Logistics Analytics in India: Spot Delays from Trip Data

Every trip leaves a trail: when it started, where it stopped, how long it. When it arrived. Most transporters keep that trail in a notebook or a spreadsheet. Logistics Analytics turns it into answers about why trips run instead of a guess after the customer complains.

What the India data says

An article in Inbound Logistics from March 2018 reported, citing a 2014 World Bank report, that up to 60 percent of an Indian truck move consisted of time spent stationary. It named state border checks as one reason and pointed to the July 2017 introduction of GST as an effort to reduce that drag.

Three cautions. The figure is from 2014. The article is from 2018, so it predates GST’s full effect. The article gives no sample or method. Does not break the stationary time into waiting, loading and idling. We use it to show why the question is worth asking, not as a number.

Logistics Analytics: what trip data can show

This section is our view. Trip data answers four questions.

Where does time go? Split each trip into driving, loading, unloading, waiting at borders and halts. The biggest block is the place to look.

Which routes are late? Some routes are slow every week. Some only on certain days. A pattern points to a cause while one late trip points to luck.

Which stops cause delays? A depot or customer site where trucks regularly wait is a conversation to have with that site.

Which trips never match the plan? A gap between actual time is the most useful number you can track.

None of this needs models at first. A clean log of start, stop and arrival times reviewed weekly finds most of the problems. The harder part is getting drivers and dispatchers to record the data consistently, so make recording easy before you make analysis clever. A driver who must type into a form at a halt will skip it, while one tap on a phone will not.

What to ask before you start

1. Which question do you most want answered?
2. Are start, stop and arrival times recorded for every trip?
3. Who reviews the numbers? How often?
4. What will you change when you find a delay pattern?
5. Can the data be exported to your reporting tools?

How We Approach Logistics Analytics

Our Data Analytics service covers six areas: BI and visualisation, big data, predictive and prescriptive analytics, customer analytics, data warehousing and integration and data governance. Our fleet platform Easylogy keeps trip history. Raises alerts for delays, which gives a team the raw record to analyse. For logistics analytics, we start with the question your trip data can answer.

If your team is exploring logistics analytics to find delays, we’d welcome the conversation.