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Note for Industrial Engineering and Industrial Management - IE by Chirag Khanduja

  • Industrial Engineering and Industrial Management - IE
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  • COLLEGE - KPLC
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Chirag Khanduja
Chirag Khanduja
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Process Layout Fixed Position Layout Work Flow Diagram Flow Process Chart Computerized Techniques for Plant Layout CORELAP, CRAFT, ALDEP, PLANET, COFAD, CAN-Q Chapter 10: Quality Analysis and Control Statistical Quality Control Control Chart Control Chart for Variables X– Chat and R – Chart Control Chart for Variables C – Chart and P – Chart Chapter 11: Process Capability Operation Characteristic Curve (OC Curve) Sampling Plan (Single, Double, Sequential Sampling Plan) Work Sampling Total Quality Management (TQM) ISO Just in Time (JIT) Operations Research Chapter 12: Graphical Method Chapter 13: Simplex Method Chapter 14: Transportation Model Chapter 15: Assignment Model Chapter 16: Queuing Model Chapter 17: Value Analysis for Cost/Value Chapter 18: Miscellaneous Wages Plan, Depreciation Load Chart, Mass Production Gantt Chart Others Page 2 of 318

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Note “Asked Objective Questions” is the total collection of questions from:20 yrs IES (2010-1992) [Engineering Service Examination] 21 yrs. GATE (2011-1992) and 14 yrs. IAS (Prelim.) [Civil Service Preliminary] Copyright © 2007 S K Mondal Every effort has been made to see that there are no errors (typographical or otherwise) in the material presented. However, it is still possible that there are a few errors (serious or otherwise). I would be thankful to the readers if they are brought to my attention at the following e-mail address: swapan_mondal_01@yahoo.co.in S K Mondal Page 3 of 318

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Forecasting S K Mondal 1. Chapter 1 Forecasting Theory at a Glance (For IES, GATE, PSU) Forecasting means estimation of type, quantity and quality of future works e.g. sales etc. It is a calculated economic analysis. 1. Basic elements of forecasting: 1. 2. 3. 4. Trends Cycles Seasonal Variations Irregular Variations 2. Sales forecasting techniques: a. b. c. d. e. f. g. h. i. j. k. l. m. n. I. Historic estimation Sales force estimation Trend line (or Time-series analysis) technique Market survey Delphi Method Judge mental techniques Prior knowledge Forecasting by past average Forecasting from last period's sales Forecasting by Moving average Forecasting by weighted moving average Forecasting by Exponential smoothing Correlation Analysis Linear Regression Analysis. Average method: Forecast sales for next period = Average sales for previous period Example: Period No Sales 1 2 3 4 5 6 7 5 9 8 5 8 Forecast sales for Period No 7 = 7+5+9+8+5+8 =7 6 II. Forecast by Moving Average: Page 4 of 318

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Forecasting S K Mondal Chapter 1 In this method the forecast is neither influenced by very old data nor does it solely reflect the figures of the previous period. Example: Year 1987 1988 Period Sales 1 2 3 4 1 2 Four-period average forecasting 50 60 50 40 50 55 50 + 60 + 50 + 40 = 50 4 60 + 50 + 40 + 50 Forecast for 1988 period 2 = = 50 4 Forecast for 1988 period 1 = III. Weighted Moving Average: A weighted moving Average allows any weights to be placed on each element, providing of course, that the sum of all weights equals one. Example: Period Sales Month-1 Month-2 Month-3 Month-4 Month-5 100 90 105 95 110 Forecast (weights 40%, 30%, 20%, 10% of most recent month) Forecast for month-5 would be: F5 = 0.4 × 95 + 0.3 ×105 + 0.2 × 90 + 0.1 ×100 = 97.5 Forecast for month-6 would be: F6 = 0.4 ×110 + 0.3 × 95 + 0.2 ×105 + 0.1 × 90 = 102.5 IV. Exponential Smoothing: New forecast = α (latest sales figure) + (1 − α ) (old forecast) [VIMP] Where: α is known as the smoothing constant. The size of α should be chosen in the light of the stability or variability of actual sales, and is normally from 0.1 to 0.3. The smoothing constant, α , that gives the equivalent of an N-period moving average can be calculated as follows, α = 2 . N +1 For e.g. if we wish to adopt an exponential smoothing technique equivalent to a nine2 period moving average then, α = = 0.2 9 +1 Page 5 of 318

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