Inventory optimisation is a method of balancing capital investment constraints or objectives and service-level goals over a large assortment of stock-keeping units (SKUs) while taking demand volatility into account. Companies such as Colgate-Palmolive, Delphi and Luxottica use inventory optimisation to calculate inventory targets for Stock Keeping Units in Stocking Locations (SKU-Ls).
Inventory management challenges
Every company has the challenge of matching its supply volume to customer demand. How well the company manages this challenge has a major impact on its profitability. APQC Open Standards data shows that the median company carries an inventory of 10.6 per cent of annual revenues. The typical cost of carrying inventory is at least 10.0 per cent of the inventory value. So the median company spends over 1 per cent of revenues carrying inventory, although for some companies the number is much higher.
Also, the amount of inventory held has a major impact on available cash. With working capital at a premium, it's important for companies to keep inventory levels as low possible and to sell inventory as quickly as possible. When Wall Street analysts look at a company's performance to make earnings forecasts and buy and sell recommendations, inventory is always one of the top factors they consider. Studies have shown a 77 per cent correlation between overall manufacturing profitability and inventory turns. The challenge of managing inventory is increased by the 'Long Tail' phenomenon which is causing a greater percentage of total sales for many companies to come from a large number of products with low sales frequency.
Shorter and more frequent product cycles which are required to meet the needs of more sophisticated markets create the need to manage supply chains containing more products and parts.
At the same time, planning frequencies and time-buckets are moving from monthly/weekly to daily and the number of managed stocking locations from dozens in distribution centres to hundreds or thousands at the points of sale (POS).
This leads to a large number of time series with a high level of demand volatility. This explains one of the main challenges in managing modern supply chains, the so-called 'bullwhip' effect, which often causes small changes in actual demand to cause a much larger change in perceived demand, which in turn can mislead companies to make bigger changes in inventory than are really necessary.
Without inventory optimisation, companies commonly set inventory targets using rules of thumb or single stage calculations. Rules of thumb normally involve setting a number of days of supply as a coverage target. Single stage calculations look at a single item in a single location and calculate the amount of inventory required to meet demand. One approach to planning inventory is to manage fast moving items under high service levels and slow moving items under lower service levels. The inventory mix resulting from this approach is far from optimal.
Inventory optimisation - deterministic vs. stochastic
Inventory optimisation models can be either deterministic – with every set of variable states uniquely determined by the parameters in the model – or stochastic – with variable states described by probability distributions. Stochastic optimisation takes supply uncertainty into account that, for example, 6 per cent of orders from an overseas supplier are 1–3 days late, 1 per cent are 4–6 days late, 5 per cent are 7–14 days late and 8 per cent are more than 14 days late. Stochastic optimisation also accounts for demand volatility which is priority No. 1 among the challenges faced by supply chain professionals. For example, management predicts a 65 per cent probability of selling 500 units, a 20 per cent probability of selling 400 units and a 15 per cent probability of selling 600 units. High service levels can be achieved with cost overruns, excessive inventory and fire-fighting, but higher profitability can be achieved by understanding the sources of volatility and planning appropriately. The result is a better understanding of the inventory requirements than with a deterministic approach
Inventory optimisation - single vs. multi-echelon
A sequential single-echelon approach forecasts demand and determines required inventory for each echelon separately. Multi-echelon inventory optimisation determines the correct levels of inventory across the network based on demand variability at the various nodes and the performance (lead time, delays, and service level) at the higher echelons.
Multi-echelon inventory optimisation looks at inventory levels holistically across the supply chain while taking into account the impact of inventories at any given level or echelon on other echelons. For example, if the product sold in a retailer's outlet is received from one of its distribution centres, the distribution centre represents one echelon of the supply chain and the outlet another one. It should be clear that the amount of stock needed at the outlets is a function of the service received from the distribution centre. The better the service that is provided upstream, the smaller the protection that is needed downstream. The goal of multi-echelon inventory optimisation is to continually update and optimise safety stock levels across all of these echelons.
Multi-echelon inventory optimisation represents the state of the art approach to optimise inventory across the end to end supply chain. Modelling multiple stages allows other types of inventory, including cycle stock and prebuild along with safety stock due to time phased demands, to be accurately predicted. As part of inventory optimisation, supplier performance, customer service and internal asset metrics should be continuously monitored to enable continuous improvement.
Inventory optimisation benefits
Several companies have achieved financial benefits by employing inventory optimisation. A study by IDC Manufacturing Insights found that many organisations that utilised inventory optimisation reduced inventory levels by up to 25 per cent in one year and enjoyed a discounted cash flow above 50 per cent in less than two years.
Electrocomponents, distributor of electronics and maintenance products based in the UK, increased profits by £36 million by using inventory optimisation to achieve higher service levels while reducing inventory. BP-Castrol used inventory optimisation to reduce finished goods inventory by an average of 35 per cent in two years while increasing service levels (defined as line fill rates) by 9 per cent.