bye bye motors

8/3/2019 Bye Bye Motors

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Bye-Bye Looks to Boost After-Sales Distribution Efficiency 1

Bye-Bye Motors – one of the large automakers in India – assembles and sells vehicles for a wide range of

uses – passenger cars, utility vehicles, trucks, commercial passenger carriers, and defence vehicles. Atthe beginning of 2011 it was producing a range of products in each category of use. For instance, in thecategory of passenger car, Bye-Bye made four different products in the range of small-medium size,each with multiple models. In the truck category, it offered models for medium and heavy commercialvehicles, intermediate commercial vehicles, light commercial vehicles, and small commercial vehicles.Some of the products were also exported in other South-Asian countries, South Africa, and somecountries in Europe.

Over the past decade the competition in the Indian auto market has intensified with several domesticand multinational firms setting up manufacturing and supplier bases and producing vehicles for a widerange of markets. With increasing competition, automakers are under constant pressure to improveperformance on multiple dimensions – quality, productivity, fast delivery, new products/models, andefficient after sales service. As the majority of large automakers in India have installed moderndesigning laboratories, IT systems, production lines and testing facilities, they matched each other onquality and productivity of manufacturing and assembly. However the performances varied on aspectslike efficiency of new product development and after-sales service.

Distribution and After-Sales

Bye-Bye distributes the spare parts for its products all across India via a complex network of over 5000channel partners. These are distributors, dealers, dealer branches, and Bye-Bye owned BBASS (Bye-Bye

Auto Service Stations).

For distributing the commercial vehicles’ spares, Bye -Bye has established four big warehouses, one ineach major region of India – North, West, South, East. Over 50,000 parts are supplied to the channelpartners from these warehouses. The channel partners place orders for spare parts almost every day.

In order to improve utilization of trucks, attempts are made to send a truck to cover multipledestinations that lie roughly on the same route. The routes from a warehouse to a destination andbetween different destinations are known and distances and travel times well estimated.

About 4-10 destinations are typically served by a truck trip. A truck waits to fill up a reasonable load

before leaving from a warehouse. This waiting time could be up to three days. On a given daydispatches are made from the warehouses for about 1700 destinations (channel partners). A largechannel partner receives its delivery once every 2-3 days, whereas a smaller partner receives once aweek. A truck typically takes 2-5 days to return to its origin warehouse. About three-fourth of alldispatches are within the region.

1 Prepared by Rahul Pandey in 2011, [email protected]



The Supply Chain head of Bye- Bye’s commercial vehicle spares division admits that , in spite of theirmanual efforts to improve utilization of trucks, a lot of trucks go PTL (part load) instead of FTL (full load).Besides high transport cost of PTL trips, they are exposed to more damages as the truckers carry othermaterials in the unused space.

The Supply Chain head believes that by a scientific method of “milk run” routing and distributionscheduling Bye-Bye can save significant cost without compromising the service level performance.

The after-sale service quality depends a great deal on the availability of the right mix of spare parts atthe channel partner’s facility. With proliferation of products and their spare parts, the distributors anddealers are often hard pressed to meet customer requirements.

Need for a Distribution Routing and Dispatch Scheduling Tool

In late 2010, the Supply Chain head and his team began to estimate back-of-the-envelope milk-runroutes for certain regions. These manually calculated milk runs were tried out for a couple of months

and the responses of both warehouses and channel partners were positive. People realized that such anapproach yields benefits.

Encouraged by these informal trials, the Supply Chain head now wanted to make the methodology morerobust and scale it up all over India. He is convinced of the need to develop a scientific model forrecommending optimal routes and dispatches of trucks, and integrating it into a software tool,interfaced with the existing ERP system. This will permit automated calculations of optimal routes anddispatch schedules , and quick ‘what -if’ scenario analysis.

He invited a supply chain analytics consultant for an initial brainstorming session to scope the problemand figure out if such a model and tool could be developed and implemented effectively.

A lot of questions came up in the brainstorming session which witnessed intense debates andarguments. While there was little consensus on the best answers to these questions, it became obviousby the end of day that the discussions have resulted in greater clarity about the issues involved. It alsobecame clear that answers to these questions are crucial to model the problem effectively and make theproposed tool useful and practical to implement.

Time horizon and rolling period:

Will the model be run on the morning of every day?

Will the model recommend daily dispatches and routes of trucks for the next several days or for just one day at a time?

Geographical scope:

Will the model be run for each warehouse separately or for all the warehouses (i.e. for all India)together? What are the implications of each option?



Decisions expected from the model:

Detailed route for every truck to be dispatched from each warehouse

Contingency (back-up) re-routing of trucks in case of unexpected events

Optimization objectives:

Meet all orders from channel partners within reasonable time limit (based on service leveltargets in terms of maximum number of days from order to delivery)

Minimize total travel time (or transport cost) of trucks

Major input data in the model:

Master data:

o List of warehouses and destination points in each region

o Feasible routes from warehouse to destinations and between destinations, and traveltime or cost on each leg/route – both within and across regions

(Should warehouse-warehouse movements be considered too?)

o List of spare parts and equivalent truck volume per unit of each

o Service level requirement (no. of days from order to delivery)

o For each warehouse: List of available trucks and capacity of each

o Priority of destinations

Transaction data:

o Daily orders from each destination

o Trucks available at warehouse; Status of trucks in transit

What other constraints in the system should be considered?

Question for discussion

Can you answer the questions raised in the brainstorming session, and then propose a model to theSupply Chain head that can recommend optimal distribution routes and schedules?

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