Waiting line management: unit 11. The waiting line is a list of customers who remains waiting for getting certain goods or services from service center. Understanding waiting lines or queues and learning how to manage them is one of the most important areas in operation management. In organizations or in personal life‚ there are examples of processes which generates waiting lines or queues. Such waiting lines occur because the current services facility is insufficient to provide service at that
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Waiting Line System Queuing Systems Queuing System Input Characteristics Queuing System Operating Characteristics Analytical Formulas Single-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times Multiple-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times Economic Analysis of Waiting Lines Slide 1 Structure of a Waiting Line System Queuing theory is the study of waiting lines. Four characteristics of a queuing system are:
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the naïve method‚ a two-period movingaverage‚ and exponential smoothing with an α = 0.2. (Hint: Use naïve to start the exponentialsmoothing process.)MonthSales Naïveb) Compare the forecasts using MAD and decide which is best.Exponential = MAD = Σ | actual – forecast | = 13.73 8+8+4+7/4= 6.75nNaïve = MAD = Σ | actual – forecast | = 8.25 10+10+5+8/4= 8.25n2 period = MAD = Σ | actual – forecast | = 8.25 2.5+15+10+8/4= 8.25nAnswer: Exponential is bestc) Using your method of choice‚ make a forecast
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Exponential Distribution • Definition: Exponential distribution with parameter λ: λe−λx x ≥ 0 f (x) = 0 x s). = = = = = P (X > s + t|X > t) P (X > s + t‚ X > t) P (X > t) P (X > s + t) P (X > t) e−λ(s+t) e−λt e−λs P (X > s) – Example: Suppose that the amount of time one spends in a bank is exponentially distributed with mean 10 minutes‚ λ = 1/10. What is the probability that a customer will spend more than 15 minutes in the bank? What is the probability that a customer
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1150 1087 1170 1196 1084 2008 1270 1137 1170 1155 1104 2009 1290 1186 1207 1259 1154 2010 x 1214 1236 1287 1195 B) Five-year moving average = 141.9 Three-year moving average = 78.6 Exponential smoothing (w = .9) = 45.7 Exponential smoothing (w = .3) = 110.9 C) I would use the exponential smoothing w=.9 because of the trending factor 6) A) In 2010 = 11450 units B) Sales would go from 11450 to 1100‚ which is about 3.9% reduction/loss C) Sales would decrease from 11450 to 9650
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Characteristics of a Queuing System Difficulty: Moderate 4) The average time each customer spends in the queue is referred to as: A) W B) Wq C) L D) Lq E) ρ Answer: B Page Ref: 377 Topic: Single-Server Queuing System With Poisson Arrivals and Exponential Service Times (M/M/1 Model) Difficulty: Moderate 5) In a drive-in fast food restaurant‚ customers form a single lane‚ place their order and pay their bill at one window‚ and then pick up their food at a second window. This queuing configuration
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Anderson‚ D. R.‚ et.al.‚ (2011) Quantitative Business Methods 11th ed. Retrieved from http://analysights.wordpress.com/2010/05/13/forecast-friday-topic-exponential-smoothing-methods/ Retrieved from http://www.decisionmakingsolutions.com/quotes_about_business.html Retrieved from http://www.logisitik.com/learning-center/forecasting-management/item/460-exponential-smoothing.html Retrieved from http://www.sanmiguel.com.ph/corporate/company/
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producció. Carles Ramírez Estrada Marcel Subirana Florats Curs 2011-2012 G23 22/03/2012 4.47 City Cycles has just started selling the new Z-10 mountain bike‚ with monthly sales as show in the table. First‚ co-owner Amit wants to forecast by exponential smoothing by initially setting February’s forecast equal to January’s sales with α=1. Co-owner Barbara wants to use a three-period moving average. 1. Is there a strong lineal trend in sales over time? 2. Fill in the table with what Amit
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DEMAND FORECASTING Demand forecasting is the process of predicting future average sales on the basis of historical data samples and market intelligence. The volatility of demand from an average level is supplied from the safety inventory. Any forecast is likely to be wrong‚ so the focus should be on understanding the range of potential forecast errors and the level of safety inventory that will cater for peak demand. An important additional calculation is forecast bias. This is the cumulative
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accurately as possible‚ the population and wheat requirements in Punjab province for the year 2010-11. For this purpose a time series data regarding population‚ wheat production and wheat requirements were collected from the National statistics. An exponential smoothing model specific for forecasting was applied. Different weights ranging from 0.3-0.9 were given to the past data to get the precise estimates. Results revealed that a quantity of 7.55 million tons wheat will be surplus in Punjab as the total
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