Module 4 Process planning for final year mechanical engineering

process planning

1. PRODUCT PLANNING

2. PRODUCT PLANNING Product planning requires Vision

3. Product Planning A product is a goods or a service Product Planning comprised of two elements Product development Conceive, develop, produce, and test Product management Commercialized, sustained, eventually withdrawn

4. PRODUCT PLANNING Product planning is the process of searching ideas for new products, screening them systematically, converting them into tangible products, introducing the new product in the market and formation of product policies and strategies. It’s the evaluation of the range, mix, specifications and pricing of existing and new products in relation to present and future market requirements and competition; planning of product range, mix, specifications and pricing to satisfy company objectives and specify the research, design and development support required.

5. Product Planning Roles Resource Allocation All companies are resource-constrained People, time, money Product Mix coordination Optimal mix of products to fill market targets Marketing Program support Information about product performance Product Portfolio evaluation Cash, profitability, market position, strategic value

6. Six Types of New Products Cost Improvements possibly same item with cost reductions (different than price reduction) Product Improvements New and improved features Line Extensions Tartar control toothpaste, whitening toothpaste Market Extensions New Category Entries Kodak selling batteries New-to-the-World Products Cell phone, DVD player, etc.

7. Why is Product Planning Important? One-third of a companies sales come from products introduced in the past 5 years Over 90% of product concepts fail during product development process Of the ones that make it to market, about a third fail 31 % of commercial products, 46% of consumer products 27% of product line extensions fail 31% of new brands in existing categories fail 46% of new products in new categories fail

8. How to recognize the Need for product planning From current deficiencies From anticipated deficiencies Societal Political Ecological Technological Economical

9. Constituents of product planning Market and marketing analysis Analysis of market potential The performance of feasibility study Detail need analysis Identification of alternative configurations Screening and evaluation of available alternatives Selection of preferred approach

10. Constituents of product planning contd …. Feasibility study includes following information For system operational concept Identification of prime mission of system Defining the operating characteristics of the system Identification of quantity of equipments, personnel and facilities etc. Anticipated time that the system will be in operational use Anticipated usage of the system and its elements

11. Constituents of product planning contd …. Effectiveness factors Definition of environment in which system is expected to operate For system maintenance concept Maintenance, support and repair policies Establishment of supportability requirements Requirement for total logistic support Maintenance concept evolve from deployment profile Organizational Intermediate Depot

12. Constituents of product planning contd …. Output of feasibility study is a proposal converting the technical characteristics of the preferred system configuration. Feasibility study and advance planning data are reviewed for future course of action

13. Constituents of product planning contd …. Advance Planning Product System Evaluation, selection and Justification Reviewed from business point of view Evaluated in terms of life cycle cost costs associated with planning, R&D, investment, operation & support and system phase out. An estimate of market share, knowledge of time of need, revenue can be projected in a year and comparison with the anticipated cost.

14. Constituents of product planning contd …. Advance Planning B. Product Specifications and Plans Development of formal specifications and planning documentations specifications: Technical design requirements planning : managerial related activities

15. Constituents of product planning contd …. Advance Planning contd … B-1 Classification of specifications System specifications: Technical, operational and support requirements Development specifications: For R&D purpose Procurement specifications: Process specifications: service that is performed on any product or material Material specifications:

16. Constituents of product planning contd …. Advance Planning contd … B-2 Planning Documentation Individual supporting plan to cover the various phases of product life cycle which includes: Organization structure and responsibilities programme functions and tasks, schedules, processes and procedures and cost projections. All plans are prepared by those who are responsible for execute

17. Constituents of product planning contd …. C. Product Acquisition Plan Process of acquiring a product from commencing with identification of need and extending through delivery of the product. Involve: Research, design, production etc. Plan must involve Definition of tasks: Scheduling of the tasks Line of Balance Organization of tasks Cost/ schedule/ performance Corrective action

18. Constituents of product planning contd …. D. Product Evaluation Plan: Requirements for the product in terms of performance and effectiveness It includes Test and evaluation requirements Categories of test and evaluation Test preparation phase Test and evaluation procedure Data collection, analysis and corrective action method Test and evaluation reporting

19. Constituents of product planning contd …. E. Product use and logistic support plan Product marketing and sales strategies The deployment and distribution of product The recommended procedure for product operation Logistic details

20. Constituents of product planning contd …. F. Product (system) proposal represent a recommended course of action based on technical feasibility study and advance product planning Proposal are classified as Internal Proposal External Proposal

21. process PLANNING

22. What is PP

23. Process Plan

24. Process Planning

25. Process planning establishes the shortest route Followed from raw material to finished part or product Activities associated with process planning list of operations to be performed and their sequence specification of the Machines and equipments required

26. Process planning necessary tooling Jigs fixtures gives the manufacturing detail with respect to feed speed and depth of cut for each operation it gives the estimated processing time of operations. all the information is represented in the form of a document called the process sheet or route sheet.

27. the information of the process is used for …. The important document for costing and provides the information on the various details like setup and operation time for each job. The machine and manpower requirement can be computed from the setup and operational times. Helps to carry out scheduling. The material movement can be traced. It helps in cost reduction and cost control It helps in to determine the efficiency of a work centre.

28. the information required for process planning 1. Assembly and component drawings and bill of material: These details give the information regarding the general description of the part to be manufactured raw material specifications, dimensions and tolerances required, the surface finish and treatment required

29. the information required for process planning contd … 2. Machine or equipment details with respect to: the various possible operations that can be performed the dimensions that can be machined the accuracy of the dimensions that can be obtained available fees and speeds on the machine

30. the information required for process planning contd … 3. the standard time for operation and details of set up time for each job: This help to compute the standard time of the operation and hence the production rate. 4. Availability of tooling.

31. factors affecting process planning Volume of production Delivery dates of components or products Accuracy and process capability of Machines The skill and expertise of Manpower Material specifications Accuracy requirement of components of Arts

32. Steps in process planning Detailed study of component drawings to identify the salient features that influence process selection, machine selection, inspection stages and tooling required. List the surface to be machined. The surface to be machined are combined into basic operations this step helps in selection of machine for operation. Determined the speed feed and depth of cut for each operation.

33. Steps in process planning Estimate the operation time Find the total time to complete the job taking into account the loading and unloading times handling time and other allowances Represent the details on the process sheet

34. Process Planning Approaches Manual Systems Computer Aids Variant System Experimental Generative System

35. Manual Process planning Man-variant process planning

36. Computer Aids

37. Variant (Retrieval) CAPP Methodology

38. Variant CAPP

39. Variant CAPP

40. Variant CAPP

41. Generative CAPP Methodology

42. Knowledge based process planning

43. C OMPUTER-AIDED PROCESS PLANNING ADVANTAGES 1. It can reduce the skill required of a planner. 2. It can reduce the process planning time. 3. It can reduce both process planning and manufacturing cost. 4. It can create more consistent plans. 5. It can produce more accurate plans. 6. It can increase productivity.

44. WHY AUTOMATED PROCESS PLANNING • Shortening the lead-time • Manufacturability feedback • Lowering the production cost • Consistent process plans

45. What is Forecasting? Process of predicting a future event Underlying basis of all business decisions Production Inventory Personnel Facilities I predict You will pass with Distinction

46. Short-range forecast Up to 1 year, generally less than 3 months Purchasing, job scheduling, workforce levels, job assignments, production levels Medium-range forecast 3 months to 3 years Sales and production planning, budgeting Long-range forecast 3 + years New product planning, facility location, research and development Forecasting Time Horizons

47. Influence of Product Life Cycle 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 Introduction – Growth – Maturity – Decline

48. Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services Uses of Forecasts

49. Characteristics of Forecasts They are usually wrong! A good forecast is more than a single number Includes a mean value and standard deviation Includes accuracy range (high and low) Aggregated forecasts are usually more accurate Accuracy erodes as we go further into the future. Forecasts should not be used to the exclusion of known information

50. Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use

51. What Makes a Good Forecast? It should be timely It should be as accurate as possible It should be reliable It should be in meaningful units It should be presented in writing The method should be easy to use and understand in most cases.

52. Steps in the Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”

53. Product Life Cycle Best period to increase market share R&D engineering is critical Practical to change price or quality image Strengthen niche Poor time to change image, price, or quality Competitive costs become critical Defend market position Cost control critical Introduction Growth Maturity Decline Company Strategy/Issues Internet Flat-screen monitors Sales DVD CD-ROM Drive-through restaurants Fax machines 3 1/2” Floppy disks Color printers

54. Product Life Cycle Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Introduction Growth Maturity Decline OM Strategy/Issues Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focus Enhance distribution Standardization Less rapid product changes – more minor changes Optimum capacity Increasing stability of process Long production runs Product improvement and cost cutting Little product differentiation Cost minimization Overcapacity in the industry Prune line to eliminate items not returning good margin Reduce capacity

55. Types of Forecasts Economic forecasts Address business cycle – inflation rate, money supply, housing starts, etc. Technological forecasts Predict rate of technological progress Impacts development of new products Demand forecasts Predict sales of existing product

56. The Realities! Forecasts are seldom perfect Most techniques assume an underlying stability in the system Product family and aggregated forecasts are more accurate than individual product forecasts

57. Types of Forecasting Methods Forecasting methods are classified into two groups:

58. Qualitative Methods

59. Forecasting Approaches Used when situation is vague and little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet Qualitative Methods

60. Forecasting Approaches Used when situation is ‘stable’ and historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of color televisions Quantitative Methods

61. Overview of Qualitative Methods Jury of executive opinion Pool opinions of high-level executives, sometimes augment by statistical models Delphi method Panel of experts, queried iteratively

62. Overview of Qualitative Methods Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then aggregated Consumer Market Survey Ask the customer

63. Overview of Quantitative Approaches Naive approach Moving averages Exponential smoothing Trend projection Linear regression Time-Series Models Associative Model

64. Set of evenly spaced numerical data Obtained by observing response variable at regular time periods Forecast based only on past values Assumes that factors influencing past and present will continue influence in future Time Series Forecasting

65. Trend Seasonal Cyclical Random Time Series Components

66. Naive Approach Assumes demand in next period is the same as demand in most recent period e.g., If May sales were 48, then June sales will be 48 Sometimes cost effective and efficient

67. MA is a series of arithmetic means Used if little or no trend Used often for smoothing Provides overall impression of data over time Moving Average Method Moving average = ∑ demand in previous n periods n

68. January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Month Shed Sales Moving Average (12 + 13 + 16)/3 = 13 2 / 3 (13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1 / 3 Moving Average Example 10 12 13 ( 10 + 12 + 13 )/3 = 11 2 / 3

69. Used when trend is present Older data usually less important Weights based on experience and intuition Weighted Moving Average Weighted moving average = ∑ (weight for period n) x (demand in period n) ∑ weights

70. January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Weighted Month Shed Sales Moving Average [(3 x 16) + (2 x 13) + (12)]/6 = 14 1 / 3 [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = 20 1 / 2 Weighted Moving Average 10 12 13 [(3 x 13 ) + (2 x 12 ) + ( 10 )]/6 = 12 1 / 6 Weights Applied Period 3 Last month 2 Two months ago 1 Three months ago 6 Sum of weights

71. Increasing n smooths the forecast but makes it less sensitive to changes Do not forecast trends well Require extensive historical data Potential Problems With Moving Average

72. Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant (  ) Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data Exponential Smoothing

73. Exponential Smoothing New forecast = last period’s forecast + a (last period’s actual demand – last period’s forecast) F t = F t – 1 + a (A t – 1 - F t – 1 ) where F t = new forecast F t – 1 = previous forecast a = smoothing (or weighting) constant (0  a  1)

74. Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20

75. Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 New forecast = 142 + .2(153 – 142)

76. Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 New forecast = 142 + .2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars

77. Choosing  The objective is to obtain the most accurate forecast no matter the technique We generally do this by selecting the model that gives us the lowest forecast error Forecast error = Actual demand - Forecast value = A t - F t

78. Common Measure of Error Mean Absolute Deviation (MAD) MAD = ∑ |actual - forecast| n

79. Exponential Smoothing with Trend Adjustment When a trend is present, exponential smoothing must be modified Forecast including (FIT t ) = trend exponentially exponentially smoothed (F t ) + (T t ) smoothed forecast trend

80. Exponential Smoothing with Trend Adjustment F t = a (A t - 1 ) + (1 - a )(F t - 1 + T t - 1 ) T t = b (F t - F t - 1 ) + (1 - b )T t - 1 Step 1: Compute F t Step 2: Compute T t Step 3: Calculate the forecast FIT t = F t + T t

81. Exponential Smoothing with Trend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t ) Forecast, F t Trend, T t Trend, FIT t 1 12 11 2 13.00 2 17 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10

82. Exponential Smoothing with Trend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t ) Forecast, F t Trend, T t Trend, FIT t 1 12 11 2 13.00 2 17 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 F 2 = a A 1 + (1 - a )(F 1 + T 1 ) F 2 = (.2)(12) + (1 - .2)(11 + 2) = 2.4 + 10.4 = 12.8 units Step 1: Forecast for Month 2

83. Exponential Smoothing with Trend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t ) Forecast, F t Trend, T t Trend, FIT t 1 12 11 2 13.00 2 17 12.80 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 T 2 = b (F 2 - F 1 ) + (1 - b )T 1 T 2 = (.4)(12.8 - 11) + (1 - .4)(2) = .72 + 1.2 = 1.92 units Step 2: Trend for Month 2

84. Exponential Smoothing with Trend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t ) Forecast, F t Trend, T t Trend, FIT t 1 12 11 2 13.00 2 17 12.80 1.92 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 FIT 2 = F 2 + T 1 FIT 2 = 12.8 + 1.92 = 14.72 units Step 3: Calculate FIT for Month 2

85. Exponential Smoothing with Trend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t ) Forecast, F t Trend, T t Trend, FIT t 1 12 11 2 13.00 2 17 12.80 1.92 14.72 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 15.18 2.10 17.28 17.82 2.32 20.14 19.91 2.23 22.14 22.51 2.38 24.89 24.11 2.07 26.18 27.14 2.45 29.59 29.28 2.32 31.60 32.48 2.68 35.16

86. Exponential Smoothing with Trend Adjustment Example | | | | | | | | | 1 2 3 4 5 6 7 8 9 Time (month) Product demand 35 – 30 – 25 – 20 – 15 – 10 – 5 – – Actual demand (A t ) Forecast including trend (FIT t )

87. Trend Projections Fitting a trend line to historical data points to project into the medium-to-long-range Linear trends can be found using the least squares technique y = a + bx ^ where y = computed value of the variable to be predicted (dependent variable) a = y-axis intercept b = slope of the regression line x = the independent variable ^

88. Least Squares Method Time period Values of Dependent Variable Deviation 1 Deviation 5 Deviation 7 Deviation 2 Deviation 6 Deviation 4 Deviation 3 Actual observation (y value) Trend line, y = a + bx ^

89. Least Squares Method Time period Values of Dependent Variable Deviation 1 Deviation 5 Deviation 7 Deviation 2 Deviation 6 Deviation 4 Deviation 3 Actual observation (y value) Trend line, y = a + bx ^ Least squares method minimizes the sum of the squared errors (deviations)

90. Least Squares Method Equations to calculate the regression variables b = S xy - nxy S x 2 - nx 2 y = a + bx ^ a = y - bx

91. Least Squares Example b = = = 10.54 ∑ xy - nxy ∑ x 2 - nx 2 3,063 - (7)(4)(98.86) 140 - (7)(4 2 ) a = y - bx = 98.86 - 10.54(4) = 56.70 Time Electrical Power Year Period (x) Demand x 2 xy 1999 1 74 1 74 2000 2 79 4 158 2001 3 80 9 240 2002 4 90 16 360 2003 5 105 25 525 2004 6 142 36 852 2005 7 122 49 854 ∑ x = 28 ∑ y = 692 ∑ x 2 = 140 ∑ xy = 3,063 x = 4 y = 98.86

92. Least Squares Example b = = = 10.54 S xy - nxy S x 2 - nx 2 3,063 - (7)(4)(98.86) 140 - (7)(4 2 ) a = y - bx = 98.86 - 10.54(4) = 56.70 Time Electrical Power Year Period (x) Demand x 2 xy 1999 1 74 1 74 2000 2 79 4 158 2001 3 80 9 240 2002 4 90 16 360 2003 5 105 25 525 2004 6 142 36 852 2005 7 122 49 854 S x = 28 S y = 692 S x 2 = 140 S xy = 3,063 x = 4 y = 98.86 The trend line is y = 56.70 + 10.54x ^

93. Least Squares Example | | | | | | | | | 1999 2000 2001 2002 2003 2004 2005 2006 2007 160 – 150 – 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – 60 – 50 – Year Power demand Trend line, y = 56.70 + 10.54x ^

94. Least Squares Requirements We always plot the data to insure a linear relationship We do not predict time periods far beyond the database Deviations around the least squares line are assumed to be random

95. Seasonal Variations In Data The multiplicative seasonal model can modify trend data to accommodate seasonal variations in demand Find average historical demand for each season Compute the average demand over all seasons Compute a seasonal index for each season Estimate next year’s total demand Divide this estimate of total demand by the number of seasons, then multiply it by the seasonal index for that season

96. Seasonal Index Example Jan 80 85 105 90 94 Feb 70 85 85 80 94 Mar 80 93 82 85 94 Apr 90 95 115 100 94 May 113 125 131 123 94 Jun 110 115 120 115 94 Jul 100 102 113 105 94 Aug 88 102 110 100 94 Sept 85 90 95 90 94 Oct 77 78 85 80 94 Nov 75 72 83 80 94 Dec 82 78 80 80 94 Demand Average Average Seasonal Month 2003 2004 2005 2003-2005 Monthly Index

97. Seasonal Index Example Jan 80 85 105 90 94 Feb 70 85 85 80 94 Mar 80 93 82 85 94 Apr 90 95 115 100 94 May 113 125 131 123 94 Jun 110 115 120 115 94 Jul 100 102 113 105 94 Aug 88 102 110 100 94 Sept 85 90 95 90 94 Oct 77 78 85 80 94 Nov 75 72 83 80 94 Dec 82 78 80 80 94 Demand Average Average Seasonal Month 2003 2004 2005 2003-2005 Monthly Index 0.957 Seasonal index = average 2003-2005 monthly demand average monthly demand = 90/94 = .957

98. Seasonal Index Example Jan 80 85 105 90 94 0.957 Feb 70 85 85 80 94 0.851 Mar 80 93 82 85 94 0.904 Apr 90 95 115 100 94 1.064 May 113 125 131 123 94 1.309 Jun 110 115 120 115 94 1.223 Jul 100 102 113 105 94 1.117 Aug 88 102 110 100 94 1.064 Sept 85 90 95 90 94 0.957 Oct 77 78 85 80 94 0.851 Nov 75 72 83 80 94 0.851 Dec 82 78 80 80 94 0.851 Demand Average Average Seasonal Month 2003 2004 2005 2003-2005 Monthly Index

99. Seasonal Index Example Jan 80 85 105 90 94 0.957 Feb 70 85 85 80 94 0.851 Mar 80 93 82 85 94 0.904 Apr 90 95 115 100 94 1.064 May 113 125 131 123 94 1.309 Jun 110 115 120 115 94 1.223 Jul 100 102 113 105 94 1.117 Aug 88 102 110 100 94 1.064 Sept 85 90 95 90 94 0.957 Oct 77 78 85 80 94 0.851 Nov 75 72 83 80 94 0.851 Dec 82 78 80 80 94 0.851 Demand Average Average Seasonal Month 2003 2004 2005 2003-2005 Monthly Index Expected annual demand = 1,200 Jan x .957 = 96 1,200 12 Feb x .851 = 85 1,200 12 Forecast for 2006