Predictive technology in remote competitive product service markets

Abstract

Competition for service of products between the original equipment manufacturer (OEM) and a fringe firm is studied using a sequential game-theory model. The OEM utilises predictive technology to reduce the product failure rate, and the fringe firm provides efficient service at a reasonable price. Because of market remoteness, the OEM is incapable of matching the local fringe firm’s service quality. The price of service offered by the two firms, the effective failure rate of the product when serviced by the OEM, and the fringe firm’s service quality drive the number of products in the service market. Further, the captive market segment for OEM service and the products’ failure rate as seen by each firm drive that firm’s service demand. In such a setting, the OEM invests in predictive technology that reduces the effective failure rate of the products serviced by it. We find predictive technology benefitting both the firms at higher failure rates, thus acting as a Pareto improvement while implying co-existence in such remote product service markets. We further find the OEM dominating the fringe firm with higher price and profits only when the OEM captive market segment is large.

Keywords:

• Competition
• service
• predictive technology
• remote markets

Acknowledgements

The author thanks Prof Omkar D. Palsule-Desai, IIM Indore, for his timely guidance and sharp insights in writing this paper. Further, the author also acknowledges healthy discussions with domain experts in the Power Plant Industry, without which this paper would not be possible. Finally, the author appreciates the constructive feedback and suggestions from the anonymous referees that enriched this paper.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 The aggregate number of products $m_o+m_f$ in the market is a function of the product price as well. Since the OEM is the only supplier of the product in our model, we ignore the impact of price and focus only on the competition between the OEM and the fringe firm in the service market.

2 We do not perform the comparison of $k_{\Pi }$ and k_p.

3 We already found $k_{\Pi }>k_o$ and k_p > k_o. The relative position of $k_{\Pi }$ with respect to k_p is driven by the other parameters of the model, viz., β, θ, ν_f, ν_o, c_f, α, τ, and κ.