Different inventory optimization solutions have been developed. Some are based on rules of thumb, others use stock ratio policies, while still others leverage sophisticated calculations to determine inventory. Despite this, there's evidence all is not well; U.S. Census bureau trade inventory statistics reveal a 10 percent increase in the inventory to sales ratios from 1.25 in 2010 to 1.39 in November 2019.
Reasons for this include changes in consumer behavior, the swing to omni-channel retailing and increased supply chain complexity that have outstripped the capabilities of old-school inventory optimization solutions. What's needed is advanced inventory optimization based on prescriptive analytics that's able to truly optimize inventory, reduce inventory costs and ensure stock is available when and where needed.
What Is Inventory Optimization
On the SKU level, inventory optimization helps determine the best places to hold inventory. SKU rationalization provides a way for enterprises to figure out which products to carry more of and which to scale back on. In addition to cutting excess inventory, SKU rationalization improves product availability.
A key factor is the analysis of inventory drivers, determining stock holding policies and understanding what drives stock movement. Cycle stock is the portion used to directly satisfy demand and is driven by demand and replenishment cycles. Safety stock serves as a buffer inventory to cater for unexpected supply and demand variability.
Conventionally, the key to managing safety stock and reducing inventory levels is attempting to understand and reduce demand variability. Other inventory drivers to consider include raw materials constraints, lead times and seasonality. Inventory KPIs (key performance indicators) measure how an existing supply chain is performing and identify areas where there's need to improve.
Emerging Consumer and Technology Trends
Consumer behavior is changing. According to Statistica, E-commerce will account for 22 percent of all global retail sales. While this figure includes all sales, the percentage of online general merchandise sales was higher and, in some instances, approaching the volume of brick-and-mortar store sales. These figures show that retailers can't afford to stand back from omni-channel retailing.
In other areas, online consumer packaged goods sales are growing at a rapid rate as the number of grocery and supermarket chains selling online increases. In clothing, consumers want to find products online, try them on in the store and order the color, size or model they want directly. Some stores are experimenting with mobile apps that allow customers to select and scan items and pay without having to go through the till.
These changes illustrate the need for real-time control over inventory and for systems in place to move inventory to where it's needed.
Why Traditional Inventory Optimization Solutions Fail to Produce Results
Traditional inventory control practices can't cope with rapid change, nor are they suitable for complex distribution strategies where stock is kept in multiple locations, let alone omni-channel retailing. Rule of thumb and stock holds based on arbitrary rules are particularly vulnerable, and, in order to avoid stockout, rely on excessively high inventory levels. Although inventory control software fares somewhat better, the best that many linear solutions can achieve is stock recommendations based on historic usage. Few, if any, have the intelligence to use demand to optimize inventory.
This is probably the reason why most users aren't making progress with inventory management. In fact, 93 percent of users depend on Excel spreadsheets for inventory control, and worryingly, 68 percent feel they can't function without spreadsheets.
Adapting Inventory Management to Meet Changing Supply Chain Requirements
As businesses adapt to the new realities, they need to adapt to new ways to efficiently manage and distribute inventory.
Omni-channel retailing means businesses have to gear up to keep retail stores adequately stocked while managing the distribution of numerous small orders all over the country. This may mean various smaller distribution centers strategically located for easy last-mile delivery. Safety stock strategies need to be reviewed and multi-echelon inventory management techniques applied that consider the entire supply network. These tools should have the capability to determine where stock is concentrated in the supply chain, whether it is as raw materials or as stock held in manufacturers' warehouses, regional warehouses or in last-mile warehouses.
Advanced and accurate sales forecasting techniques are needed to improve forward sales planning so supply chain managers can gear-up inventory to meet demand. Sophisticated sales support software able to determine regional preferences, seasonal demand and the response to marketing campaigns is necessary. These are capabilities conventional EPR, MRP and supply chain software just doesn't have; what's required is the next big thing in inventory optimization solutions.
Using Advanced Analytics to Drive Inventory Optimization Solutions
There's a good reason why conventional inventory control software and ERP doesn't have the abilities referred to above, and that's because its core function is to manage and control millions of small but critical transactions. Without that capability, there would be no control.
The millions of pieces of data generated by these transactions provide an enormous source of relevant information. This, together with other data, can be analyzed using advanced analytics software to determine answers to key business questions. As the name suggests, prescriptive analytics is a forward-looking solution allowing managers to determine answers to key questions, such as how to optimize stockholding or minimize logistics costs.
Based on linear and non-linear programming techniques, prescriptive analytics allows you to model your business and evaluate different scenarios to determine which, of several options, achieves the best result in terms of predetermined criteria. A prescriptive analytics model can accurately represent part or all of a business unit. Using structured and unstructured data derived from the business, prescriptive analytics applications can identify optimal inventory and other strategies in your organization.
Benefits of Next-Generation Supply Chain Inventory Optimization Solutions
Next-generation prescriptive analytics offers numerous possibilities for improving and optimizing inventory. Unlike some rules-based techniques, prescriptive analytics solutions take business constraints into account, are able to evaluate trade-offs and present directly actionable solutions. Using fifth-generation constraint-based programming, solutions such as River Logic's Enterprise Optimizer provide real answers to the complex inventory problems facing numerous industries.
In one instance, a globally-known snack food giant couldn't meet demand for its most profitable product portfolio. This was despite having a full ERP solution, a point optimization solution, a well-known network design tool and numerous spreadsheet applications. Using Enterprise Optimizer, the company identified that previous assumptions about transport costs outweighing the advantage of producing these products at one or two plants instead of at each plant, were wrong. In the first week, this company achieved $332,000 cost savings and identified further achievable savings.
Looking ahead, companies will need to start viewing inventory trade-offs holistically, considering the end-to-end value chain. With ever-changing consumer behaviors, it's only going to become harder for supply chains to stay profitable and competitive. Silo'd approaches to inventory management simply won't cut it in the coming years!
