MSC337 Marymount University Operations Management Assignment


Marymount University

Question Description

1. you have to find 6 scholarly Journal articles about (Project Management). Each one needs to be cited in APA formatting and included in the Word document.and I want you to write 6 pages. each article you have to write 1 pages.

2. Create a PowerPoint presentation that cover the 6 topic of articles and a summary of the articles. Each article has to be in separate slide.

3. You need to link the content of the articles to the topics covered in the Operations textbook.

5. Essentially create an annotated bibliography.

6. I attach an example of the paper.

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Running head: Business Forecasting 1 Business Forecasting: An Annotated Bibliography Name Marymount University MSC – 337-A Operations Management Dr. Cynthia Knott May 1, 2018 Business Forecasting 2 Introduction The field of forecasting includes supply forecasting, such as for agricultural commodities or the oil industry, and extends to economic forecasting, such as GDP forecasts produced by the Federal Reserve Bank. The six articles cited in this annotated bibliography are published in The Journal of Business Forecasting (JBF), a publication of the Institute of Business Forecasting and Planning. Established in 1982, JBF is a peer-reviewed academic journal edited by Dr. Chaman Jain, Professor of Economics at St. John’s University. Articles in the journal are written for corporate demand planners and S&OP professionals. The field of business demand forecasting is dynamic, and practitioners need to stay abreast of current trends in the field. In his letter to readers in the current issue of the journal (Winter 2017-2018), Dr. Jain comments on the expanding role of ecommerce, the advent of automated forecasting software, and the growing need for demand planners to adopt the increasing availability of sophisticated analytical tools. Forecasting methods are covered in chapter four of Heizer and Render’s (2014) textbook, Operations Management: Sustainability and Supply Chain Management (104-136). The articles annotated here build on that material and seek to create general familiarity with the field of business forecasting. Using an evaluative annotation approach, each article has been given an extended exposition of its content. The accompanying PowerPoint presentation, rather than introducing articles alphabetically by author (mea culpa to APA), approaches the bibliography in a thematic sequence. Beginning with an overview of forecasting models, the topics cover the use of external data and economic indicators, matching forecasting methods to product segments, the impact accurate forecasts have on inventory and financial metrics, and talent management as an important part of the forecasting process. The annotated bibliography that follows is alphabetical by author’s last name. Business Forecasting 3 Chase, Charles W. (Winter 2015-2016). The Importance of Product Segmentation. The Journal of Business Forecasting; 34, 4, 36-40. Charles Chase is an expert in sales forecasting, market response modeling and supply chain management; he has worked for companies such as Johnson & Johnson, Coca-Cola, and Heineken, among others (p. 36). Companies are understanding that not all their products are forecastable, says Chase, and “are asking themselves what is forecastable and what is not, and how can they segment their products to get the most accuracy across their product portfolio” (p. 37). Forecastability is impacted by consumer demand and data constraints. Demand planners must segment their brands and products and apply the correct forecasting method to obtain the best performance across their product lines. Many practitioners realize poor forecast performance simply because they use one methodology to forecast all of their products (p. 37). The author states that in many cases the forecast accuracy is less than 70%, whereas desired results would be in the 85% to 95% range (p. 37). Product groups have different data patterns, depending on their life cycles and marketing and sales support. When demand planners “cleanse” a product’s historical demand data, they also lose data on sales promotion responses and seasonality, which can result in their ERP (Enterprise Resource Planning) system picking a moving average model instead of a seasonal exponential model (p. 37). The right model can improve forecasts by 10% across the firm’s product portfolio. Chase recommends decomposing each data set by product or brand to find the trend, seasonality, cyclical variance, and unexplained error (p. 37). This kind of time series analysis can uncover true demand patterns. The value that Chase brings to demand planning and forecasting is his concept of segmenting products and brands into four quadrants and then overlaying the best forecasting method over each quadrant to achieve better forecast results. The four general product quadrants Business Forecasting 4 are: 1) low value, low forecastability, 2) low value, high forecastability, 3) high value, low forecastability and 4) high value, high forecastability (p.37). He then provides more specific product characteristics for each quadrant: 1) slow moving, 2) new products, 3) fast moving, and 4) steady state (p.37). The table below, reproduced from the article, shows these four quadrants and product portfolio management principles: New Products High Value Low Forecastability Product Line Extensions New Products Short Life Cycle Products High Priority Products Strong Trend Seasonal Fluctuations Possible Cycles Advertising Driven Sales Promotions Fast Moving High Value High Forecastability Low Priority (Regional Specialty Products) Some Trend Seasonal Fluctuations Intermittent Data Low Priority Products Strong Trend Highly Seasonal Possibly Cycles Minor Sales Promotions Steady State Products Low Value High Forecastability Company Value Slow Moving Products Low Value Low Forecastability Low <------------------ Forecastability ---------------> High The appropriate forecasting methodologies can also be segmented into four quadrants and matched with the segmented product characteristic quadrants shown above. For example, slow moving, low forecastability products can only be approximately forecasted using some trend and seasonal fluctuation analysis and moving averaging, whereas high value, high forecastability products show strong trend and season fluctuations and are responsive to sales promotions and advertising (p. 38). This group of products can be further analyzed using causal models (dynamic regression, multiple linear regression) to reduce unexplained variance. Chase’s breakout of statistical methods into four corresponding quadrants (not shown here) can then be overlaid on top of the product characteristics segmentation table, above. In brief, new product demand can be Business Forecasting forecasted using judgmental methods, clustering and data mining. Slow moving products can benefit from moving averages, sales force composites and intermittent demand models. Causal methods work well for the fast-moving products quadrant, and time series methods such as exponential smoothing work well for the steady-state products quadrant (p. 39). Chase underscores the point that more sophisticated statistical methods are needed to improve on time series methods which are useful for segmenting product group demand history into basic trend, seasonality, cyclical and unexplained variance (p. 40). Heizer and Render (2014) explain time series methods in chapter four of Operations Management (pp. 108-125). Chase goes the additional step in this article by mapping the most applicable statistical and forecasting methods against the segmented product characteristics framework to help demand planners achieve better forecasts. 5 Business Forecasting 6 Chase, Charles W. (Spring 2016) Forecast Accuracy Has No Impact on Inventory! Really? The Journal of Business Forecasting, 35, 1, 26-29. Charles Chase is an expert in sales forecasting, market response modeling, and supply chain management. Currently with the SAS Institute, Inc., he has worked for companies such as Johnson & Johnson, Coca-Cola, and Heineken (p. 26). Chase’s main argument here is that companies have sorted themselves out into two supply chain camps: demand or supply. In his opinion, firms have swung back and forth between these two camps; in recent years, companies have abandoned supply chain strategies because the use of buffer inventory is not as cost efficient as it was (p. 26). He states, “although poor forecasting has been identified as the root cause, companies continue to use traditional statistical methods like moving averaging and non-seasonal exponential smoothing models, which are only accurate one period into the future” (p. 26). There is a lot of room for forecasting accuracy. Based on his work with 100 companies over 10 years, “the average forecast accuracy is between 50-65 percent at the aggregate level, and between 35-45 percent at the lower mix levels” (p. 27). A classic cause of poor demand forecasts is the use of moving average and non-seasonal exponential smoothing models that are only accurate a few periods into the future. Upper and lower forecast ranges tend to be cone shaped beyond that point, whereas more advanced statistical methods like ARIMA and dynamic regression are more accurate farther out (p. 28). The use of better models translates into lower safety stock. Often businesses’ ERP (Enterprise Resource Planning) software only supports the simpler methods. The author says that demand forecasting and planning has received “little attention and investment in people, analytics, and technology over the past decade” (p. 28). Companies have shown 10-30 percent improvements in forecast accuracy by using holistic modeling based on predictive analytics. Businesses often “cleanse” their products’ demand history by breaking it down into two streams: 1) baseline, and 2) promoted. The promoted stream is actually a combination of seasonality and promoted volume, which can be spiky. The baseline Business Forecasting 7 stream tends to be a moving average. Demand planners often try to put these two data streams back together with “the result, 1+1 now equals 5” (p. 29). Advanced analytics for supply planning together with better forecasts creates a synergy effect of an additional 15-30 percent reduction in finished goods inventory (p. 29). The result is reduced inventory costs, increased revenues and profit, and more available working capital (p. 29). Companies that are moving toward becoming more demand-driven are doing better with demand forecasts and reducing inventory safety stock. New forecasting models, sometimes referred to as “Consumption Based Modeling”, link downstream data to upstream data using a process called “Multi-Tier Causal Analysis” (MTCA) (p. 29). Heizer and Render (2014) clarify “downstream” as distributors and retailers, and “upstream” as suppliers (p.447). Chase references his own Spring 2015 article in The Journal of Business Forecasting for more information about the correlation between upstream and downstream data. By not relying entirely on either demand or supply forecasts businesses can be better at solving their supply chain challenges. A holistic view of supply and demand can lead to more success in building a responsive supply chain. Heizer and Render (2014) cover forecasting methods in chapter four of Operations Management and explain supply chain logistics and inventory models in chapters 11 and 12, respectively. Business Forecasting 8 Homareau, Jack. (Fall 2015) Forecasting Sales Volumes with Economic Indicators. The Journal of Business Forecasting, 34, 3, 32-34. The author brings many years of macroeconomic modeling and forecasting experience to this article, which outlines a methodology for forecasting sales volume using economic indicators. Specifically, he showed how to create a baseline product forecast for US automobile sales using U.S. housing starts data as the economic driver. The Housing Starts data series correlates well with automobile sales volume, and along with product promotions and marketing plans can improve sales forecasts. He also evaluated the forecasting model’s performance. The sales data used in this example are quarterly U.S. auto sales from 1999 through 2012. Actual sales for 2013 were used to evaluate the model’s predictions for 2013 sales. The methodology used was a simple regression model, with housing starts as the explanatory variable and auto sales as the response variable (pp. 31-32). For 2013, actual U.S. housing starts (rather than projected U.S. housing starts) were used to forecast auto sales over the same period. Data from the U.S. Census Bureau was converted from monthly to quarterly totals. Homareau’s next step was to calculate the quarterly percentage change in both data series on a year-over-year (YoY) basis. Then he developed the regression model, where x is the YoY quarterly percentage change in housing starts, and y is the YoY quarterly percentage change in U.S. auto sales (pp. 33-34). The resulting coefficient of correlation is 0.74, and the regression line equation is Y=1.1 + 0.5 x. The third step was to prepare 2013 forecasts. The forecasted auto sales growth rate for 2013 Q1 was calculated at 18.1%, based on U.S housing starts growth of 33.9% in Q1 of 2013. Automobile sales growth rates for Q1- Q4 of 2013 are calculated below (p. 34). Period 2013 Q1 2013 Q2 2013 Q3 2013 Q4 Housing Starts Growth Rate 33.9 17.0 13.0 12.9 Vehicle Sales Growth Rates (YoY: quarterly) 1.1 + (.05 x 33.9)= 18.1 1.1 + (.05 x 17.0) = 9.6 1.1 + (.05 x 13.0) = 7.6 1.1 + (.05 x 12.9) = 7.6 Business Forecasting 9 This growth rate is multiplied against 2012 Q1 actual sales to obtain projected 2013 Q1 sales of 4,179.9. The following table shows these calculations and 2013 sales projections (p. 34). Period 2013 Q1 2013 Q2 2013 Q3 2013 Q4 Motor Vehicles Sales (000) 3,539.3 (2012-Q1) 3,883.1 (2012-Q2) 3,699.5 (2012-Q3) 3,665.8 (2012-Q4) Projected YoY: Quarterly Growth Rates 18.1 9.6 7.6 7.6 Projected Quarterly U.S. Motor Vehicles Sales (000) 3,539.3 x 1.181 = 4,179.9 3,883.1 x 1.096 = 4,255.9 3,699.5 x 1.076 = 3,980.7 3,665.8 x 1.076 = 3,944.4 The final step in the process was to compare forecasted sales with actual sales, and compute the MAPE (Mean Absolute Percentage Error). For 2013 Q1, actual sales were 3,754.6 and projected sales were 4,179.9. The forecast error here is 425.3, with a MAPE of 10.2%. The following table shows these results as well as the calculated overall 2013 MAPE of 2.9% (p. 34). The author Period 2013 Q1 2013 Q2 2013 Q3 2013 Q4 MAPE Projected Sales (000) 4,179.9 4,255.9 3,980.7 3,944.4 Actual Sales (000) 3,754.6 4,210.3 4,028.8 3,890.0 Absolute Forecast Error 425.3 45.6 48.1 54.4 Absolute % Error 10.2 1.1 1.2 1.4 2.9 states this error rate is fairly good by industry standards, but that an additional economic variable and some manual adjustments by a person with “domain knowledge” (auto industry sales data background), would have improved the projected sales for 2013 and reduced forecast error. Heizer and Render (2014) cover regression analysis and MAPE in chapter four of Operations Management (pp. 106-132). It was helpful to see Homareau use quarterly percentage change in the data series in his regression rather than actual auto unit sales regressed on the actual number of housing starts. The use of percentage growth rate will be a helpful data transformation tool in other cases where the explanatory and dependent variables may be measured on different scales. I entered all of the author’s data into Excel and ran the regression. I can confirm the coefficient of Business Forecasting 10 correlation is 0.74; however, the regression model is more accurately stated as Y = 1.1 + 0.455 x (the author rounds it up to Y= 1.1 + 0.5 x). The more accurate coefficient will result in a smaller forecast error. See the Excel “Summary Output” below for more results. This article will be quite helpful for students who are learning how to forecast business data using a simple regression model that includes U.S. economic data as the explanatory variable (independent variable). SUMMARY OUTPUT Regression Statistics Multiple R 0.736882537 R Square 0.542995873 Adjusted R Square Standard Error 0.53385579 8.227878293 Observations 52 ANOVA df SS Regression 1 4021.815363 4021.81536 67.6979812 Residual 50 3384.89906 Total 51 7406.714423 Coefficients MS Standard Error t Stat F 59.4082023 P-value Significance F 4.72186E-10 Lower 95% Upper 95% Intercept 1.115357672 1.158543528 0.96272401 0.34031911 -1.211645488 3.442360833 Housing YoY % Change 0.455566491 0.059105591 7.70767165 4.72186E-10 0.336849417 0.574283565 Business Forecasting 11 Jain, Chaman. (Winter 2006/2007) Benchmarking Forecasting Models. The Journal of Business Forecasting, 25, 4, 14-17. The purpose of this article is to help business forecasters determine how to apply the right model to their data sets, taking into consideration that data sets have patterns. The author describes three types of forecasting models, identifies fundamentals of modeling, discusses the types of models used in business, and concludes with model selection. The three types of models are: 1) Time Series, 2) Cause-and Effect, and 3) Judgmental (p. 14). Time Series models are based on the assumption that the future will resemble the past. They work best for short-term forecasting due to data stability in the short run. They include: 1) Simple- and Moving Averages, 2) Trend, 3) Exponential Smoothing, 4) Decomposition, and 5) Autoregressive Integrated Moving Average (ARIMA) (p. 14). Cause-and- Effect Models involve independent and dependent variables, where the average relationship between the two can be projected into the future. Jain provides examples: 1) Regression, 2) Econometric, and 3) Neural Network. Judgmental Models can be used when there is no historical data, when the market is volatile, and/or when a very long-term forecast is contemplated (p. 14). Jain offers nine fundamentals of models: 1) Datasets form patterns and the model should attempt to capture the pattern as best as possible. The actual model will include pattern plus error. 2) 100% accuracy is not necessary. Minimization of error is the goal. 3) More data are not necessarily better. In the consumer products industry, most companies use data of 30 months or less. 4) Sophisticated models are not always better. Start with a simple model and move to more complex models. 5) There is no magic model. Change in the real world means models need to adapt to the datasets being analyzed. 6) Models age with time. Over time, data is usually dynamic and models will not always work as they used to. 7) Each model has its own data requirements. Some models require 35 or more observations to prepare a forecast. 8) Statistical forecasts are nothing more than baseline forecasts. The art and science of forecasting may require a judgmental overlay. Business Forecasting 12 9) Forecasts should not be prepared in isolation. It is important to consult stakeholders such as marketing or finance people (pp. 15-16). Based on a 2006 survey, Time Series models are used by 72% of industries, Cause-and Effect by 17% and Judgmental by 11% (p. 16). Jain reports that within Time Series model usage, Averages and Simple Trend are used 60% of the time, Exponential Smoothing 30%, and other methods are used 10%. Cause-and-Effect models are being used more often in business forecasting, with Regression used the most. Judgmental models that are used most are Survey (50%), followed by Analog at 27% (p. 17). Jain provides bar charts that show all the model usage survey results. Software systems often have an internal “expert system” that select the best model for the application. An example of a criterion might be minimizing Mean Absolute Percentage Error (MAPE). Forecasting software systems often are based on time series models and some provide an expert system in regression. Forecasters can use suggested software tools or select their own model preference. Jain recommends starting by choosing an acceptable error rate and then using a simple model before progressing to complex models (p. 17). Monitoring the model’s performance is important, as is using “ex post forecasts” to determine if the model is good. Heizer and Render (2014) present this mater ...
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Project Management Annotated Bibliography




Badewi, A. (2016). The impact of project management (PM) and benefits management (BM)
practices on project success: Towards developing a project benefits governance
framework. International Journal of Project Management, 34(4), 761-778
This article discusses the impacts of applying both Project Management and Business
Management on various projects. Apparently, the article articulates on how the success of
every project relies on sound and profound Project Management and Business
Management used. The major topics covered in the article are Project Management,
Benefits Management, Success of Projects, Project Governance and Change
Management. I found this article essential and beneficial because most of the information
given can be applied in Companies to enhance the success of projects in these Companies
or Organizations. For instance, the article discusses in details the advantages of
combining both Benefit Management and Project Management Practices in
Organizations. The article was written by Amgad Badewi who is a high-ranking Lecturer
in Project Management, and with vast knowledge on Project Management which make
this article interesting and very educative. This article is special and fascinating compared
to other articles on Project Management since it compares the practices of Project
Management and Benefit Management and their roles on the success of any project in the
Organization. Initially, I didn’t know that the success of projects in Organizations can be
affected by Benefits Management. I thought that poor Project Management practices are
the only one that affects the success of projects in Companies. To this extent, therefore,
this article can be of great help to students especially on their research projects since it
offers relevant information on the impact of both Business Management and Project
Management of the success of Projects.



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