Tulasi S Sastri
A company is engaged in procurement of paddy, process it, sell and distribute rice products. Operations are characterized by high volumes and movement of materials on a large scale. Every day, about 150 trucks reach the plant with paddy and a similar number leave the plant with rice products. Collection points for paddy and delivery points for rice products are not far from each other, such that it is possible to plan return loads for each truck.
The company has its own fleet of trucks and is conscious about efficiency of operations and cost control. It had a logistics department which worked in close liaison with planning function. GPS devices have been installed on each of the trucks, but they have been not of much use because of high volume of transactions.
Typically, a truck leaves the plant with rice products loaded, fuel topped up, mileage recorded, delivers the load usually in a radius of about 250 km, picks up paddy from a nearby collection point and returns to the plant, next day.
Each transaction of sale and purchase are identified by unique identification numbers, Daily Dispatch Number (DDN) and Daily Purchase Number (DPN).
Problem:
Possible areas of mischief by truck drivers are going an extra mile carrying unauthorized products picked up en-route, cover up distance and time through excuses like traffic or break downs or delay in return load being available, apart from pilferage of fuel.
Solution:
All the collection and destination points are known and are engaged in recurring transactions. Their distance to and fro plant is known. Similarly normal time it takes for a truck to ply to these points also is known. A master has been created with these details for each collection and destination point. Connecting these details with each DDN / DPN, it was possible to create required logistics base for each transaction.
Each truck identified with a truck number and driver, has been provided with a card, to be used as a log sheet, to manually record information when a truck is leaving the plant and included details like DDN / DPN, fuel, running meter reading in km and actual time of the truck leaving the plant. Truck drivers have been advised to record time and km at each destination and collection point. At the end of each week, a new card has been issued and the previous log sheet’s data has been captured into the ERP system in use.
Weekly reports generated for each truck reported total km run during a week, with load and empty, time engaged in both the situations, percentage deviation from normal time and distance, highlighting variations above 20%, notional freight earned and relating it to costs incurred, and fuel efficiency in terms of distance per liter of fuel consumed. This provided phenomenal information for monitoring operations as well as for improving the planning process.
For more articles from me please read Translating Operations into Money – Cases in Business Management, available for online purchase on Amazon.in, Flipkart, Book adda, Notion Press or visit www.operationstomoney.com .
Thank you for your attention.
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