Understand the role of operations in both production and service enterprises. Introduce Lean thinking and structured operations improvement tools. Introduce a range of quantitative methods and highlight their application in the decision making process for solving real world problems. Provide an understanding of optimal decisions under constraints. Provide an understanding of design and analysis of operations under uncertainty. To provide students with modeling and software capabilities that can be applied to operations design and analysis.

Lean Thinking and Operations Introduce students to lean thinking and operations improvement tools used within DMAIC (Define-Measure-Analyze-Improve-Control) projects. Related lean thinking to operations modeling methods. Operations Modeling - Software: Introduce and provide students with base skills to use software to solve operations optimization models. The focus is primary on introducing the student to spread sheet modeling, but brief introductions to other modeling and optimization software will be given. Students will apply software modeling skills obtained here to subsequent topics. Operations Modeling Under Constraints Basic definition of Linear programming, demonstrate method via graphical method, model formulation applications in operations. Simplex method, Artificial starting solution method, interpretation of simplex tableau, sensitivity analysis. Transport model, Assignment model, Shortest Route model, Network Minimisation model, Maximum Flow Model, Transshipment model. Introduce binary and integer applications in operations analysis, integer solution methods such as branch-and-bound and meta heuristics solution methods. Decision Making Under Uncertainty. Introduce decision making under uncertainty. Introduce basics of simulation using spreadsheets. Introduce basic queuing and inventory models.

On successful completion of this module students will be able to: 1. Identify the difference between linear and non-linear models and understand where they can be applied. 2. Given a problem description, develop a linear or integer programming models. 3. Apply solution methods for linear, network, and integer-programming models. 4. Apply basic decision making models under uncertainty. 5. Be able to develop spreadsheet models and implement quantitative solution methods.

N/A

Use of computers.

The module will be delivered through formal lectures and computer laboratories and tutorials.