Low fill rates, stockout and inventory pile ups are a massive problem across the globe for most consumer product companies. Studies show that companies are losing up to 12% of their annual revenues because of inventory issues. Stockout and wastage can be reduced by having agile supply chains that can react quickly to any changes on demand or supply side.
The key to creating agile supply chains is to have accurate demand predictions that are refreshed frequently, incorporate impact of business / external factors such as pricing changes, and constantly align these predictions with the supply side constraints to provide optimal recommendations on inventory levels of the raw materials that should be maintained from a manufacturing standpoint to keep up with the ever-changing consumer demand.
Business Vertical:
Food & Beverages
Region:
India
Size of the firm:
Medium
Main Challenge:
Reduce stockout and improve product availability
Challenge
The client is a manufacturer of 70+ specialty beverages offered through several packaging options like single serving, tetra packs, pouches, etc. They sell through a wide network of 200+ channels categorized into direct website, ecommerce marketplaces, modern and general trade. Just like many of their contemporaries, the client is perpetually facing the issue of stockout which in turn translates to millions of dollars of lost revenues. Their entire process of demand planning, raw material procurement and replenishment of finished products is manual, biased on gut feel and completely based on spreadsheets. Their demand planning process uses an approach based on average daily orders and with static min-max days norms. As a result, they are not able to align the manufacturing side of things seamlessly to the expected demand.
In addition to this, they have little visibility into the performance of their raw material suppliers as they deal with a wide variety of raw materials and multiple suppliers for each one of them. The client tends to go with their default suppliers for simplicity rather than the ones that give best lead times / fill rates / prices. Their replenishment plans are also based on backward looking daily averages which are highly influenced by stockout. Naturally, considering past stockout lead to lower average sales leading to less-than-optimal replenishment quantities, which can lead to further stockout. As a result, they are not able to quickly adapt to their ever-changing consumer demand preferences, align the right suppliers that can meet their requirement and replenish the stores with the right product quantities at the right time.
Solutions Proposed
- Automate raw material procurement plans powered by accurate SKU + Geography + Distributor + Retailer level demand predictions
- Continuously score suppliers based on their performance metrics
- Optimize replenishment of finished goods to individual distributors across different channels based on from future looking demand than backward looking averages
- Surface early warning on stockout for proactive procurement / replenishment
What Qentelli Did
After a careful assessment of the client’s status-quo and the target state of supply chain management, we have evaluated and selected a product that is a best fit for the client and also gives us the ability to fine tune the algorithm and continuously monitor the feedback. The deployed solution is intended to bring all interrelated functions tied to sales, demand, pricing, inventory, and purchase orders all on one platform to create a system of intelligence that can enable seamless flow of information across the entire supply chain.
Picking an AI/ML solution is an ergonomic decision considering the demands of the project and ease of modifying the algorithms to suit the changing needs of the business. Here are some of the unique aspects of our approach:
Omnichannel demand sensing - The state-of-the art AI/ML engine deployed in client’s system provides accurate bottom-up predictions for each SKU + Warehouse + Distributor + Retailer combination. This considers the impact of several demand drivers such as factors such pricing, promotions, and holidays. Our demand sensing engine also tries to minimize the impact of historical stockout and anomalies which leads to more robust predictions of future demand.
Automated raw material procurement - This functionality translates SKU + Geography + Distributor + Retailer level demand predictions into raw materials required using Bill of Materials from a manufacturing standpoint. This in turn gets combined with existing inventory and purchase order data to dynamically calculate safety stock based on historical errors and determine the right quantities to order given the future demand and current inventory runway.
Best buy supplier scoring - The supplier scoring mechanism that is built into the AI engine continuously tracks supplier performance based on lead times, fill rates and pricing and recommends the best supplier for each SKU for every planning cycle.
Optimized replenishment with early warnings on stockout - The deployed solution is built to be able to scale understanding the impact of seasonality, holidays, neighborhood effects for each SKU in each geography/ channel, replenishment quantities and safety stocks are determined using future looking daily demand predictions than using traditional min-max based systems. Additionally, early warnings on stockout help in proactively minimizing loss of sales.
Outcomes
20%
Less over/understocking
5% - 10%
Improvement in monthly fill rates
With end-to-end automation powered by advanced AI algorithms, our solution has been able to reduce the client’s understocking and overstocking occurrences by around 20% which translates to 5% - 10% improvement in fill rates per month. Entire planning process has been cut down from several days to just a few hours which is a major productivity boost to the operations planning team.
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