ENGM 742: Engineering Management and Labor Relations Decision Making

Categories of Decision Making Tools Routine vs. Nonroutine Objective vs. Bounded Rationality Level of Certainty

Decision Making Under Certainty Linear Program: Used to determine optimal allocation of an organization’s limited resources State the problem Decision Variables Objective function Constraints OR Web: http://ie.sdsmt.edu/orweb/or.html

Sec. 3.1Alt. Prototype Problem Determine the appropriate number of tractors and shakers to make so as to maximize total profit. Constraints Do not exceed demand for either product Do not exceed capacity of assembly area

Sec. 3.1Alt. Model Variables Profit X1 = number of tractors to manufacture X2 = number of shakers to manufacture Profit Z = 3X1 + 5X2 (in $1,000’s)

Sec. 3.1Alt. Model Constraints Demand for tractors X1 < 8,000 Demand for shakers X2 < 6,000 Capacity of Assembly 3X1 + 4X2 < 36,000 Non-negativity X1 > 0 , X2 > 0

Sec. 3.1Alt. Model Max Z = 3X1 + 5X2 s.t. X1 < 8,000 X2 < 6,000

Sec. 3.2 Some Terminology Corner Point Feasible Sol. (CPF) (0,6) (4,6) 2 4 6 8 10 12 8 6 4 2 Corner Point Feasible Sol. (CPF) (0,6) (4,6) (8,3) (8,0) (0,0)

Decision Making Under Risk Expected Value Sum: (probability of future * value of future) Decision Trees Graphical method for finding the expected value Queuing Simulation

K-Corp 125 -90 40 60 80 20 -50 No Competition (.8) New Design Re-engineer New Design do nothing No Competition (.8) Competition (.2) Competition (.5) No Comp. (.5) New do no No Comp. (.3) Comp. (.7) 125 -90 40 60 80 20 -50

K-Corp (60) (38) 125 -90 40 60 80 20 -50 No Competition (.8) Re-engineer New Design do nothing No Competition (.8) Competition (.2) Competition (.5) No Comp. (.5) New do no No Comp. (.3) Comp. (.7) 125 -90 40 60 80 20 -50 (60) (38)

K-Corp (60) [60] (38) 125 -90 40 60 80 20 -50 No Competition (.8) Re-engineer New Design do nothing No Competition (.8) Competition (.2) Competition (.5) No Comp. (.5) New do no No Comp. (.3) Comp. (.7) 125 -90 40 60 80 20 -50 (60) [60] (38)

K-Corp (82) (50) (60) [60] (38) (-50) 125 -90 40 60 80 20 -50 Re-engineer New Design do nothing No Competition (.8) Competition (.2) Competition (.5) No Comp. (.5) New do no No Comp. (.3) Comp. (.7) 125 -90 40 60 80 20 -50 (82) (50) (60) [60] (38) (-50)

K-Corp (82) [82] (50) (60) [60] (38) (-50) 125 -90 40 60 80 20 -50 Re-engineer New Design do nothing No Competition (.8) Competition (.2) Competition (.5) No Comp. (.5) New do no No Comp. (.3) Comp. (.7) 125 -90 40 60 80 20 -50 (82) [82] (50) (60) [60] (38) (-50)

K-Corp (38) (60) [60] (82) (50) (-50) [82] 125 -90 40 60 80 20 -50 Re-engineer New Design do nothing No Competition (.8) Competition (.2) Competition (.5) No Comp. (.5) New do no No Comp. (.3) Comp. (.7) 125 -90 40 60 80 20 -50 (38) (60) [60] (82) (50) (-50) [82]

K-Corp (38) (60) [60] (82) (50) (-50) [82] 125 -90 40 60 80 20 -50 Re-engineer New Design do nothing No Competition (.8) Competition (.2) Competition (.5) No Comp. (.5) New do no No Comp. (.3) Comp. (.7) 125 -90 40 60 80 20 -50 (38) (60) [60] (82) (50) (-50) [82]

Decision Making Under Uncertainty Maximax Maximin Equally likely Aspiration-Level

Aspiration-Level Aspiration: max probability that payoff > 60,000 P{PA1 > 60,000} = 0.8 P{PA2 > 60,000} = 0.3 P{PA3 > 60,000} = 0.3 Choose A2 or A3

Maximin Select Aj: maxjminkV(jk) e.g., Find the min payoff for each alternative. Find the maximum of minimums Sell Land Choose best alternative when comparing worst possible outcomes for each alternative.

Bayes’ Decision Rule E[A1] > E[A3] > E[A2] choose A1

Forecasting Essential preliminary to effective planning Engineering manager must be concerned with both future markets and future technology

Why Forecasting? New facility planning Production planning Work force scheduling Sensitivity analysis

Long Range Forecasts Design new products Determine capacity for new product Long range supply of materials

Short Range Forecasts Amount of inventory for next month Amount of product to produce next week How much raw material delivered next week Workers schedule next week

Sales Forecast

Sales Forecast

Sales Forecast

Sales Forecast

Forecasting Quantitative Methods Time Series Methods Moving Average Weighted Moving Average Exponential Smoothing Association or Causal Method Multiple Regression

Forecasting Qualitative Methods Judgment Methods Expert Opinion Delphi Historical Counting Methods Market Testing Market Survey

Delphi Method Eliminates effects of interactions between members Experts do not need to know who other experts are Delphi coordinator asks for opinions, forecasts on subject

Which Method? Textbook authors suggest: Select a few methods Make forecasts Take simple average

Forecasting New Products First use judgmental Expert opinions Consumer intentions

Management Science Characteristics Systems view of the problem Team approach Emphasis on use of formal mathematical models and statistical and quantitative techniques

Management Science Process Formulate the Problem Construct a Mathematical Model Test the Model Derive a Solution from the Model Apply the Model’s Solution to the Real System

For Review Compare and contrast mission and vision statements. Provide an example of how each is used by middle managers. Which of the forces in Porter’s five forces model is the most important in making decisions? Why? What role do goals and objectives play in the strategic planning process? Provide one example of how a forecast can be used to help management decision making and one example of how a forecast can mislead a management decision.