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Case Study

Larsen and Toubro: Spare Parts Forecasting

Hegde Prakash , Jaiswal Ruchi ,Kulkarni Suhruta ,Unnikrishnan Dinesh Kumar
Analytics (6519), Statistical Analysis (1210), Demand Forecasting (10376), Inventory Management (1147)

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Abstract  

Larsen and Toubro (L&T) was India’s largest technology, engineering, construction and manufacturing company. Construction and Mining Business (CMB) sold equipment such as dozer shovels, dozers, dumpers, hydraulic excavators, motor graders, pipe layers, surface miners, tipper trucks, wheel dozers and wheel loaders. CMB also provided the services of equipment installation and commissioning and other maintenance services. Supply of spare parts was critical, since the customer faced severe losses owing to equipment unavailability. Forecasting was done on an ad hoc basis based on the experience of the planning personnel. The value of each spare part ranged from INR 10 to 8 million. It was critical to maintain a correct balance for the spare-parts inventories, since unavailability led to loss of revenues, decreased profitability, customer dissatisfaction, and also gave rise to the fake products industry. Excess inventory led to high inventory carrying costs, working capital lock-in and also a possibility of spare parts becoming obsolete. Vijaya Kumar, Deputy General Manager of CMB, had to arrive at a forecasting methodology with an error of less than 10% for the 20,000 odd spare-parts. This warranted for 20,000 forecasting models; however, this was not only time consuming but also very expensive to develop and manage. Kumar wanted to build the forecasting model quickly so that he could roll out the forecasting strategy on a pan-India basis within a few weeks.

Learning Objective 

The case is ideal for teaching various forecasting techniques such as moving average, exponential smoothing, Croston’s method, and auto-regressive integrated moving average (ARIMA) based on the data from a large manufacturing firm. The case is suitable for teaching the nuances of forecasting as techniques for different types of demands use metrics such as mean absolute percentage error (MAPE) and Bayesian information criteria (BIC).

  • Pub Date:
    01 Jan 2015
  • Source:
    IIM-B
  • Discipline:
    Operations Management
  • Product#:
    1231
  • Keywords:
    Analytics (6519), Statistical Analysis (1210), Demand Forecasting (10376), Inventory Management (1147)
  • Length:
    Pdf : 11 page(s) ,

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