From predictive analytics to machine learning to superior customer engagement, AI brings unlimited potential which can facilitate a more efficient supply chain, better inventory management, and accurate demand forecasting.
Over the years, FMCG companies have been trying to digitize their operations and use technology to their advantage. While the companies that have adopted technology effectively are already seeing the huge benefits, the others are trying to catch up to stay competitive.
The growing proliferation of AI promises to be an important game-changer for FMCG since it can bring some fundamental changes in the way the business is run while unlocking huge benefits in terms of efficiency as well as profitability.
From predictive analytics to machine learning to superior customer engagement, AI brings unlimited potential. It can help facilitate a more efficient supply chain, better inventory management, and accurate demand forecasting.
An IBM survey found that retail and brand executives expect intelligent automation capabilities to help reduce operating costs by an average of up to 7 percent.
Therefore, rather than view AI with suspicion, FMCG companies will benefit greatly from enthusiastically embracing the winds of change.
Supply Chain and Logistics
With digitization, FMCG companies have much better visibility into their operations. When this data is used in conjunction with machine learning algorithms, it can help identify operational inefficiencies and mitigate them through network route optimization. AI can also help predict the exact delivery time for a consignment and also help in fraud detection.
FMCG businesses can now analyze their production lines and quickly pinpoint inefficiencies. For companies that deal with perishable goods, an efficient supply chain that uses strategic warehouse locations directly translates into greater profitability, since there are fewer instances of expired goods, etc.
Demand Forecasting and Inventory Management
Apart from internal customer and sales data, AI can help study a variety of data from external sources including demographic data, social media feeds, and other general data on user preferences. It can also find and interpret historical data and apply relevant insights. When we apply machine learning techniques and deep learning frameworks to this vast repertoire of data, we can forecast demand far more accurately.
Machine learning works especially well for demand forecasting due to its ability to process large volumes of data very quickly, compared to traditional methods. Also, unlike most regular data processing methods, AI algorithms can be trained to read even unstructured data in formats such as video, audio, image, or text formats. Therefore, companies have access to an even larger pool of data. Also, AI-based demand forecasting algorithms eliminate the need for human interpretation, which reduces the chance of errors.
The data insights derived from the existing vast pools of data can help create interesting insights based on deep learning. For instance, AI can help forecast demand more accurately for individual products such that they maintain the right amount of stock. Too much stock can languish and expire, thereby resulting in losses. At the same time, unavailability of products can lead to disappointed customers, not to mention lower profits. AI can help ensure that products that are in demand are always available to customers who wish to purchase them.
With real-time sales/consumption data coupled with predictive analytics, FMCG players can achieve optimal inventory management to make sure that customers always have access to their products when they wish to buy them.
Given the limitless potential of AI, FMCG businesses will benefit from studying it to understand how they can apply it effectively to their organizations.
The author is Co-Founder and CEO of Mate Labs, a B2B AI company which was the only organization that was selected to represent India at Google Demo Day Asia 2019.
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