Operations and Competitiveness - Hatem Masri

Operations and Competitiveness - Hatem Masri

Operations and Supply Chain Management Introduction Chapter 1 Lecture Outline What Operations and Supply Chain Managers Do Operations Function Evolution of Operations and Supply Chain Management Globalization and Competitiveness Operations Strategy and Organization of the Text Learning Objectives for This Course

What Operations and Supply Chain Managers Do What is Operations Management? What is Operations? design, operation, and improvement of productive systems a function or system that transforms inputs into outputs of greater value What is a Transformation Process?

a series of activities along a value chain extending from supplier to customer activities that do not add value are superfluous and should be eliminated Transformation Process Physical: as in manufacturing operations Locational: as in transportation or warehouse operations Exchange: as in retail operations Physiological: as in health care Psychological: as in entertainment Informational: as in communication

Operations as a Transformation Process INPUT Material Machines Labor Management Capital TRANSFORMATION PROCESS OUTPUT Goods Services Feedback & Requirements Operations Function

Operations Marketing Finance and Accounting Human Resources Outside Suppliers Copyright 2011 John Wiley & Sons, Inc. How is Operations Relevant to my Major? Accounting

Information Technology Management As an auditor you must understand the fundamentals of operations management. IT is a tool, and theres no better place to apply it than in operations. We use so many things you learn in an operations classscheduling, lean

production, theory of constraints, and tons of quality tools. How is Operations Relevant to my Major? Economics Marketing Finance

Its all about processes. I live by flowcharts and Pareto analysis. How can you do a good job marketing a product if youre unsure of its quality or delivery status? Most of our capital budgeting requests are from operations, and most of our cost savings, too. Evolution of Operations and Supply Chain Management Craft production Division of labor

process of handcrafting products or services for individual customers dividing a job into a series of small tasks each performed by a different worker Interchangeable parts standardization of parts initially as replacement parts; enabled mass production Evolution of Operations and Supply Chain Management Scientific management

Mass production systematic analysis of work methods high-volume production of a standardized product for a mass market Lean production adaptation of mass production that prizes quality and flexibility Historical Events in Operations Management Era Industrial Revolution Scientific

Management Events/Concepts Dates Originator Steam engine Division of labor Interchangeable parts Principles of scientific management Time and motion studies Activity scheduling chart Moving assembly line 1769 1776 1790 James Watt

Adam Smith Eli Whitney 1911 Frederick W. Taylor 1911 1912 1913 Frank and Lillian Gilbreth Henry Gantt Henry Ford Historical Events in Operations Management Era Human Relations

Operations Research Events/Concepts Dates Originator Hawthorne studies 1930 1940s 1950s 1960s 1947 1951 Elton Mayo Abraham Maslow Frederick Herzberg Douglas McGregor

George Dantzig Remington Rand 1950s Operations research groups 1960s, 1970s Joseph Orlicky, IBM and others Motivation theories Linear programming Digital computer Simulation, waiting line theory, decision theory, PERT/CPM MRP, EDI, EFT, CIM

Historical Events in Operations Management Era Events/Concepts JIT (just-in-time) TQM (total quality management) Quality Strategy and Revolution operations Dates Originator 1970s 1980s 1980s Reengineering 1990s

Six Sigma 1990s Taiichi Ohno (Toyota) W. Edwards Deming, Joseph Juran Wickham Skinner, Robert Hayes Michael Hammer, James Champy GE, Motorola Historical Events in Operations Management Era Events/Concepts Internet Revolution

Internet, WWW, ERP, 1990s supply chain management Globalization Dates Originator E-commerce 2000s WTO, European Union, Global supply chains, Outsourcing, Service Science 1990s 2000s ARPANET, Tim Berners-Lee SAP,

i2 Technologies, ORACLE, Dell Amazon, Yahoo, eBay, Google, and others China, India, emerging economies Historical Events in Operations Management Era Events/Concepts Dates Originator Green Revolution Global warming, An Inconvenient Truth, Kyoto

Today Numerous scientists, statesmen and governments Evolution of Operations and Supply Chain Management Supply chain management management of the flow of information, products, and services across a network of customers, enterprises, and supply chain partners Globalization Why go global?

favorable cost access to international markets response to changes in demand reliable sources of supply latest trends and technologies Increased globalization results from the Internet and falling trade barriers Copyright 2011 John Wiley & Sons, Inc. Hourly Compensation

GDP per Capita Trade in Goods, % of GDP Productivity and Competitiveness Competitiveness Productivity ratio of output to input Output

degree to which a nation can produce goods and services that meet the test of international markets sales made, products produced, customers served, meals delivered, or calls answered Input labor hours, investment in equipment, material usage, or square footage Measures of Productivity Osborne Industries C6*C8 C7*C9 C5/C6

C5/C7 C5/C13 Productivity Growth Percent Change in Input and Output Strategy and Operations How the mission of a company is accomplished Provides direction for achieving a mission Unites the organization Provides consistency in decisions Keeps organization moving in the right direction Strategy Formulation 1.

2. 3. 4. 5. Defining a primary task What is the firm in the business of doing? Assessing core competencies What does the firm do better than anyone else? Determining order winners and order qualifiers What qualifies an item to be considered for purchase? What wins the order? Positioning the firm How will the firm compete? Deploying the strategy Strategic Planning

Mission and Vision Voice o f the Busines s Marketing Strategy Copyright 2011 John Wiley & Sons, Inc. Corporate Strategy Operations Strategy he t f o

e c i o V r Custome Financial Strategy Order Winners and Order Qualifiers Source: Adapted from Nigel Slack, Stuart Chambers, Robert Johnston, and Alan Betts, Operations and Process Management, Prentice Hall, 2006, p. 47 Positioning the Firm

Cost Speed Quality Flexibility Positioning the Firm: Cost Waste elimination Examination of cost structure relentlessly pursuing the removal of all waste looking at the entire cost structure for reduction potential

Lean production providing low costs through disciplined operations Positioning the Firm: Speed Fast moves, Fast adaptations, Tight linkages Internet Service organizations always competed on speed (McDonalds, LensCrafters, and Federal Express)

Manufacturers Customers expect immediate responses time-based competition: build-to-order production and efficient supply chains Fashion industry two-week design-to-rack lead time of Spanish retailer, Zara Positioning the Firm: Quality Minimizing defect rates or conforming to design specifications

Ritz-Carlton - one customer at a time Service system designed to move heaven and earth to satisfy customer Employees empowered to satisfy a guests wish Teams set objectives and devise quality action plans Each hotel has a quality leader Positioning the Firm: Flexibility

Ability to adjust to changes in product mix, production volume, or design Mass customization: the mass production of customized parts National Bicycle Industrial Company offers 11,231,862 variations delivers within two weeks at costs only 10% above standard models Policy Deployment Policy deployment

translates corporate strategy into measurable objectives Hoshins action plans generated from the policy deployment process Policy Deployment Derivation of an Action Plan Using Policy Deployment Balanced Scorecard Balanced scorecard measuring more than financial performance 1. finances 2. customers 3. processes

4. learning and growing Key performance indicators set of measures to help managers evaluate performance in critical areas Balanced Scorecard Worksheet Balanced Scorecard Radar Chart Dashboard Operations Strategy Services Products

Capacity Facilities Copyright 2011 John Wiley & Sons, Inc. Human Resources Sourcing Process and Technology Quality Operating Systems Organization of This Course:

Operations and Supply Chain Management Introduction Designing Products Designing Services Processes and Technology Capacity and Facilities Planning Managing Projects Supply Chain Management and Design Supply Chain Procurement and Distribution Globalization Forecasting

nventory Management Aggregate Sales and Operations Planning Resource Planning Systems Learning Objectives of this Course 1. Demonstrate an understanding of the concepts of operations management and recognize the current trends in operations and project management. 2. Analyze operations strategies and issues involved in designing products, processes, and services. 3. Design supply chains and recognize the role of information systems in business operations. 4. Employ decision models and techniques in business operations. Chapter 1 Supplement Decision Analysis Support Tools and Processes Outline

Decision Analysis Decision Making without Probabilities Decision Analysis with Excel Decision Analysis with OM Tools Decision Making with Probabilities Expected Value of Perfect Information Sequential Decision Tree Decision Analysis Quantitative methods

a set of tools for operations manager Decision analysis a set of quantitative decision-making techniques for decision situations in which uncertainty exists Example of an uncertain situation demand for a product may vary between 0 and 200 units, depending on the state of market Copyright 2011 John Wiley & Sons, Inc. Decision Making Without Probabilities

States of nature Events that may occur in the future Examples of states of nature: Decision making under risk high or low demand for a product good or bad economic conditions probabilities can be assigned to the occurrence of states of nature in the future

Decision making under uncertainty probabilities can NOT be assigned to the occurrence of states of nature in the future Copyright 2011 John Wiley & Sons, Inc. Payoff Table Payoff table method for organizing and illustrating payoffs from different decisions given various states of nature Payoff

outcome of a decision States Of Nature Decisiona b 1 Payoff 1a Payoff 1b 2 Payoff 2a Payoff 2b Decision Making Criteria Under Uncertainty Maximax Maximin

choose decision with the maximum of the maximum payoffs choose decision with the maximum of the minimum payoffs Minimax regret choose decision with the minimum of the maximum regrets for each alternative Decision Making Criteria Under Uncertainty Hurwicz

choose decision in which decision payoffs are weighted by a coefficient of optimism, alpha coefficient of optimism is a measure of a decision makers optimism, from 0 (completely pessimistic) to 1 (completely optimistic) Equal likelihood (La Place) choose decision in which each state of nature is weighted equally Southern Textile Company STATES OF NATURE DECISION Expand Maintain status quo Sell now

Good Foreign Competitive Conditions $ 800,000 1,300,000 320,000 Poor Foreign Competitive Conditions $ 500,000 -150,000 320,000 Maximax Solution STATES OF NATURE DECISION Expand Maintain status quo Sell now

Expand: Status quo: Sell: Good Foreign Competitive Conditions $ 800,000 1,300,000 320,000 Poor Foreign Competitive Conditions $ 500,000 -150,000 320,000 $800,000 1,300,000 Maximum 320,000 Decision: Maintain status quo

Maximin Solution STATES OF NATURE DECISION Expand Maintain status quo Sell now Expand: Status quo: Sell: Good Foreign Competitive Conditions $ 800,000 1,300,000 320,000 Poor Foreign Competitive Conditions

$ 500,000 -150,000 320,000 $500,000 Maximum -150,000 320,000 Decision: Expand Minimax Regret Solution States of Nature Good Foreign Poor Foreign Competitive Conditions Competitive Conditions $1,300,000 - 800,000 = 500,000 1,300,000 - 1,300,000 = 0 1,300,000 - 320,000 = 980,000 Expand: Status quo: Sell:

$500,000 - 500,000 = 0 500,000 - (-150,000)= 650,000 500,000 - 320,000= 180,000 $500,000 Minimum 650,000 980,000 Decision: Expand Hurwicz Criteria STATES OF NATURE DECISION Expand Maintain status quo Sell now = 0.3 Good Foreign Competitive Conditions

$ 800,000 1,300,000 320,000 Poor Foreign Competitive Conditions $ 500,000 -150,000 320,000 1 - = 0.7 Expand: $800,000(0.3) + 500,000(0.7) = $590,000 Maximum Status quo: 1,300,000(0.3) -150,000(0.7) = 285,000 Sell: 320,000(0.3) + 320,000(0.7) = 320,000 Decision: Expand Equal Likelihood Criteria STATES OF NATURE DECISION

Expand Maintain status quo Sell now Good Foreign Competitive Conditions Poor Foreign Competitive Conditions $ 800,000 1,300,000 320,000 $ 500,000 -150,000 320,000 Two states of nature each weighted 0.50 Expand: $800,000(0.5) + 500,000(0.5) = $650,000 Maximum Status quo: 1,300,000(0.5) -150,000(0.5) = 575,000 Sell: 320,000(0.5) + 320,000(0.5) = 320,000

Decision: Expand Decision Analysis with Excel = MAX(C6:D6) = MIN(C6:D8) = MAX(F6:F8) = MAX(G7:H7) = C19*E7+C20*F7) = .5*E8+.5*F8 = MAX(D6:D8)-F7 Decision Making with Probabilities Risk involves assigning probabilities to states of nature

Expected value a weighted average of decision outcomes in which each future state of nature is assigned a probability of occurrence Copyright 2011 John Wiley & Sons, Inc. Expected Value EV (x) = n p(xi)xi i =1 where xi = outcome i p(xi) = probability of outcome i

Copyright 2011 John Wiley & Sons, Inc. Decision Making with Probabilities STATES OF NATURE Good Foreign Competitive Conditions DECISION Expand Maintain status quo Sell now $ 800,000 1,300,000 320,000 p(good) = 0.70 Poor Foreign Competitive Conditions

$ 500,000 -150,000 320,000 p(poor) = 0.30 EV(expand): $800,000(0.7) + 500,000(0.3) = $710,000 EV(status quo): 1,300,000(0.7) -150,000(0.3) = 865,000 Maximum EV(sell): 320,000(0.7) + 320,000(0.3) = 320,000 Decision: Status quo Decision Making with Probabilities: Excel Expected value computed in cell D6 Expected Value of Perfect Information EVPI

maximum value of perfect information to the decision maker maximum amount that would be paid to gain information that would result in a decision better than the one made without perfect information EVPI Good conditions will exist 70% of the time Poor conditions will exist 30% of the time

choose maintain status quo with payoff of $1,300,000 choose expand with payoff of $500,000 Expected value given perfect information = $1,300,000 (0.70) + 500,000 (0.30) = $1,060,000 Recall that expected value without perfect information was $865,000 (maintain status quo) EVPI= $1,060,000 - 865,000 = $195,000 Sequential Decision Trees

A graphical method for analyzing decision situations that require a sequence of decisions over time Decision tree consists of Square nodes - indicating decision points Circles nodes - indicating states of nature Arcs - connecting nodes Evaluations at Nodes Compute EV at nodes 6 & 7

EV(node 6)= 0.80($3,000,000) + 0.20($700,000) = $2,540,000 EV(node 7)= 0.30($2,300,000) + 0.70($1,000,000)= $1,390,000 Decision at node 4 is between $2,540,000 for Expand and $450,000 for Sell land Choose Expand Repeat expected value calculations and decisions at remaining nodes Decision Tree Analysis $1,290,000 $2,000,000 0.60 Market growth

2 (-$ Exp 80 an 0, d 00 0) 0.40 N om a gro rket wth 1 $225,000 $2,540,000 and Exp ,000) 00 (-$8

$1,740,000 , rs a k e ar 3 y ff) M ( o t h pay w o gr $0 0.60 $1,160,000 4 et nd La )

se 0 0 ha ,0 rc 200 P u (-$ 3 $1,360,000 Sell la 0.80 et Mark h t grow 6 0.20 nd

$3,000,000 $700,000 No ma gro rket wt h $450,000 0.40 $790,000 gr No ow m a t $0 h (3 rke pa ye t yo ar ff) s,

5 et $1,390,000 Mark owth r g e ous ) h e 0.30 r Wa 0,000 0 7 (-$6 0.70 No ma grow rket

Sell lan d th $210,000 $2,300,000 $1,000,000

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