Data, artificial intelligence, the food chain and supply chain

Supply chain management has become ever more challenging as retailers struggle to differentiate themselves and keep up – especially in the competitive market of food retailing.

Ai & DataConsumer trends in grocery shopping — including fresh and healthy, dinner and meal to go options, foodvenience, as well as increased competition with retailers like Aldi and Lidl — have changed the landscape. The need for sophisticated supply chain solutions is increasing and this part of the business is becoming a major differentiator to the success of the organization.

Above all things, consumers are demanding convenience. As retailers struggle to keep up and engage customers, they need innovative ways to understand and adapt quickly. This need for speed is no longer just nice to have, and most retailers lack speed due to numerous factors including old technology or various technology silos, or not having a single source of the truth for a means to manage data. These are major hurdles for retailers and cause delays in management of data. This issue needs to be prioritized as one of their most important assets.

A few key success factors for competitive advantage are:

  • Tying the data, demand planning and consumer visibility together and . . .
  • Having an artificial intelligence (AI) algorithmic aspect, offering dynamic and auto-adaptive decision making across all supply chain

Data and forecast data

A vast amount of data fuels the supply chain. And retailers need to efficiently and quickly access the data and make predictions. Demand forecasting and supply chain planning solutions have been around for a while and there are many great and mature solutions in the market, yet retailers continue to be challenged and need more adaptive ways to manage forecast data. Forecasting is a challenge for retailers and promotional forecasting and running profitable promotions is even more complicated. (See also related viewpoint on demand forecasting and the role of AI).

Let’s look at data, demand and consumer visibility and as mentioned earlier around retailers’ struggle with silos. For example, consider the silos between merchandising and marketing and the struggle to synchronize promotional forecast planning. Often, plans don’t match beyond promotional performance and there can be lags in launch execution. With one platform to connect all the data needed and providing one view, life becomes much easier.

Tied together, promotional plans, execution, historical data as well as consumer insight data work in concert to help make informed decisions. Federated consumer data infused into a forecasting engine, coupled with machine learning and predictive algorithms can locate patterns and explain highly detailed consumer behavior.

AI is the concept of machines being able to carry out “smart” tasks mimicking human thinking. Simply defined, AI is machines doing things for people, leveraging smart process to automate repetitive activities and to learn from actions taken. We believe this is important, and this next level of predictive forecasting including consumer insights can add a layer of 1-3% profit.

Machine learning depends on data, and so retailers need to be able to trust their data and the technology it’s running through, so being in an environment offering one platform for data certainly is key for speed and predictable outcomes. Harnessing that data and positioning it for AI really offers the next level of efficiencies.

When choosing a supply chain solution, it’s important that it be rooted in FMCG and built from the ground up to address the specific needs of our industry. It should leverage patterns, clusters and automation, and new tools that all work fluidly toward a cohesive forecasting process.

Consumer visibility and forecasting

It may seem obvious to state this, but it’s a point often missed by retailers today. Success depends on a clear view of your customer data and behavior. Retailers require up-to-date data and enough of it so artificial intelligence tools can best take advantage of the data. Small data pools can be less effective in terms of AI’s ability to recognize patterns and anomalies in data, and therefore not be as impactful as when drawing from larger data sources.

It’s also key that retailers use software solutions that combine federated forecasting data that can be blended with demand forecasting AI algorithms to provide real value, true business insights that enhance business forecasts that improve decision making. Better decision making that, ultimately, improves product availability and makes for happier customers.

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