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Stressed over Excess Inventory?

Originally published May 20, 2008

If you are like most retailers, you may be wondering about four questions:

  1. How do I increase my fill rates without increasing inventory costs?

  2. Is it possible to decrease inventory levels by 10%, for example, without impacting service levels?

  3. How can I leverage all the product and customer data in our CRM, ERP and MRP systems for service?

  4. How can I reduce COGS (bottom-line) and improve service revenue (top-line)?

A good answer to these question lies within software for inventory optimization.

Product inventory optimization software is designed to address the need for inventory forecasting and product management needs for retailers and manufacturers. The utilization of inventory forecasting software provides significant financial benefits to the organization. Good inventory forecasting software should provide the user with the ability to have data integration that consolidates different data sources and business intelligence, thereby providing basic reporting for the product managers as well as a sophisticated forecasting tool that can handle both large data sets and sparsely populated data collections. Using this system, analysts can utilize the extensive forecasting capability to help them better understand and predict the trends and activity associated with their products for both sales and replenishment. With their findings, product managers can better manage and optimize their current and projected inventory, providing capital savings for the CFO as well as increasing customer satisfaction.

A product inventory optimization solution provides organizations with the ability to calculate periodic review inventory replenishment policies for distribution and/or retailer systems, thus enabling product managers to maintain adequate stock levels and improve customer satisfaction. The solution provides essential aid to decision making by helping organizations answer three fundamental questions of inventory management:

  1. What is the optimal inventory range by SKU to achieve a specific service level based on current inventory policies?

  2. Which items have crossed policy thresholds and therefore should be re-ordered to restock inventory?

  3. How much should be ordered?

The solution offers two approaches to define monthly replenishment requirements:

The first approach can be leveraged if there is a constraint on regional stock coverage. When this is true, the engine uses historical demand data, re-order lead time, beginning inventory at the distribution center (DC), beginning inventory at central warehouse, optimal production quantity, required stock coverage in the region, and required stock coverage at the central warehouse to define monthly replenishment requirement by region by SKU.

When there are no constraints on regional stock coverage, the solution uses historical demand data; re-order lead time; associated inventory cost data, which includes the cost of replenishment, the cost of holding inventory and the optional cost of backordering (stock-outs); and target service levels to calculate optimal inventory replenishment policies. For example, the sophisticated policy calculation algorithm in SAS SPO accounts for variability in demand data and supply, thus helping organizations compare and choose from various scenarios.

The policies produced by a product inventory optimization solution should perform better than the standard EOQ (economic order quantity) policies, which do not account for variation in customer demand and replenishment order lead times. This approach helps manufacturers and retailers develop better replenishment strategies for repeatedly ordered items.

The second approach helps manufacturers and retailers develop better replenishment strategies for repeatedly ordered items. The solution uses historical demand data, replenishment lead time, inventory cost and target service levels to quickly calculate accurate replenishment policies that help manufacturers and retailers determine when each item should be ordered and in what quantity. An underlying sophisticated policy calculation algorithm helps manufacturers and retailers calculate optimal policies such that they can achieve target customer service levels while minimizing ordering and inventory holding costs.

An effective implementation of a product inventory optimization solution consists of not only the installation of the software components, but also the consulting and training services that go with it. This article discusses all three of them.

What the Different Software Components Should Do

Forecast Server Software Features: Forecast server software has an easy-to-use GUI. It allows the choice of forecast automation level. Users can choose the level of automation for the forecasting process: re-diagnose and identify candidate models, re-estimate existing model parameters or generate forecasts using existing models and parameters. No programming is required. Users just point-and-click their way to powerful forecasting capabilities.

Product Inventory Optimization Software Engine Features: The engine should help the organization to develop better replenishment strategies for repeatedly ordered items. The solution should use historical demand data and replenishment lead time to quickly calculate accurate replenishment policies that help the product manager to determine when each item should be ordered and in what quantity. The underlying policy calculation algorithm should also be used to calculate optimal policies to achieve target stock coverage. These accurate inventory policies will help determine how much of each item should be ordered and when they should be ordered for each region by SKU.

Business Intelligence Software Features: Business intelligence software provides the following benefits to the organization: comprehensive and centralized data, self-service reporting, ad hoc query and analysis, slice and drill down reporting, integrated analytics and integration with Microsoft Office products. Most BI products integrate with Microsoft Office. This integration enables the product manager to do self-reporting.

Data Integration Software Features: Data integration software is a powerful, configurable and comprehensive software that empowers the IT department to access virtually all data sources; extract, cleanse, transform, conform, aggregate, load and manage product data; support data warehousing, migration, synchronization and federation initiatives; support both batch-oriented and real-time master data management (MDM) solutions; and create real-time data integration services in support of service-oriented architectures. There is no wait when you have a good data integration environment.

Data Mining Software Features: Software for data mining supports the entire data mining process with a broad set of tools. It allows the customer statistical modeling group, their business managers and their IT department to interface with other groups and create accurate business-driven data mining models in a seamless process, enabling the entire team to collaborate more efficiently.

This software should be easy to use and include an intuitive user interface that incorporates common design principles established for data mining software. It should also have additional navigation tools for moving easily around the workspace. The GUI can be tailored for all analysts' needs via flexible, interactive property sheets, code editors and display settings. It will enhance accuracy of predictions and easily surface reliable business information. It will also create better performing models with new innovative algorithms that enhance the stability and accuracy of predictions, which can be verified easily by visual model assessment and validation metrics.

The software also eases the model deployment and scoring process. Scoring – the process of applying a model to new data – is the end result of many data mining endeavors. The software automates the tedious scoring process and supplies complete scoring code for all stages of model development in SAS, C, Java and PMML. The scoring code can be deployed in a variety of real-time or batch environments or directly in relational databases. The outcome is faster implementation of data mining results.

One desirable characteristic of data mining is that it provides scalable processing for large installations with millions of SKUs. It has an architecture that scales from single-user to large enterprise solutions. It provides server-based processing and storage. Data mining should be done by a software component that is fully prepared to perform with grid computing, parallel processing and multi-threaded predictive algorithms.

What Consulting and Training Services Are Needed?

The implementation services associated with a product inventory optimization initiative can be broken down into four areas of consulting services needed. These four areas include:

  1. Installation and configuration.

  2. Analytic and forecast consulting.

  3. SPO implementation consulting.

  4. Training services.

The objective of the consulting services is to provide the initial setup and direction to help the customer take ownership and continue the ongoing expansion of the system capabilities. This is because these systems are extremely interactive and are developed in an iterative way to adjust to extreme competitive situations. The consulting engagement requires assigned and dedicated resources from the customer to be active participants and to receive the appropriate knowledge transfer and system training along with collaboration for the analytic needs of the organization. This includes working with internal resources to obtain access to the existing data warehouse environment and understand the data elements that are available for analysis.

It may sound like a lot, but it is worth many dollars in savings!

  • Al CordobaAl Cordoba
    Al Cordoba is the Vice President of global sales for Qualex Consulting Services, a 15-year SAS Integrator. Previous to Qualex, Al worked for 13 years in different management positions for SAS Institute. Al began as a regional sales support manager in Washington, DC. He moved to Brazil to start the SAS subsidiary as General Manager. Promoted to SAS Vice President, he started six additional SAS subsidiaries in Latin America. Al won several awards for sales excellence and grew the business by over 300%.
     
    Previous to SAS, he acquired over 7 years of health insurance consulting experience with Blue Cross and Blue Shield Association in Washington, DC.  He also worked for Chevy Chase Bank in their IT Department, Lockheed International in the Engineering & Science division, and Steptoe and Johnson, the third largest law firm in the Nation's capital. Al has Master's degrees in computer systems management from the University of Maryland and quantitative analysis from SUNY/Syracuse University.

     

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