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The current pilot project at Verband KVA Thurgau once again confirms that our approach is proving its worth.


The aim is to calculate fill level forecasts for the underfloor containers (UFC) based on historical weighing data at the push of a button and to derive the optimum emptying times for the UFC from this.

On this basis, emptying schedules can be automatically dispatched in a matter of seconds, and optimal routes can be created, which are ultimately transmitted directly to the navigation system of the respective collection vehicle.


Added values:

  • This automation means that personnel resources at the haulage company can be used more efficiently for other tasks.

  • Personnel deployment plans or maintenance cycles of the collection vehicles can be planned and optimized in the long term.

  • In addition, simulations for future investments - for example, in the vehicle fleet - are simplified, as increasing emptying requirements can be forecast with increasing UFC density.

  • Above all, however, the environment benefits: thanks to the need-based and route-optimized emptying of the UFCs, travel kilometers and thus CO2 emissions are significantly reduced.


For its monthly rolling projections, Coop Mineraloel AG relies on a system developed and implemented by us, which forecasts sales and expenses for the entire Coop service station and Pronto store network - based on the latest algorithms - at the click of a mouse.


The forecasting methods we use are derived from classical and more recent time series analysis. This allows us to consider all possible variables, such as seasonal patterns, trends, calendars and vacation effects. Where appropriate for time series, machine learning methods such as neural networks, classification and regression trees can also be used.


The massive reduction in manual activities frees up capacities for new tasks and the susceptibility to errors approaches zero. Thanks to the automation of monthly extrapolation, it is possible for controlling to present different scenarios even in times of Covid-19 crisis, allowing management to map uncertainties and react proactively to changes.


The advantages are obvious: thanks to the newly gained information density and the new forecasts, logistic processes can be optimized and made more efficient.



Added value


  • Less effort for non-value-added activities

  • More precise results and greater transparency thanks to increased information density

  • Clarification of interrelationships and cross-functional dependencies (across a wide range of business areas) through disclosed cause-and-effect chains

  • Massively increased responsiveness through automation - calendar orientation is increasingly a thing of the past

  • Objectified forecasts that provide a fact-based picture

  • Multiple use and reusability of algorithms provided by s-peers

  • Wide range of evaluation options in assured quality

  • Early possibility of performance evaluation through simulation

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