose $1M/hour production line might go down for want of a spare part;
- Make-to-order and/or Engineer-to-order machines and parts.
A good spare parts inventory management software solution will address these.
The Real World is Full of Constraints: Logistical, Production, Cash Flow, —
Developing an optimal inventory strategy requires dealing with real-world constraints. Sometimes logistical considerations have a major impact on inventory strategy. For example, Eco-Bags Products, a manufacturer of eco-friendly reusable bags, has one main supplier in India for all of their products (about 300 different SKUs). The most cost-effective way to ship from their supplier is ocean shipment of a full container load (FCL). It is somewhat less expensive than ‘less than container’ (LCL) shipments and much less expensive4 and more environmentally friendly than shipping by air. Carbon footprint is an important consideration for Eco-Bags Products, driven by their ecological values and mission.
One full container equates to about three to four months of demand across all products for Eco-Bags Products. Therefore, they try to consolidate all of their orders to create a full container load shipment every three to four months. Another constraint is the minimum order size that the factory will accept, which generally ranges from 1,000 to 5,000 units, depending on the product. The economics of logistics and production constraints force Eco-Bags Products into these infrequent consolidation of orders, meaning they will hold more inventory and take bigger out-of-stock risks than they would in theoretically ideal circumstances (i.e. if they could order each SKU separately, to be shipped at its own ideal time for replenishment).
Furthermore, because their supplier is in India, Eco-Bags Products has to work around the monsoon season, when things tend to shut down. In addition, lead times from the supplier change from time-to-time, depending on what other demands are being placed on the factory. On top of all that, cashflow constraints prevent Eco-Bags Products from ordering the full optimal quantities of items they need. All of these challenges, combined with unexpected surges in demand, force them to airship some items from time-to-time.
Since 2005, Eco-Bags Products has been using NetSuite’s cloud ERP platform to run and grow their company. The company uses NetSuite (in combination with custom code and Excel spreadsheets) to generate a time-phased view of future demand and inventory levels, in order to calculate which products and quantities to include in their next orders, as well as identify when air shipments are needed. This is where the balancing act comes into play; deciding which SKUs to replenish (potentially overstocking some of them) and which to wait on (taking a chance they will run out before the next FCL shipment). Having all their data in a single system helps Eco-Bags Products manage these inventory challenges.
In the Fourth and Final installment of this series, we look at how small businesses can acquire sophisticated inventory management capabilities normally associated with larger enterprises.
1 It should be noted that some safety stock will still be needed for many reasons: to absorb variability or uncertainty in demand and supply, to account for lead times, to enable optimal order sizes, to compensate for any remaining inaccuracies in the forecast, and to provide other benefits. — Return to article text above
2 An inventory optimization system will not reduce inventory at all locations. Rather it recommends the optimal levels, which may mean increasing inventory for certain SKU locations, while decreasing inventory for other SKU locations. — Return to article text above
3 Product demand is based on sales — how many and at what locations product is bought. Spare parts demand depends on where products are actually used and thereby where the repairs occur, as well as on the longevity of parts (mean-time-to-failure), warranties, service level agreements, maintenance policies, and other factors. Product inventory requirements are typically constrained to the life of the product, whereas spare parts are needed after the end-of-life of the product, often for many years. — Return to article text above
4 Shipping by boat is about 50X-100X less expensive per ton-mile than shipping by air. Ocean transport also produces about 60X less greenhouse gases per ton-mile than air transport. — Return to article text above
To view other articles from this issue of the brief, click here.