What is the difference between risk and uncertainty?
Risk
Present when managers know the possible outcomes of a particular course of action and can assign probabilities to them
Uncertainty
The future is unknown, and probabilities cannot be given for outcomes
What is the expected utility theory?
 Actual decisions made depend on the willingness to accept risk
 Expected utility theory allows for different attitudes towards risktaking in decision making
 Managers are assumed to derive utility from earning profits
 Managers are assumed to derive utility from earning profits
 Managers make risky decisions in a way that maximises expected utility of the profit outcomes

Utility function measures utility associated with a particular level of profit

Index to measure level of utility received for a given amount of earned profit

 Managers attitude toward risk
 Determined by the manager’s marginal utility of profit

Marginal utility (slope of utility curve) determines attitude towards risk
How can you tell if someone is risk averse from the utility curve?
You are risk averse with respect to a gamble if you prefer the expected value of the gamble with certainty to the gamble itself.
You are risk averse if the expected value is greater than the certainty equivalent.
Explain the flaws in the utility model by comparing it with the prospect model
Explain how to carry out project planning and control
Project planning and control
Stage 1: Understand the project environment
 Geosocial environment
 Geography and national culture
 Econopolitical environment
 Economy and government
 Business environment
 Customers, competitors and suppliers
 Internal environment
 Company strategy, resources and other projects
Stage 2: Project definition
 Aim, strategy and scope
Stage 3: Project planning
 Objectives: what is the goal and estimate of cost/ time
 Project scope: how to approach, feasibility, major tasks
 Contract requirements: reporting and performance, responsibilities
 Schedules: activities, tasks, timelines, milestones
 Resources: budget and budget control
 Personnel
 Control: monitoring and evaluating progress and performance
 Risk analysis
 Identify activities
 Estimate the times and resources for activities
 Identify relationship and dependencies between activities
 Identify time and resource schedule constraints
 Fix the schedule for time and resources
Stage 4: Technical execution
Stage 5: Project control
 Earned value analysis
 Probabilistic analysis: program evaluation and review technique
 Most likely time (m), optimistic time (a), pessimistic time (b)
 Mean = a + 4m + b / 6
 Variance = (b – a / 6) ^2
 Use expected times to identify critical path, and compute slack and project time
 Total project variance = Sum of variance of critical path activities
 Project variance is a measure of the risk involved in the project
 Crashing project networks
 Process of reducing time spans on activities so that the project is completed in less time.
 Focus must be on critical path activities
 In order to decide which activity to crash, the ‘crash cost slope’ of each is calculated (crash cost per time period).
 Crash the activity on the critical path which has the lowest crash cost slope.
Explain the components of a simple queuing system. Give examples
The calling population
 The population which customers/jobs originate
 The size can be finite or infinite (the latter is most common)
 Can be homogeneous (only one type of customer/job) or heterogeneous
The arrival process
 Determines how, when and where customer/jobs arrive to system
 The important characteristic is the customers/jobs inter arrival times
 Correct specification of the arrival process requires data collection of interarrival times and statistical analysis
The queue configuration
 Specifies the number of queues
 Their location
 Effect on customer behaviour (balking or reneging)
 The max size the queue can hold (infinite/finite capacity)
Service mechanism
 Can involve one or several service facilities with one or several parallel service channelsThe service provided by a server is characterised by its service time
 Typically involves data gathering and statistical analysis
 Most analytical queuing models are based on the assumption of exponentially distributed service times
The queue discipline
 Specifies the order by which jobs in the queue are served
 Most common principle is FIFO
 Other rules are: LIFO, SPIT, EDD
 Can entail prioritisation based on customer type
Examples of world queuing systems:
Commercial queuing systems
 Commercial organisations serving external customers
 E.g. dentist, bank, ATM, petrol stations, plumber, garage …
Transportation service systems
 Vehicles are customers or servers
 E.g. vehicles waiting at toll stations and traffic lights, trucks or ships waiting to be loaded, taxi cabs, fire engines, lifts and buses
Business – internal service systems
 Customers receiving service are internal to the organisation providing the service
 E.g. inspection stations, conveyor belts, computer support …
Social service systems
 E.g. ER at a hospital, waiting lists for organ transplants, waiting lists for primary school places
What are the advantages of multiple line queues vs single line queues
Multiple line vs single
Multiple:
 Service provided can be differentiated
 Labour specialisation possible
 Customer has more flexibility
 Balking behaviour may be deterred: several medium length queues are less intimidating
Single
 Guarantees fairness
 No customer anxiety regarding choice of queue
 Most efficient set up for minimising time in the queue
 Jockeying (queue switching) is avoided
Explain the importance of variability in queuing
If there were no variability, there would be no need for queues to occur
Statistically, the usual measure for indicating the spread of a distribution is its standard deviation sigma.
However, variation does not only depend on standard deviation.
To normalise standard deviation, it is divided by the mean of its distribution. The measure it called the variation of the distribution.
Describe the different between steady and transient state
Steady state condition
 Enough time has passed for the system state to be independent of the initial state as well as the elapsed time
 The probability distribution of the state of the system remains the same over time (is stationary).
Transient condition
 Prevalent when a queuing system has recently begun operations
 The state of the system is greatly affected by the initial state and by the time elapsed since operations startedas
Explain Little's Law
What is the probability that there is n jobs in the system in a queue in the M/M/1 model.
In the M/M/1 model what is:
Expected number of customers in the system
Expected time a job spends in the system
Expected number of customers in queue
Expected time a job spends in the queue
What are the different shortage costs in queuing and how do you analyse design costs trade offs?
1. External customers arrive to the system
 Profit organizations
 The shortage cost is primarily related to lost revenues “Bad Will”
 Non profit
 The shortage cost is related to a societal cost
2. Internal customers arrive to the system
 The shortage cost is related to productivity loss and associated profit loss
Usually it is easier to estimate the shortage costs in situation 2 than in situation 1.
What is Operations Research? Give examples of the different types
OR professionals aim to provide a rational basis for decision making by seeking to understand and structure complex situations and to use this understanding to predict system behaviour and improve system performance.
Done using analytic and numeric techniques to develop and manipulate models of organisational systems.
Types of OR models
 Linear programming: objective function and constraints are all linear functions of the decision variables
 Network flow programming: special case of linear program where situation can be modelled as a network
 Integer programming: variables are required to take integer values
 Nonlinear
 Dynamic programming: process described in terms of states, decisions, transitions and returns. Problem is to find sequence that maximises total return.
 Stochastic programming: Uses random variables for some aspects of the problem. Expression can be written for the expected value of the objective.
What is the common terminology for linear programming?
What are the assumptions in linear programming?

Proportionality

contribution of each activity Xj to the value of the objective function Z is proportional to the level of the activity Xj as represented by the CjXj term in the objective function. Similarly, the contribution of each activity to the lefthand side of each functional constraint is proportional to the level of the activity Xj, as represented by the AijXj term in the constraint.

Additivity

Every function in a linear programming model (whether the objective function or the function on the lefthand side of a functional constraint) is the sum of the individual contributions of the respective activities.

Divisibility

Decision variables in a linear programming model are allowed to take any values, including noninteger values, that satisfy the functional and nonnegativity constraints.

Since each decision variable represents the level of some activity, it is assumed that the activities can be run at fractional levels.

Certainty

The value assigned to each parameter of a linear programming model is assumed to be a known constant.
Proportionality

contribution of each activity Xj to the value of the objective function Z is proportional to the level of the activity Xj as represented by the CjXj term in the objective function. Similarly, the contribution of each activity to the lefthand side of each functional constraint is proportional to the level of the activity Xj, as represented by the AijXj term in the constraint.
Additivity

Every function in a linear programming model (whether the objective function or the function on the lefthand side of a functional constraint) is the sum of the individual contributions of the respective activities.
Divisibility

Decision variables in a linear programming model are allowed to take any values, including noninteger values, that satisfy the functional and nonnegativity constraints.

Since each decision variable represents the level of some activity, it is assumed that the activities can be run at fractional levels.
Certainty

The value assigned to each parameter of a linear programming model is assumed to be a known constant.
Describe the algorithm for shortest path problem
Objective of the nth iteration:
 Find the nth nearest node to the origin
Input to the nth iteration:
 n1 nearest nodes to the origin, including their shortest path and distance from the origin. (These nodes, plus the origin, will be called solved nodes)
Candidates for the nth nearest node:
 Each solved node that is directly connected by a link to one or more unsolved nodes provides one candidate – the unsolved node with the shortest connecting link to this solved node. (Ties provide additional candidates).
Calculation of the nth nearest node
 For each such solved node and its candidate, add the distance between them and the distance of the shortest path from the origin to this solved node. The candidate with the smallest such total distance is the nth nearest node (ties provide additional solved nodes), and its shortest path is the one generating this distance.
Applications
 Minimising the distance travelled
 Minimising the total cost of a sequence of activities
 Minimising the total time of a sequence of activities
Describe the mimimum spanning tree algorithm and its applications in the real world
Algorithm to solve the MST problem
1. Select any node arbitrarily, and then connect it to the nearest distinct node
2. Identify the unconnected node that is closest to a connected node, and the connect these two nodes. Repeat this step until all nodes have been connected.
3. Tie breaking: Ties for the nearest distinct node (step 1) or the closest unconnected node (step 2) may be broken arbitrarily, and the algorithm must still yield an optimal solution. However, such ties are a signal that there may be (but need not be) multiple optimal solutions. All such optimal solutions can be identified by pursuing all ways of breaking ties to their conclusion.
Applications of the MST problem
 Design of telecommunication networks
 Design of a lightly used transportation network to minimise the total cost of providing the links
 Design of a network of high voltage electrical power transmission lines
 Design of a network of wiring on electrical equipment
 Design of a network of pipelines to connect a number of locations
Explain the augmenting path algorithm
An augmenting path is a directed path from the source to the sink in the residual network such that every arc on this path has strictly positive residual capacity. The minimum of these residual capacities is called the residual capacity of the augmenting path because it represents the amount of flow that can feasibly be added to the entire path.
1. Identify an augmenting path by finding some directed path from source to sink in the residual network such that every arc on this path has strictly positive residual capacity. (if no augmenting path exists, the net flows already assigned constitute an optimal flow pattern)
2. Identify the residual capacity c* of this augmenting path by finding the minimum of the residual capacities of the arcs on this path. Increase the flow in this path by c*.
3. Decrease by c* the residual capacity of each arc on this augmenting path. Increase by c* the residual capacity of each arc in the opposite direction on this augmenting path. Return to step 1.
Some applications of the maximum flow problem
• Maximise the flow through a company’s distribution network from its factory to its customers
• Maximise the flow through a company’s supply network from its vendors to its factories
• Maximise the flow of oil through a system of pipelines
• Maximise the flow of water through a system of aqueducts
• Maximise the flow of vehicles through a transportation network
Explain the maxflow mincut theorem
Maxflow mincut theorem
The theorem states that, for any network with a single source and sink, the maximum feasible flow from the source to the sink equals the minimum cut value over all cuts of the network.
Equivalently, optimality has been attained whenever there exists a cut in the residual network whose value is zero.
What are the examples of some logical constraints?
What is a supply network?
Supply Network: A set of connected but geographically dispersed firms involved in making and delivery of product/service to end customers
What are the different supply network decisions?
Strategic
 investment in plants: numbers, locations
 introduction of new products: BOMs used
 manufacturing technology
 creation of logistics network
 make vs buy, supplier selection
Tactical
 manufacturing system
 inventory policy
 procurement policy
 IT system and information flow
 customer strategies, demand planning, forecasting
Scheduling of resources (labour, machine, vehicles)
Routing of raw materials and finished products
Solicitations of bids/quotations, order processing
What is procurement, explain the difference between direct and indirect procurement
Means purchasing inputs used in the firm’s value chain
 Raw material
 Supplies
 Consumable items
 Assets such as machinery, lab equipment, office equipment, buildings
Direct purchasing: buying for primary activities
Indirect purchasing: providing supplies and services for support activities
What are the different procurement strategies?
Performance based partnership
 High dependence on one supplier
 Used for strategic products
Competitive bidding
 In general, no longterm supply contract, rather multiple sourcing
 Used for interchangeable products
Securing continuity of supply
 Securing supply of bottleneck products, if necessary, at additional cost
 Reducing dependence on supplier by developing alternative products and looking for alternative suppliers
Category management and eprocurement solutions
 MRO (maintenance, repair, operating supplies) products require a purchasing strategy which is aimed at reducing administrative and logistic complexity
 Electronic catalogues
 Article catalogue (standardisation of product assortment)
Plot purchasing’s impact on financial results vs supply risk
How do you choose a sourcing strategy?
Single vs. multiple sourcing
 Assessment with regards to dependence, supply risk and transaction costs
Global vs. local sourcing
 Local sourcing preferred when product is a hightech product for which specification often changes; high flexibility and precision required in terms of delivery
Partnership or competitive relationship
 Competitive relationship mostly used when commodities are purchased, when the products are purchased in in large volumes and when many suppliers are available
Buying on contract or buying on spot basis

Contract buying preferred when prices are expected to rise

Advisable to choose a mix between contract and spot buying
Price agreement vs. performance agreement
 Performance agreement often used when services are purchased (Service level agreement)
 Price agreements might be sufficient if standard quality products are purchased (for example certain types of fabric)
What is the objective of locations strategy? What factors impact how it is picked?
Objective of location strategy: to maximise the benefits of location to the firm
Location decisions can be determined by:
 Marketing strategy
 Compete on cost: find low cost location
 Compete on level of responsiveness: close to transportation networks/market
 Cost of doing business
 Growth
 Potential access to more customers
 Expanding product portfolio
 Depletion of resources
 Industries where resources determine key success
Factors influencing location decisions
 Economic
 Tariffs, taxes, trade concession, capital subsidies
 Temporal
 Competition, demand patterns, industry dynamics, presence of related industries (clustering), skilled employees’ engagement
 Physical location
 Labour cost, developed infrastructure, proximity to market, cost of inputs, competitors locations, specialised inputs
 Organisational factors
 Strategic role of a factory amongst multiple plants
Why do companies go abroad? What should they consider?
 M&A
 Business growth
 Faster lead times/cost reduction
 Increase of offshoring
Aspects countries should consider:
 Country level
 Political risks, legislation, economic issues (currency), location, labour, availability of suppliers
 Region level
 Labour, cost, regulations, proximity to resources, land cost
 Site level
 Site size/cost, distribution systems, proximity to suppliers, environmental impact, clustering
Why are location decisions so important?
Irreversible allocation of the firm’s capital – long term/strategic decision
Business continuity
Impact on supply chain performance
 Lead times
 Inventory
 Responsiveness to demand
 Flexibility
 Quality