BankUSA Help Desk - Case Study Brent Schmitz Business 4208 Notre Dame de Namur July 28‚ 2013 Abstract The purpose of this case study is to recommend how to increase the overall effectiveness and improve the planning of the Help Desk business unit for BankUSA. This study will look at what are the service management characteristics of the customer service representative‚ create a suggested mission statement for the Help Desk and review which forecasting technique is best used by the
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SCM 404 Demand Fulfillment Spring 2014 1. Implied Demand Uncertainty (IDU) has important implications for the structure and performance of a supply chain. Consider the table below from class on 1/9/13. For each customer characteristic or need‚ explain the meaning of the “+” or “-“ and explain why that characteristic has that effect. (3 points) Customer Need Impact on IDU Quantity of individual order + Response time (customer desired lead time) - Variety of products + Service level
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Experiment 13 Charging and Discharging Capacitors 1. Introduction In this experiment you will measure the rates at which capacitors in series with resistors can be charged and discharged. The time constant RC will be found. Charging a capacitor. Consider the series circuit shown in Fig. 1. Let us assume that the capacitor is initially uncharged. When the switch S is open there is of course no current. If the switch is closed at t=0‚ charges begin to flow and an ammeter will be able
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Averages Method: Moving average of order N is the arithmetic average for the most recent N observations Exponential smoothing method: It combines both demand and forecast. The Current forecast is the weighted average of the last forecast and the current demand 1.1.2 Trend based methods: Regression Analysis: fits straight line to a set of data Holt’s Method : type of double exponential smoothing. Allows for simultaneous smoothing on the series and on the trend 1.2 Qualitative methods:
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Eight Steps to Forecasting • Determine the use of the forecast □ What objective are we trying to obtain? • Select the items to be forecast • Determine the time horizon of the forecast □ Short time horizon – 1 to 30 days □ Medium time horizon – 1 to 12 months □ Long time horizon – more than 1 year • Select the forecasting model(s) |Description |Qualitative Approach |Quantitative Approach
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RADIATION DETECTION IN VEGETABLE LEAVES: A COMPARATIVE CASE STUDY IN COASTAL AND HINTERLAND REGIONS OF AKWA IBOM STATE BY Godfrey T. Akpabio‚ Ime E. Essien And Bassey E. Bassey. Department of Physics‚ University of Uyo‚ Uyo. ABSTRACT Radioactive radiation level was detected for five samples of vegetable leaves namely: Water leaf‚ Fluted Pumpkin‚ Editan Lasientera africana and Afang Gnetum africanum. These vegetable leaves were collected from Uyo (interland region) and Ibeno
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Complex Numbers and Applications ME50 ADVANCED ENGINEERING MATHEMATICS 1 Complex Numbers √ A complex number is an ordered pair (x‚ y) of real numbers x and y. For example‚ (−2.1‚ 3.5)‚ (π‚ 2)‚ (0‚ 0) are complex numbers. Let z = (x‚ y) be a complex number. The real part of z‚ denoted by Re z‚ is the real number x. The imaginary part of z‚ denoted by Im z‚ is the real number y. Re z = x Im z = y Two complex numbers z1 = (a1‚ b1) and z2 = (a2‚ b2) are equal‚ written z1 = z2 or (a1‚ b1) = (a2‚ b2)
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Nicole-line breaks mean new slide important questions Forecasts are needed to predict demand all different teams within the company need the forecast different users have different time requirements and detail reqts you might have to collect more data if you don’t have enough cost depends on the scope of the project need to engage the users‚ so have to provide a feedback system The top chart appears to be a ore difficult to forecast but they just narrowed the y axiz 2nd chart down
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percent error (MAPE) are shown. Correlation Bias MAD MSE MAPE Naïve -- 541.38 6865.52 69‚856‚200 .19 Moving Average (3 periods) -- 491.36 6‚138.27 59‚540‚560 .17 Weighted Moving Average (3 period; .6‚ .3‚ .1) -- 424.81 6‚501.58 61‚107‚180 .18 Exponential smoothing (alpha = 0.5) -- 794.28 5‚880.56 50‚755‚960 .16 Trend Analysis .54 0.00 4‚355.70 31‚285‚700 .12 Seasonal Additive Decomposition .97 0.00 1‚251.26 2‚386‚650 .03 It is obvious that the superior
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PROBLEM 4.1 A) (374 + 368 + 381) / 3 = 374.33 Pints B) Forecast: (381 x .1) = 38.1 (368 x .3) = 110.4 (374 x .6) = 224.4 38.1 + 110.4 + 224.4 = 372.9 Pints C) Week Of Pints Used Forecast with exponential smoothing applied 31st Aug 360 360.00 7th Sep 389 360.00 14th Sep 410 365.80 21st Sep 381 374.64 28th Sep 368 375.91 5th Oct 374 374.33 12th Oct 374.26 Forecast : 374.26 Pints PROBLEM 4.5 A) (3700 + 3800) / 2 = 3750 Miles
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