Operations Management I For Confederation of Indian Industry (CII) Session III Chapter 6,

1. R M Harindranath MBA MCA (Ph D) MCT Operations Management I For Confederation of Indian Industry (CII) Session III Chapter 6,

2. Chapter VI: Demand Forecasting for Production Planning

3. Forecasting • What are forecasts? • Forecasts are estimates of the occurrence, timing, or magnitude of uncertain future events. • Operations managers are primarily concerned with forecasts of demand.

4. Benefits from improved forecasting • better strategic information, • better marketing information, • better financial information, • better operations information, • increased customer service, • better allocation of scarce resources, • increased manufacturing and operating efficiency, • higher productivity, • stability in planning, • reduced finished goods inventory, • elimination of waste, • more flexibility to respond to customer preferences, • increased profitability, • And most importantly, increased Return on Investment.

5. Demand Characteristics • Five basic components of most demand time series are: • Average : The sum of the demand observations of each period divided by the number of data periods • Trend: Systematic increase or decrease in the average of the series over time. • Seasonal influence: A predictable increase or decrease in demand depending on the time of day, week, month, or season. • Cyclical movement: Less predictable increase or decrease in demand over longer periods of time (years or decades) • Random (unforecastable) “noise” error

6. Demand Characteristics • Four of the components of demand – average, trend seasonal influence, and cyclical movements – combine in varying degrees to define the underlying time pattern of demand for a product or service • Random error is the component of demand that makes every forecast wrong

7. Forecasting Approaches Three types of forecasting techniques are used for demand forecasting: • Judgement • Causal • Time-series • Most forecasting methods fall into two broad categories: those that rely on the subjective assessments of a person or group of persons as known as qualitative or quantitative methods

8. Judgmental Methods • When adequate historical data are lacking, such as when a new product is introduced or technology is expected to change, firms rely on managerial judgement and experience to generate forecasts • The most widely used judgmental techniques are sales forces estimates, executive opinion, market research and Delphi method.

9. Judgmental Methods - Sales Force Estimates • Sales force is sometimes used to forecast demand for a product based on its experience and contacts with customer • Sales force know more about the customers needs

10. Judgmental Methods - Executive Opinion • Executive opinion is a forecasting method in which the opinions and experiences of one or more managers are summarized to arrive at a single forecast. • For a new product or service is being considered, the sales force may not be able to make accurate demand estimates • Costly because it takes valuable executive time • Subject to biases due to individual attitudes and situations.

11. Judgmental Methods – Market Research • A Systematic approach to determine consumer interest in a product or service by creating and testing hypotheses through data-gathering surveys • Surveys are administered by telephone, through mailings, or in personal interviews to a representative sample • Excellent for the short term, good for the medium term, and only fair for the long term

12. Judgmental Methods – Delphi Method • Uses expert opinion to reach consensus about a decision regarding a future event • A panel of experts, often from different parts of the country, respond individually to the issue in question • A questionnaire format is often used. • The results of the questionnaire are tabulated and summarized statistically by a coordinator, who sends the summations back to the participants to give them an opportunity to modify their responses • Responses that differ significantly format he norm are often asked to be justified.

13. Causal Methods • Causal methods are used when historical data are available and the relationship between the factors to be forecasted • They provide the most sophisticated forecasting tools and are very good for predicting turning points in demand and preparing long-range forecasts. • Linear regression is one of the best-known and most commonly used causal methods

14. Linear Regression Analysis • one variable, called a dependent variable, is related to one or more independent variable by a linear equation. • the dependent variable is a function of only one independent variable, and therefore the theoretical relationship is a straight line: • Y = A + BX Where Y = dependent variable X = independent variable A = Y-intercept of the line B = slope of the line

15. The Carpet City Store in Athens has kept records of its sales (in square yards) each year, along with the number of permits for new houses in its area.

17. Solution. Projecting the hand fit line causes it to intercept with the Y axis at about 7,000 Sq.meters. This could be interpreted as the demand when no new houses are built; that is, probably as replacemet for old carpeting. To estimate the slope, two points are selected, such as; • Year x y • 1989 10 10,000 • 1993 30 17,000 • From algebra the slope is calculated as • b = y(93) – y(89) = 17,000 – 10,000 = 7,000 =350 • x(93) – x(89) 30 – 10 20 • Y = 7,000 + 350 x • Now suppose that there are 25 permits for houses to be built in 1995. The 1995 sales forecast would therefore be • 7,000 + 350 (25) = 15,750 square yards.

18. Times Series methods • A time series is simply a historical record of demand at fixed time intervals • The time intervals can be of any length, from hours to years •  The major disadvantages of time series methods are they require a large amount of historical data

19. Time Series - Simple Moving Averages • Estimate the average of a demand time series • Most useful when demand has no pronounced trend or seasonal influence. • Moving average • An = Forecast for the coming period • n = Number of periods to be averaged • Di = Demand for the initial period

20. Weighted Moving Averages • Each historical demand in the average can have its own weight • Emphasizes recent demand over earlier demand • Weighted moving average • Wi = Weight to be given the actual occurrence for period t-1 • ( W2 for t-2 and Wn for t-n ) • n = Number of periods to be averaged • Di = Demand for the initial period

21. Exponential Smoothing • Sophisticated weighted moving average method that calculates the average of a time series by giving recent demands more weight that earlier demands • Most frequently used formal forecasting method because of its simplicity and the small amount of data needs to support it • F t+1 =  . Dt+ (1-). Ft • Changing the value of “” effectively changes the weighting: a large value gives more emphasis to the more recent demand values; a small value gives more equal weighting to past demand values

22. Thank you • Let us begin VII Chapter in another 5 minutes

23. Chapter VII: Aggregate Planning

24. Aggregate Planning • Planning is generally divided in three time horizons: • Long Term • 5+ years into the future • R&D, plant location, product planning • Principally judgement-based • Medium Term • 1 season to 2 years • Aggregate planning, capacity planning, sales forecasts • Mixture of quantitative methods and judgement • Short Term  • 1 day to 1 year, less than 1 season • Demand forecasting, staffing levels, purchasing, inventory levels • Quantitative methods

25. Defn: Aggregate Planning • Aggregate planning is the process of planning the quantity and timing of output over the intermediate range (often 3 to 18 months) by adjusting the production rate, employment, inventory, and other controllable variables. •  In a manufacturing firm, an aggregate plan is called a production plan. • A production plan is a statement of its plant’s production rates, work-force levels, and inventory holdings based on estimates of customer requirements and capacity limitations. • It links the strategic goals with plans for the individual products (i.e. the master production schedule)

26. Aggregate Planning • Three dimensions of aggregation: • Product families - Based on similar demand requirements and common processing, labor, and materials requirements. • Labor - Considerations include work-force flexibility, physical and geographic locations. • Timing - The planning horizon is the length of time covered by the aggregate plan.

27. Aggregate Planning • Purpose of Aggregate Planning • -- To plan for dealing with the quantity and timing of demand • -- Inputs to aggregate planning •  1.Available resource over the planning period • 2.Forecast of expected demand • 3. Policies regarding changes in employment levels (use of overtime, layoffs, subcontracting, etc.)

28. Demand and Capacity Options • Demand can be modified or influenced in several ways as below: • Demand Options • 1. Pricing • 2. Promotion • 3. Back orders • 4. New demand

29. Decision Options • 1. Pricing: Differential pricing is often used to reduce peak demand or to 1 build up demand in off-peak periods • 2. Advertising and promotion: This is another method used to stimulate or in some cases smooth out demand • 3. Backlog or reservations: In some cases demand is influenced by asking customers to wait for their orders or by reserving capacity in advance (reservations) • 4. Development of complementary products: Firms with highly seasonal demands may try to develop products which have counter cyclical seasonal trends

30. Capacity options • 1. Hire and lay off workers • 2. Use overtime/slack time • 3. Part-time workers • 4. Inventories • 5. Subcontracting

31. Basic Strategies for Meeting Uneven Demand • 1. Maintain a level work force • 2. Maintain a steady output rate • 3. Match demand period by period • 4. Use a combination of decision variables

32. Techniques for Aggregate Planning • General procedure • 1.Determine demand for each period • 2.Determine capacities (regular time, overtime, subcontracting) for each period • 3.Identify company or departmental policies that are pertinent (maintain a safety stock, maintain a reasonably stable workforce) • 4.Determine unit costs for regular time, overtime, subcontracting, holding inventories, back orders, layoffs, and other relevant costs • 5.Develop alternative plans and compute the cost for each • 6.If satisfactory plans emerge, select the one that best satisfies objectives

33. Aggregate Planning in Services • Differences between manufacturing and services which impact aggregate planning • 1. Services occur when they are rendered • 2. Demand for service can be difficult to predict • 3. Capacity availability can be difficult predict • 4. Labor flexibility can be an advantage in services

34. Planning Strategies • 1.Chase strategy - Adjusts output rates or work-force levels to match demand over the planning horizon without using anticipation inventory or under time. • Accomplished through hires and layoffs, overtime, and extra shifts or subcontracting. • 2. Level strategy - Maintains a constant production rate or work-force level over the planning horizon.

35. End of chapter VII

36. Chapter VIII: Master Production Scheduling

38. Master production schedule • The master production schedule (MPS) is an anticipated build schedule for end products • The master production schedule is a dis-aggregation (break down) of the aggregate plan • This master schedule will include which products to produce, how much to produce in each period • The master production schedule (MPS) is an anticipated build schedule for end products

39. Break down the Aggregate Plan

40. DEVELOPING MASTER PRODUCTION SCHEDULES • When our available inventory is projected to become negative, that is the time to create a new MPS "shipment" quantity

41. End of chapter VIII

42. Chapter IX: Detailed Scheduling Methods for Single Machine & Dispatching Rules

43. Scheduling • Scheduling is defined as the allocation of resources over a period of time to perform a collection of jobs given the constraints of the system. • Scheduling is one major aspect of manufacturing which can make or break a company. • Since manufacturing has existed, so has the problem of efficient scheduling. • Good schedules can lead to many benefits for a company, while bad schedules can be disastrous. • A good schedule will not harm a company, while a bad one can have dire consequences.

44. Benefits • Improved production efficiency • Improved customer service levels • Minimized costs • Reduced waste

45. Objectives in Scheduling • Meet customer due dates • Minimize job lateness • Minimize response time • Minimize completion time • Minimize time in the system • Minimize overtime • Maximize machine or labor utilization • Minimize idle time • Minimize work-in-process inventory

46. Gantt Chart • A schedule is often depicted through a Gantt Chart • The vertical axis typically represents tasks, or, in the case of manufacturing, resources in the system (machines, operators, etc.) • Time is the dimension of the horizontal axis • It is an excellent pictorial monitoring technique for review and action

48. Scheduling Types • Single Machine Scheduling • Batch Scheduling • Assembly line Scheduling • Flow Scheduling

49. Scheduling Types - Single Machine Scheduling • Single machine scheduling is the assignment of jobs to be processed on a single machine over time. • Similar to the asymmetric Traveling Salesman Problem (ATSP) except that the end job is not directly connected to the start job • Single machine scheduling models real-world problems in flexible manufacturing systems and is therefore an important problem from an applications point of view

50. Performance of Priority Sequencing Rules • Earliest due date (EDD) - Performs well with respect to: minimizing percentages of jobs past due, minimizing the maximum amount of time a job is late. Performs poorly with respect to: job flow time, work-in-process inventory, utilization. • First come, first serve (FCFS) - Perceived as being fair. Performs poorly with respect to all performance measures • Shortest processing time (SPT) - Performs well with respect to: average job flow time, work-in-process inventory, minimizing percentages of jobs past due, utilization. • Performs poorly with respect to: minimizing the maximum amount of time a job is late, minimizing total inventory (it pushes work to finished goods before it is needed), adjusting schedules when due date changes (due date is not used in the calculation of priority