Marketing & Retail
Risk Management and Optimisation Examples
Chapter 9: Retail Ordering Policy Risk
In Book 1: Novel Six Sigma Approaches to Risk Assessment and Management
ISBN: 9781522527039
Author: Vojo Bubevski (Independent Researcher)
Abstract
The method is applied to Retail Ordering Policy to manage the associated risk. DMAIC framework applies proven stochastic techniques as follows: 1. Define: Stochastic optimisation determines the optimal retail ordering policies to maximise profit; 2. Measure: Simulate every determined optimal ordering policy and calculate profits, risks, and Six Sigma metrics to measure against specified target limits; 3. Analyse: Analyse simulation results and identify and quantify the main contributors to the profits variability by using sensitivity analysis; 4. Improve: The optimal retail ordering policies are ranked based on their profits and associated risk factors. The technically best optimal retail ordering policy is recommended to the management for implementation; 5. Control: The Control stage is elaborated by reusing the data and presenting stochastic optimisation and simulation models for ongoing management of the optimal strategy. Some changes are applied to the data and models, however, to emulate the scenario of an implemented strategy.
Keywords: Retail optimal ordering policy; Retail ordering policy risk; Six Sigma; DMAIC; Stochastic Optimisation; Monte Carlo simulation.
The Results
In the Improve stage, the analysis results of the two optimal ordering policies are compared, and the less risky optimal policy is acknowledged (Table 5).
The two optimal schedules are compared considering the Mean Profit (µ), Standard Deviation (σ), Process Capability (Cp), Process Capability Index (Cpk), Sigma Level (σ-L), Sharpe Ratio (SR), and the profit Regression Mapped Value (RMV) of Total Season Demand, i.e., the only risk factor. The comparison shows Mean Profit (µ) for Policy 2 is reduced by $305.13 (i.e., 3.97%). However, Standard Deviation (σ) has been reduced by $393.69 (i.e., 12.38%), and Regression Mapped Value (MRV) has been reduced by $401.49 (i.e., 13%). Process Capability (Cp), Process Capability Index (Cpk), and Sigma Level (σ-L), are all reasonably increased. Finally, Sharpe Ratio (SR) is increased by 11%, i.e., the profit per unit of risk relating to the Risk-Free rate has been increased by 11%. Also, should be emphasised that the reduction of Standard Deviation is greater than the reduction of profit. Therefore, Policy 2 is less risky than Policy 1 and importantly gains more profit per unit of risk taken versus the Risk-Free Rate. So, it is the best technical solution and should be recommended to the management for implementation.
Table 5: The Optimal Ordering Policies Analysis Results
| Profit µ | σ | Cp | Cpk | σ-L | SR | RMV | |
| Policy 1 | $7,686.75 | $3,180.47 | 0.4302 | 0.3990 | 1.2471 | 2.285 | $3,178.27 |
| Policy 2 | $7,381.62 | $2,786.78 | 0.5025 | 0.4841 | 1.4842 | 2.526 | $2,776.78 |
Chapter 2: Optimal Pricing Strategy in Marketing
In Book 5: Risk & Decision Analysis in Risk Management for Businesses
ISBN: 978-620-2-79642-2
Author: Vojo Bubevski (Independent Researcher)
Abstract
This chapter presents a Risk Analysis of the optimal pricing strategy in a two-channel market, Web and Retail. If the price in one channel is set low, it will not only create a higher demand in that channel, but it will impact the demand through the other channel. Hence, pricing decisions are not obvious. A stochastic optimisation model is used to find the pricing strategy that maximises the mean profit from the two channels. What-If and Sensitivity Analyses are performed, which deliver a complete assessment of the output’s probability, i.e., the optimal profit, and quantify the output variability based on changes in associated inputs. The risk analysis results support management decision-making on whether to apply the pricing strategy with the optimal profit.
Keywords: Risk & Decision Analysis, Marketing Pricing Strategy, New Product Profitability, Financial Risk, What-If Analysis; Sensitivity Analysis; Monte Carlo simulation; Stochastic model.
The Results
The optimisation completion report was presented. The report displayed “Optimisation Completed” at the top of the window. At the bottom, it reports that the maximal profit value achieved as “Best=365944.3956” (i.e., in dollars ($)). Next, it reports that the “Best” is achieved in the 989th trial. Then, it reports the Profit’s initial value by displaying “Original=309133.7500” (i.e., in dollars ($)). Subsequently, it shows the pre-defined number of trials by displaying “Trials=1000”. Finally, it reports the optimisation elapsed time by displaying “Time=00:02:08”.The simulation calculated the results which are as follows. The Profit Mean is $365,944.40 with a Standard Deviation of $34,449.70, i.e., 9.414%, which is a low risk. There is a 5% probability that Profit will be below $307,516; a 90% probability that it will be in the range of $307,516 – $425,878; and a 5% probability that it will be above $425,878.