Operations Control Key Sources: Data Analysis and Decision Making (Albrigth, Winston and Zappe) An Introduction to Management Science: Quantitative Approaches to Decision Making (Anderson, Sweeny, Williams, and Martin), Essentials of MIS (Laudon and Laudon), Slides from N. Yildrim at ITU, Slides from Jean Lacoste, Virginia Tech, …. Introduction to Systems and Models 1

Outline Systems Models Models and IS Modeling process From modeling to decision making 2

Systems A set of elements or components that work together and interact to accomplish goals Some systems share common characteristics, including: – A system has structure, it contains parts (or components) that are directly or indirectly related to each other; – A system has behavior, it contains processes that transform inputs into outputs (material, energy or data); – A system has interconnectivity: the parts and processes are connected by structural and/or behavioral relationships. – A system's structure and behavior may be decomposed via subsystems and sub-processes to elementary parts and process steps. – There is a “boundary.” 3 inputprocessingoutput feedback customer and metrics

System Characteristics Simple vs. complex Diversity of inputs Diversity of outputs (standard versus customized) Role of the customer in defining the output -Number of steps/ components -Diversity of “steps” or sequences that can be used to achieve output -Number of performance measures 4

System Characteristics Open vs. closed Level and frequency of information flow with its external environment. The role of external decisions on how the system operates and adjusts The role of internal decisions on the external systems (it feeds) 5

System Characteristics Adaptive vs. non-adaptive – Related to openness level – If information flows in, how the system reacts/ adjusts to this information. Information could be just “to know” – non adaptive Information could be used to modify forecasts, production plans, staffing plans. – If no information flow, cannot be adaptive. 6

Systems Characteristics Stable vs. dynamic Associated with real time information for “operational” systems Relates to the “speed” in which information flows and in which it adapts/ modifies plans subject to this information. Subject to the type of decision. – Operational / day to day decisions could be highly dynamic. – Long term decisions are typically stable, but could be dynamic within its context. EG. Every month we change suppliers. –. 7

Systems Characteristics Performance and standards – Efficiency: A measure of what is produced divided by what is consumed. – Effectiveness: A measure of the extent to which a system achieves its goals.. System variable – A quantity or item that can be controlled by the decision maker. E.g., the price a company charges for a product. System parameter – A value or quantity that cannot be controlled by the decision maker. E.g., cost of a raw material. 8

Models Models are representations of real systems/ objects. – Conceptual models Qualitative models that highlight important connections in real world systems and processes. They are used as a first step in the development of more complex models. – Statistical models Characterize system based upon its statistical parameters such as mean, mode, variance or regression coefficients. – Mathematical models Analytical models (set of equations) and numerical models (behavior / trend of data). 9

Conceptual Models 10

Other Models 11

Models One goal is to give insight into interrelationships of key variables. Advantages of using models: – Experimenting with models versus real systems. Faster Less expensive Less risk Does not disrupt real system Analysis of alternatives – Models for systems/products that do not yet exist. – Models to predict behavior of “uncontrollable” systems. – Key consideration: The level of detail: cost versus “confidence” and analysis ability. 12

Models Basic characteristics of “business” quantitative models: – Decision variables (what management controls). – Constraints and systems characteristics (what is “given”). – Objective function (the goal of the system) - an output of “running “ the model. Stochastic models: consider system randomness. Deterministic models: assume randomness is minimal, thus variation is ignored. 13

Models Reasons for quantitative analysis/ models to support decision making (why do this?) – The system/problem is complex. – The system/problem is important. Strategic value Risk reduction Profits Customer service – In some cases: the system/problem is new thus the goal is to get insights into the design. – In other cases: the processes/problem is repetitive, we want to increase efficiency of decision making (time and cost). 14

Models and business strategy Could be related to any of the components of Porter’s Competitive Forces Model – Traditional competitors – New market entrants – Substitute products and services – Customers – Suppliers 15

Models and business strategy Models can be used to support decision making aimed at – Low-cost leadership, – Product differentiation, – Focus on market niche, – Strengthen customer and supplier intimacy. 16

Models and Information Systems Most models are data driven. Information systems integration to model development, implementation, and operation. For example – Inventory / sales models Products Vendors Quantities available and incoming – Product design models Customer preferences Design options Economic indicators 17

Modeling process System observation and analysis – processes and their characteristics – What is controlled, what are external constraints – available data, needed data/ static or dynamic 18

Linkages to external / to multiple functions: – High for operational – Low for Strategic 19 Frequency of Use Level of detail Operational Strategic Tactical Modeling process

Selection of modeling technique – cost and complexity, – effect of variability, – what is the goal of the analysis. Consultant problem: recommending a hammer when we need a wrench… we know a modeling method so let’s use it. 20 Modeling process

7 Steps of Problem Solving First 5 steps are the process of decision making and often are based on models. – Define the problem / analyze the environment. – Identify the set of alternative solutions. – Determine the criteria for evaluating alternatives. – Evaluate the alternatives (experimentation). – Choose an alternative (make a decision) – Implement the chosen alternative. – Evaluate the results. 21 Modeling process

Assumptions remove some complexity -> solvable. (AKA - simplifying assumption) Assumptions are your friends! – Assumptions are necessary to solve problems. – Without them, we are stuck with “real-world” complexity that is essentially unsolvable. A good assumption: – Simplifies the problem statement/model significantly. – Is intuitively plausible to the “domain experts”. 22 Role of Assumptions from Dr. Michael Gorman, U of Dayton Modeling process

Model development – Days… weeks …. – Information from multiple sources. – Software and data driven. – Justification of assumptions (previous slide). Model verification – Is the model representing what we understand of the product/ system? – Does it have the right constraints? Can we eliminate any? 23 Modeling process

Model validation (for existing systems) – Given a set of similar inputs would the model and the system generate the “same” results? What if analysis/ Sensitivity Analysis – Running the model at various levels of inputs (the decision variables) …. evaluates how a solution/decision (or its performance) changes given changes to one or more of the constraints/ assumptions. – This can indicate how different options are “dominant”, based on different variable levels. – Serves to indicate solution/ recommendation robustness. 24 Modeling process

From Modeling to Decision Making Models provides insights and multiple alternatives. Management selects course of action. Implementation and “retest” – Models are used to evaluate best implementation and keep decisions “optimal”. – Real time models: support DM using real time data. 25

Current buzzword related to business models: Analytics Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. 26

Some large players in Analytics 27