Upping the retail game with AI/ML based Automated Replenishment Ordering

Authored by Manu George Mathew | Client Partner - Analytics ; Shrushti Govardhan | Business Analysts @ BigTapp Analytics

Supply Quotient is a solution that focuses on optimizing and automating the supply chain with a view of the retailers network of stores, distribution centers and suppliers combined with broader insights on operational environment. We aim to help organizations in the retail industry optimize their supply chain by providing an easy-to-use platform incorporating all essential processes and insights allowing users to make quicker, smarter, and more efficient data driven decisions to support the ever-changing needs of a business.

Retailers in the modern retail space who do not utilize automated retail replenishment increase their likelihood in facing several challenges such as inaccurate inventory tracking, overstocking, understocking, inefficiency in manual processes, difficulty in forecasting demand, and lack real-time visibility into their inventory levels and sales data. Such problems often translate to wasted resources, lost sales, dissatisfied customers, and a complexity to make informed decisions about restocking, pricing, and promotions. 

Supply Quotient aims to help retailers overcome the challenges while also improving their inventory management, operational efficiency, and customer satisfaction Our platform also incorporates notable features which enable the user to make quick adjustments, and long-term strategic changes that will give the business a competitive advantage.

Prebuilt Dashboards

1.  Flexibility for you to slice, and dice based on a store, products, product categories and other entities.
2.  Get insights on your inventory, sales, supplier performance etc.
3.  10+ key metrics required by retailers in optimizing the supply chain available in the dashboards.

Intra Network Stock Transfers 

1.  Goods that are moved between different locations within a company's supply chain network are considered.
2.  The intra-network includes transfers between warehouses, distribution centres, and retail stores.
3.  Notice optimised inventory levels and reduction in unnecessary transportation costs.

Lead Time Inference

1.   Predict the accurate delivery time of goods based on historical data.
2.   Improve your planning and forecasting processes by predicting lead time.
3.  Ensure that you have the right amount of inventory on hand to meet customer demand. 

Automated Order Generation

1.  Automatically generate orders based on inventory levels, customer demand, and other factors.
2.  Reduce the risk of stockouts by automating the order process.
3.  Ensure that you are always ordering the right amount of inventory to meet demand

Centralised Store for Data

1.  Gain insights and optimise the supply chain by providing a platform for centralised storage of data.
2.  Includes data from various sources, such as inventory levels, supplier information, delivery times, and more.
3.  Make better decisions about your supply chain operations and identify opportunities for improvement. 

* From a study done in May 2018

https://www.retaildive.com/news/out-of-stocks-could-be-costing-retailers-1t/526327/

*From an article published in June, 2022

References:

https://insideretail.asia/2020/02/26/out-of-stocks-the-main-reason-shoppers-abandon-physical-stores/

https://risnews.com/ai-supply-chain-report-21

https://www.genpact.com/insight/the-evolution-of-forecasting-techniques-traditional-versus-machine-learning-methods

https://www.birlasoft.com/articles/how-machine-learning-is-reshaping-the-future-of-demand-forecasting

https://www.retailexpress.com.au/blog/automated-replenishment-how-an-algorithm-can-optimise-your-inventory