Consumers buy differently – AI predicts unerringly

Rain and cold, sunshine and heat – all this influences our everyday lives and especially consumer behavior. Because you buy the umbrella when it rains and the barbecue meat when the sunshine is announced for the weekend.

As simple as that sounds, there are two challenges for manufacturers and retailers when it comes to precise sales forecasts: You can’t know what the weather will be like in the short term. Production and supply chains have to be planned and goods produced for longer.

In addition, they are very arbitrary, so manufacturers and retailers always plan enough buffer for their goods. This, in turn, evokes exact sales planning for e.g. producers of grilled meat (perishable goods) for their planning and strategic development.

Probably the most frequently used planning tool in companies is the Excel spreadsheet in combination with one’s own personal empirical values or those of long-standing employees. But in the agile and often unpredictable market environment, this approach quickly reaches its limits.

With the use of artificial intelligence or machine learning, planning is possible that automatically and continuously adapts to market changes, integrates cannibalization effects and recognizes product life cycles. This promises a higher forecasting quality than classical statistical forecasting models.

Every company prepares forecasts for its goods sales that are as accurate as possible, and the effort involved is usually enormous. But how good are these forecasts and are they sufficient? With personal experience, you run into problems at the latest when the responsible employees are on vacation or, in the worst case, leave the company.

Algorithms, on the other hand, are trained, generate knowledge from historical values and data; they automatically recognize patterns and correlations. If a company has an extensive database, the greater the chance that all the interrelationships can be covered. Once these are identified, they can be applied to new data to make predictions for future sales.

AI or machine learning, if successfully implemented, can optimize sales planning or even implement demand-driven workforce and material requirements planning, but also realize production, warehouse and transportation capacity planning.

So there is a solution to the above challenge: the use of artificial intelligence! Now, unfortunately, many companies still lack internal expertise in this specialized area, so that an implementation of this technology requires external support.

Questions like: Do we buy a standard software or do we have a solution programmed for our individual needs? And how much is the investment? Choosing the right solution seems complex at first. With an AI potential workshop, you make it easier for yourself to identify application scenarios with high AI potential, and with a low initial investment!