Automotive Production

Risk Management, Optimisation, Decision Analysis, and Prediction Example

Chapter 3: Optimising Plant Expansion for Electric Cars

In Book 4: Risk Management for Businesses with Stochastic Six Sigma DMAIC Method

ISBN: 978-620-2-67095-1

Author: Vojo Bubevski (Independent Researcher)

Abstract

This chapter presents a marketing scenario in the electric car industry. A manufacturer of electric cars currently has the capacity to produce a known number of cars per year. The company believes that demand for electric cars will increase in the next few years, so it wants to expand its capacity. To finance this, the company plans to divert profits from car sales to a fund for eventual expansion. The management must decide on: i): the percentage of profits to divert to expansion, up to and including the expansion year; ii) the year to start expansion; and iii) the level of expansion, which must be one of three specified units of annual capacity. There are the following sets of uncertainties involved: i) the market size for electric cars; ii) the market share of the company; and iii) competitor improvements. The objective is to maximise the net profit of the company for the next five years. Stochastic optimisation is utilised to achieve the objective.

Keywords: Business Risk Management, Risk Assessment, Electric Car Industry, Finance, Stochastic Model, Monte Carlo Simulation, Six Sigma DMAIC

The Results

Optimisation results are presented in Table 9 including Total Units Sold, Units Sold Pre-Expansion, Profit Diverted to Expansion and Profit Net of Expansion Cost.

Table 9: Optimisation Results

Decisions results are shown in Table 10 including Trial Variables of Profit % Diverted to Expansion, Years from Now to Expand and Extra Capacity, and Expansion Start Year (ExpStartY).

Table 10: Decisions Results including Trial Variables

It should be noted that the Trial Variables are iteratively changed by the optimisation model until the maximal net profit is reached. Therefore, the maximal net profit of $3,416,189,584 is achieved with the Trial Variable values shown in Table 10.