mask layout

Predict work
using real-time data

Forecast duration of individual work items and utilize available capacity in the most optimal way.
When machines, material and frontline workers get connected into one digital ecosystem, it allows to not only make predictions about machine conditions or failures, it also means manual work duration and quality can be predicted. This is enabled by a machine learning system that uses historic data to make forecasts.
predict time to finish

Prediction of time to finish

An individual task builds the basis of all further predictions. From looking at historical data, the WORKERBASE platform is able to calculate an expected time-to-finish for a task. This takes into account the available contextual information such as weekday, shift, product being worked on, previous incidents and other information. While providing predictions, our tool is compliant with all worker related regulations. The information is used anonymously inside the system and therefore can't be used as a performance tracking tool.

Prediction of capacity utilization

Predicting workload is one of the most important aspects in real-time optimization of a production flow. Workload is the sum of all individual’s time to finish for current and future tasks. When combined with planning information such as production plans, this allows to predict the expected workload of an area during the day. It surfaces capacity problems in certain areas before they occur and allows to take preventive action early on. Possible solutions are change of production sequences or placing additional staff in certain areas.
prediction of capacity utilization
digital workflows in the smart factory

Prediction of yield rates and rework

When having a complete dataset that includes production plans, workload estimations and historical information on quality, yield rates can be effectively forecasted. This allows to extend the capacity planning to the quality and rework areas and thereby ensuring shortest possible rework times.

Benefits of predictive work

Anticipate future events based on historic data and coordinate measures to minimise the potential negative impacts of these events. Orchestrate work based on realtime predictions and directly turn data into action to streamline production.
Increase
machine uptime
Improve
throughput
Reduce
waste
Increase worker
utilization

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