case study: Optimizing inventory replenishment with AI based models

Customer is a popular candy maker that supplies its products to different retail stores across the city of Brisbane. Customer has to work all through the day when the festival arrives, especially during Christmas, New Year and not to mention during some unconventional events like Halloween.  He is intended to increase his production to reach a large number of customers across the city and improve his business scalability.


Many times his supply chain is unable to deliver to the destination, creating uproar across all sites from manufacturing to supply chain and distributors to retailers.  Customer follows a traditional method of inventory management that often put it to risks during transit of products. It also discovered that its inventory management is not updated to keep everyone informed about its product availability and improves his service. One such incident left customer frustrated as it cost him millions of dollars as a loss.

As is with their out-of-dated inventory management, customer packed products in cartons with barcodes not in sync with the numbers saved in the computer. It was done hastily only meet high demand of the products during festive season.  This mismatched inventory caused the retailers to empty shelves too faster and resulted in supply shortage. Not only did their mismatched inventory caused them huge loss, but it also added to damage to their customer loyalty. The inattentive mistake cost them reduced number of orders from different stores. Since all this happened due to supply chain mismanagement and lack of replenishment optimization, customer wanted to adopt a well-advanced system that could help design a better optimized inventory management.


SynergyLabs AI based inventory allocation and replenishment models could be used to improve the situation. We aimed at reducing dependence on the traditional inventory management system and overcome challenges embedded into inventory allocation and replenishment. We wanted to work in compliance with higher demand of each SKU/store.  To reach this target, we built some strategies so that we no longer follow the trends of outdated models of inventory management.

With the model in integration with AI and ML, we overlooked some poor activities such as,

·         extracting data from the system and building inventory challenges

·         determining the necessity of allocation and replenishment buildup based on seasonal demand

·         learning different tactics to know situations for applications

·         manually allocating different work list such as purchase orders and warehouse management

·         manually updating data system with inputs reflecting replenishment necessity yearly basis

·         working with different departments to determine different requirements of product development

How we set our plan

We emphasized on building an AI based model integrated with Mixed Integer Linear Programming (MILP) capability. To bring this model into action, we combined different algorithms and science into one solution. Based on neural network system, the model is capable of assessing different parameters of inventory management such as product demand, current inventory requirements, challenges, shelf stocking, and pricing determination during item ordering, shipping and warehousing. Extracting data from these parameters and split those into meaningful insights would help customer maximize profit using MILP optimization model.

How our system works

Our model creates alerts to manage the inventory. It is capable of automating tasks of bulk purchase orders and inventory and also seasonal replenishment. Model can offer profitable suggestion regarding inventory quantities for stores. It saves time for employees engaged in complex activities, thus improving productivity and helps grow business.


SynergyLabs AI-based MILP model enables replenishment optimization with enhanced accuracy. Thus, it helps customers meet demand with the productive manner and earn a good profit.