BRIDGEi2i Case Study - Improved vehicle utilization through predictive maintenance
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Bridgei2i assisted a large Middle Eastern taxi operator in optimizing its preventive maintenance schedule for over 3000 taxis to maximize road availability and revenue. Utilizing historical data, they developed a simulation-based optimization model that aims to increase daily taxi availability by over 50 vehicles while balancing workshop load and reducing maintenance costs. The improved scheduling is expected to enhance inter-service mileage by about 60%.
BRIDGEi2i Case Study - Improved vehicle utilization through predictive maintenance
- 1. Customer Case Study BRIDGEi2i helps a large transport operator in Middle East improve vehicle availability and workshop load pattern through a data driven preventive maintenance schedule. Business Challenge The client is one of the largest taxi operators in the Middle East which operates 3000+ taxis. A major objective for the company is to maximize availability of taxis on road for customers and in turn also increase the revenue potential for drivers. While few of the taxis get off road due to repair needs, many of them need to undergo periodic preventive maintenance at periodically. Currently the schedule is not optimized and typically heavy during certain days of the week. Opportunity lies in building an optimum preventive maintenance schedule which ensure higher fleet availability and properly balance utilization of workshop capacity across days. BRIDGEi2i Solution BRIDGEi2i engaged in a data driven consulting engagement to build a schedule that learns from historical workshop workload patterns, taxi mileage and recommended preventive maintenance thresholds. The historical data was analyzed to build a simulation based optimization model which tries to optimize taxi availability with constraints set for significantly smoothening workshop load pattern, minimal vehicle wear and tear and lower preventive maintenance cost. Learning from historical data patterns Vehicle mileage and existing load on workshop were analyzed to build accurate assumptions on constraints Optimum Scheduling Logic based on Insights A simulation based logic for scheduling does a realistic prediction of vehicle mileage and devises a rule to prioritize and slot vehicles for preventive maintenance For more details contact us: enquiries@bridgei2i.com Information Insight Impact © BRIDGEi2i Analytics Solutions
- 2. Customer Case Study Business Impact A simulation was performed to assess impact of the potential shift to an insight based scheduling logic. The new schedule for preventive maintenance is expected to allow more than 50 additional cars on road daily, smoothen the volatility of workshop load and at the same time improve inter service mileage by about 60%. About BRIDGEi2i BRIDGEi2i is an analytics solutions company partnering with businesses globally, helping them achieve accelerated outcome harnessing the power of data. BRIDGEi2i helps companies to BRIDGE the gap between INFORMATION, INSIGHT and IMPACT in their journey to institutionalize data driven decisions across the enterprise. For more details contact us: enquiries@bridgei2i.com Information Insight Impact © BRIDGEi2i Analytics Solutions