Data Mining for Combining Forecasts in Inventory Management

2005 ◽  
pp. 703-707
Author(s):  
Chi Kin Chan
Keyword(s):  
Data Mining ◽  
Inventory Management ◽  
Traditional Approach ◽  
The Past ◽  
Aggregate Information ◽  
Forecasting Methods ◽  
Combining Forecasts ◽  
Forecasting Method ◽  
The Future ◽  
Single Method

The traditional approach to forecasting involves choosing the forecasting method judged most appropriate of the available methods and applying it to some specific situations. The choice of a method depends upon the characteristics of the series and the type of application. The rationale behind such an approach is the notion that a “best” method exists and can be identified. Further that the “best” method for the past will continue to be the best for the future. An alternative to the traditional approach is to aggregate information from different forecasting methods by aggregating forecasts. This eliminates the problem of having to select a single method and rely exclusively on its forecasts.

Data Mining for Combining Forecasts in Inventory Management

2008 ◽  
pp. 2792-2797
Author(s):  
Chi Kin Chan
Keyword(s):  
Data Mining ◽  
Inventory Management ◽  
Traditional Approach ◽  
The Past ◽  
Aggregate Information ◽  
Forecasting Methods ◽  
Combining Forecasts ◽  
Forecasting Method ◽  
The Future ◽  
Single Method

The traditional approach to forecasting involves choosing the forecasting method judged most appropriate of the available methods and applying it to some specific situations. The choice of a method depends upon the characteristics of the series and the type of application. The rationale behind such an approach is the notion that a “best” method exists and can be identified. Further that the “best” method for the past will continue to be the best for the future. An alternative to the traditional approach is to aggregate information from different forecasting methods by aggregating forecasts. This eliminates the problem of having to select a single method and rely exclusively on its forecasts.

Combination of Forecasts in Data Mining

2011 ◽  
pp. 589-593
Author(s):  
Chi Kin Chan
Keyword(s):  
Data Mining ◽  
Traditional Approach ◽  
Forecast Accuracy ◽  
Performance Improvements ◽  
The Past ◽  
Aggregate Information ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Combination Of Forecasts ◽  
Single Method

The traditional approach to forecasting involves choosing the forecasting method judged most appropriate of the available methods and applying it to some specific situations. The choice of a method depends upon the characteristics of the series and the type of application. The rationale behind such an approach is the notion that a “best” method exists and can be identified. Further that the “best” method for the past will continue to be the best for the future. An alternative to the traditional approach is to aggregate information from different forecasting methods by aggregating forecasts. This eliminates the problem of having to select a single method and rely exclusively on its forecasts. Considerable literature has accumulated over the years regarding the combination of forecasts. The primary conclusion of this line of research is that combining multiple forecasts leads to increased forecast accuracy. This has been the result whether the forecasts are judgmental or statistical, econometric or extrapolation. Furthermore, in many cases one can make dramatic performance improvements by simply averaging the forecasts.

Data Mining for Optimal Combination Demand Forecasts

2011 ◽  
pp. 106-122
Author(s):  
Chi Kin Chan ◽  
Heung Wong ◽  
Wan Kai Pang ◽  
Marvin D. Troutt
Keyword(s):  
Data Mining ◽  
Inventory Management ◽  
Moving Average ◽  
Weighting Method ◽  
Forecasting Methods ◽  
Combining Forecasts ◽  
Simple Exponential Smoothing ◽  
The Individual ◽  
Selection Of

This chapter is a case study in combining forecasts for inventory management in which the need for data mining in combination forecasts is necessary. The need comes from selection of sample items on which forecasting strategy can be made for all items, selection of constituent forecasts to be combined and selection of weighting method for the combination. A leading bank in Hong Kong consumes more than 300 kinds of printed forms for its daily operations. A major problem of its inventory control system for such forms management is to forecast their monthly demand. The bank currently uses simple forecasting methods such as simple moving average and simple exponential smoothing for its inventory demands. In this research, the individual forecasts come from well-established time series models. The weights for combination are estimated with quadratic programming. The combined forecast is found to perform better than any of the individual forecasts. Some insights in data mining for this context are obtained.

scholarly journals Advising an Appropriate Forecasting Method for a Snacks Item (Biscuit) Manufacture Company in Bangladesh

2018 ◽  
Vol 66 (1) ◽  
pp. 55-58
Author(s):  
Nandita Barman ◽  
M Babul Hasan ◽  
Md Nayan Dhali
Keyword(s):  
Time Series ◽  
Inventory Management ◽  
Linear Trend ◽  
Exponential Smoothing ◽  
Demand And Supply ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Exponential Smoothing Method ◽  
Manufacture Company

In this paper, we study the most appropriate short-term forecasting methods for the newly launched biscuit factory produces different types of biscuits. One of them is nut-orange twisted biscuits. As it is a newly launched biscuit factory, it does not use any scientific method to find future demand of their products to produce for the purpose of sales. Having an error free production as well as a good inventory management we try to find an appropriate forecasting method for the sets of data we analyzed for that specific production. Several forecasting methods of time series forecasting such as the Moving Averages, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. can be applied to estimate the demand and supply for these companies. This paper focuses on selecting an appropriate forecasting technique for the newly launched biscuit company. For this, we analyze Exponential Smoothing method as used to time series. We observe from the empirical results of the analysis that if the data has no trend as well as seasonality, Exponential Smoothing Forecasting Method processes as the most appropriate forecasting method for the factory. If the data experiences linear trend in it then Holt’s Forecasting Method processes as the most appropriate forecasting method for the sets of data we analyzed. Dhaka Univ. J. Sci. 66(1): 55-58, 2018 (January)

scholarly journals Who Controls the Past Now Controls the Future: Counter-Terrorism, Data Mining and Privacy

2016 ◽  
Author(s):  
Wayne N Renke
Keyword(s):  
Data Mining ◽  
International Security ◽  
Right To Privacy ◽  
Counter Terrorism ◽  
The Past ◽  
Needed Information ◽  
The Future ◽  
Security Concerns

Recent world events have created international security concerns and a demand for counter-terrorist measures. Information fuels counter-terrorism. "Data mining" has been touted as a means for acquiring needed information. This article describes data mining, explores its social, political and personal risks, then assesses its impact on the Charter-protected right to privacy. The author proposes a framework for the constitutionally appropriate regulation of data mining. Data mining is portrayed as a potentially valuable counter-terrorism tool which must be governed responsibly, if its costs are not to exceed its benefits.

scholarly journals Data Mining and Official Statistics: The Past, the Present and the Future

Big Data ◽  
2014 ◽  
Vol 2 (1) ◽  
pp. 34-43 ◽  
Author(s):  
Hossein Hassani ◽  
Gilbert Saporta ◽  
Emmanuel Sirimal Silva
Keyword(s):  
Data Mining ◽  
Official Statistics ◽  
The Past ◽  
The Future

An Overview of the Forecasting Methods Used in Real Estate Housing Price Modelling

2015 ◽  
Vol 73 (5) ◽  
Author(s):  
Mohan Munusamy ◽  
Chitrakala Muthuveerappan ◽  
Maizan Baba ◽  
Mat Naim Abdullah @ Mohd Asmoni
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Housing Price ◽  
Method Error ◽  
The Past ◽  
Housing Price Index ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Critical Constraints ◽  
Types Of Error

Forecasting is very fundamental in real estate where the past transactions become the evidences while decision making for the present and the future. Several techniques and validation approached that were commonly used in housing price index forecasting. Beside the appropriate forecasting method, error calculation is one of the critical constraints in accuracy out of all methods. This paper overview the available methods and the types of error being considered in forecasting techniques. Then the forecasting methods, namely Multiple Regression Analysis (MRA) and Artificial Neural Network which are highly applied in forecasting modelling are compared over its error accuracy.  

scholarly journals LUMPY DEMAND FORECASTING USING LINEAR EXPONENTIAL SMOOTHING, ARTIFICIAL NEURAL NETWORK, AND BOOTSTRAP

2018 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Sinta Rahmawidya Sulistyo ◽  
Alvian Jonathan Sutrisno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inventory Management ◽  
Spare Part ◽  
Service Level ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Spare Parts Inventory

Lumpy demand represents the circumstances when a demand for an item has a large proportion of periods having zero demand. This certain situation makes the time series methods might become inappropriate due to the model’s inability to capture the demand pattern. This research aims to compare several forecasting methods for lumpy demand that is represented by the demand of spare part. Three forecasting methods are chosen; Linear Exponential Smoothing (LES), Artificial Neural Network (ANN), and Bootstrap. The Mean Absolute Scaled Error (MASE) is used to measure the forecast performance. In order to gain more understanding on the effect of the forecasting method on spare parts inventory management, inventory simulation using oil and gas company’s data is then conducted. Two inventory parameters; average inventory and service level; are used to measure the performance. The result shows that ANN is found to be the best method for spare part forecasting with MASE of 0,761. From the inventory simulation, the appropriate forecasting method on spare parts inventory management is able to reduce average inventory by 11,9% and increase service level by 10,7%. This result justifies that selecting the appropriate forecasting method is one of the ways to achieve spare part inventory management’s goal.

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