This document analyzes demand forecasting methods for four pharmaceutical products. Four forecasting methods - naive, cumulative mean, simple moving average, and exponential smoothing - were evaluated based on mean error, mean absolute percentage error, and mean squared error. Visual Basic for Applications was used to optimize parameters for simple moving average and exponential smoothing. The best method for each product was determined to be the one with the lowest mean squared error. Forecasts and 90% confidence intervals are presented for next-month demand.
Analysis of Forecasting Sales By Using Quantitative And Qualitative MethodsIJERA Editor
This paper focuses on analysis of forecasting sales using quantitative and qualitative methods. This forecast should be able to help create a model for measuring a successes and setting goals from financial and operational view points. The resulting model should tell if we have met our goals with respect to measures, targets, initiatives
On Confidence Intervals Construction for Measurement System Capability Indica...IRJESJOURNAL
Abstract: There are many criteria that have been proposed to determine the capability of a measurement system, all based on estimates of variance components. Some of them are the Precision to Tolerance Ratio, the Signal to Noise Ratio and the probabilities of misclassification. For most of these indicators, there are no exact confidence intervals, since the exact distributions of the point estimators are not known. In such situations, two approaches are widely used to obtain approximate confidence intervals: the Modified Large Samples (MLS) methods initially proposed by Graybill and Wang, and the construction of Generalized Confidence Intervals (GCI) introduced by Weerahandi. In this work we focus on the construction of the confidence intervals by the generalized approach in the context of Gauge repeatability and reproducibility studies. Since GCI are obtained by simulation procedures, we analyze the effect of the number of simulations on the variability of the confidence limits as well as the effect of the size of the experiment designed to collect data on the precision of the estimates. Both studies allowed deriving some practical implementation guidelinesin the use of the GCI approach. We finally present a real case study in which this technique was applied to evaluate the capability of a destructive measurement system.
Analysis of Forecasting Sales By Using Quantitative And Qualitative MethodsIJERA Editor
This paper focuses on analysis of forecasting sales using quantitative and qualitative methods. This forecast should be able to help create a model for measuring a successes and setting goals from financial and operational view points. The resulting model should tell if we have met our goals with respect to measures, targets, initiatives
On Confidence Intervals Construction for Measurement System Capability Indica...IRJESJOURNAL
Abstract: There are many criteria that have been proposed to determine the capability of a measurement system, all based on estimates of variance components. Some of them are the Precision to Tolerance Ratio, the Signal to Noise Ratio and the probabilities of misclassification. For most of these indicators, there are no exact confidence intervals, since the exact distributions of the point estimators are not known. In such situations, two approaches are widely used to obtain approximate confidence intervals: the Modified Large Samples (MLS) methods initially proposed by Graybill and Wang, and the construction of Generalized Confidence Intervals (GCI) introduced by Weerahandi. In this work we focus on the construction of the confidence intervals by the generalized approach in the context of Gauge repeatability and reproducibility studies. Since GCI are obtained by simulation procedures, we analyze the effect of the number of simulations on the variability of the confidence limits as well as the effect of the size of the experiment designed to collect data on the precision of the estimates. Both studies allowed deriving some practical implementation guidelinesin the use of the GCI approach. We finally present a real case study in which this technique was applied to evaluate the capability of a destructive measurement system.
Week 4 forecasting - time series - smoothing and decomposition - m.awaluddin.tMaling Senk
Forecasting - time series - smoothing and decomposition methods
Smoothing Method as Moving Averages and exponetial methods. The steps for decomposition methods and example of it. Case study for smothing methods in Single Exponential Smoothing, Double Exponential Smoothing and Triple Exponential Smoothing
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A NOVEL PERFORMANCE MEASURE FOR MACHINE LEARNING CLASSIFICATIONIJMIT JOURNAL
Machine learning models have been widely used in numerous classification problems and performance measures play a critical role in machine learning model development, selection, and evaluation. This paper covers a comprehensive overview of performance measures in machine learning classification. Besides, we proposed a framework to construct a novel evaluation metric that is based on the voting results of three performance measures, each of which has strengths and limitations. The new metric can be proved better than accuracy in terms of consistency and discriminancy.
A Novel Performance Measure for Machine Learning ClassificationIJMIT JOURNAL
Machine learning models have been widely used in numerous classification problems and performance measures play a critical role in machine learning model development, selection, and evaluation. This paper covers a comprehensive overview of performance measures in machine learning classification. Besides, we proposed a framework to construct a novel evaluation metric that is based on the voting results of three performance measures, each of which has strengths and limitations. The new metric can be proved better than accuracy in terms of consistency and discriminancy.
PARA QUE SEPAS QUIÉN TE CANTA POR LAS MAÑANASdegatitos
DEFENDER LA VIDA DE UN ANIMAL NO ES UN ACTO DE COMPASIÓN SINO DE JUSTICIA
El Rugido hace Historia
https://www.facebook.com/elRugidohaceHistoria
www..rugidohacehistoria.com.ar
https://www.youtube.com/user/elRugidohaceHistoria
www.raddioeme.com
jueves 20.30 a 22hs
Week 4 forecasting - time series - smoothing and decomposition - m.awaluddin.tMaling Senk
Forecasting - time series - smoothing and decomposition methods
Smoothing Method as Moving Averages and exponetial methods. The steps for decomposition methods and example of it. Case study for smothing methods in Single Exponential Smoothing, Double Exponential Smoothing and Triple Exponential Smoothing
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A NOVEL PERFORMANCE MEASURE FOR MACHINE LEARNING CLASSIFICATIONIJMIT JOURNAL
Machine learning models have been widely used in numerous classification problems and performance measures play a critical role in machine learning model development, selection, and evaluation. This paper covers a comprehensive overview of performance measures in machine learning classification. Besides, we proposed a framework to construct a novel evaluation metric that is based on the voting results of three performance measures, each of which has strengths and limitations. The new metric can be proved better than accuracy in terms of consistency and discriminancy.
A Novel Performance Measure for Machine Learning ClassificationIJMIT JOURNAL
Machine learning models have been widely used in numerous classification problems and performance measures play a critical role in machine learning model development, selection, and evaluation. This paper covers a comprehensive overview of performance measures in machine learning classification. Besides, we proposed a framework to construct a novel evaluation metric that is based on the voting results of three performance measures, each of which has strengths and limitations. The new metric can be proved better than accuracy in terms of consistency and discriminancy.
PARA QUE SEPAS QUIÉN TE CANTA POR LAS MAÑANASdegatitos
DEFENDER LA VIDA DE UN ANIMAL NO ES UN ACTO DE COMPASIÓN SINO DE JUSTICIA
El Rugido hace Historia
https://www.facebook.com/elRugidohaceHistoria
www..rugidohacehistoria.com.ar
https://www.youtube.com/user/elRugidohaceHistoria
www.raddioeme.com
jueves 20.30 a 22hs
Francesco Micali : Dal sito internet al network diocesano - Mediabeta srlf.micali
Intervento di Francesco Micali per il seminario di studi "Diocesi in Rete" organizzato dal Servizio Informatico e dall'Ufficio Comunicazioni Sociali della Conferenza Episcopale Italiana.
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ForecastingBUS255 GoalsBy the end of this chapter, y.docxbudbarber38650
Forecasting
BUS255
Goals
By the end of this chapter, you should know:
Importance of Forecasting
Various Forecasting Techniques
Choosing a Forecasting Method
2
Forecasting
Forecasts are done to predict future events for planning
Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan
Forecasts are made on many different variables
Forecasts are important to managing both processes and managing supply chains
3
Key Decisions in Forecasting
Deciding what to forecast
Level of aggregation
Units of measurement
Choosing a forecasting system
Choosing a forecasting technique
4
5
Forecasting Techniques
Qualitative (Judgment) Methods
Sales force Estimates
Time-series Methods
Naïve Method
Causal Methods
Executive Opinion
Market Research
Delphi Method
Moving Averages
Exponential Smoothing
Regression Analysis
Qualitative (Judgment) methods
Salesforce estimates
Executive opinion
Market Research
The Delphi Method
Salesforce estimates: Forecasts derived from estimates provided by salesforce.
Executive opinion: Method in which opinions, experience, and technical knowledge of one or more managers are summarized to arrive at a single forecast.
Market research: A scientific study and analysis of data gathered from consumer surveys intended to learn consumer interest in a product or service.
Delphi method: A process of gaining consensus from a group of experts while maintaining their anonymity.
6
Case Study
Reference: Krajewski, Ritzman, Malhotra. (2010). Operations Management: Processes and Supply Chains, Ninth Edition. Pearson Prentice Hall. P. 42-43.
7
Case study questions
What information system is used by UNILEVER to manage forecasts?
What does UNILEVER do when statistical information is not useful for forecasting?
What types of qualitative methods are used by UNILEVER?
What were some suggestions provided to improve forecasting?
8
Causal methods – Linear Regression
A dependent variable is related to one or more independent variables by a linear equation
The independent variables are assumed to “cause” the results observed in the past
Simple linear regression model assumes a straight line relationship
9
Causal methods – Linear Regression
Y = a + bX
where
Y = dependent variable
X = independent variable
a = Y-intercept of the line
b = slope of the line
10
Causal methods – Linear Regression
Fit of the regression model
Coefficient of determination
Standard error of the estimate
Please go to in-class exercise sheet
Coefficient of determination: Also called r-squared. Measures the amount of variation in the dependent variable about its mean that is explained by the regression line. Range between 0 and 1. In general, larger values are better.
Standard error of the estimate: Measures how closely the data on the dependent variable cluster around the regression line. Smaller values are better.
11
Time Series
A time seri.
Determining Measurement Uncertainty Parameters for Calibration Processestheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Procedure outcomes and cost analysis using the Macon Value Formula
Value analysis duration and Place of service effect on Value
Competition on Value for Patients in Pain Management
Case StudiesMemorial HospitalMemorial Hospital is a privately .docxtidwellveronique
Case Studies
Memorial Hospital
Memorial Hospital is a privately owned 600-bed facility. The hospital provides a broad range of health care services, including complete laboratory and X-ray facilities, an emergency room, an intensive care unit, a cardiac care unit, and a psychiatric ward. Most of these services are provided by several other hospitals in the metropolitan area. Memorial has purposely avoided getting involved in any specialized fields of medicine or obtaining very specialized diagnostic equipment because it was felt that such services would not be cost-effective. The General Hospital, located only a few miles from Memorial, is affiliated with the local School of Medicine and offers up-to-date services in those specialized areas. Instead of trying to compete with General Hospital to provide special services, Memorial Hospital has concentrated on offering high-quality general health care at an affordable price. Compared with the much larger General Hospital, Memorial stresses close personal attention to each patient from a nursing staff that cares about its work. In fact, the hospital has begun to place ads in newspapers and on television, stressing its patient-oriented care.
However, the hospital's administrator, Janice Fry, is concerned about whether the hospital can really deliver on its promises, and worries that failure to provide the level of health care patients expect could drive patients away. Janice met recently with the hospital's managerial personnel to discuss her concerns. The meeting raised some questions about how the hospital's quality of health care could be assured. Jessica Tu, director of nursing, raised the question, "How do we measure the quality of health care? Do we give patients a questionnaire when they leave, asking if they were happy here? That does not seem to answer the question because we could make a patient happy, but give them lousy health care." Several other questions were asked concerning the hospital's efforts to keep costs down. Some people were concerned that an emphasis on costs would be detrimental to quality. They argued that when a person's life is at stake, costs should not be of concern.
After the meeting, Janice began thinking about these questions. She remembered reading recently that some companies were using total quality management (TQM) to improve their quality. She liked the idea—if it could be used in a hospital.
1. Discuss some ways that a hospital might measure quality.
2. What are the potential costs of quality for Memorial Hospital? How could the value of a human life be included?
3. Are there any ideas or techniques from TQM that Janice could use to help Memorial focus on providing quality health care?
4. What measures could Memorial use to assess the quality of health care it is providing?
Forecasting
BUS255
Goals
By the end of this chapter, you should know:
Importance of Forecasting
Various Forecasting Techniques
Choosing a Forecasting Method
2
Forecasti ...
Production Planning and control: Forecasting techniques – causal and time series models, moving average,
exponential smoothing, trend and seasonality; aggregate production planning; master production scheduling; materials
requirement planning (MRP) and MRP‐II; routing, scheduling and priority dispatching, concept of JIT manufacturing
System.
Project Management: Project network analysis, CPM, PERT and Project crashing.
International journal of applied sciences and innovation vol 2015 - no 2 - ...
Supply Chain Planning Paper
1. SUPPLY CHAIN PLANNING
DEMAND ANALYSIS PROJECT
G. Lauson, M.S., P.E.
January 31, 2017
Purpose
This paper presents the findings of an analysis of four (4) pharmaceutical products. Four (4)
forecasting methods were applied and three (3) error measurements were applied to those
forecasting methods to determine which method most accurately estimated next-future-month
demand for each product. Monthly data was selected at the request of the manufacturing
division, as this was determined by them as optimum for their production schedule planning.
Note that all forecasting and error methods / descriptions were taken from a Coursera / Rutgers
University online course titled “Supply Chain Planning” which the author completed in January
2017. The course is part of a five-course offering within Coursera’s “Supply Chain Management
Specialization.”
Forecasting Methods
The following forecasting methods were evaluated for accuracy with respect to each of the four
pharmaceutical products. A brief explanation of the rationale behind each method is provided,
along with how the method is computed.
1. Naïve Method. The simplest of the four, this method uses the prior-period actual demand
as the forecast for the next-period demand. The computation is:
Ft = Dt-1
2. Cumulative Mean Method. This method averages all demand from prior periods – that
average becomes the forecast. Based on information provided in the above-referenced
Coursera course, this is a very stable forecast that averages out all noise; however, that
stability may be contrary to current demand conditions. The assumption behind this method
is that “all prior data is equally useful.” The computation is:
Ft = Σ i = 1 to t–1 [Di] / (t – 1)
3. Simple Moving Average (SMA) Method. An adaptable forecasting method that can be
made reactive or stable. Companies with stable demand tend to like this indicator. The
computation is:
Ft = Σ i = (t-N+2) to t–1 [Di] / N
4. Exponential Smoothing (or Exponential Moving Average (EMA)) Method. A weighted
2. average of all past demand; most of the weight is on the latest observed period and the
remainder of the weights decline exponentially. α changes the weights and varies how much
weight (in the weighted average) is applied to each period. The computation is:
Ft = α * [Dt-1] + (1 – α) * Ft-1
Error Estimation Methods
The following methods were used to estimate the error associated with each of the above
forecasting methods.
1. Mean Error. This is the simplest forecast accuracy measure, and is more a measure of
bias than accuracy. This error measurement averages the difference between true demand
and associated forecast for the forecasted time periods. The computation is:
ME = Σ i = 1 to N [Di - Fi] / N
2. Mean Absolute Percentage Error. The next-simplest forecast accuracy measure
measures accuracy (as opposed to bias). Averages the absolute value of the difference
between true demand and associated forecast (divided by true demand) for the forecasted
time periods. The computation is:
MAPE = Σ i = 1 to N ABS[(Di - Fi) / Di] / N
3. Mean Squared Error. Probably the most important error measurement; it averages the
squared differences between true demand and associated forecast for the forecasted time
periods. Squaring gives more weight to large errors (which are the ones we want to avoid;
small errors can be tolerated). Large errors surprise us and make life and planning much
more difficult. The computation is:
MSE = Σ i = 1 to N (Di - Fi)2
/ N
Application of VBA/Excel
Visual BASIC for Applications (VBA) is a programming language that is attached to all Microsoft
applications, including Excel. VBA programming was used in this project to optimize the Simple
Moving Average span (i.e., N, the number of demand values used to compute the Moving
Average), as well as to optimize the weight parameter, α, that is the controlling part of
Exponential Smoothing. The VBA program applied trial span and alpha values and evaluated
each value according to the following selection criterion:
Minimize [1 * ABS(ME) + 2 * MAPE + 3 * MSE]
The above criterion applies judgment-based weights (i.e., the "1," "2," and "3" in the above
expression) that emphasize the relative importance of the Mean Error (ME), the Mean Absolute
2 / 6
3. Percent Error (MAPE), and the Mean Squared Error (MSE) in determining the Simple Moving
Average and Exponential Moving Average ability to forecast accurately. Span and alpha values
were then subjected to visual inspection for each of the four products (via graphed data). The
spans and alphas were visually acceptable from the following perspectives:
1. Minimized bias.
2. Minimized error.
3. Trend capture.
4. Responsiveness to non-random fluctuation.
Forecasting Decisions
The optimum forecasting method was decided on the basis of the smallest Mean Squared Error
(MSE) value. The standard deviation (s) was computed from the MSE by taking the square root
of the MSE; s was then used to determine the forecasting confidence interval. The confidence
interval computations are:
LCL = Fi – si * 1.64 (where 1.64 is the 90-percent confidence normal-distribution z-score)
UCL = Fi + si * 1.64
The 90-percent confidence z-score was selected on the basis of a visually-suitable width
confidence interval (i.e., a judgment was applied in selecting the 90-percent interval).
The following table provides the summary monthly data on which the best forecasting methods
for each product are based. Again, the criterion of relevance used to determine the best
forecasting method is the smallest Mean Squared Error value.
MONTHLY DATA ACCURACY ASSESSMENT
Product A Product B Product C Product D
Method E MAPE MSE E MAPE MSE E MAPE MSE E MAPE MSE
NM -2,040 16.8% 90,837,443 -49 23.4% 2,887,528 9 12.7% 1337 -41 20.0% 70,472,694
CMF -9,720 30.6% 206,087,159 94 15.5% 1,418,775 71 32.8% 8635 4,402 29.0% 138,201,620
MA -4,252 17.4% 68,944,942 -163 15.6% 968,698 13 11.2% 1146 -607 21.2% 66,255,361
EMA -2,888 16.4% 75,744,998 201 15.3% 1,296,510 13 11.2% 1118 -46 18.8% 64,604,584
Best Method NM EMA MA NM EMA MA NM MA EMA NM EMA EMA
The following table provides the next-future month forecasts for each product. The Mean is the
forecast, and the Lower-Control and Upper-Control Limits provide the 90-percent probable
extreme-value range for the next-future month demand. Note that Sales and Operations
Planning (S&OP) can be brought to bear on critical products, with an informed team selecting
the “best” forecast from within the 90-percent confidence interval; i.e., from within the estimated
future demand range: [LCL, UCL].
3 / 6
4. MSE-BASED MONTHLY DATA FORECAST
Product
A
Product
B
Product
C
Product
D
Mean 28,908 4,936 296 20,187
UCL 42,526 6,550 351 33,369
LCL 15,291 3,322 241 7,005
Sy/x 8,303 984 33 8,038
n 24 24 26 26
z90% 1.64 1.64 1.64 1.64
Finally, the following monthly graphs provide visual aids of the selected forecasting methods and
of the applied confidence interval statistics. True demand values are represented via blue line;
forecast values are represented via red line. Note that each chart’s present-time (zero) value is
at its right side; past-time (negative) values are to the left.
PRODUCT A (3m SMA) w/ 90% Confidence Interval
10,094
20,094
30,094
40,094
50,094
60,094
70,094
80,094
90,094
100,094
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
Months (0 is Present Time)
Demand
PRODUCT A MA_A LCL UCL
4 / 6