Relevant bibliographies by topics / Inventory control - Forecasting - Statistical methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Inventory control - Forecasting - Statistical methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Inventory control - Forecasting - Statistical methods"

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Rehmani, Khurram, Afshan Naseem, Yasir Ahmad, Muhammad Zeeshan Mirza, and Tasweer Hussain Syed. "Development of a hybrid framework for inventory leanness in Technical Services Organizations." PLOS ONE 16, no. 2 (2021): e0247144. http://dx.doi.org/10.1371/journal.pone.0247144.

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Inherent uncertainties in demand and supply make it problematic for supply chains to accomplish optimum inventory replenishment, resulting in loss of sales or keeping excessive inventories. To cope with erratic demands, organizations have to maintain excessive inventory levels, sometimes taking up to one-third of an organization’s annual budget. The two most pressing concerns to handle in inventory management are: how much to order and when to order. Therefore, an organization ought to make the correct and timely decisions based on precise demand information to avoid excessive inventory accumulation resulting in enhanced competitive advantage. Owing to the significance of inventory control and analysis, this paper reports on developing and successfully implementing a hybrid framework for optimum level inventory forecasting in Technical Services Organizations. The proposed framework is based on a case study of one of Pakistan’s leading Technical Services Organization. The paper presents a statistical analysis of historical data and a comprehensive fault trend analysis. Both these analyses set a solid foundation for the formulation of a comparative analysis matrix based upon price and quantity based analysis of inventory. Finally, a decision criterion (Forecasting Model) is proposed using three primary forecasting techniques with minimum error calculations. The study’s finding shows a forecast error of 142.5 million rupees in the last five years, resulting in the accumulation of more than 25 thousand excessive inventory stock. Application of price and quantity based analysis identifies that 65% of the annual budget is significantly dependent upon only 9% (in terms of quantity) of "High Price and Small Quantity" Items (HS). These HS items are forecasted through three different forecasting methods, i.e., Weighted Moving Average, Exponential Smoothing, and Trend Projection, with Minimum Absolute Deviation to significantly reduce the forecasting error while predicting the future required quantity. The research work aims to contribute to the inventory management literature in three ways. First, a new comparative analysis matrix concept for identifying the most critical items is introduced. Second, a Multi-Criteria Forecasting Model is developed to capture a wide range of operations. Third, the paper suggests how these forecasting criteria can be integrated into a single interactive DSS to maintain optimum inventory level stock. Even though the DSS framework is based on data from a single organization, the application is expected to manage inventory stock in a wide range of manufacturing and services industries. This study’s proposed hybrid framework is the first of its kind that encapsulates all four dimensions of inventory classification criteria, forming a multi-criteria hybrid model within a DSS framework.
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K.T., Vinod, S. Prabagaran, and O. A. Joseph. "Dynamic due date assignment method." Journal of Manufacturing Technology Management 30, no. 6 (2019): 987–1003. http://dx.doi.org/10.1108/jmtm-06-2017-0112.

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Purpose The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness. Design/methodology/approach A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation. Findings The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop. Research limitations/implications Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines. Practical implications The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error. Originality/value Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.
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David, Engmir, Irwan Budiman, and Jusra Tampubolon. "Decreasing Total Inventory Cost by Controlling Inventory in Motorcycle Dealer." Jurnal Sistem Teknik Industri 22, no. 2 (2020): 41–49. http://dx.doi.org/10.32734/jsti.v22i2.3930.

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This research was conducted at one of the motorcycle dealers in Indonesia. Besides selling motorcycles, this dealer also provides services to repair motorcycles and sells genuine motorcycle parts. Inventory management which the company carried out is still not good enough because there are still demand for spare parts from consumers that cannot be fulfilled by the company. The purpose of this study is to draw up a plan to control spare parts by paying attention to the spare parts that need to be considered, estimating the exact number of spare parts demand, knowing the smallest total inventory cost, knowing the amount of safety stock needed, and knowing when to reorder. In preparing the spare parts control, the methods used are ABC analysis, demand forecasting method, and EOQ method. The results of this study are plans to control the inventory of Tire, Rr. such as the forecasting sales of Tire, Rr. as many as 17338, economic order quantity of Tire Rr are 2158 units, the number of safety stocks of Tire, Rr. needed in 2020 are 1738 units, and the reorder point in 2020 is 8 times with the total inventory cost for Tire, Rr. in 2020 is Rp. 30,009,005.
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Sani, B., and B. G. Kingsman. "Selecting the Best Periodic Inventory Control and Demand Forecasting Methods for Low Demand Items." Journal of the Operational Research Society 48, no. 7 (1997): 700. http://dx.doi.org/10.2307/3010059.

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Sani, B., and B. G. Kingsman. "Selecting the best periodic inventory control and demand forecasting methods for low demand items." Journal of the Operational Research Society 48, no. 7 (1997): 700–713. http://dx.doi.org/10.1038/sj.jors.2600418.

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Sani, B., and B. G. Kingsman. "Selecting the best periodic inventory control and demand forecasting methods for low demand items." Journal of the Operational Research Society 48, no. 7 (1997): 700–713. http://dx.doi.org/10.1057/palgrave.jors.2600418.

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Cahyadewi, Desya Rachmasari, A. A. P. Agung Suryawan Wiranatha, and I. Ketut Satriawan. "Analisis Peramalan Permintaan dan Pengendalian Persediaan Bahan Baku Body Scrub Powder di CV. Denara Duta Mandiri." JURNAL REKAYASA DAN MANAJEMEN AGROINDUSTRI 8, no. 3 (2020): 360. http://dx.doi.org/10.24843/jrma.2020.v08.i03.p05.

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This study aims to analyze the demand forecasting of body scrub powder products in October 2019 and the level of raw material inventory body scrub powder optimal in order to minimize the total cost of inventory in CV. Denara Duta Mandiri. Forecasting analysis using time series model with 3 methods of Moving Averages with 2, 3 and 4 month time period, exponential smoothing method with ? = 0.1, ? = 0.5, ? = 0.9 and Trend Projection. To analyze the value of forecasting accuracy, the method used are Mean Absolute Deviation (MAD) and Mean Squared Error (MSE). The results show that the Trend Projection method is the most effective method that can be used as the basis for determine body scrub powder products demand, with the result of forecasting demand is 562 kg and the value of forecasting accuracy, MAD = 76,97, MSE = 9.760,12. The results of Economic Order Quantity (EOQ) showed that total cost of raw material inventory control body scrub powder at CV. Denara Duta Mandiri by using EOQ smaller than the methods used by the company. CV. Denara Duta Mandiri management should try to apply the EOQ method in terms of supply of raw materials so that the company can better minimize inventory costs. 
 Keywords: demand forecasting, raw material, Economic Order Quantity (EOQ)
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Hajej, Zied, Aime C. Nyoungue, Aminu S. Abubakar, and Kammoun Mohamed Ali. "An Integrated Model of Production, Maintenance, and Quality Control with Statistical Process Control Chart of a Supply Chain." Applied Sciences 11, no. 9 (2021): 4192. http://dx.doi.org/10.3390/app11094192.

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This article investigates integrated maintenance, production, and product quality control policy for a supply chain consisting of a single machine producing only one type of product, a main storage warehouse, and multi-purchases warehouses. The variation of the production rate and its use over time impact the manufacturing system’s degradation degree. Hence, the machine is subject to a random failure that directly affects the quality of the products. The goal of this study is to establish an optimal production and delivery planning with inventory management considering the production, holding, and delivery costs, and then an appropriate maintenance strategy, considering the influence of the production rate on the system degradation. Also, we provide a quality control policy to reduce the proportion of non-compliant products by using the statistical process control chart to forecast production. Forecasting the production aims to satisfy the varying demands during a finite horizon under service and quality levels. Numerical examples are presented to justify the effectiveness of the suggested strategy.
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Shilul Imarah, Thukas, and Roni Jaelani. "ABC ANALYSIS, FORECASTING AND ECONOMIC ORDER QUANTITY (EOQ) IMPLEMENTATION TO IMPROVE SMOOTH OPERATION PROCESS." Dinasti International Journal of Education Management And Social Science 1, no. 3 (2020): 319–25. http://dx.doi.org/10.31933/dijemss.v1i3.149.

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The study aims to test the effectiveness of the implementation of several inventory control methods to improve the smooth operation in a trading company of industrial equipements components that have difficulty in maintaining inventory data accuracy, inventory shortages and in other hand excess unrequired inventory. The research data used is taken from the ERP report data and manual reports created by the perpetrators of the operational activities of the Inventory Control for the period 2016 - 2018. The sampling method used is purposive sampling of the product sales data in years 2016 – 2018 as much as 2498 Stock Keeping Unit (SKU) which is then processed using the method of ABC analysis and FSN (Fast Moving, Slow Moving and None Moving) analysis to get 10 SKUs that belongs to the category A and F (Fast Moving) group as a sample of research. The research method uses the Quantitative method with the use of ABC analysis, Forecasting and Economic Order Quantity. The results shows that the implementation of ABC analysis effectively reduced the workload of periodic counting and is able to improve data accuracy to be higher level. The exponential smoothing forecast method shows the least gap to the actual value and EOQ effectively optimizes ordering and holding costs and reduces the risk of failure in Inventory Control and positively affects the smoothness of the operation process.
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Duan, Wen Xi. "Farming Herdsman Fatstock Feeding Amount Forecasting and Control." Applied Mechanics and Materials 556-562 (May 2014): 3571–74. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3571.

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The profit of raising fatstock is subordinated to the random variables of the normal distribution. The average feeding profit is established by using the statistical methods. Moreover, the function between the feeding profit and raising amount is determined by adopting the method of linear regression. The function relationship among the purchase price, demand and production capacity of raising fatstock is gained by using the binary regression method. Therefore, the price of the meat livestock in the future will be forecast. However, the amount of raising fatstock can be controlled according to the differences between the production capacity and the demand.
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Dissertations / Theses on the topic "Inventory control - Forecasting - Statistical methods"

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何添賢 and Tim Yin Timothy Ho. "Forecasting with smoothing techniques for inventory control." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B42574286.

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Sani, Babangida. "Periodic inventory control systems and demand forecasting methods for low demand items." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309040.

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Tai, Hoi-lun Allen, and 戴凱倫. "Stochastic models for inventory systems and networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37681758.

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Southworth, M. S. "An alternative approach to inventory control and forecasting methods in the public and private sectors." Thesis, Cranfield University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.237724.

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Harrington, Robert P. "Forecasting corporate performance." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54515.

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For the past twenty years, the usefulness of accounting information has been emphasized. In 1966 the American Accounting Association in its State of Basic Accounting Theory asserted that usefulness is the primary purpose of external financial reports. In 1978 the State of Financial Accounting Concepts, No. 1 affirmed the usefulness criterion. "Financial reporting should provide information that is useful to present and potential investors and creditors and other users..." Information is useful if it facilitates decision making. Moreover, all decisions are future-oriented; they are based on a prognosis of future events. The objective of this research, therefore, is to examine some factors that affect the decision maker's ability to use financial information to make good predictions and thereby good decisions. There are two major purposes of the study. The first is to gain insight into the amount of increase in prediction accuracy that is expected to be achieved when a model replaces the human decision-maker in the selection of cues. The second major purpose is to examine the information overload phenomenon to provide research evidence to determine the point at which additional information may contaminate prediction accuracy. The research methodology is based on the lens model developed by Eyon Brunswick in 1952. Multiple linear regression equations are used to capture the participants’ models, and correlation statistics are used to measure prediction accuracy.<br>Ph. D.
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Alm, Jonathan, and Kiöhling Marcus von. "Lagerstyrningsmetoders påverkan på totalkostnad : Möjliga ufall för lager med säsongsvarierad efterfrågan." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Logistik och verksamhetsledning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-45423.

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Purpose – To analyze the impact on total cost by inventory control methods under the influence of seasonal demand. The purpose will be answered with following research questions: What inventory control methods can be used when there is seasonal demand? What is the impact of inventory control methods on total cost under the influence of seasonal demand? Method – The study was conducted as a case study and the empirical data was collected through interviews and document study. Both of these contributed to the basis for the analysis and for the calculations in the test of the study. Literature study was conducted and included theories for inventory control methods to answer the first research question, as well as formulas for the methods used to answer the second research question. Findings – It appears from the study, the inventory control methods that can be used when there is seasonal demand and during current planning environment is periodic ordering system and cycle service method. These have been tested further in the study. Seasonal index was considered an important method since it dimensions demand which to a high degree regulate the inventory levels and thereby the result of the inventory control methods. Further the study compares none theoretical inventory control methods and theoretical inventory control methods impact on total cost. It is shown that carrying costs, as a part of total cost, can be reduced by 25% during the peak season and 62% during off-season. This without changing the deliverability. Alternatively, the deliverability can be increased by 10% by using inventory control methods without increasing the total cost of the inventory. Implications – The theoretical contribution of the study is that it has increased the knowledge concerning inventory control methods when there is seasonal demand, and the possible results they might bring. The empirical contribution of the study is that companies can use the study as an indication of the economic benefits and motivation for implementing theoretical inventory control methods. Limitations – The tested inventory control methods did not alter the ordering cost, which to a high degree can have an impact on the total cost. The study also shows a possible impact on the inventory control during the current planning environment. If the planning environment changes, the result of the study can be different.
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Haddad, Khaled, University of Western Sydney, College of Health and Science, and School of Engineering. "Design flood estimation for ungauged catchments in Victoria : ordinary and generalised least squares methods compared." 2008. http://handle.uws.edu.au:8081/1959.7/30369.

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Design flood estimation in small to medium sized ungauged catchments is frequently required in hydrologic analysis and design and is of notable economic significance. For this task Australian Rainfall and Runoff (ARR) 1987, the National Guideline for Design Flow Estimation, recommends the Probabilistic Rational Method (PRM) for general use in South- East Australia. However, there have been recent developments that indicated significant potential to provide more meaningful and accurate design flood estimation in small to medium sized ungauged catchments. These include the L moments based index flood method and a range of quantile regression techniques. This thesis focuses on the quantile regression techniques and compares two methods: ordinary least squares (OLS) and generalised least squares (GLS) based regression techniques. It also makes comparison with the currently recommended Probabilistic Rational Method. The OLS model is used by hydrologists to estimate the parameters of regional hydrological models. However, more recent studies have indicated that the parameter estimates are usually unstable and that the OLS procedure often violates the assumption of homoskedasticity. The GLS based regression procedure accounts for the varying sampling error, correlation between concurrent flows, correlations between the residuals and the fitted quantiles and model error in the regional model, thus one would expect more accurate flood quantile estimation by this method. This thesis uses data from 133 catchments in the state of Victoria to develop prediction equations involving readily obtainable catchment characteristics data. The GLS regression procedure is explored further by carrying out a 4-stage generalised least squares analysis where the development of the prediction equations is based on relating hydrological statistics such as mean flows, standard deviations, skewness and flow quantiles to catchment characteristics. This study also presents the validation of the two techniques by carrying out a split-sample validation on a set of independent test catchments. The PRM is also tested by deriving an updated PRM technique with the new data set and carrying out a split sample validation on the test catchments. The results show that GLS based regression provides more accurate design flood estimates than the OLS regression procedure and the PRM. Based on the average variance of prediction, standard error of estimate, traditional statistics and new statistics, rankings and the median relative error values, the GLS method provided more accurate flood frequency estimates especially for the smaller catchments in the range of 1-300 km2. The predictive ability of the GLS model is also evident in the regression coefficient values when comparing with the OLS method. However, the performance of the PRM method, particularly for the larger catchments appears to be satisfactory as well.<br>Master of Engineering (Honours)
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Peck, Lara. "Impacts of weather on aviation delays at O.R. Tambo International Airport, South Africa." Diss., 2015. http://hdl.handle.net/10500/22201.

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Weather-related delays in the aviation sector will always occur, however, through effective delay management and improved weather forecasting, the impact and duration of delays can be reduced. The research examined the type of weather that caused departure delays, due to adverse weather at the departure station, namely O. R. Tambo International Airport (ORTIA), over the period 2010 to 2013. It was found that the most significant weather that causes such delays are thunderstorms, followed by fog. Other noteworthy elements are rainfall, without the influence of other weather elements, and icing. It was also found that the accuracy of a weather forecast does not impact on the number of departure delays, and thus departure delays due to weather at the departure station are largely unavoidable. However, the length and impact of such delays can be reduced through improved planning. The study highlights that all weather-related delays can be reduced by improved weather forecasts, effective assessment of the weather forecast, and collaborative and timely decision making. A weather impact index system was designed for ORTIA and recommendations for delay reductions are made.<br>Geography<br>M. Sc. (Geography)
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Books on the topic "Inventory control - Forecasting - Statistical methods"

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Sihn, Wilfried. Ein Informationssystem für Instandhaltungsleitstellen. Springer, 1992.

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Lang, Michel. Estimation de la crue centennale pour les plans de prévention des risques d'inondations. Éditions Quæ, 2007.

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Jaech, John L. Training manual on statistical methods for nuclear material management. Division of Reactor Accident Analysis, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1988.

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Post, Claudia. Über die Anwendbarkeit geostatischer Verfahren und Optimierung von Daten zur Bewertung der hydraulischen und geologischen Gegebenheiten als Grundlage für Sanierungsmassnahmen am Beispiel des Ronneburger Erzreviers. Lehrstuhl für Ingenieurgeologie und Hydrogeologie der RWTH, 2001.

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United States. Office of National Drug Control Policy. Drug Control Research, Data, and Evaluation Committee. Federal drug-related data systems inventory: Report of the Drug Control Research, Data, and Evaluation Committee. 2nd ed. Executive Office of the President, Office of National Drug Control Policy, 2003.

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United States. Office of National Drug Control Policy. Drug Control Research, Data, and Evaluation Committee. Federal drug-related data systems inventory: Report of the Drug Control Research, Data, and Evaluation Committee. 2nd ed. Executive Office of the President, Office of National Drug Control Policy, 2003.

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United States. Office of National Drug Control Policy. Drug Control Research, Data, and Evaluation Committee. Federal drug-related data systems inventory: Report of the Drug Control Research, Data, and Evaluation Committee. 2nd ed. Executive Office of the President, Office of National Drug Control Policy, 2003.

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United States. Office of National Drug Control Policy. Drug Control Research, Data, and Evaluation Committee. Federal drug-related data systems inventory: Report of the Drug Control Research, Data, and Evaluation Committee. 2nd ed. Executive Office of the President, Office of National Drug Control Policy, 2003.

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United States. Office of National Drug Control Policy. Drug Control Research, Data, and Evaluation Committee. Federal drug-related data systems inventory: Report of the Drug Control Research, Data, and Evaluation Committee. 2nd ed. Executive Office of the President, Office of National Drug Control Policy, 2003.

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United States. Office of National Drug Control Policy. Drug Control Research, Data, and Evaluation Committee. Federal drug-related data systems inventory: Report of the Drug Control Research, Data, and Evaluation Committee. 2nd ed. Executive Office of the President, Office of National Drug Control Policy, 2003.

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Book chapters on the topic "Inventory control - Forecasting - Statistical methods"

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Solis, Adriano O. "Forecasting Lumpy Demand: Statistical Accuracy and Inventory Control Performance." In Supply Management Research. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-08809-5_4.

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de Oliveira, Alexandre Crepory Abbott, Jéssica Mendes Jorge, Andrea Cristina dos Santos, and Geraldo Pereira Rocha Filho. "Neural Network with Specialized Knowledge for Forecasting Intermittent Demand." In Advances in Transdisciplinary Engineering. IOS Press, 2020. http://dx.doi.org/10.3233/atde200113.

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Demand forecasting is an essential part of an efficient inventory control system. However, when the demand has an intermittent or lumpy behavior, forecasting it becomes a challenging task. Several methods have been developed to solve this issue, but nonetheless, they only consider the information about the occurrence of demand, failing to assess the drivers of the data behavior. With the current digitalization of the industry, more data is available and, therefore, the chances of finding a causal relationship between the available data and the demand increases. Considering that, this paper proposes a single-hidden layer neural network for forecasting irregularly spaced time series with attributes conveying information about the past demand, seasonality of the data and specialized knowledge about the process. The neural network proposed is compared with benchmark neural networks and traditional forecasting methods for intermittent demand using three different performance measures on actual demand data from an industry operating in the aircraft maintenance sector. Statistical analysis is conducted on comparison results to identify significant differences in the forecasting methods according to each performance measure.
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Strike, P. W. "Forecasting and control." In Statistical Methods in Laboratory Medicine. Elsevier, 1991. http://dx.doi.org/10.1016/b978-0-7506-1345-3.50016-9.

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Saha, Esha, and Pradip Kumar Ray. "Statistical Analysis of Medical Data for Inventory Management in a Healthcare System." In Analytics, Operations, and Strategic Decision Making in the Public Sector. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7591-7.ch008.

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Statistical analysis is a powerful technique in the field of healthcare that enables drawing meaningful insights from a study in which medical data are collected through real-time observations, survey, and from medical records. In the field of healthcare, there are several research problems ranging from clinical to operational, such as decisions on organ transplant, operating room planning, appointment scheduling, resource allocation, layout design, demand forecasting, and many more. One such operational problem dealt with in this chapter is the inventory management in healthcare systems. Inventory management in healthcare is an upcoming area of research as efficiently managing inventory acts as a prerequisite for planning and decision making in a healthcare system. This requires statistical analysis of the medical data. The medical data is segmented and summarized using numerical and graphical descriptive statistical methods to draw conclusions in order to significantly help the healthcare personnel in managing the healthcare systems.
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Aman, Zineb, Latifa Ezzine, Yassine Erraoui, Younes Fakhradine El Bahi, and Haj El Moussami. "Seeking Accuracy in Forecasting Demand and Selling Prices: Comparison of Various Methods." In Forecasting in Mathematics - Recent Advances, New Perspectives and Applications. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.93171.

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The need for a good forecast estimate is imperative for managing flows in a supply chain. For this, it is necessary to make forecasts and integrate them into the flow control models, in particular in contexts where demand is very variable. However, forecasts are never reliable, hence the need to give a measure of the quality of these forecasts, by giving a measure of the forecast uncertainty linked to the estimate made. Different forecasting models have been developed in the past, particularly in the statistical area. Before going to our application on real industrial cases which highlights a prospective study of demand forecasting and a comparative study of sales price forecasts, we begin, in the first section of this chapter, by presenting the forecasting models, as well as their validation and monitoring.
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Conference papers on the topic "Inventory control - Forecasting - Statistical methods"

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Lei, Ming, Zihan Yin, Shalang Li, and Qian Tan. "Intermittent demand forecasting and inventory control with multiple temporal and cross-sectional aggregation and disaggregation methods." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393070.

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Zhang, Liang, Jin Wen, and Yimin Chen. "Systematic Feature Selection Process Applied in Short-Term Data-Driven Building Energy Forecasting Models: A Case Study of a Campus Building." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5073.

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An accurate building energy forecasting model is a key component for real-time and advanced control of building energy system and building-to-grid integration. With the fast deployment and advancement of building automation systems, data are collected by hundreds and sometimes thousands of sensors every few minutes in buildings, which provide great potential for data-driven building energy forecasting. To develop building energy forecasting models from a large number of potential inputs, feature selection is a critical procedure to ensure model accuracy and computation efficiency. Though the theory of feature selection is well developed in statistics and machine learning fields, it is not well studied in the application of building energy modeling. In this paper, a feature selection framework proposed in an earlier study is examined using a real campus building in Philadelphia. This feature selection framework combines domain knowledge and statistical methods and is developed for short-term data-driven building energy forecasting. In this case study, the feasibilities of using this feature selection framework in developing whole building energy forecasting model and chiller energy forecasting model are studied. Results show that, for both whole building and chiller energy forecasting applications, the model with systematic feature selection process presents better performance (in terms of cross validation error of forecasted output) than other models including that with conventional inputs and that uses only single feature selection technique.
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Li, Gong, Jing Shi, and Junyi Zhou. "Short Term Wind Speed Forecasting Based on Bayesian Model Averaging Method." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-13055.

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Abstract:
Wind energy has been the world’s fastest growing source of clean and renewable energy in the past decade. One of the fundamental difficulties faced by power system operators, however, is the unpredictability and variability of wind power generation, which is closely connected with the continuous fluctuations of the wind resource. Good short-term wind speed forecasting methods and techniques are urgently needed since it is important for wind energy conversion systems in terms of the relevant issues associated with the dynamic control of the wind turbine and the integration of wind energy into the power system. This paper proposes the application of Bayesian Model Averaging (BMA) method in combining the one-hour-ahead short-term wind speed forecasts from different statistical models. Based on the hourly wind speed observations from one representative site within North Dakota, four statistical models are built and the corresponding forecast time series are obtained. These data are then analyzed by using BMA method. The goodness-of-fit test results show that the BMA method is superior to its component models by providing a more reliable and accurate description of the total predictive uncertainty than the original elements, leading to a sharper probability density function for the probabilistic wind speed predictions.
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Răducu, Camelia Mădălina. "LEARNING STRATEGIES AND SCHOOL MOTIVATION IN EXPERIENTIAL LEARNING VS. TRADITIONAL LEARNING." In International Psychological Applications Conference and Trends. inScience Press, 2021. http://dx.doi.org/10.36315/2021inpact032.

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"Introduction: In recent years, European innovation policies in education have focused on preventing early school leaving and functional illiteracy. In this context of innovation in education, experiential learning has proven to have unique qualities for both teachers and students. Thus, the main motivation of this paper was to show that experiential teaching methods and techniques in primary education are able to produce significant improvements in learning strategies and school motivation in young students. Objectives: The aim of this this study was to explore the differences in learning strategies and school motivation on young students who had benefitted from Experiential Learning, in contrast with those following direct learning instructional methods Methods: This study was performed using two groups of subjects. The first group (experimental group) included 60 students taught by experiential methods and the second group (control group) included 60 students taught by traditional methods. All students were in the fourth grade in an urban school. Differences in learning strategies and school motivation were explored by applying School Motivation and Learning Strategies Inventory - SMALSI (Stroud &amp; Reynolds, 2006) to both the experimental group and the control group. SMALSI is structured in 9 dimensions - 6 strengths: study strategies, note-taking / listening skills, reading / comprehension strategies, writing skills / research, strategies used in tests, techniques for organizing / managing time; and 3 weaknesses are: low academic motivation, test anxiety, concentration difficulties / paying attention. To determine the differences in the students’ mean scores, descriptive as well as inferential statistical analyses were performed on the data. Results: The results showed that an experiential teaching model produces positive results in all evaluated strengths and in two of the three weak points investigated, namely in academic motivation and test anxiety. Statistically insignificant effects are in terms of attention / concentration difficulties, they may be more dependent on physiological and psychological maturation and less on the teaching methods, but also may be a direction of further research. Conclusions: The findings of this study could significantly help teachers looking for viable solutions to optimize students school results, increase school motivation and improve learning strategies in primary school."
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