Engineering Management Lecture 3 PDF

‫كلية الهندسة – قسم الهندسة الكهربائية‬ ‫العام الدراسي‪2023 /2022 :‬م – الفصل الدراسي الثاني‬ ‫المقرر‪ :‬اإلدارة الهندسية‬ ‫الفرقة‪ :‬الرابعة‬ ‫رقم المحاضرة‪3 :‬‬ ‫عنوان المحاضرة‪PLANNING AN...

‫كلية الهندسة – قسم الهندسة الكهربائية‬ ‫العام الدراسي‪2023 /2022 :‬م – الفصل الدراسي الثاني‬ ‫المقرر‪ :‬اإلدارة الهندسية‬ ‫الفرقة‪ :‬الرابعة‬ ‫رقم المحاضرة‪3 :‬‬ ‫عنوان المحاضرة‪PLANNING AND FORECASTING II :‬‬ ‫‪Mr. MUBARAK MOHAMMED‬‬  SYLLABUS  Introduction to Engineering Management  Planning and Forecasting  Decision Making  Planning Production Activity  Leading Technical People  Organizing and Controlling  Engineering Project Management 2  OBJECTIVES  Identify the qualitative methods of planning.  Identify and perform the quantitative methods of planning.  Qualitative Methods  Jury of Executive Opinion  Involves small group of high-level managers to estimates demand by working together.  Combines managerial experience with statistical models  Relatively simple and quick forecasting method.  This approach is subject to severe judgmental biases that can result in poor forecasts. Pool opinions of high-level executives, sometimes augment by statistical models. Top Level Executive  Qualitative Methods  Delphi method  Forecasting process and structured communication framework based on the results of multiple rounds of questionnaires sent to a panel of experts.  After each round of questionnaires, the experts are presented with an aggregated summary of the last round, allowing each expert to adjust their answers according to the group response.  This approach eliminates need for group meetings and bias inherent.  This approach can take a lot of time to reach consensus. Panel of experts, queried iteratively  Qualitative Methods  Sales Force Composite  The sales agents forecast the sales in their respective territories, which is then consolidated at branch/region/area level, after which the aggregate of all these factors is consolidated to develop an overall company sales forecast.  This method is the bottom-up approach  Sales reps know customers’ wants.  Tends to be overly optimistic. The sales force composite methods encompasses the aggregate judgments of the entire sales force.  Qualitative Methods  Consumer Market Survey  Involves gathering data directly from consumers, customers, or market participants to make predictions about future demand for a product or service.  The data collected through surveys can provide Consumer valuable insights into consumers' opinions, Market Survey attitudes, and buying behavior. Complete Enumeration Method The forecast's accuracy will depend on the size and Sample Survey Method representativeness of the sample, the quality of the End-use Method survey questions, and the ability to generalize the findings to the larger population.  Quantitative Methods  Simple Moving Average Method  Moving Average (MA) is a series of arithmetic means.  Used if little or no trend.  Used if no seasonal or cyclical variations  Used often for smoothing  Provides overall impression of data over time 1 n Fn1  t 1 At n  Quantitative Methods  Example For data given below find the forecast value of 2023 using 3 period moving average, 5 period moving average. Period Actual Value Solution: 2018 100 Period Actual Value MA Period 3 MA Period 5 2019 102 2018 100 2020 105 2019 102 2021 108 2020 105 2022 109 2021 108 2023 ? 2022 109 MA-3 = 102.33 2023 ? MA-3 = 105 MA-5 = 104.8 MA-3 = 107.33  Quantitative Methods  Weighted Moving Average Method  Used when trend is present  Older data usually less important  Weights based on intuition  Weights often lay between 0 & 1.  The sum of weights is equal to 1.0 Fn1  t 1 wt At where  n n t 1 wt  1  Quantitative Methods  Example For data given below find the forecast value of 2023 using 3 period weighted moving average with weights of (0.2, 0.3, 0.5). Period Actual Value Solution: 2018 100 Period Actual Value WMA Period 3 2019 102 2018 100 2020 105 2019 102 2021 108 2020 105 2022 109 2021 108 2023 ? 2022 109 MA-3 = 103.1 2023 ? MA-3 = 105.9 MA-3 = 107.9  Quantitative Methods  Exponential Smoothing Method  Is a form of weighted moving average method  Weights decline exponentially  Most recent data weighted most  Requires smoothing constant (α) which Ranges from 0 to 1.  Subjectively chosen by experience.  Some little record keeping of past data.  Quantitative Methods  Example For data given below find the forecast value of 2023 using exponential smoothing of (α=0.4) Solution: Actual Forecast Period Value Value 2018 100 99 2019 102 104 2020 105 107 F2003 = αA2002 + 1 − α F2002 2021 108 110 2022 109 111 F2003 = 0.4 109 + 1 − 0.4 × 111 = 110.2 2023 ? ?  Quantitative Methods  Regression Analysis Method  This is an “Exploratory Forecasting Method”.  It develops “logical” relationships between variables.  Tries to minimize sum of the squares of the deviations.  Gives best fit for a line passing through the data. a  y  bx n ( xi yi )   xi  yi y  a  bx b n ( x )  ( xi ) 2 2 𝑦ത = σ 𝑦𝑖 𝑥ҧ = σ 𝑥𝑖 i 𝑛 𝑛  Quantitative Methods  Example For data given below find the forecast value of June using linear regression method Solution: b  n (x y )  i x i y  i i n ( x 2 i )  ( x )i 2 Period Sales Period Sales 𝐗𝟐 𝐗 𝐢 𝐘𝐢 ∴ b = 1.4 (X) (Y) February 2 σ 𝑦𝑖 σ 𝑥𝑖 March 3 0 2 0 0 𝑦ത = = 4 & 𝑥ҧ = = 1.5 𝑛 𝑛 April 5 1 3 1 3 a  y  bx ∴ a = 1.9 May 6 2 5 4 10 June ? 3 6 9 18 ∴ y = a + bx = 1.9 + 1.4x Σ 6 16 14 31 𝐅𝐨𝐫 𝐣𝐮𝐧𝐞: 𝐱 = 𝟒 =⇒ 𝐲 = 𝟕. 𝟓 𝑛=4 16 17