Managing the Supply Chain with a Retail Data Warehouse

Having an effective data warehouse is essential for successful inventory and supply chain management. It captures the necessary data, giving retailers the information they need to optimize their strategies and maximize their advantages.

Failing to properly manage the supply chain can have catastrophic results - success can turn to failure, profits to losses, and market share can quickly be lost to competitors. Customers won't hesitate to go to the competition if their desired products are unavailable due to inadequate supply strategies. Furthermore, having products on shelves that don't sell is also a waste of resources. A successful supply chain management strategy relies on sophisticated data analysis to anticipate demand, and predict any changes that could affect it.

Capturing all relevant data in a single, central location (a data warehouse) is a key to gaining competitive advantage. However, achieving this requires effort and resources to ensure that the data is of high quality. Many retailers have yet to invest in capturing all the necessary data for a customer-centric data warehouse.

Those retailers who are willing to invest the effort can take advantage of the opportunity created. When considering what main areas and sub-areas the data warehouse should cover, the first question is what analytic subject areas should be included?

It is our opinion that the retailer should concentrate on five key areas:

Managing an inventory of products or items.

Inventory management divides the data about inventory into separate parts according to several metrics, keeping track of the supply chain from purchase order to store. This allows for analysis and adjustment of inventory to meet customer needs.

The efficiency of the supply chain can be gauged by measuring metrics such as stock turnover for the best and worst performing products, total investment in inventory over time, amount of inventory in each stage of the supply chain (order, shipping, en route to store, on shelves) at a given time, products that are getting returned often (and why), and in-stock position of product in each store.

Depending on the type of retailer, type of goods sold and their brand image, inventory management can be seen in a variety of ways. For instance, a grocery store that is mid-priced and sells items like bread and milk may only offer a few SKUs and use full replenishment. This means that they only need to track the rate of sale and quantity on hand in order to trigger automated re-ordering systems.

An understanding of the competitor's pricing is a very important data point for effective inventory management, as it is essential to price staple items competitively.

In comparison to the previous example, a fashion retailer selling seasonal apparel items has a more complex set of decisions. Although the profit per item may be higher, analysis of SKUs across a particular style or class is necessary in deciding whether to reduce the price or advertise the product. Due to the seasonal period being relatively short, it may be necessary to purchase a season's worth of merchandise in just a few buys to prevent having to offer large discounts at season's end.

Managing vendors

Vendor management examines the retailer's connection with each supplier and quantitatively evaluates each vendor's value to the retailer. This evaluation can be informed by analyzing the accuracy of product sales and return predictions, the vendor's punctuality and flexibility, the frequency of product returns, the retailer's payment speed, and shared sales and stock data. Increasing the retailer's use of this data can further improve their relationship with vendors.

The value of having a vendor business intelligence (BI) extranet has been discussed in previous articles. This type of BI-based application allows vendors to gain an understanding of the retailer's perspective of their performance, helping to reduce the communication gap that may form between supplier and retailer. A vendor scorecard with high-level metrics, as well as the ability to dig deeper into order and shipment level detail to search for any irregularities, can be beneficial to the collaboration between the two.

Analysis of Product Costs

Product cost analysis offers greater precision in understanding costs by examining markups, discounts, and other costs such as shipping, storage, and stocking. Additionally, it can provide answers to key business questions, such as: How can product costs be lowered while still improving both product and customer profitability? What effect do changes in sale prices and product mixes have on profit and revenue? Cost of goods sold is often the most indistinct metric in the retail industry, as it is universally understood from an accounting perspective, yet many retailers interpret it differently. To combat this, creating a unified database of the components of cost and using it as the foundation for calculating derived costs can minimize variations between databases.

Merchandise and Collection Organization

Merchandise and assortment planning assesses the efficacy of assortments, stores, and departments. It can optimize store clusters and assortment plans based on real results by examining data such as the open-to-buy position in comparison to the previous year, profiles of store clusters, and the performance of clusters in different locations.

Merchandise and assortment planning, while not typically seen as part of the supply chain, still trigger actions in the supply chain, such as orders for goods production and delivery to distribution centers and stores. These two processes are increasingly important in the retail industry and rely heavily on data and analytical tools for effective functioning. While some retailers may choose to purchase specialized tools for these functions, the inputs (e.g. SKU sales history) and outputs (assortments, financial targets) should be stored in a data warehouse and analyzed over time. Distribution center operations is also important, as it allows retailers to monitor distribution center performance, analyze employee costs, shipments and receipts, shrinkage, and weeks of supply, and investigate ways to reduce costs through retraining or reassignment, alternative shipping methods, and changes in handling methods.

A retailer should source the following types of data into their data warehouse to track the data subject areas: Merchandise and Assortment Plans (objectives related to inventory and sales), Financial Plans and Targets, Open-to-Buy Data, Assortment Plans by Store and Vendor-Centric Data (Contracts, Deals, Purchase Orders, Incentives for Bulk Purchases and Baseline Inventory Costs). This data should be compared with actual results to create better strategies for minimizing direct costs of re-supply.

Segment-specific product information can be used to optimize shelf space allocation for fashion, food, and other product segments in each store, according to their respective sales volumes. Physical and calculated inventories, inventory receipts and adjustments, supplier shipments, and intra-enterprise item movements are essential to maintaining optimal inventory levels. Additionally, data on transfers between distribution centers and stores can help to detect excessive handling issues.

The Whole Picture

By leveraging their retail data warehouses, many retailers can gain a comprehensive understanding of their supply chain by combining detailed supply chain data and analysis with sales and customer data. This will enable them to gain a complete picture of both demand and supply side factors in their retail business.