5 inventory analytics best practices to achieve inventory optimization
Inventory optimization is the provision of the right inventory, in the right quantities and at the right locations, to meet the supply and demand of parts and materials in the enterprise. Significant benefits exist for organizations that optimize their inventory by reducing inventory items and stock levels, thus avoiding associated carrying costs and obsolescence write-downs. Indirectly, organizations can generate savings by using time formerly spent on inventory management to ensure physical assets’ reliability and availability.
However, significant challenges to inventory optimization lie in the volume of inventory transactions that must be analyzed and the complexity of the analysis required to identify trends in inventory use. Optimized asset inventory management allows organizations to meet such challenges head-on. Indeed, opportunities for incorporating analytics exist in many areas of inventory management. Specifically, the following well-established inventory management best practices are ideal for analytics.
Spare parts, an essential component of the availability of any system, have intermittent consumption patterns and usually have only one specific use, and organizations can often source them only from the system manufacturer. For these reasons, many organizations overstock spare parts to avoid costly system downtime. Unfortunately, overstocking incurs its own costs—and they can be significant.
Often, organizations use manufacturer lifetime and degradation data to inform their approach to stocking spares. But such data can be imperfect, making it unreliable. Organizations can use inventory analytics to identify items that are trending toward being out of stock, providing a means of stock management more reliable than supplier data. In addition, research has shown that monitoring technology can reduce the need for spare part inventory.
To maintain ideal inventory stock levels, organizations must accurately classify inventory. ABC analysis, a particularly popular classification system, classifies inventory by the relative priority of each item against other items in the inventory. A-classed items, the most important, typically make up 5 to 10 percent of inventory. B-classed items typically make up the next 15 to 25 percent of inventory. C-classed items, the least important, make up the remaining 65 to 80 percent of inventory.
But such classification is rarely as straightforward as it sounds. Indeed, ABC analysis requires ongoing review and revision to achieve optimal item distribution. Organizations can use inventory analytics to flag obsolete items, ensure efficient item distribution and identify critical spare parts.
Many organizations rely on corrective maintenance procedures that lead to unscheduled inventory demands. To help address intermittent demand, organizations should implement forecasting practices such as the following, while taking care to understand what forecasting is—and what it is not:
- Forecasting demand and planning supply
- Communicating, cooperating and collaborating
- Eliminating islands of analysis
- Using tools wisely
- Emphasizing forecasting
- Measuring everything
With such practices as these in place, inventory analytics can make use of the wealth of data that organizations generate, and organizations can capitalize on an analytics solution designed for asset inventory management.
Obsolescence is an unavoidable fact of inventory management, but unfortunately, many organizations manage it reactively. To manage obsolescence proactively, organizations must be able to answer questions such as the following: How can we anticipate obsolescence? What contingency plans have we in place? What are our most important needs? Should we maintain items, or replace them? How do we ensure safety?
Such organizations should develop an obsolescence risk assessment process, helping inventory managers assess the probability that items will become obsolete and flag the items that are at greatest risk of becoming so. To augment the process, inventory managers can use analytics to identify already obsolete items, identify items that the organization can afford to manage reactively and reduce the manual effort involved in computing the probability of obsolescence. Incorporating analytics into the obsolescence process gives organizations the data they need to apply mitigation strategies such as supplier agreements, systems upgrades or risk mitigation purchases.
Organizations commonly strive to avoid excess stock, which not only is unproductive but also gives a poor return on investment. Stock liquidation strategies typically attempt to liquidate stock for maximal return or at minimal expense. However, as in many areas of life, an ounce of prevention is indeed better than an ounce of cure.
By optimizing reorder points for inventory items, organizations can avoid excess stock conditions. Although such optimization is not a trivial process, inventory analytics developed by IBM can help inventory managers ensure that they have sufficient inventory to handle fluctuating demand while still keeping costs down. The potential benefits are substantial, ranging from accelerated analysis to quantified financial impact to automatic optimization—all of which lead to increased cash flow and inventory cost savings.