Machine Learning for the FMCG Industry

A detailed observation on the potential benefits of using modern Machine Learning technologies in the FMCG vertical

Executive Summary

The unique characteristics of the FMCG industry make it an ideal candidate for Machine Learning and associated technologies. These characteristics include very large volumes of transactions and data, and a large number of data sources that influence projections. These characteristics mean that traditional analytics technologies struggles with the volume and complexity of the data which is exactly where Machine Learning is best suited.Most horizontals of the industry are candidates for optimisation including improving the effectiveness of marketing campaigns, increasing the performance of the sales team, optimising the supply chain and streamlining manufacturing. The FMCG industry has been relatively slow to adopt these cutting edge technologies which gives an early entrant an opportunity to strongly outperform its competitors.

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Introduction

FMCG Introduction

FMCG (Fast Moving Consumer Goods) refers to organisations that sell products in large quantities. These products are usually inexpensive, the volumes sold are large and may often have a short shelf life. Profits on individual items is very small and large volumes are required to have a viable business.These characteristics offer many challenges and also many opportunities.

This paper investigates these challenges and opportunities in detail and focuses on the use of Machine Learning technologies to optimise processes to increase profits for FMCG companies.

Machine Learning Introduction

The following list should serve as a refresher when thinking about Machine Learning vs traditional analytics and business intelligence:1. Unstructured Data

Modern Big Data technologies and advanced machine learning algorithms can analyse data in any format, such as images, videos, text, emails, social media messages, server, logs, etc. Whereas traditional analytics can only analyse structured data in databases.

2. Combine Data

Modern technologies allows us to quickly merge datasets together and form rich data collections that can merge internal company data with external public data sets. This allows the data scientist to enrich sales and marketing data for instance with government social demographic statistics. Traditional analytics is usually performed on data silos and when data sets are combined this is usually done at a huge expense by building data warehouses which still only usually have internal company data.

3. Future vs Past

Machine Learning is often called predictive analytics as one of its major use cases is to predict the future. Advanced machine learning algorithms will ingest all your data and find patterns that can then be used to make accurate inferences about the future. These predictions are qualified with an accuracy metric so management can make intelligent decisions based on these predictions. Traditional analytics rarely tries to infer future events and only deal with explaining and visualising past events.

4. Answers vs Reports

Using the predictive power of machine learning, management can start asking smart questions from their data. Questions such as:

  • What is the optimal marketing campaign to increase market awareness for product X
  • How many of product Y should we product to reduce oversupply next winter season
  • What sales rep should I use to manage our new customer to maximise potential profit

This is very different from existing business intelligence suites which usually deliver dry reports or charts which are very often misinterpreted.

5. Speed of delivery

Traditional analytics / business intelligence implementations can take years to complete. They are intrusively integrated into an organisations IT and as such move very slowly. Modern machine learning technologies allow for management to get answers from their data very quickly and efficiently. A simple question can be answered in weeks not years.

6. Machine analysis vs human interpretation

Machine Learning uses advanced computer algorithms to analyse unlimited quantities of data. This analysis is done totally impartially and free from any biases that are common in many manual analysis. The outputs from these algorithms are also very easy to interpret and leave very little room for misrepresentation making them very objective and quantifiable tools for decision making.

Machine Learning in FMCG

The FMCG (Fast Moving Consumer Goods) industry is an ideal target for Predictive Analytics and Machine Learning. There are several unique attributes of the industry that makes this so; these are:

  • The massive volumes involved
  • Access to good quality sales data
  • Short shelf life
  • Current forecasting techniques are relatively inaccurate
  • Current marketing strategies are less than optimal
  • Current manufacturing practices are less than ideal
  • Current supply chain strategies are less than optimal
  • Consumer numbers are very large

We now explore each of these attributes in detail.

1. Large volumes / access to good quality sales data

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The number of sale transactions available to modern FMCG organisations is huge. This data can usually be purchased from retailers and is of very high quality. This sales data forms the backbone for any predictive model as increasing sales should always be the primary objective of any predictive project.Most large FMCG companies also have very good systems in place that record data at every stage of a product’s lifecycle. From manufacturing to delivery to marketing and sales. These systems usually have very high quality data and require very little data cleansing to be valuable.

Given the enormous volumes of transactions generated by FMCG this data is usually very hard to analyse manually as it overwhelms most brave analysts. Currently many organisations have not gone beyond basic analysis at a very high aggregated level, for instance: sales for the week, sales for a store, etc. And where they do drill down deeper into the data, this is usually done by senior analysts with years of experience (and biases) at a huge cost.

2. Short shelf life

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FMCG products usually have a short shelf life meaning that the costs of oversupply and over manufacture can be significant. Given also, the large volumes of products any optimisation to the oversupply (or undersupply) problem can result in very large ROI. The over/under supply problem is again a perfect candidate for machine learning technologies.

3. Sales and marketing

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If your goal is to increase sales then having accurate sales forecasting is critical. With an accurate forecasting model you can create simulations that allow managers to do quality “what if” analysis. Currently sales forecasting is inaccurate and senior management lack the confidence in these numbers. Having the ability to merge many data sources (sales, marketing, digital, demographics, weather, etc.) greatly improves the quality of sales forecasts when compared to traditional predictions which are traditionally done on isolated and aggregated sales figures.Once the sales data is merged with the marketing data we can start making very accurate marketing predictions also. Questions like:

  • Which product should we promote this month
  • What type of campaign will be most profitable for this product
  • What consumer segment should we target
  • How can we get value from our social media data and use current consumer sentiment to create timely marketing campaigns

4. Manufacturing and supply chain

Most large FMCG have wonderful ERP systems that hold a wealth of hidden value in their data. This data can be used to create models that can answer several critical questions.

  • How can we guarantee on time delivery
  • How can we shorten the time to manufacture a product
  • How can we increase the yield for a product
  • How can we minimise product returns / complaints

PredictBench

PredictBench is a product that enables you to get the most value from your data. It is quick and efficient and does not need to involve your IT department. You do not have to understand reporting, statistics or any form of data analysis techniques. You just ask us what questions you want answered and using the latest Machine Learning technologies; we give you those answers.If you are interested in learning more please feel free tocontact me.

PicNet

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Founded in 2002, PicNet has been a leading provider of IT services and solutions to Australian businesses.PicNet helps organisations use technology to increase productivity, reduce costs, minimise risks and grow strategically.

Apr, 30, 2015

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