Adjusting the logistic function to develop a more realistic product life cycle model
Document Type
Article
Publication Date
2021
Abstract
Product life cycle (PLC) is one of the essential issues that influence supply chains. A good understanding of this cycle enables better demand forecast and hence optimal planning of resources, investments, marketing and sales, it is also useful for customer availability forecasting. In literature, a logistic model is used to describe the first three stages of the PLC of any product: introduction, growth, and maturity. However, this paper discusses the derivation of a mathematical model that can capture the other stages of the PLC, declining, phase out and obsolescence and explains the uses of the derived model. Two numerical examples are provided to illustrate the proposed model and the improvement it provides over the traditional models. Finally, a sensitivity analysis is performed to illustrate the effect of changing the model parameters on the overall PLC shape. The results show that the proposed model is more reliable and realistic in capturing the PLC of any product and perform more accurate forecasting of future values.
Recommended Citation
Rehim, S.E.D.A., Mostafa, N.A. and Mohamed, T.A. (2021) ‘Adjusting the logistic function to develop a more realistic product life cycle model’, Int. J. Product Lifecycle Management, Vol. 13, No. 3, pp.205–223.
Comments
The aim of this study was to capture the perfect PLC curve. Not all the products pass through the first three stages; such as bakery products that start with high demand and decrease over time, the longer the product stays the higher the probability for the product to be obsolete. So, in this case, the second equation that describes the declining stage is used and this curve is mostly applied to fresh products in the market. Moreover, when merging the two graphs together the shape factor plays a role in shifting the declining curve to the point when the growing curve ends. Not only it can be used in merging the two graphs but also when using only one curve, and shifting the growth or decline of the curve to delay or rush the stages of the cycle. This equation is much simpler than other equations, and its parameters can be extracted from different historical studies, and most importantly it can draw the seasonality of the product by calculating one or more cycles throughout time. Pricing can be controlled relative to the demand throughout time. Furthermore, by accurately drawing the PLC, the maximum quantity needed can be known. Therefore, companies responsible for making perishable products, can now know the quantity needed to be produced over a certain period of time. The limitation of this study was the lack of historical data, which has led to limit the scope and sample size. Future work may be directed towards implementing the proposed model for real data.