Data analytics has also helped the operations managers make better decisions about how to assign the chutes in order to balance the system load more effectively. This assistance has been particularly constructive for the “young team” who were fairly new to the organisation.
Through dashboards that are visible to all operations staff, the CEP provider now has transparency into all its processes and has been able to transform its operations from “what we think we know” to “what we know”. Meanwhile, its management team uses the data to measure KPIs and create forecast models for future performance, whether that’s week-over-week or year-over-year performance.
Using data to test and validate a new system design
A large Scandinavian postal house was looking to extend its sortation system but was concerned its new system design would actually result in lost capacity as it would impact on the OCR/VCS process. So, its system design team used the system’s data to test whether the proposed design would lead to recirculation of the parcels.
Dashboards were able to show the timing of the OCR/VCS process, proving to the team that even in peak hours, the OCR/VCS system could respond to more than 95 percent of the parcels in less than the required 15 seconds. Through data testing, the team could confirm its system design was valid and viable.
How ambitious does a distribution centre need to be?
But how ambitious does a distribution centre need to be before it benefits from utilising its data? To illustrate, let’s look at each of the three cases above:
- Central European case: This operator was between the descriptive and predictive stage of data maturity. But even with a fairly basic level, it could still benefit on both the maintenance and operational levels.
- US case: This operator was also at the descriptive stage of data maturity, using real-time data visualisation to help its operational staff use decision science to propose immediate actions that improve system operations on the fly.
- Scandinavian case: This operator, too, was at the descriptive stage, yet its maintenance and operational staff could make an informed decision as early as the design stage of the proposed system extension.
The above examples are all at the basic end of digitalisation. Nevertheless, even this level of data maturity clearly offers benefits.
Moreover, these examples are just a small corner of the benefits a CEP operator will gain from logging and centralising data for visualisation and for making data-driven decisions. Imagine having a visualised overview of the complete system!
With data analytics, CEP operators can work more efficiently towards specific goals, in their roles as maintenance, operations, or management professionals. And with predictive and prescriptive analytics, digitalisation can go even further; predicting what is likely to happen and prescribing action to take to prevent future problems.