MSc Supply Chain & Logistics Management

This module aims to provide students with a critical understanding of the fundamental building blocks of Supply Chain Management (SCM) and e-Supply Chains from a strategic perspective with a view to developing their capabilities in modelling, analysing, diagnosing and re-designing/improving supply chains.

It covers Understanding the Supply Chain; Strategies alignment; Supply Chain performance; Supply Chain benchmarking; Sourcing decisions; Supply Chain network design; Sustainability in the supply chain and case studies.

At the end of this module students will be able to:

• display an understanding of the fundamental building blocks of supply chains, including terminology; factors associated with SCM (business, technological, logistical and legal factors) and the relationship between traditional management functions and technology (such as marketing, purchasing, IT etc)
• demonstrate a critical understanding of how to analyse and diagnose supply chains from a strategic perspective by modelling supply chains; analysing supply chain practices and performance and drawing up supply chain improvement/development strategies for a chosen business
• display an understanding of sustainability issues in modern supply chains including key concepts; methods to assess sustainability and the ability to propose strategic improvements for the sustainability of supply chains

Assessment and feedback is in the form of 40% individual work and 60% group work.

This module aims to provide students with an in-depth knowledge and understanding of those key concepts, methods, tools and techniques that are fundamental to effective and efficient running of supply chain operations from suppliers’ suppliers to customers’ customers.

The module covers an Introduction to supply chain operations; Demand management; Master planning; Material management; Capacity management; Inventory management; Distribution management; and Case studies.

On completion the participants will be able to design and manage operational supply chain planning, scheduling and controlling systems for complex and disparate operations. In particular, students will:

• Understand the basics of supply chain operations in relation to planning, operating, scheduling and controlling, including knowledge of supply chain operation practices (demand management, master planning, material management, capacity management and inventory management); understanding the impact of supply chain operation practices within commercial and social contexts and global trends in supply chain operation practices
• Identify and assess different methods, tools and techniques for managing plans, operations and materials of end-to-end supply chains
• Demonstrate the ability to apply those methods, tools and techniques in different contexts, including manufacturing and service sectors, with a critical awareness of drivers and obstacles in real life and the ability to produce sensible solutions to overcome them

Assessment and feedback is in the form of 2 in-class tests (60%) and one coursework (40%).

This module aims to introduce students to the 'softer' aspects of engineering management. Given some key organisational and technological issues, the main focus is to examine the relationship between “human” elements and change management from an engineering-oriented perspective.

At the end of this module students will be able to:

• discuss key issues in organisation and technology by critically defining and discussing key characteristics of an organisation and evaluating the impact of technology on the workplace of the future
• understand modern people management concepts and practices by evaluating leadership and motivation techniques using analytical approaches and will demonstrate critical understanding of the role of people in a modern organisation
• identify the challenges and consequences of change, including defining and discussing the skills required to handle organisational change and the drivers and obstacles towards organisational change
• understand the impact of organisational and technological issues on people when managing changes. This includes identifying drivers and obstacles from organisational, technological and human perspectives using analytical methods as well as creating strategies to help implement changes

Assessment and feedback is given in the form of:

• group presentations and a group report
• an individual essay

The class is devoted to the concepts and approaches to effective performance measurement and management at an organisational level.

Performance Measurement and Management (PMM) is one of the key factors for survival and prosperity of business systems. Business analysts can typically expect to be involved in the design of PMM systems and in the evaluation of performance information.

This module focuses on the different approaches to performance measurement and management commonly used across public and private sectors. The class focuses at the strategic, and organisational level of PMM while providing the essential knowledge and skills at the technical level.

The module starts with presenting the background of performance measurement to provide the students with an understanding of the roots of some of the common problems in measuring and managing performance in organisations. This will follow with discussing PMM from five different but highly interrelated perspectives, namely stakeholder, customer, comparative, operations and integrative perspectives. Students are encouraged to get more insight into the techniques and concepts by carrying out directed readings as well as completing a group and an individual assignment.

The class will conclude by discussing implementation and managerial issues in measuring business performance.

This module aims to develop a critical understanding of operation, structure and implementation issues around enterprise resource planning (ERP) systems as used in industry. Students learn how a typical ERP system works using an up-to-date SAP training package and consider the real-life use of a typical software package within an organisation.

The module covers: Business Excellence; What is ERP?; Business Planning and Control; Expected Benefits; How does ERP work?; ERP Pre-Requisites; Selecting the right system; Implementation Planning.

At the end of this module students will have a critical understanding of:

• terminology relating to MRP, ERPII and ERP
• the benefits and limitations of using ERP systems for making operational, tactical and strategic decisions within businesses, including the ways in which ERP systems may support or hinder decision making at different levels
• learning to operate and build a product in a SAP environment
• the scope, implications, logic, critical requirements to facilitate successful implementation of ERP systems, in particular the relationship between ERP systems, other ICT systems, business processes and human factors

Assessment and feedback is in the form of a 40 minute quiz (40%) and one coursework (60%).

This module aims to provide students with skills and knowledge relating to the use of engineering practices in Project Management with particular respect to the project triple constraint: time, cost and quality.

The module covers: project management principles, concepts and processes; organisational influences, project stakeholders and project lifecycle; project scoping such as project definition, project objectives, project deliverables, and work breakdown structure; Project planning and scheduling: definition of events, activities and nodes, network diagram, analysis of critical path, PERT method and use of industry standard software packages; Project controlling: cost estimate, budget setting, risk identification and assessment and contingency planning.

At the end of this module students will be able to:

• demonstrate a good understanding of project management practices and practical skills to manage project scope – including translating project specifications into work packages
• define and schedule project activities using tools such as critical path and PERT methods; estimate cost and determine budget using analogous and three-point estimating methods; identify and control quality standards using cost of quality and other tools
• develop a good understanding of the inter-dependency between various project management knowledge areas, such as managing projects under constraints; identifying and assessing risks and developing contingency plans
• understand the importance of project stakeholders and their impact on project management, including managing stakeholder relationships

Assessment and feedback is in the form of a group report (50%) and an individual project (50%).

This module aims to introduce students to the practical reality of analysing and solving supply chain management problems in real life. Students explore a series of case studies and real life problems relating to supply chain management (SCM) in a range of industries.

At the end of this module students will be able to:

• apply concepts and principles relevant to the management of supply chains and networks to the analysis of case studies and relatively unstructured problems for a range of industries
• select and apply appropriate problem-structuring methods, data collection & analysis methods and conceptual models for a given problem in SCM
• formulate a range of alternative options to deal with a given problem in SCM; to evaluate such options in terms of their feasibility, acceptability, vulnerability, and strategic fit; and to make appropriate recommendations to senior management

One of the four assignments consists of a prepared case study that must be tackled by students on an individual basis and is assessed through an individual report. The other three assignments are done on a group basis, assessed by formal business-style presentations as well as written reports.

The aim of the individual project is to allow students to combine the skills learned in other modules of the course and apply them within a significant project in a specific area of design, manufacture, or engineering management. This will be achieved through students carrying out work into a particular topic relating to their course and preparing a dissertation that documents the project.

On completion of the module the student is expected to be able to:

• define a valid project in a cutting-edge field of study relevant to the student’s degree – with an appropriate methodology and work plan for the project
• plan, manage and complete project, involving where appropriate technical analysis and independent critical thinking. This involves giving a thorough, logical and critical review of the subject matter; using appropriate tools, processes and levels of analysis in the project and applying project management techniques to manage a successful project
• document their project using suitable presentation techniques (such as language, figures, writing, layout, structure etc.); showing clear evidence of the value of the project and its outcomes and describing the project with clarity

Based on the work of a project, a student will submit an individual dissertation that will account for 90% of the final mark for the class. An interim project justification report will account for the remaining 10% of the mark.

The class is devoted to the concepts and approaches to effective performance measurement and management at an organisational level. This module focuses on the different approaches to performance measurement and management (PMM) commonly used across the public and private sectors.

This module covers: the use of spreadsheets for forecasting, simulation, optimisation, data analysis, and to support technical report writing; different types of forecasting techniques; method application using spreadsheet models and principles of effective computer programming through the development of macros within spreadsheets.

At the end of this module students will be able to:

• create simple but appropriately organised spreadsheet models for complex problems, then use the spreadsheet to support traditional operational research techniques such as forecasting, optimisation and simulation
• describe the main categories of forecasting technique and their data requirements
• construct and interpret forecasts using smoothing methods and decompositional analysis
• calculate and interpret forecast errors to track accuracy of forecasts
• understand effective forecasting systems, basic principles of computer programming and VBA for developing macros within Excel

Assessment is in the form of class tests (30%) and an assignment (70%).

This module aims to provide students with an in-depth understanding of the key principles, concepts, tools and techniques of total quality management and continuous improvement together with an awareness of how these can be used to design and deliver an integrated continuous improvement programme.

The module covers: Introduction to Total Quality Management including definitions, basic elements and quality costing; ISO Quality Management System Standards; Quality improvement tools; Reliability Engineering and Continuous Improvement Concepts (FMEA, Lean methodologies, Kaizen, Poka Yoke, Theory of constraints).

At the end of this module students will be able to:

• understand the key principles, concepts, tools and techniques of total quality management and continuous improvement
• apply key principles, concepts, tools and techniques of total quality management and continuous improvement, including planning for real-life application of tools and techniques
• formulate improvement strategies within a particular context

Assessment and feedback is in the form of one group work (a case study report, 40%) and one individual coursework (a journal article, 60%).

This module aims to raise awareness of the role lean principles and green concepts can have in reducing the environmental impact of logistics activities. It also provides students with a critical understanding of trade-offs and win-win opportunities which exist between logistics activities and environmental issues through case studies. Students will gain a realistic appreciation of how organisations can increase the value of both operational and environmental choices in the context of logistics.

The module covers: Lean principles and Green concepts, such as cleaner production and eco-efficiency; the links between lean and green and implications for logistics management and operations management and logistics.

At the end of this module students will be able to:

• understand and examine how lean principles fit with green concepts in logistics
• describe and address trade-offs between logistical decisions and environmental impacts
• understand how to integrate operations management with green concepts to improve logistical performance
• discuss the key trends in lean and green logistics management

Assessment and feedback is in the form of a Group Case Study Presentation (30%) and an essay (70%).

This module aims to provide a series of strategic frameworks for managing high-technology businesses. The main focus is on the acquisition of a set of powerful analytical tools which are critical for the development of a technology strategy as an integral part of business strategy. These tools can provide a guiding framework for deciding which technologies to invest in, how to structure those investments and how to anticipate and respond to the behaviour of competitors, suppliers, and customers. The course should be of particular interest to those interested in managing a business for which technology is likely to play a major role, and to those interested in consulting or venture capital.

At the end of this module students will be able to:

• Demonstrate a comprehensive understanding of the role and importance of technology in business strategy formulation process
• Develop the ability to critically assess concepts, tools and techniques of managing technology for both stable and turbulent business environments
• Evaluate complexity and develop appropriate technology strategy models for specific cases

Grades will be determined by class participation assessed through four two-page papers on case studies, which may be written in groups of 4 people (40%), and an individual final technology strategy report based on an in-depth exploration of technology strategy in an assigned industry (60%). There is no final exam.

This module aims to introduce students to the principles of Lean and Six Sigma. From Continuous Improvement approaches to organisational requirements, the module covers the critical success factors needed to support sustainable and effective business transformation.

The module covers: an Introduction to Lean Thinking, Six Sigma, and Lean Six Sigma (LSS); Comparing and Contrasting Lean & Six Sigma; DMAIC Continuous Improvement Methodology; LSS project characterisation and selection; Lean and Six Sigma metrics; Overview of basic Lean Tools and Techniques including: affinity diagram, project charter, project selection matrices, SPC, Ishikawa, 5 Why’s, 5S, SMED, DoE, etc.; Evolution of Lean Six Sigma (from manufacturing to service environments and the implications of each).

At the end of this module, students will be able to:

• gain an appreciation for Lean Six Sigma as a Continuous Improvement methodology, and understand the implications of its application in manufacturing, transactional and service processes
• apply the Lean Six Sigma methodology (DMAIC) and basic Continuous Improvement tools to solve real world problems
• evaluate the Critical Success Factors and fundamental barriers in the execution of both Lean & Six Sigma initiatives

Assessment and feedback is in the form of an exam (35%) and an assignment in the form of a project report (60%) and project presentation (5%).

This module aims to introduce students to the theories and principles of Systems Thinking. The module also introduces the methods, tools and techniques for modelling, analysing, improving and designing systems in a variety of organisations including industrial, commercial and public sector.

The module covers: Systems theory, concepts and approaches; Hard and soft systems analysis and systems dynamics; Systems and organisational performance – including leadership in a systems environment and ‘design’ in a systems environment and Practical application of Systems Thinking.

At the end of this module students will be able to:

• show clearer understanding and knowledge of hard and soft approaches and how they can be used to deal with complexity and system behaviour in a business context
• develop understanding of fundamental cybernetic principles that form the foundations of Checkland’s Soft System Methodology and Beer’s Viable System Model
• develop knowledge and skills in systems analysis and business process modelling
• critically evaluate the most appropriate methodology to model, analyse and design engineering/business systems across a range of organisations
• demonstrate an understanding of how to model a business system and to develop a solution to solve a business system problem
• develop an awareness of the importance of system approaches in management interventions

Assessment and feedback is in the form of a group presentation and one coursework in the form of a reflective diary.

This module aims to introduce statistical modelling to support business and management decisions. The module provides a basis for students to learn a range of widely used methods ranging from effective presentation of data to development of sophisticated statistical models.

This module covers two main areas: Part one covers an introduction to the basic theory and application of statistical modelling, including data analysis, probability theory, distributions and moments, estimation and hypothesis testing. Part two focuses on regression modelling and multivariate analysis. While key background theory will be presented, the emphasis is on the generation and interpretation of output from commercially available software. Throughout, there is an emphasis on the use of statistical analysis to help support decision-making and the management of business and industrial problems.

At the end of this module students will be able to:

• display and interpret data using appropriate visual displays and select, construct and interpret summary statistics
• understand probabilistic reasoning and compute probabilities for simple problems
• use graphical methods to identify appropriate models and estimate parameters
• apply and interpret formal statistical estimation procedures and goodness-of-fit tests
• develop and validate appropriate simple and multiple linear regression models
• understand the basic principles of classification methods
• understand the basic principles of Analysis Of Variance (ANOVA)
• use SPSS to perform appropriate statistical analyses

Assessment and feedback is in the form of an exam (50%) as well as a group report and class test (50%).

This module aims to introduce students to modelling techniques that can be used to help support decision-makers. Business simulations are one such important modelling technique. This class focuses on two specific business simulation techniques; discrete-event simulation and system dynamics.

This module covers: a rational approach to simulation using a number of examples from manufacturing and service operations; the use of probabilistic distributions in simulation where the emphasis is on ‘relevance for use’ rather than the underlined statistical theories and techniques; basic and advanced techniques of Simul8 (a popular DES software); complex business problems through the construction of causal loop diagrams and computer software used specifically for system dynamics modelling.

At the end of this module students will be able to:

• understand the main uses of business simulations and when to use the two different types; discrete event simulation and system dynamics
• approach a DES project in a rational way, using performance measures
• investigate certainty of output and validation of a DES model
• use Simul8 software and some of its advanced tools
• appreciate how a causal loop diagram, representing a given system, can be translated into a quantitative system dynamics model
• appreciate how system dynamics models can be used to investigate systems behaviours so that practical recommendations can be made for improvements

Assessment and feedback is in the form of an assignment (50%) and an exam (50%).

This module aims to introduce the fundamental techniques of risk management, risk-informed decision making and the general principles of risk analysis and its place in risk management, as well as the chance to develop skills in applying these methods to a variety of engineering examples.

The module covers: modelling approaches and methods used by industry currently to manage risk; tools and techniques that are gaining popularity in industry but are not widespread; the basic principles of uncertainty and consequence modelling and the tools and techniques required to apply these principles and Industry standard processes and software tools.

At the end of this module students will be able to:

• understand theory that underpins standard approaches to elicitation of expert judgment
• understand basic theory of fault and event tree modelling
• understand the standard approaches to modelling dependency between random variables
• develop the ability to assess the robustness of a risk model
• understand the standard methods used in ALARP decision-making
• appreciate the consequences of choosing specific measures for risk

Assessment and feedback is in the form of an exam (50%) and one group assignment (50%)

This module considers the distinctive characteristics of Service Operations Management and how these differ from manufacturing. Through the modelling of service operations, the module illustrates how the different types of service operations can be effectively and efficiently managed.

The module covers: Service Characteristics, dimensions and typologies; Design in services; Service capacity and yield management; Strategic links in services; Productivity and quality management in services; Cultural issues in services and New challenges in services (including service science).

At the end of this module students will be able to:

• understand the complexity of service operations, including their unique features and how they differ from manufacturing
• use taxonomy models to differentiate services from different perspectives and to be able to critically evaluate and use these models as appropriate
• critically discuss the use and applicability of strategic and conceptual models and theories developed for service operations in areas such as service design, capacity, strategic links in services (including customer satisfaction and customer loyalty) as well as cultural effects in services
• understand the complexity of the subject of ‘productivity’ and its relationship with quality in services
• appreciate the importance of considering services as a system
• understand the new challenges and issues in service operations including professional services, uses of IT and difficulties in measurement

Assessment and feedback is in the form of a group assignment (30%) and an individual assignment (70%).

This module aims to prepare students to be state-of-the-art business analysts in the era of big data. It gives them knowledge of and ability to use business analytics tools to make sense of both qualitative and quantitative data, identify the main messages in those data and report such messages back to decision-makers appropriately in order to help them to make better decisions.

The module covers: Application and interpretation of visualisation and advanced analysis techniques alongside an investigation of what makes good data, and both theoretical knowledge in the form of analytics techniques as well as new software skills in relevant analytics software.

At the end of this module students will be able to:

• understand what makes good data collection and be able to critically appraise data quality
• understand a range of different business analytics techniques and be able to apply them to large, complex data sets
• apply appropriate techniques for visualization of high dimensional complex data
• select the appropriate business analytics technique and interpret the results to answer specific questions posed by a client
• apply appropriate qualitative data mining tools

Assessment and feedback is in the form of a class test (30%) and a group assignment (70%).