FOOD FOR THOUGHT | data science in the food industry
Are you struggling with data and how to get the most value out of it as a food manufacturer? In recent years Data Science has emerged as a powerful tool in a large number of industries such as retail, finance, and supply chain. However, little has been done with it in the food industry.
This blog explores how Data Science can help your company to compose the optimal product portfolio and go beyond the obvious. In particular, we explore how Data Science can be used to predict the product's potential performance, uncover hidden relations between products, and identify customer needs and wants.
WHAT IS DATA SCIENCE in the food industry?
Starting out with Data Science can be overwhelming at times. you can find a lot about it on the internet. But to begin our story lets first get a clear understanding of what Data Science entails:
"The field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making."
By applying Data Science methodologies on their own data, food manufacturers can make more informed decisions about what products to invest in and how to allocate their resources.
Data Science assists portfolio managers in a number of ways. First, it can provide insights into which products are most likely to be successful. By analyzing historical sales data of (similar) products, it is possible to identify patterns that provide an indication on how the (new) product might perform in the future. Data Science algorithms that can help you with this are SARIMA, ARIMA.
Second, Data Science can help optimize the mix of products in a portfolio. By analyzing historical sales data it is possible to uncover previously unknown associations between products. This provides product portfolio managers with additional insights next to metrics like revenue and volume, in their product-portfolio-management-process. Data Science algorithms that can help you with this are ASSOCIATION RULE MINING or MARKET BASKET ANALYSIS.
Third, Data Science can help improve individual products in a portfolio. By analyzing external data on past product performance, such as online product reviews, social media posts and tweets, it is possible to identify and understand the social sentiment of the product. Data Science algorithms that can get you starterd with this are SENTIMENT ANALYSIS or TEXT MINING.
By using Data Science methods companies get a clear insight into their customers’ behavior, preferences, and current sentiment. When using this insight it leads to an optimal product portfolio that meets the customer’s expectation.
How data science saved the product portfolio
Product data, both internal and external, is a goldmine for companies looking to optimize their product portfolios. However, within the food industry many are still struggling to get the most out of it. In this section, we'll take a look at a real-world example from one of our clients and how Data Science algorithms saved the product portfolio.
Within the food-industry we see products being evaluated based on revenue and volume over a certain amount of time. This is normal. However, at one client this led to a crisis with a number of large customers (worth 35% of their annual revenue, approx. EUR 87.5M).
Our client divested a product with very low revenue and volume. Within 30 minutes after sending out the comms to a small group of customers the phones were ringing. Two large customers were raising hell. Threatening to take their business elsewhere. Leaving the account managers flabbergasted and product management to revert the divestment of the product instantly.
At that time we were expanding the client’s data & analytics-platform with Data Science features. We were asked to analyze the “underperforming” product ASAP. The client felt they might have missed something important. but they just couldn’t get to the bottom of it.
Remember, the client executed their product-portfolio-management-process as usual. The product was evaluated by revenue and volume. After running all of the product’s historical sales-data through the data science features we found the root-cause of the raging customers. Our client missed one small thing, the ‘ underperforming’ product was a service product.
The service product was part of every order placed by the large customers. In fact, it was one of the main reasons they stated with our client. By applying a Data Science algorithm called ASSOCIATION RULE MINING (ARM) we identified a very strong association between this service product and the high revenue and volume products.
Since then the output of ARM and other data science features complement the original dataset that is used in the product-portfolio-management-process and these types of crises are a thing of the past.
The next step for our client is to start collecting, analyzing and combining external data with their own data to service their customers even better!
conclusion
Through the use of Data Science you can analyze your company’s product portfolio from a whole new perspective. By analyzing past performance, Data Science algorithms enable us to also predict future trends for similar products. In addition, it lets us understand which products are most popular, generate the most revenue, and are strongly associated with one another.
You can then use this information to make decisions about which products to keep in your portfolio and which products to discontinue. Data Science is an essential tool for any company that wants to optimize its product portfolio and stay ahead of the competition.
about the the author
Sjors Otten
Management
“Insights without action is worthless”
Sjors Otten is a pragmatic and passionate data & analytics architect. He excels in leveraging the untapped potential of your organization’s data. Sjors has a solid background in Business Informatics and Software Development.
With his years of experience on all levels of IT, Sjors is the go-to-person for breaking down business- and IT-strategies in workable and understandable data & analytics solutions for all levels within your organization whilst maintaining alignment with the defined corporate strategies.