Abstract:
Purpose – The purpose of this paper is to develop a model for the production planning decision of a dairy
plant in a multi-product setting under supply disruption risk and demand uncertainty while determining the
optimal product-mix and material planning requirement.
Design/methodology/approach – A mixed-integer nonlinear programming model is proposed to
determine the optimal product-mix that maximizes the expected profit of a dairy. The data are collected
through visits to the dairy site, conducting brainstorming sessions with the plant manager and marketing
head at the corporate office. Disruption data are collected from the India Meteorological Department, Odisha.
Findings – From the analysis, it is recommended that the dairy should not produce curd during the planning
period. Moreover, turnover from toned, double toned and baby food is maximum than that of the curd and these
products are produced in the planning period. The expected profit increases from its present value when an
optimal product-mix is followed. Sensitivity analysis is performed to analyze the effect of demand uncertainty,
supply disruption and production quota. The expected profit decreases as the supply failure probability increases.
Research limitations/implications – The model is implemented in a dairy plant under Orissa State
Cooperative Milk Producers Federation, Odisha, India. The proposed methodology has not been validated,
theoretically. The concerned dairy is based on the Indian context, but the authors believe that the study is
highly relevant to other dairies as well.
Practical implications – This study provides a methodology for dairy plant managers to plan
production effectively under supply disruption risk with demand uncertainty. It also suggests material
requirement planning at different factories of the dairy plant.
Originality/value – This paper develops a mathematical model for the production planning decision of a
dairy plant that determines the optimal product-mix, which maximizes the expected profit of a dairy under
disruption risk and demand uncertainty (in the Indian context).