Demand forecasting and inventory management filetype pdf

According to Y in , case study research method can be define as an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used.

As define by Yin , case study can be carried out using single case study or multiple case studies. Single cases may be used to confirm or challenge a theory, or to represent a unique or extreme case. Single-case studies are also ideal for revelatory cases where an observer may have access to a phenomenon that was previously inaccessible.

In this study, one of the objectives is to confirm the theory that the combination forecasting method is better than the individual forecasting method. Thus, single case study is suitable in this study. According to Yin , documents could be letters, memoranda, agendas, study reports, or any items that could add to the data base.

In this study, secondary data relevant to the forecasting will be taken from the CCTV distributor company. Data taken will be past data kept by the company. The data that will be involved in this study is the monthly order unit and monthly sold out unit for the sample product..

Data Analysis: Data analysis consists of examining, categorizing, tabulating, testing or otherwise combining both the quantitative and qualitative evidence to address they initial propositions of a study Yin, In this study, the data analysis will be carried out using three individual forecasting method and two combination forecasting method.

The forecasting result will be generating using Forecast X Software. The forecasting result will be evaluate using two forecasting measurement. The forecasting methods: The first forecasting method that will be used in this study is the moving average method. The second forecasting method that will be used in this study is the Holt-W inters exponential smoothing method. The third forecasting method that will be used is the simple linear regression method.

The fourth and the fifth method will be the combination forecasting method using different method in determined the weight for the forecast. Instead of choosing the best model from among the forecasting method, a more reasoned approach is to combined the forecasts in order to obtained a forecast that is more accurate than any if the separate predictions.

The detail of the formula for the furcating method will be shown below; According to Frechtling , the general equation for the single moving average is: 3. In order to apply this method and to determine the best value for the weights of each method involves, a two step regression process is used. First, a standard multiple regressions of the actual values on the value predicted from the individual forecasting method. According to W ilson and Keating , the formula can be express as this: 3.

The result of regressing the actual values on the two forecast series, without an intercept, yields the desired result to determine the best weights to be used in combining the forecast. Several methods have been devised to summarize the error generated by a particular forecasting method. In this study, two accuracy measurements will be used. The first accuracy measurement is the mean absolute percentage error MAPE. The second accuracy measurement will be used is the root-mean square error RMSE.

ForecastX software: In order to carry out the forecasting calculation job, The ForecastX software will be used. It is software that well trusted by the industry as well as the academic industry.

Hence, it is the suitable software to be used in this study. Thousands of companies of all sizes use the ForecastX software successfully today. Besides in the industry, the ForecastX software also had well known reputation in the academic industry. They had established education partner relationship with many of the university.

In order to provide a clearly picture about the operation of the Forecast X software in forecasting, the general operation steps and operation flow chart is provided in below. Step 1: Enter the data into the excel software and start ForecastX.

Step 2: In the data capture dialog box identify the data that want to used and specify the data contain dates as well as the descriptive label Step 3: If a data transformation is necessary, select the transform button in the forecast method tab. On the data transformation screen, use the data series to display drop-down menu to select the data that need to analyze. Step 4: If data adjustment is needed, select the adjust button in the forecast method tab.

Next, use the data series to display drop-down menu to select the data that need to analyze. In the start row and end row box, select the range in the data set to adjust. Next, specify the adjust value and percentage. Step 5: In the forecast method tab, select the forecasting technique in the method selection dialog box. Step 6: In the statistics tab, select the statistic that desired Step 7: In the report tab, select the report type. Advances have occurred in the development of methods based on combining forecasts.

W e have also occurred for methods based on statistical data, such as extrapolation and rule-based forecasting methods. Most recently, gains have come from the integration of statistical and judgmental forecasts. Demand forecasting allows retailers to make better decisions about which prices to adjust and when, which products to promote, and what promotional tactics to deploy, in order to achieve objectives. T he benefits are significantly more profound and productive than a simple sales forecast.

The best informed decisions will help we increase profits, sales or market share. By combining forecast we knowledge of past performance under similar circumstances with forward-looking promotional pricing plans, we can make better buying, allocation, and replenishment decisions.

In turn, we will reduce the cost of over-stocks and minimize the frequency of out- of-stocks. Understanding consumer expectations at given times and under different market conditions delivers tangible benefits to both on the demand side and supply side of business. Rudisill and L. Litteral, Arinze, B. Kim and M. Anadarajan, Armstrong, J. Blecher, L. Chan, K. Kingsman and H. W ong, Chandra, C. Grabis, Frechtling, D. Gardner, E. Anderson-Fletcher and A.

W icks, Exponential Smoothing. Golineli, R. Parigi, Hibon, M. Evgeniou, Kapetanios, G. Labhard and S. Price, Kerkkanen, A. Korpela and J. Huiskonen, Koning, A. Franses, M. Hibon and H.

Stekler, Korpela, J. Tuominen, Lam, K. Mui and H. Yuen, Makridakis, S. Hibon, Phelan, M. Mcgarraghy, ISBN Sander, N. Ritzman, McSharry, Tiacci, L. Women in Manufacturing APICS, through mangement partnership with The Manufacturing Institute, explores how manufacturing and supply chain can attract, retain and advance women.

Even though reality often varies from the forecast, the variation usually is very slight, so only modest adjustments are required. Part 1 David Ross. English Simplified Chinese Portuguese Spanish. In my next blog we will be investigating in greater detail the dichotomies between demand and supply and how master scheduling can solve the problem. Instead, it zpics on a very narrow, yet very pragmatic, topic: Is your company doing big things worthy of recognition?

The scope of this network includes some elements that you command, but it also demands reliance on partner organizations that extend far managemebt your direct control. When determining how to best approach capacity, there are three basic strategies to consider. Are You Having Chewy Conversations? Sales and Operations Planning from an Omnichannel Perspective. This means that your plans need to be good, not perfect.

One of my master planners recently asked me: Marketing and sales normally work with inventories at the aggregate level, usually by product family. Some of them demamd conducted supply review meetings, and a few held formal portfolio review meetings. This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are as essential for the working of basic functionalities of the website.

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