Aim
The course aims to deepen the students’ knowledge and understanding of methods for developing the management of production and inventory systems, both from a theoretical and applied perspective. An important aspect is to enhance the students’ ability to structure and manage complex tasks, processes and assignments in the form of projects.

Concrete objectives are to provide the students with:

• knowledge and understanding of project management from a theoretical and an applied perspective

• enhanced knowledge of quantitative models for control of production and inventory systems

• training and enhanced ability to conduct and manage industry projects relating to the management of production and inventory systems

Knowledge and understanding
For a passing grade the student must

• be able to use advanced quantitative methods for control and evaluation of production and inventory systems,

• understand and be able to apply the principles for effective project management,

• show the ability to independently structure and solve industry based project assignments of investigative nature.

• understand the limitations in commonly used production control models, and quantitative modelling of these systems. An important issue that the student must be able to analyse is the strengths and weaknesses with “Pull” oriented CONWIP systems in relation to “Push” oriented MRP systems.

• understand the principles for hierarchical planning of production and personnel, scheduling and shop floor planning within a “Pull” oriented production system. Moreover, to be able to apply quantitative models to improve the control at all these levels.

• critically examine and choose an appropriate model description to effectively manage commonly seen production systems. A special focus is placed on generalised KANBAN systems (CONWIP systems). This is contrasted to the MRP based systems studied in the course MIO030 Production and Inventory Control.

• be able to explain, apply and evaluate different types of quantitative models for efficient control of CONWIP type systems.

• correctly perform statistical analysis of data from stochastic processes that constitute input data to quantitative production and inventory control models. A special focus is placed on analysis of customer demand data and different types of process and activity times. This involves, among other things, fitting of distribution functions and various types of hypothesis testing.

• understand different principles for determining cost parameters (such as holding costs, shortage costs and set up costs) commonly used in production and inventory control models.

• be able to explain and apply quantitative models for control of single- and multi-stage inventory systems, with uncertain demand, and/or uncertain lead-times. This involves the computation of various types of service levels and expected costs, and the optimization of control parameters like reorder points, order quantities and order-up-to levels.

• know important concepts and terms often used in project management.

• have an understanding for differences in project processes depending on scope of work.

• conduct requirement engineering, project planning, and project control for a fictitious project (with adequate supervision provided).

• be able to communicate with potential project stakeholders.

• be able to apply and integrate knowledge acquired during their education to solve real business problems within the area of production management and logistics.

• independently acquire new knowledge within areas relevant for solving the project assignment at hand.

• independently conduct and manage a real company based project of investigative nature with high demands on structured project management, and reporting/documentation of results, both in terms of oral presentations and written reports.

Skills and abilities
For a passing grade the student must

• aggregate production planning (LP and MIP models)

• scheduling and shop floor planning with focus on CONWIP systems (deterministic and stochastic models)

• stochastic models for evaluation and optimisation of single-echelon systems under different assumptions: continuous review, periodic review, continuous normally distributed demand, discrete compound Poisson demand, single and multi periods, complete backordering and lost sales, service levels (S1, S2 and S3) and backorder costs, (R,Q), (S-1,S) and (s,S) systems, deterministic and stochastic lead-times (dependent and independent), uncoordinated and coordinated replenishments.

• stochastic models for evaluation and optimisation of multi-echelon (supply chain) inventory systems under different assumptions: serial and distribution systems, the METRIC approximation versus exact evaluation, (S-1,S) and (R,Q) policies.

in area of project management:

• be able to apply tools and knowledge from previous courses to determine the stakeholders’ requirements and develop a demand specification and project goals,

• be able to describe the project’s work plan through a project planning process,

• report deviations and progress and modify the project plan if necessary,

• present both orally and in writing, the implementation and progress of the project process, gained experiences, and project results.

Contents
The course consists of three integrated parts or sections:

Methods and principles for effective project management

This part of the course deals with different types of projects and project processes, requirement engineering, project planning, organisation and control of projects, team work, the role of the project leader, and projects as part of the firm’s business development.

Advanced methods for control of production and inventory systems

This part of the course deals with advanced quantitative methods for control of single- and multi-echelon production and inventory systems.

Live company based project in the area of production management and logistics

This project provides the students with the opportunity to apply the theoretical knowledge they have acquired in past and present courses in the field of production management and logistics. In the project, the students are confronted with real problems, which require choice of appropriate models to analyse the situation. It also requires development and proposal of suitable solution approaches and improvement strategies. The work is reported in a detailed and well structured technical report.

Section on project management

This part of the course provides the students with the foundation effective project management. During the course the students will work with two projects where their skills in project management can be put to use, first a fictitious project to hone their skills in applying the studied theory on project work and project management, and then a large live company project in the field of production management and logistics (see description below).

Project management treats the project as a component in the firm’s business development. Today’s business projects require integration between the product development activities and the development of the manufacturing system for a certain product or product family. Project specification is an important tool in project management for defining the project identity. The course treats concepts as project processes, demand specification, project preparation, project control, project economy, team work, project organisation, the role of the project leader, project termination and learning experiences.

Introduction to project management

Project types, the project process, business projects, the project specification

Project requirements and planning

Project types, requirement engineering, objectives, project planning

Project control

Project economy, concept phase, system design, detailed design, project control.

Project structure

Project plan, project organisation, risk, project evaluation.

Project communication

Project documentation, communication, business law, procurement, commissioning, project termination, Capability Maturity Model.

Production development projects

Project process for production development

Section on advanced quantitative models for production and inventory control

This part of the course aims at deepening and expanding the students’ knowledge in quantitative modelling of production and inventory systems both from a theoretical and applied perspective. The starting point for the content treated in the course is the theories and methods studied in the course MIO030 Production and Inventory Control. We discuss challenges associated with applying quantitative models in practice, for example, when it comes to determining cost parameters and distribution fitting.

Live company based project in production management or logistics

The live company based project offers the opportunity to apply the theoretical knowledge acquired in past and present courses in production management or logistics. The choice of production inventory system depends on the production environment, the requirements and prioritised criteria. In the project, the students are confronted with real business problems, which require them to analyse, propose and develop appropriate methods for solving the problem at hand. The exact nature of the problems facing the students vary with the companies and the projects they offer. The projects are carried out in groups of 3-4 students. The students are encouraged (but not required) to use their own contacts in industry and design a project that fits their interests. All projects needs to be approved by the course supervising team before inclusion in the course. The project work and the obtained results are documented in a detailed technical report and presented orally to the class both during the project and after it is completed.

Literature
Axsäter S. Inventory Control, Second edition. Springer, New York, 2006.
Cleland, D & Ireland, L., Project Management. Strategic design and implementation. McGraw-Hill, 2002.
Course compendium
Scientific research papers:
A1 Hopp, W.J. and M.L. Spearman (2004), “To Pull or Not to Pull: What is the Question?”, Manufacturing and Service Operations Management, Vol. 6 (Spring 2004), pp. 133-148.
A2 Spearman, M.L. and M.A. Zazanis (1992), “Push and Pull Production Systems: Issues and Comparisons”, Operations Research, Vol. 40 (3), pp 521-532.
A3 Hopp, W.J., Spearman, M.L. and I. Duenyas (1993), “Economic Production Quotas for Pull Manufacturing Systems”, IIE Transactions, Vol 25 (2), pp. 71-79.
A4 Krajewski, L.J., King, B.E., Ritzman L.P. and D.S. Wong (1987), “Kanban, MRP and Shaping the Manufacturing Environment”, Management Science, Vol. 33 (1), pp. 39-57.
A5 Spearman, M.L. and R.Q. Zhang (1999), “Optimal Lead Time Policies”, Management Science, Vol. 45 (2), pp. 290-295.
A6 Andersson J. and J Marklund (2000), “Decentralized Inventory Control in a Two-Level Distribution System”, European Journal of Operational Research, Vol. 127 (3), pp.483-506.

Code: 0107. Name: Take Home Assignments.
Higher education credits: 3. Grading scale: TH. Assessment: Take home exams/assignments in production and inventory control, and project management, together with a live, relatively large, industry project conducted at an industrial company. The final grade in the course is a synthesis of the performance across these different activities. Contents: The assignment deals with: advanced quantitative methods for control of single- and multi-echelon production and inventory systems. a fictitious project to hone their skills in applying the studied theory on project work and project management.

Code: 0207. Name: Project.
Higher education credits: 6. Grading scale: UG. Assessment: Take home exams/assignments in production and inventory control, and project management, together with a live, relatively large, industry project conducted at an industrial company. The final grade in the course is a synthesis of the performance across these different activities. Contents: The project provides the students with the opportunity to apply the theoretical knowledge they have acquired in past and present courses in the field of production management and logistics. In the project, the students are confronted with real problems, which require choice of appropriate models to analyse the situation. It also requires development and proposal of suitable solution approaches and improvement strategies. The work is reported in a detailed and well structured technical report.