Retail supply chain structures are complex. Outliers at an SKU level are a norm rather than an exception. Supply chain teams must ensure the availability of the right number of products to the customers based on various parameters such as product demand, promotions of the retailer, promotions by the competition, changes in customer tastes and preferences and others. Anomaly detection for supply chain managers can detect the outliers and assist in effectively managing the waste and availability metrics in a simpler, faster and better way.
The primary objective of supply chain is to minimise the cost of ensuring product availability. The focus is to decrease waste in the entire supply chain. Most of the time, supply chain is in firefighting mode. By pulling all the levers in their hand, the supply chain teams ensure that the product is available when the customer needs it.
Firefighting happens for a wide variety of reasons. Managing promotions is one of the major causes of strain in supply chain. Supply chain team works on every promotion with the uplift guidelines provided by the commercial teams and in certain cases they also get the inputs from marketing teams. Despite the improvements in technology, forecasting promotional sales remains a nightmare.
When there is a promotion, supply chain teams are in enormous stress to coordinate manufacturing and moving the products. Knowing the promotional uplift to guarantee product availability is key to the success of a promotion. Since it is difficult to predict, having an AI based anomaly detection engine especially for supply chain managers, can tell you the success and failure of the promotions in real time. Using this information, the supply chain team can prioritise, adjust the flow of products within the system and ensure that the right products are available at the right time during the promotions cycle. One can use the insights, beef up the manufacturing and increase sales during a stellar promotional performance. Similarly, during an abysmal promotional performance, the production could be cut to the extent possible and waste could be minimised.
Also, with the advent of ecommerce, demand for large number of unique items with relatively small quantities sold is on the rise. These long tail items suddenly pick up sales and quickly stocks out. Variances like these are naturally hard to predict, makes inventory planning notoriously difficult. Most retailers do not capitalise on this demand because (given the small volumes) they are not aware of it. And it becomes impossible to hold the right amount of inventory in hand to handle this sporadic demand. Anomaly detection for supply chain managers will alert the right person, who can then flex their capabilities and satisfy these sudden spikes in demand without adding unnecessary inventory to the system.
Supply chain provides guidelines for distribution to ensure that products seamlessly move through the warehouses and is stocked for the right amount of time. The guidelines are typically based on historical data and understanding from the commercial teams. AI based anomaly detection can help the supply chain in flexing the guidelines to distribution and optimise the flow through the supply chain.
Anomaly detection for supply chain managers will help them gain insights into various factors while it is happening. The real time insights thus produced will equip the managers with all the necessary information. One can flex the supply chain by not moving products that are having a considerable dip in the sales, use those resources and increase availability of the items that are high in demand. In summary, using the power of AI, supply chain managers can manage by exceptions, increase product availability, decrease waste and subsequently generate higher profits.
To know more about how anomaly detection can help supply chain managers in retail.
Kumar is a principal consultant at CrunchMetrics. He is an alumnus of IIT- Madras and IIM- Calcutta. As an entrepreneur, he has co-founded an analytics company and then an omni channel retail company. He has worked in advisory roles for Fortune 500 companies such as Deloitte and Tesco in various multinational locations. He has also worked in technology roles for MNCs such as Cognizant and Virtusa. He is a Good Reads author with the pen name Khun S. Kumar and has published seven novellas in Amazon.