For capital intensive businesses, effective Inventory Optimisation represents a unique opportunity to generate substantial return on investment by minimising inventory investment and generating free cash flow, improving service level performance, and increasing profits by reducing supply chain operating costs as a percentage of sales.

GRA's Inventory Optimisation techniques optimise the complex relationships between costs, capacities, target service levels and variability/risk (eg forecast error and supplier delivery performance) for each item in each location across the supply chain network.

However, there are many different techniques available under the heading of Inventory Optimisation, and it is important to understand which technique is appropriate.

At a fundamental level, there are two tiers of inventory optimisation, Strategic and Operational/Tactical.

Strategic Inventory Optimisation

Strategic Inventory Optimisation focuses on supply chain structure or policy, and as such, its maximum refresh frequency tends to be annually or when a fundamental change to the supply chain is being considered.

Operational/Tactical Inventory Optimisation

Whereas Strategic Inventory Optimisation focuses on questions of structure and policy, Operational/Tactical Inventory Optimisation deals with the recurring operational processes of demand and supply planning which may occur daily, weekly or monthly. Operational/Tactical Inventory Optimisation aims to keep inventories optimally balanced for the specified service level as various factors change. For example, inventories should adjust dynamically in response to changing demand patterns and risk factors (such as forecast error and supplier performance).

It is also important to note that for Operational/Tactical Inventory Optimisation to be truly effective, it must be genuinely integrated with demand planning and replenishment planning processes. For example, if forecast accuracy increases, safety stocks should automatically decrease leading to an automatic decrease or deferral of replenishment requirements. The use of static inventory policies such as 'weeks supply' prevent inventory optimisation as they are not responsive to changes in supply chain variability and risk, service level targets or costs. In the previous example, an improvement in forecast accuracy has no direct impact on a safety stock policy of 'two weeks of cover'.

Following are some of the prevailing Inventory Optimisation techniques with notes on their application.

Inventory Deployment Optimisation (IDO) and Network Flow Optimisation (NFO)

These techniques determine where to store or produce inventory and how inventory should flow from source of supply to consumption. These decisions need to be made cognisant of the whole supply chain and are often made in the context of strategic supply chain planning. For example, where products should be stored will depend on factors including:

• demand profiles
• the number of sites (which can also be a variable in the analysis)
• the carrying cost at each site
• procurement and/or production costs at each site
• the function, capacity and capability of each site
• production, storage and transportation capacities and constraints for each node in the supply chain
• the relative value of the inventory and its pursuant profit margin versus transportation costs (eg a cement distributor is likely to have more sites holding inventory to minimise shipments)
• all fixed and variable costs by resource, node and channel
• customer service levels
• supplier and customer lead times
• supply chain variability and risk profiles

Multi-Echelon Optimisation (MEO)

MEO can be applied in environments where product and/or component substitution is possible. In traditional inventory optimisation techniques, inventory levels for both Finished Goods and Bill of Material (BOM) components are set on an item-by-item basis based on service level, variability and cost optimisation techniques. However, if the aim is to achieve a service level of 95%, having three interchangeable items stocked at a 95% provides an actual service level of 99.9%+, so inventories are excessive relative to service level requirements. The same is true if three warehouses all stock the same product (provided that all warehouses can deliver to the customer). MEO techniques factor in substitution possibilities across the manufacturing and distribution network and set the optimal stock levels based on service level targets across the interchangeable inventory population. In environments where the substitution potential is high, MEO techniques can deliver inventory reductions of 10%-30% above traditional techniques. The computer manufacturing and assembly industry is a good example of an ideal MEO environment as it has many interchangeable components and redundant stocking across a large, distributed supplier base.

Service Level Optimisation (SLO)

SLO is a technique that determines the optimal service level policy for a given set of constraints. As stated previously, the traditional method uses service level as the constraint (ie the lowest possible inventory for a given service level). Using this method, it often takes 50% more safety stock to increase service levels from 95% to 99% as an additional standard deviation of supply chain variability must be carried. As a result, the significant increase in inventory and carrying costs for a relatively small increase in service level can erode profit margins. SLO provides an alternate approach that sets service level policies at the point where profit margins are maximised, based on the trade-off between profit margin, revenue realisation and inventory related costs. SLO can also be used to determine the ideal set of service level policies by item given an aggregate inventory budget constraint. For example, if there is a $5 million inventory holding constraint, SLO can determine the mix of service level policies that maximises total fill rate to customers. This technique may assign higher service levels to faster moving lower value items and lower service levels to slower moving higher value items.

Marginal Analysis & Optimal Sparing

Marginal Analysis is used primarily in Maintenance Repair & Overhaul (MRO) industries. It seeks to determine the cost optimal mix of rotables, repairables and spares to deliver maximum asset availability (eg aircraft, power generator) for a given budget. Conceptually, this technique shares some similarities with MEO and SLO, but the mathematical approaches are different given the unique characteristics of MRO environments (eg very slow moving items, repair cycle turnaround time, etc).

Constraint Based Optimisation

This technique is used to determine inventory strategies given the presence of significant critical constraints which are common in constrained manufacturing, scheduling and processing environments. Examples of these environments include grain processing and distribution, petroleum processing and distribution, process manufacturing (eg float glass production, dairy product manufacturing) and commodity export shipping (eg coal export scheduling across an interconnected rail and port system).

Benefits resulting from the effective application of Inventory Optimisation include:

• 20-40% inventory investment reduction
• increased service levels ranging up to 99.9%
• significant working capital improvement
• compressed cash-to-cash cycles
• the ability to fund business initiatives from operating cash flow (OCF) improvements
• improved return on capital employed / net assets
• improved debt to equity ratios
• 10%-15% reduction in supply chain operating costs
• improved capacity and fixed asset utilisation
• improved relationships with suppliers and customers
• integration of business goals and operational practices

By way of example the Supercheap Auto Group achieved the following documented results with the application of both strategic and operational/tactical inventory optimisation techniques:

• reduced inventories by 17% (or improved operating cash flow by $23 million) whilst increasing service levels via advanced demand management & inventory optimisation
• increased margins and gained market share in a depressed sales environment
• In FY06, added 30 new stores or $60 million in revenue with no additional investment in inventory
• $100m invested in opening new stores over three (3) years with only $40m increase in net debt
• avoided need for additional DC (fixed asset) investment due to inventory and operating efficiency improvements