With increased demand uncertainty, it has become very important to continuously monitor distribution performance. We assist our clients in real-time monitoring of inventory planning & replenishment decisions and in updating plans when required, ultimately in decreasing such costs as markdowns and in improving customer service.

We provide services in developing Distribution Requirements Planning (DRP) dashboards to assist DRP managers in taking near real-time DRP decisions. DRP involves three important decisions: where to keep inventory, how much inventory to keep, and when to replenish inventory. With increased demand uncertainty and with increased number of stock keeping units (SKUs), DRP has become very complex and thereby very important to provide customers required products with required quantities on time while maintaining low logistics costs and low inventory holding costs.

To assist our clients,  we first select and prepare appropriate DRP data that can be leveraged for improving DRP decision making. For instance, following DRP data can be used to improve DRP decision making:

  • Daily ERP sales to Customers & Company Owned Stores
  • Daily inventory available with Distribution Centers (DC) & Company Owned Stores
  • Daily Electronic Data Interchange (EDI) sales of Customers & Company Owned Stores
  • RFID data for tracking inventory (if available)
  • Daily remaining inventory of Customers & Company Owned Stores

Next, we integrate and visualize diverse and vast amount of DRP data stored in big data stores using such  visualization techniques as bar charts and geographic maps in near real-time to assist DRP managers in taking  precise inventory planning & replenishment decisions.

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Effective DRP would not only help companies in improving their sales performance through improved customer service but also in improving cost efficiency in this competitive market. Specifically, companies can reduce their inventory holding costs, logistics costs, out of season sales discount costs and stock out costs by effectively managing their DRP process. Overall, effective DRP could help companies in improving their profits, ultimately improving their stock performance.

For instance, Zara is a data-driven fast fashion retailer that builds its business around RFID which it uses for inventory tracking and DRP decision making.  Following Zara, many apparel companies have increased their usage of RFID to manage sales, inventory and distribution of their products. However, the vast amount of data produced using RFID create challenges for data management and for effectively utilizing RFID data. In particular, companies use RFID tags at Stock Keeping Unit (SKU) level and use RFID readers at different levels to track inventory of their products throughout their supply chain. Multiple readings taken by RFID readers at different stages in the distribution and inventory management process often produce redundant information which needs to be stored in big data stores for further analysis. Moreover, semi-structured nature of RFID data makes it difficult to be managed through traditional Relational Data Base Management Systems (RDBMS).

Recent developments through research in big data provide opportunities to effectively manage vast amount and wide variety of DRP data. For instance, recent developments in managing big data through scalable SQL and NoSQL data stores provide opportunities to effectively manage vast amount and variety of DRP data through horizontal scaling and schema-flexible data management. In fact, organizations such as Zara, Walmart and Amazon are already using these new technologies to effectively manage their DRP process.

Moreover, new developments in data visualization can be used to integrate diverse and vast amount of DRP data stored in big data stores and to visualize this integrated data in near real-time for effective DRP. For instance, daily ERP sales to customers, daily inventory available with DC, daily EDI sales of Customers, RFID data and daily remaining inventory of customers can be integrated and can be visualized on geographic maps in near real-time to assist DRP planners in taking  precise inventory replenishment decisions.

In the absence of daily remaining inventory information, companies can use financial year starting inventory of their Customers & Company Owned Stores to calculate daily remaining inventory in the following manner:

Daily Remaining Inventory = Financial Year Starting Inventory + Year-To-Date ERP Sales  – Year-To-Date EDI Sales

Overall, diverse and vast amount of DRP data stored in big data stores can be  blended using data visualization tools such as Tableau so that they can be processed into one package for near real-time data visualization. These near real-time dashboards can assist  DRP planners in real-time monitoring of inventory planning & replenishment decisions and in updating plans when required, ultimately in decreasing such costs as markdowns and in improving customer service.

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