How can your distribution centre unlock the potential of digitalisation?
Digitalisation and data analytics exist on a handful of different levels that vary in how advanced they are. The collection of data is considered Level 1 on the analytics maturity curve. Levels 2 and 3 are described below.
Generally speaking, distribution centres should always remember that:
- All levels of data analytics are valuable
- Distribution centres can’t become fully-fledged digital operations overnight
- You climb the data analytics ladder one step at a time
Level 2: Descriptive analytics
The first ‘next step’ of data analytics. Monitor every part of the system and store the information digitally. The aim is to answer the most basic question: what happened?
The objective of descriptive analytics is to understand precisely what happened, but not why the event took place. For example, a distribution centre suddenly experiences an increase in breakdowns. Approaching this problem entails looking at data that covers where the failures in the sortation system took place. Which equipment was affected? At what time did the failures take place? What amounts and types of parcels were going through the sortation system leading up to, and during, the failure?
You could say that descriptive data analytics lays out all the facts retrospectively, but without connecting any of the dots. In today’s CEP industry, almost all distribution centres carry out some form of descriptive analytics.
Read more: How data analytics will change the work of distribution professionals.
Level 3: Diagnostic analytics
The first level took care of the ‘what’. Diagnostic analytics, the second level, adds another piece to the puzzle by addressing the ‘why’.
Diagnostic analytics is essentially based on the same numbers as descriptive analytics, but there is a vastly increased ability to analyse data at the diagnostic level. Instead of simply stating what happened, diagnostic analytics compares new data with older data, looks at correlations and identifies patterns. For example, a distribution centre might be able to identify that a combination of volume, parcel packaging and size of parcels might cause specific equipment to falter.
This allows for the management team to make data-based considerations with regards to a better way to structure the sortation process or allocate operators to maintain a specific flow. Operators can also base their work on data and react when digital predetermined conditions are met instead of having to respond once breakdown occurs.
Diagnostic data analytics is an extremely valuable tool. It remains, though, an instrument based on what has already happened. The final two levels of the digital analytics ladder separate themselves by taking data analytics a step further: analysing what is going to happen in the future.