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How to Automate Your Replenishment and Purchase Process?

Retail business owners today face the dual challenge of having an optimum replenishment methodology for the depleting stock while maintaining a seamless purchase process on the supply chain management front.

The questions they frequently come across are…

The primary reason for the enormity of the challenge is the inability of the systems to look at sales, inventory and purchase in a holistic sense, and the lack of the capability to forecast the demand based on documented heuristics and other external factors. In this document we will analyze these questions and propose insightful solutions to address them.
The following are the primary reasons for the situation we are in

Departmentalization of Processes
A lot of retail organizations in the interest of addressing challenges specific to a certain business process through focused human resource skills have ended up in departmentalization or in other words the compartmentalization of processes and relegating them to silos. The software solution vendors have contributed to the issue with module based constrained solutions without an open architecture and lack of integration capabilities.

The lopsided ‘One size Fits all’ Approach
The fact that purchase predominantly is a standard function has been over emphasized by software solutions providers leading to a rigid and ‘one size fits all’ approach. It is true that the overall structure of the purchase process is quite standard across industry sectors, at the same time there are some very critical domain specific requirements that one needs to address. The variations can range from approval workflows, Reorder Policy, output print formats, vendor evaluation processes, goods acceptance mechanisms and many more.

Technology Acumen of the Ecosystem
When we look at the purchase process in the retail ecosystem, we have a variety or stakeholders, some of them are internal to the organization like the Finance and Stores, while others are external like the vendors and contractors. Not all the stakeholders would be ready or can match the system needs of the retail organization, and would require certain tailoring/customizations to be able to adapt to the defined IT processes.
The following are the solution approaches to address the challenges mentioned above

Accurate and Timely Documentation

For any digitization strategy to work, the fundamental step is to have a single source of truth with timely and accurate recording of all transactions. A good, accurate and sizable transaction data becomes the bedrock of any decision support and predicative analytics and demand forecasting system. This seemingly trivial act is often compromised with delayed and wrong entries. Enough control mechanisms have to be enabled to arrest any lacunae in the processes.

Demand Forecasting and Automated Replenishment Requests

By definition demand forecasting is the process of estimating and predicting customer demand of products or services by using informal methods such as educated guesses, and quantitative methods, such as the use of historical sales data and takes into account external factors like market trends, seasonal demands, yearly growth rate, economic conditions, upcoming promotions among others.

For a system-based demand forecasting also called as perpetual inventory system, you need to have clean, well classified master data along with sizable historical sales transaction data.

Based on the Safe Stock principle, setting Reorder Level and Order Quantity that gets reflected in the automated replenishment requests which are fed to the purchase process.

Automating the Purchase Process

Automation of the replenishment requests mentioned in the previous section is the first step towards automating the purchase process. The next in line would be the automated generation of draft purchase orders based on the requisitions and preferred/ supplied vendor history and performance. The draft purchase orders should go through an approval mechanism based on the defined approval chain, which should be customized based on the value and type of item. The algorithm also needs to consider the lead time, opening hours and holidays to avoid outages at the wrong hour. One needs to include the goods in transit as well applying the replenishment purchase function. Having a comprehensive purchase management software that addresses the above areas is imminent. Machine Learning is being applied to study the heuristics of the past performance and suggest predictions. Posibolt is a leading provider of Retail ERP, Inventory and Point of Sale (POS) solutions across retail formats and variety of Distribution Centers whose customers are spread in 10 countries across Africa, Europe, Middle East and Asia.
For more details visit www.posibolt.com or send in your queries to info@posibolt.com

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