Product Life‐Cycle Management of Packaged Software

Amit Mehra, Abraham Seidmann, Probal Mojumder

Production and Operations Management · 2013 · 49 citations · 37 references

Concepts

TL;DR

Software products lose value over time due to technological and economic obsolescence, creating opportunities for firms to sell upgrades that offer higher utility than older versions. The authors extend their model in three ways to demonstrate the robustness of the results. In a growing market with homogeneous customers, the model shows that optimal upgrade intervals increase monotonically over the product life cycle driven by demand and cost; empirical evidence supports this, and comparative statics reveal that higher obsolescence or network externalities can lengthen early intervals and shorten later ones, while higher market growth always shortens intervals, and forward‑compatible upgrades extend intervals; additionally, because upgrade costs depend on intervals, these insights inform managerial cost planning.

Abstract

A software product becomes less valuable for its consumers over time due to technological and economic obsolescence. As a result, firms have an opportunity to introduce and sell upgrades that provide higher utility to consumers compared to an older and out‐of‐date software product. In a market that is growing and consists of homogeneous customers, we prove that the optimal upgrade intervals are monotonically increasing throughout the product's life cycle solely because of demand and cost considerations. This finding is in conformity with empirical evidence, thus validating our theoretical model. We then present comparative statics results to show that increase in the rate of obsolescence or network externalities may sometimes increase upgrade intervals for early upgrades and decrease these for later upgrades in the product's life cycle, but increase in market growth rate always decreases these intervals. Further, when successive software upgrades are forward compatible, upgrade intervals are longer than when they are not. Finally, we present three separate extensions of our model to showcase the robustness of our results. Since upgrade development costs depend on upgrade intervals, these insights help managers understand how costing for upgrades changes over the product's life cycle.

References

37