Retail Ordering Policy Risk

Advances in Logistics, Operations, and Management Science - Novel Six Sigma Approaches to Risk Assessment and Management ◽

10.4018/978-1-5225-2703-9.ch009 ◽

2017 ◽

pp. 197-225

Keyword(s):

Simulation Models ◽

Stochastic Optimisation ◽

Control Stage ◽

Ordering Policy ◽

Stochastic Techniques ◽

Ordering Policies ◽

Optimal Ordering Policy ◽

Policy Risk ◽

Associated Risk Factors ◽

Optimal Ordering

The method is applied to Retail Ordering Policy to manage the associated risk. DMAIC framework applies stochastic techniques. Stochastic optimisation determines the optimal retail ordering policies to maximise profit. Simulate every determined optimal ordering policy and calculate profits, risks, and Six Sigma metrics to measure against specified target limits. Analyse simulation results and identify and quantify the main contributors to the profits variability by using sensitivity analysis. 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. Control stage is elaborated by reusing the data and presented stochastic optimisation and simulation models for ongoing management of the optimal strategy. Some changes are applied to the data and models however, in order to emulate the scenario of an implemented strategy.

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Production Scheduling Risk

Advances in Logistics, Operations, and Management Science - Novel Six Sigma Approaches to Risk Assessment and Management ◽

10.4018/978-1-5225-2703-9.ch008 ◽

2017 ◽

pp. 172-196

Keyword(s):

Six Sigma ◽

Production Scheduling ◽

Simulation Models ◽

Production Schedule ◽

Optimal Production ◽

Stochastic Optimisation ◽

Control Stage ◽

Stochastic Techniques ◽

Simulation Results ◽

Associated Risk Factors

The method is applied to Production Scheduling to manage the associated risk. DMAIC framework applies stochastic techniques. It runs stochastic optimisation to determine the optimal production schedules to minimise costs. It stimulates every determined optimal production schedule and calculate costs, risks, and Six Sigma metrics to measure against specified target limits. It analyses simulation results and identify and quantify the main contributors to the costs variability by using sensitivity analysis. The optimal production schedules are ranked based on their costs and associated risk factors. The technically best optimal production schedule is recommended to the management for implementation. Control stage is elaborated by reusing the data and presented stochastic optimisation and simulation models for ongoing management of the optimal strategy. Some changes are applied to the data and models however, in order to emulate the scenario of an implemented strategy.

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AN EOQ MODEL FOR PROGRESSIVE PAYMENT SCHEME UNDER DCF APPROACH

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595906001017 ◽

2006 ◽

Vol 23 (04) ◽

pp. 509-524 ◽

Cited By ~ 9

Author(s):

HARDIK SONI ◽

AJAY S. GOR ◽

NITA H. SHAH

Keyword(s):

Interest Rate ◽

Objective Function ◽

Present Value ◽

Payment Scheme ◽

Eoq Model ◽

Ordering Policy ◽

Ordering Policies ◽

Optimal Ordering Policy ◽

Optimal Ordering

An attempt is made to formulate optimal ordering policies for the retailer when the supplier offers progressive credit periods to settle the account. We define progressive credit periods as follows: If the retailer settles the outstanding amount by M, the supplier does not charge any interest. If the retailer pays after M but before N(M < N), then the supplier charges the retailer on the un-paid balance at the rate Ic 1. If the retailer settles the account after N, then he will have to pay an interest rate of Ic 2 ( Ic 2 > Ic 1). The objective function to be optimized is considered as present value of all future cash-out-flows. An algorithm is given to find the flow of optimal ordering policy. Analytic proofs are discussed to study the effect of various parameters on an objective function.

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