AI-enabled forecasts

At the right time, the right quantities at the right place!

Accurate forecasts are nowadays essential for businesses. AI-enabled forecasting software produces reliable predictions of your sales and consumption based on historical data, so that you can always fully meet the demand of your customers. At the same time, tied capital, depreciation, wasteful storage and spoilage of goods are avoided.

Prototypes and demonstrators available
Cross-industry deployment
Suitable for SMEs?

State of play

Already today, different forecasts are used in many sectors of the company, such as inventory management, staff deployment planning or human resource needs planning. Data-driven methods for forecasting needs have been established in recent years. In this context, statistical, univariate techniques such as exponential smoothing or ARIMA models have long been superior to all other prediction accuracy methods. However, the development of software, hardware and algorithmics has been advanced to such an extent that prediction based on AI significantly exceeds the solutions of statistical benchmarks.

Technology and deployment

Description of technology

Accurate forecasts are of immense importance for traders in order to be able to identify in good time which goods should be purchased, when and in what quantities. This is the only way they can optimise the availability of goods while minimising the cost of earmarking. Moreover, a reliable forecast has the advantage that only as many staff are employed in camps and branches as is currently needed.

Such forecasts are easy to make under stable conditions. However, retail trade is a dynamic environment influenced by many continually changing factors. These include, but are not limited to, the following variables:

— Recurring sales patterns (e.g. weekdays, public holidays, seasonalities)
— Internal business decisions (e.g. price changes, offers, – branch displays, campaigns)
— External factors (e.g. weather, local events)
— unknown factors (e.g. branch opening of a direct competitor in the neighbourhood)
It is impossible for a human to take such a wealth of factors and data sufficiently into account to produce an accurate forecast. AI algorithms (specifically machine learning algorithms) are perfect for various predictive applications: They automatically learn patterns and contexts in historical data, which can then be applied to new data to make predictions. Assuming the relevant data, it is possible to predict as many values as, for example, sales in the food trade. For example, it is possible to predict in concrete terms how many choko osterhases will be sold in a given branch within a given period of time. AI shall take into account, inter alia, the following factors: Number of days to Easter, number of Easter hares sold in previous years, weekday, general purchasing behaviour of customers. This forecast can then be used to order exactly the right number of choko osterhases at the right time.

Possible use scenarios

  • Sales forecasts
  • Commodity Income Forecasts
  • Human resources forecasts
  • Stock projections
  • Return forecasts
  • Consumption forecasts

Gradual introduction

Step 1: Identify

Identify the main drivers in your business and use AI to also detect hidden relationships. Find delineated areas where concrete and rapid benefits can be created. Assess these according to the need for optimisation in the planning, the potential relevance and reliability of automated predictions, as well as the effort needed to prepare the data. In this way, you guarantee that your work provides concrete added value.

Step 2: Implement

Take a step-by-step approach to the roll-out. From the outset, plan the evaluation in a parallel process. In this way, assess and improve the quality of results and create transparency and acceptance of the results.
Invest in building the necessary skills and improve collaboration between Controlling and Business Intelligence and Analytics experts to best combine existing technical, statistical and technical expertise.

Step 3: Integrate

Take into account the technical enablers and challenges when integrating AI-enabled forecasting software. Ensure that input data and results are delivered automatically to ensure more speed in decision support through automation.

Ask the manufacturer of your planning solution which predictive methods are supported or whether predefined models already exist for your request. This will allow you to benefit from market experience and to start quickly. If necessary, extend your business planning to individually developed solutions. This will allow you to implement a tailor-made solution.

Step four: Apply

Use machine extrapolations in parallel with existing plans and evaluate both plans. This will provide you with solid information on the safety of the meeting. Put your produced models into normal operation and use them productively as part of your business planning.

Check the validity and update your forecasting models on an ongoing basis. Only valid and up-to-date models can deliver useful results. Update your forecasting models to understand results after changes. Use the time gained by automation and optimised planning models to provide better simulations with less effort to provide more relevant planning results for decision support.

Step 6: Dismantling

Use your experience to evaluate further possible fields of intervention and the development of know-how. Then, AI-enabled forecasting software gives you the potential to bring your business planning to the next level.

Opportunities for SMEs

Optimising the availability of goods

Early prediction of market developments

Secure forecasting allows optimal order volumes

Reduction in capital commitment costs

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