One of the largest sectors of the Indian economy, the Fast Moving Consumer Goods (FMCG) industry is expected to reach $104 billion by 2020, growing at a CAGR of 20.6%. The industry is characterized by huge data volumes and a big number of transactions. On top of that, the inflow of data comes from multiple sources which influence the projections as well. In this scenario, conventional processes of data analytics become ineffective. This makes the FMCG the ideal playground for Machine Learning.

Machine Learning can help the FMCG industry to streamline its manufacturing process, hence optimizing the supply chain. Using predictive analysis, FMCG organizations can enhance the potential of their sales force and augment their marketing campaigns. But before that, you need to understand why Machine learning is superior to the traditional techniques of business intelligence gathering you have been using till now.

Machine Learning vs. Traditional Data Analysis

• Data Formats: While the usual business intelligence techniques require data to be properly structured, machine learning can analyze myriad data like text, logged information, images, videos, emails, etc.
• Human Error: Since machine learning is based on impartial algorithms, it is free from human biases and prejudices and leads to better decision-making.
• Future-focused: Traditional analytics only explains the past, while machine learning does predictive analysis – inferring what will happen in the future by studying data and detecting patterns.
• Merged Data: In traditional analytics, the focus in on the internal data. Machine learning technologies help create versatile datasets by allowing the merging of internal data with external public data.
• Answering Questions: While traditional BI methods provide visualizations which are open to interpretation, machine learning answers concrete questions like how many units of a particular product should be manufactured.
• Swiftness: Unlike traditional analytical techniques, machine learning is able to ingest and process data very swiftly and efficiently, reducing the time taken for one problem to be answered from years to weeks.

The Case for FMCG Industry

Now, let’s understand what makes the FMCG industry the ideal candidate to benefit from machine learning techniques. To begin with, businesses in this sector deal with a large number of sales transactions on a daily basis. Processing all this data manually is an uphill task and most analysts don’t dare to think beyond basics like sales in a week, or the total sales at a store, etc. Machine learning can easily handle these datasets to extract incredible insights from them. It can also extract the wealth of data hidden in the ERP systems of these organizations.

Further, given the short shelf-life of FMCG products, the cost of over or undersupply to the manufacturer is quite dear. Machine learning can solve this issue without a hiccup. An accurate forecasting model is also necessary for the sales and marketing team to prepare for their campaigns and better understand parameters like which products or target consumer groups need more focus.

So, it’s about time the FMCG industry starts leveraging modern technologies like machine learning to deliver superior products and customer experience.

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