Final Exam Study Guide
Final Exam Study Guide 41440
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Operations Management Study Guide Final Exam (JUST CH 1219 ) Lecture 12: Business Analytics Overview What is business analytics? Analytics is the use of: ● data ● information technology ● statistical analysis ● quantitative methods ● mathematical or computerbased models to help managers gain improved insight about their business operations and make better, fact based decisions. Business Analytics Applications ● Management of customer relationships ● Financial and marketing activities ● Supply chain management ● Human resource planning ● Pricing decisions ● Sports Importance of Business Analytics ● There is a strong relationship of BA with: ○ profitability of businesses ○ revenue of businesses ○ shareholder return ● BA enhances understanding of data ● BA is vital for businesses to remain competitive ● BA enables creation of informative reports Evolution ● Operations research ● Management science ● Business intelligence ● Decision support systems Scope 1. DESCRIPTIVE: “What has occurred?” The application of simple statistical techniques that describes what is contained in a data set or database. 2. PREDICTIVE: “What will occur?” An application of advanced statistical, information software, or operations research methods to identify predictive variables and build predictive models to identify trends and relationships not readily observed in a descriptive analysis. 3. PRESCRIPTIVE: “What should occur?” An application of decision science, management science, and operations research methodologies (applied mathematical techniques) to make best use of allocable resources. Examples: ● retail markdown decisions ● analytics in practice ● online retailers Data for Business Analytics Four Types Data Based on Measurement Scale: ● Categorical (nominal) data: ○ Data placed in categories according to a specified characteristic ○ Categories bear no quantitative relationship to one another Examples: ○ customer’s location (America, Europe, Asia) ○ employee classification (manager, supervisor,associate) ● Ordinal data: ○ Data that is ranked or ordered according to some relationship with one another ○ No fixed units of measurement Examples: ○ college football rankings survey responses (poor, average, good, very good, excellent) ● Interval data: ○ Ordinal data but with constant differences between observations ○ Ratios are not meaningful Examples: ○ temperature readings SAT scores ● Ratio data: ○ Continuous values ○ Ratios are meaningful Examples: ○ monthly sales delivery times Decision Model ● A decision model is a model used to understand, analyze, or facilitate decision making. ● Types of model input ○ data ○ uncontrollable variables ○ decision variables (controllable) ● Types of model output ○ performance measures ○ behavioral measures Descriptive Decision Models ● Simply tell “what is” and describe relationships ● Do not tell managers what to do Predictive Decision Models ● often incorporate uncertainty to help managers analyze risk. ● Aim to predict what will happen in the future. ● Uncertainty is imperfect knowledge of what will happen in the future. Prescriptive Decision Models ● Help decision makers identify the best solution: ○ Optimization finding values of decision variables that minimize (or maximize) something such as cost (or profit). ○ Objective function the equation that minimizes (or maximizes) the quantity of interest. ○ Constraints limitations or restrictions. ○ Optimal solution values of the decision variables at the minimum (or maximum) point. Decision Model ● Deterministic prescriptive models have inputs that are known with certainty. ● Stochastic prescriptive models have one or more inputs that are not known with certainty. ● Algorithms are systematic procedures used to find optimal solutions to decision models. ● Search algorithms are used for complex problems to find a good solution without guaranteeing an optimal solution. Lecture 13 Simulation What is a simulation? An attempt to duplicate the features, appearance, and characteristics of a real system: 1. To imitate a realworld situation mathematically 2. To study its properties and operating characteristics 3. To draw conclusions and make action decisions based on the results of the simulation Advantages of Simulations 1. Relatively straightforward and flexible 2. Can be used to analyze large and complex realworld situations that cannot be solved by conventional models 3. Realworld complications can be included that most OM models cannot permit 4. “Time compression” is possible 5. Allows “whatif” types of questions 6. Does not interfere with realworld system 7. Can study the interactive effects of individual components or variables in order to determine which ones are important Disadvantages of Simulations 1. Can be very expensive and may take months to develop 2. It is a trialanderror approach that may produce different solutions in repeated runs 3. Managers must generate all of the conditions and constraints for solutions they want to examine 4. Each simulation model is unique Monte Carlo Simulation The Monte Carlo method may be used when the model contains elements that exhibit chance in their behavior 1. Set up probability distributions for important variables 2. Build a cumulative probability distribution for each variable 3. Establish an interval of random numbers for each variable 4. Generate random numbers 5. Simulate a series of trials Probability of Demand Assignment of Random Numbers Simulation Example 1 Table of Random Numbers Queueing Simulation Example arrival rates unloading rates Inventory Simulation Example daily demand reorder lead time 1. Begin each simulation day by checking to see if ordered inventory has arrived. If it has, increase current inventory by the quantity ordered. 2. Generate daily demand using probability distribution and random numbers. 3. Compute ending inventory. If onhand is insufficient to meet demand, satisfy as much as possible and note lost sales. 4. Determine whether the day's ending inventory has reached the reorder point. If it has, and there are no outstanding orders, place an order. Choose lead time using probability distribution and random numbers. Lecture 14 Forecasting What is forecasting? The process of predicting a future event Underlying basis of all business decisions ● Production ● Inventory ● Personnel ● Facilities Influence of Product Life Cycle introductiongrowthmaturitydecline ● Introduction and growth require longer forecasts than maturity and decline ● As product passes through life cycle, forecasts are useful in projecting ● Staffing levels ● Inventory levels ● Factory capacity Types of Forecasts ● Economic forecasts ○ Address business cycle – inflation rate, money supply, housing starts, etc. ○ Predict rate of technological progress ○ Impacts development of new products ● Demand forecasts ○ Predict sales of existing products and services Seven Steps in Forecasting ● Determine the use of the forecast ● Select the item/location combination to be forecasted ● Determine the time horizon of the forecast ● Select the forecasting model(s) ● Make the forecast ● Validate and implement results Qualitative Methods ● Used when situation is vague and little data exist ○ New products ○ New technology ● Involves intuition, experience ○ e.g., forecasting sales on Internet Quantitative Methods ● Used when situation is ‘stable’ and historical data exist ○ Existing products ○ Current technology ○ e.g., forecasting sales of color televisions Overview of Qualitative Methods ● Jury of executive opinion ○ Pool opinions of highlevel experts, sometimes augment by statistical models ● Delphi method ○ Panel of experts, queried iteratively ● Sales force composite ○ Estimates from individual salespersons are reviewed for reasonableness, then aggregated ● Consumer Market Survey ○ Ask the customer Overview of Quantitative Methods Time Series Forecasting ● Set of evenly spaced numerical data ○ Obtained by observing response variable at regular time periods ○ Assumes that factors influencing past and present will continue influence in future Trend Component ● Persistent, overall upward or downward pattern ● Changes due to population, technology, age, culture, etc. ● Typically several years duration Seasonal Components ● Regular pattern of up and down fluctuations ● Due to weather, customs, etc. ● Occurs within a single year Cyclical Component ● Affected by business cycle, political, and economic factors ● Multiple years duration ● Of Random Component ● Erratic, unsystematic, ‘residual’ fluctuations ● Due to random variation or unforeseen events ● Short duration and nonrepeating Naive Approach ● Assumes demand in next period is the same as demand in most recent period ○ e.g., If January sales were 68, then February sales will be 68 ● Can be good starting point d efficient Moving Average Method ● MA is a series of arithmetic means ● Used if little or no trend ● Used often for smoothing ○ Provides overall impression of data over time Weighted Moving Average • Used when trend is present • Older data usually less important • Weights based on experience and intuition Potential Problems with Moving Average ● Increasing n smoothes the forecast but makes it less sensitive to changes ● Do not forecast trends well ● R ● Form of weighted moving average ○ Weights decline exponentially ○ Most recent data weighted most ○ Ranges from 0 to 1 constant ○ Subjectively chosen ● Involves little record keeping of pa Exponential Smoothing Example Effect of Smoothing Constants Example: Common Measures of Error ● The objective is to obtain the most accurate forecast no matter what the technique is We generally do this by selecting the model that gives us the lowest forecast error Lecture 15 Forecasting Trend Projections ● Fitting a trendline to historical data points to project into the medium to longrange ● Linear trends can be found using the least squares technique Least Squares Method ● Equations to calculate the regression variables Seasonal Variations in Data Steps in the process: 1. Find average historical demand for each season 2. Compute the average demand over all seasons 3. Compute a seasonal index for each season 4. Estimate next year’s total demand 5. Divide this estimate of total demand by the number of seasons, then multiply it by the seasonal index for that season Associative Forecasting ● Forecasting an outcome based on predictor variables using the least squares technique Correlation ● How strong is the linear relationship between the variables? ● Correlation does not necessarily imply causality ● Coefficient of correlation, r, measures degree of association ○ Values range from 1 to +1 ● Coefficient of Determination, r2, measures the percent of change in y predicted by the change in x ○ Values range from 0 to 1 ○ Easy to interpret Multiple Regression ● If more than one independent variable is to be used in the model, linear regression can be extended to multiple regression to accommodate several independent variables In the earlier example, including interest rates in the model gives the new Lecture 16 Linear Programming Linear Programming ● A method to help plan and make decisions that account for the tradeoffs involved in allocating resources ● Finds the optimal value of the objective and the corresponding values of the decision variables and the resource usage ● In practice, there are usually many decision variables and resources Structure of LP ● Decision Variables: Should describe the decisions to be made. Represent choices available to the decision maker in terms of amounts of input or output ● Objective function: The decision maker wants to maximize or minimize some function of the decision variables ● Constraints: Restrictions that limit the degree to which we can pursue our objective Ci: A constant that provides the rate of contribution to the objective function Aij: Resource consumption coefficient (how much Xi consumes of resource j) Bj: Available resource j=1....M Note that the direction of the inequalities can be all or a combination of , , or = linear mathematical expressions Lecture 17 Waiting Line Models Characteristics of waiting line systems I. Arrivals or inputs to the system Size of the population • Unlimited (infinite) or limited (finite) Pattern of arrivals • Scheduled or random, often a Poisson distribution Behavior of arrivals ● Wait in the queue and do not switch lines ● No balking (leave before joining the line) or reneging (leave while in the queue) II. Waiting Line Characteristics ● Limited or unlimited queue length ● Queue discipline firstin, firstout (FIFO) is most common ● Other priority rules may be used in special circumstances III. Service Characteristics ● Queuing system designs: ● Singlechannel system, multiplechannel system ● Singlephase system, multiphase system ● Service time distribution: ● Constant service time ● Random service times, usually a exponential distribution Queuing System Designs Little’s Law L= Average number of people in the system λ = Arrival rate into the system W= Average time spent in the system L=λW These three models share the following characteristics: ● Single phase ● Poisson arrival ● FIFO ● Unlimited queue length Poisson Distribution Assumptions – Model A (M/M/1): Single Channel with Exponential Service Times and Poisson Arrivals ● Arrivals are served on a first come, first served basis ● Arrivals are independent of preceding arrivals ● Arrival rates are described by the Poisson probability distribution, and customers come from a very large population ● Service times vary from one customer to another, and independent of one and other; the average service time is known ● Service times are described by the exponential probability distribution ● The Service rate is greater than the arrival rate (STABILITY CONDITION) Model B (M/M/S): MultiChannel with Exponential Service Times and Poisson Arrivals Model C (M/D/1): Constant Service Time Lecture 18 Aggregate Planning ● Determine the quantity and timing of production for the immediate future ● Objective is to minimize cost over the planning period by adjusting: ○ Production rates ○ Labor levels ○ Inventory levels ○ Overtime work ○ Subcontracting rates ○ Other controllable variables Required for aggregate planning: ● A logical overall unit for measuring sales and output ● A forecast of demand for an intermediate planning period in these aggregate terms ● A method for determining costs ● A model that combines forecasts and costs so that scheduling decisions can be made for the planning period ● Combines appropriate resources into general terms ● Part of a larger production planning system ● Disaggregation breaks the plan down into greater detail ● Disaggregation results in a master production schedule Aggregate Planning Strategies 1. Use inventories to absorb changes in demand 2. Accommodate changes by varying workforce size 3. Use parttimers, overtime, or idle time to absorb changes 4. Use subcontractors and maintain a stable workforce 5. Change prices or other factors to influence demand Capacity Options Changing inventory levels: ● Increase inventory in low demand periods to meet high demand in the future ● Increases costs associated with storage, insurance, handling, obsolescence, and capital investment 15% to 40% ● Shortages can mean lost sales due to long lead times and poor customer service Varying workforce size by hiring or layoffs: ● Match production rate to demand ● Training and separation costs for hiring and laying off workers ● New workers may have lower productivity ● Laying off workers may lower morale and productivity Varying production rate through overtime or idle time ● Allows constant workforce ● May be difficult to meet large increases in demand ● Overtime can be costly and may drive down productivity ● Absorbing idle time may be difficult Subcontracting ● Temporary measure during periods of peak demand ● May be costly ● Assuring quality and timely delivery may be difficult ● Exposes your customers to a possible competitor Demand Options ● Influencing demand ● Use advertising or promotion to increase demand in low periods ● Attempt to shift demand to slow periods ● May not be sufficient to balance demand and capacity ● Back ordering during high demand periods ● Requires customers to wait for an order without loss of goodwill or the order ● Most effective when there are few if any substitutes for the product or service ● Often results in lost sales ● Counter seasonal product and service mixing ● Develop a product mix of counter seasonal items ● May lead to products or services outside the company’s areas of expertise Methods for Aggregate Planning ● A mixed strategy may be the best way to achieve minimum costs ● There are many possible mixed strategies ● Finding the optimal plan is not always possible Mixing Options to Develop a Plan Chase strategy ● Match output rates to demand forecast for each period ● Vary workforce levels or vary production rate ● Favored by many service organizations Level strategy ● Daily production is uniform ● Use inventory or idle time as buffer ● Stable production leads to better quality and productivity ● Some combination of capacity options, a mixed strategy, might be the best solution Lecture 19: Short Term Scheduling Strategic Importance of ShortTerm Scheduling ● Effective and efficient scheduling can be a competitive advantage ● Faster movement of goods through a facility means better use of assets and lower costs ● Additional capacity resulting from faster throughput improves customer service through faster delivery ● Good schedules result in more dependable deliveries Scheduling Criteria 1. Minimize completion time 2. Maximize utilization of facilities 3. Minimize workinprocess (WIP) inventory 4. Minimize customer waiting time Assignment Method ● A special class of linear programming models that assign tasks or jobs to resources ● Objective is to minimize cost or time ● Only one job (or worker) is assigned to one machine (or project) Build a table of costs or time associated with particular assignments 1. Create zero opportunity costs by repeatedly subtracting the lowest costs from each row and column 2. Draw the minimum number of vertical and horizontal lines necessary to cover all the zeros in the table. If the number of lines equals either the number of rows or the number of columns, proceed to step 4. Otherwise proceed to step 3. 3. Subtract the smallest number not covered by a line from all other uncovered numbers. Add the same number to any number at the intersection of two lines. Return to step 2. 4. Optimal assignments are at zero locations in the table. Select one, draw lines through the row and column involved, and continue to the next assignment. Sequencing Jobs ● Specifies the order in which jobs should be performed at work centers ● Priority rules are used to dispatch or sequence jobs ● FCFS: First come, first served ● SPT: Shortest processing time ● EDD: Earliest due date ● LPT: Longest processing time
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