Planning Under a Recession: A Data-Driven Approach to Inventory Optimization

Do you have the correct inventory targets to optimize your investment in working capital? If you’re looking to free up funds or keep excess inventory to a minimum, here are some crucial elements you’ll need to keep top of mind.

Inventory reduction is a hot topic right now. Businesses across industry lines are seeking to right-size their inventory. Some companies are driven by the need to free up cash. For other businesses, it is about minimizing write-offs and excess inventory. Others may prepare themselves for a financial downturn. Whatever the reason, there are several key factors to consider in making these decisions to optimize your inventory. These considerations include service targets, where to deploy your inventory, the parameters and policies that drive your inventory and the ability to leverage advanced algorithms. In our experience, failing to use advanced planning analytics results in less-than-optimal inventory targets. These inadequate inventory targets result in incorrect purchase order quantities to suppliers, erroneous production work orders and potential service failures.

Secondary impacts include expedited shipments, excess material handling, poor use of labor and equipment resources and increased storage costs.

Most businesses have low-hanging options to reduce inventory levels while maintaining customer service levels via advanced planning analytics. Companies can leverage advanced analytics capabilities by implementing advanced planning systems, keeping Inventory Optimization as a Service (IOaaS) with a managed services provider or hiring expert analytics consulting services.

Where is the money? It’s numbers and analytics

A recent analysis performed for a multi-billion-dollar enterprise by an NTT DATA consulting team found a material difference in the expected inventory investment required based on the capabilities of the model in use. Advanced planning analytics produced far superior options and insights while supporting similar service levels. For example, for a business with a $200 million working capital baseline, advanced analytics delivered inventory deployment options requiring about $54 million (27%) less inventory to support service objectives.

• Client inventory model in use (top-down analytics): Baseline inventory
• Detailed inventory model (bottom-up analytics): 2% reduction
• NTT DATA model using ToolsGroup SO99+ (bottom-up): 20% reduction
• NTT DATA model using ToolsGroup SO99+ Multi-Echelon Inventory Optimization (MEIO): 27% reduction

Service targets: One size does not fit all

If you are using an enterprise resource planning (ERP) system, an advanced planning system or spreadsheet models, you must review the service targets you’ve established. Companies often have the same service targets across their portfolios. Companies seeking to reduce inventory levels and lower the risk of obsolete inventory should identify service levels at the item or SKU level. Our experience is to tailor service levels by item characteristics.

For example, you may have higher targets for fast-moving items (lower obsolescence risk) and slightly lower targets for slow-moving items or items with unique characteristics. A business can use the right mix of targets to establish the data-driven targets necessary to maintain overall service objectives.

At NTT DATA we recommend a formal segmentation exercise for customers and items to identify the optimal balance between service and inventory. We also leverage purpose-built advanced analytical tools to identify inventory strategies for items with intermittent demand, such as spare parts. Using advanced analytical tools and algorithms has a bottom-line impact in supporting working capital reduction and reduced inventory write-offs.

An inventory deployment strategy entails data-driven deployment

Many planning systems will automatically establish inventory targets at all distribution locations where there’s been demand regardless of the demand velocity, variability or volume. With such planning logic in place, companies will find obsolete and excess inventory throughout their networks. It is a natural consequence of such a planning system.

Businesses seeking to manage inventory intelligently will immediately recognize the limitations of such a planning system in its ability to deploy working capital effectively or optimally. When leveraging optimization tools with solver and scenario capabilities, planners can use data, including lead time, transportation costs and working capital requirements design, to operate and deploy inventory that meets customer objectives while minimizing dead and excess inventory. You can implement multi-echelon inventory optimization (MEIO) models in such tools. For slow-moving or intermittent demand inventory, such as spare parts or long tail items, advanced systems can use probabilistic planning modeling capabilities, based on various distribution models, to optimize service and inventory.

In most industries, inventory planning should occur monthly or more frequently if driven by special events such as new locations, new channel partners, new products, sales events and pre-build requirements. Supply chain consultancies with expertise in inventory optimization are an excellent resource to provide an analytics-based point of view on target inventories at the items/SKU level by location for one-time analysis or an inventory health check.

Consulting firms like NTT DATA can also provide managed analytics services to endow clients with always-on, continuously-refreshed models for intelligent inventory deployment.

Planning based on your specific scenario

In a world where both global and local events, coupled with macro and micro policies, can change on a dime, advanced scenario planning is no longer a choice but a necessity. In most companies, scenario analysis is laborious, error-prone, iterative and requires long turnaround times. Planners performing scenario analysis often find themselves buried when the requests come. They gather and validate data, build data-driven models in Excel and generate results. Leaders then inevitably request additional scenarios in a never-ending cycle, seeking better outcomes or different situations.

Advanced supply chain planning tools and systems provide breakthrough capabilities for scenario planning and analysis. The tools are cloud-deployed, integrated with ERP or data lakes and maintained for rapid analysis and outcomes. With optimization capabilities, superior planning technologies can solve optimal scenarios, given a set of input parameters and conditions. Regardless of the situation — unplanned supplier delays, plant shutdowns, port delays or failures in the logistics network — you can readily assess the impact on inventories. The same is true for downstream events, where a strategic customer may unexpectedly reserve all your on-hand inventory. Quickly assessing options and presenting analytics-based outcomes is the key to responding to management and customer questions.

A lack of rapid scenario planning for inventory will cause suboptimal responses to stakeholders. Fortunately, newer technologies and tool capabilities are available to address inventory challenges in a timely manner.

Pre-builds: Inventory for seasonal or event-driven business

Over the past decade, companies have worked to drive high utilization of production assets and have shuttered underperforming assets. As a result, businesses that have seasonal demand must plan inventory for future consumption. Many businesses take swags or estimates at what to build and put into inventory, how much to build and when to build it. What, how much and when are all data elements that can be modeled and determined using planning techniques to minimize excess and obsolete inventory? Inventory planning coupled with service level optimization offers a means to develop better plans and results in better outcomes. Today’s planning tools have lowered the cost of entry and time to deployment to better prepare for pre-builds. Pre-build planning no longer needs to be a difficult, high-level decision. It can be supported by accessible, cloud-based inventory optimization tools. As a business looks to reduce total inventory, planning pre-builds — using data-driven and advanced analytics — result in better outcomes. Based on data inputs, advanced inventory analytics allow businesses to reduce inventory and working capital requirements. Significant inventory reduction opportunities may exist while continuing to support service levels. We can uncover intelligent reductions using advanced planning analytics.

A data-driven approach using advanced analytics provides the best outcomes. Critical capabilities in building inventory reduction plans include segmentation-based service objectives, the ability to model inventory deployment strategies, scenario-based planning with optimization solvers and advanced pre-build analytics. Strategic inventory reduction requires expertise. Businesses can develop in-house teams or work with inventory experts with access to and experience with specialized inventory optimization models.

At NTT DATA, the global Integrated Demand & Supply Planning team has expertise in inventory optimization and experience with specialized planning technologies across many industries. The team has built a center of excellence for inventory and service optimization to support its clients through an unbiased review of inventory targets and practices, expert analytics and advanced planning technology implementations.