Authors: K.R. Ravi Shankar, P. Vijayaraghavan, T.T. Narendran
Addresses: Department of Management Studies, Indian Institute of Technology (IIT) Madras, Chennai 600036, India. ' Department of Management Studies, Indian Institute of Technology (IIT) Madras, Chennai 600036, India. ' Department of Management Studies, Indian Institute of Technology (IIT) Madras, Chennai 600036, India
Abstract: |Customer support| includes all value-added product services after the product sale, such as installation, maintenance and repairs, etc. A high-level of uncertainty and fluctuations accompanies the demand for customer support services. Hence, the system needs optimal field-staffing decisions with the least risk. The problem becomes all the more challenging under restricted working hour conditions. Daily working hour duration embedded with |decision epochs| partly mitigates demand uncertainty. This research work uses this framework and assesses the volatile demand with a probability distribution. Further, this research develops a decision model using the Markov Decision Process (MDP) in a Dynamic Programmin (DP) framework to optimise the field-staffing decisions considering demand fluctuations and risk. Results of the numerical analysis show that more decision epochs reduces cost. Also, the neutral risk policies become optimal with the increase in the number of decision epochs.
Keywords: customer support; mixed weibull distribution; Markov decision process; dynamic programming; staffing policies; uncertain demand; demand fluctuations; risk; agility; agile systems; agile management.
International Journal of Agile Systems and Management, 2006 Vol.1 No.3, pp.299 - 323
Published online: 19 Sep 2006 *Full-text access for editors Access for subscribers Purchase this article Comment on this article