Data analytics in the sortation business
In current times, distribution centres have access to enormous amounts of data. Analysing these data is an extremely valuable tool for improving the sortation throughput of a distribution centre. The storage of data not only provides insight into the current performance of the system, but also enables the build-up of a historical record for future investigations and learning.
Gain insight for high efficiency and throughput
Running an efficient distribution centre is an increasingly complicated task. With e-commerce on the rise, distribution centres handle a higher number of parcels and an increasing variety of sizes, shapes and packaging. All of which represents a golden opportunity for the industry, however, it also puts a strain on sortation systems.
The best and most cost-efficient strategy for distribution centres to thrive in an age of e-commerce is through digitalisation and data analytics. Data analytics improve your business by descriptive, diagnostic, predictive and prescriptive analytics for continuous optimisation of your performance. Through data comes a new level of insight, which gives the power to dramatically increase operational efficiency and throughput.
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SORTATION SYSTEM PERFORMANCE INSIGHTS
By use of today’s tools for big data mining, streamed data can be enriched and filtered to secure the quality of the data, which is aggregated and prepared for simple visualisation. This type of data mining not only provides insight into the current performance of a parcel handling system, but also enables the build-up of a historical record for future investigations and learnings across a number of systems.
Predictive and prescriptive
At BEUMER Group we have worked with data for a number of years and have used these learnings to extend our services to the predictive and prescriptive level. BEUMER’s digital capability offers a growing suite of data-driven services to enable employees at the maintenance, operational and management levels to make better informed decisions.