Improving demand forecasting practices in the industrial context

Demand forecasting is one of the fundamental managerial tasks. Most companies do not know their future demands, so they have to make plans based on demand forecasts. The literature offers many methods and approaches for producing forecasts. When selecting the forecasting approach, companies need to estimate the benefits provided by particular methods, as well as the resources that applying the methods call for. Former literature points out that even though many forecasting methods are available, selecting a suitable approach and implementing and managing it is a complex cross-functional matter. However, research that focuses on the managerial side of forecasting is relatively rare. This thesis explores the managerial problems that are involved when demand forecasting methods are applied in a context where a company produces products for other manufacturing companies. Industrial companies have some characteristics that differ from consumer companies, e.g. typically a lower number of customers and closer relationships with customers than in consumer companies. The research questions of this thesis are: 1. What kind of challenges are there in organizing an adequate forecasting process in the industrial context? 2. What kind of tools of analysis can be utilized to support the improvement of the forecasting process? The main methodological approach in this study is design science, where the main objective is to develop tentative solutions to real-life problems. The research data has been collected from two organizations. Managerial problems in organizing demand forecasting can be found in four interlinked areas: 1. defining the operational environment for forecasting, 2. defining the forecasting methods, 3. defining the organizational responsibilities, and 4. defining the forecasting performance measurement process. In all these areas, examples of managerial problems are described, and approaches for mitigating these problems are outlined.

Designing a Demand Forecasting Process in the Fast Moving Consumer Goods Context

The purpose of this thesis was to study the design of demand forecasting processes. A literature review in the field of forecasting was conducted, including general forecasting process design, forecasting methods and techniques, the role of human judgment in forecasting and forecasting performance measurement. The purpose of the literature review was to identify the important design choices that an organization aiming to design or re-design their demand forecasting process would have to make. In the empirical part of the study, these choices and the existing knowledge behind them was assessed in a case study where a demand forecasting process was re-designed for a company in the fast moving consumer goods business. The new target process is described, as well as the reasoning behind the design choices made during the re-design process. As a result, the most important design choices are highlighted, as well as their immediate effect on other processes directly tied to the demand forecasting process. Additionally, some new insights on the organizational aspects of demand forecasting processes are explored. The preliminary results indicate that in this case the new process did improve forecasting accuracy, although organizational issues related to the process proved to be more challenging than anticipated.

Demand forecasting process design and methods in medium sized enterprise

The purpose of this thesis was to study the design of demand forecasting processes and management of demand. In literature review were different processes found and forecasting methods and techniques interviewed. Also role of bullwhip effect in supply chain was identified and how to manage it with information sharing operations. In the empirical part of study is at first described current situation and challenges in case company. After that will new way to handle demand introduced with target budget creation and how information sharing with 5 products and a few customers would bring benefits to company. Also the new S&OP process created within this study and organization for it.

Accuracy improvement of a demand forecasting model

This research concerns different statistical methods that assist to increase the demand forecasting accuracy of company X’s forecasting model. Current forecasting process was analyzed in details. As a result, graphical scheme of logical algorithm was developed. Based on the analysis of the algorithm and forecasting errors, all the potential directions for model future improvements in context of its accuracy were gathered into the complete list. Three improvement directions were chosen for further practical research, on their basis, three test models were created and verified. Novelty of this work lies in the methodological approach of the original analysis of the model, which identified its critical points, as well as the uniqueness of the developed test models. Results of the study formed the basis of the grant of the Government of St. Petersburg.

Heat Demand Forecasting Models’ Development: Use of Data Mining Tools in SQL Server Analysis Services

This thesis introduces heat demand forecasting models which are generated by using data mining algorithms. The forecast spans one full day and this forecast can be used in regulating heat consumption of buildings. For training the data mining models, two years of heat consumption data from a case building and weather measurement data from Finnish Meteorological Institute are used. The thesis utilizes Microsoft SQL Server Analysis Services data mining tools in generating the data mining models and CRISP-DM process framework to implement the research. Results show that the built models can predict heat demand at best with mean average percentage errors of 3.8% for 24-h profile and 5.9% for full day. A deployment model for integrating the generated data mining models into an existing building energy management system is also discussed.

Extended local labor markets due to high speed trains : Visualization of estimations in the Swedish national travel demand forecasting tool, SAMPERS

Wider economic benefits resulting from extended geographical mobility is one argument for investments in high-speed rail. More specifically, the argument for high-speed trains in Sweden has been that they can help to further spatially extend labor market regions which in turn has a positive effect on growth and development. In this paper the aim is to cartographically visualize the potential size of the labor markets in areas that could be affected by possible future high-speed trains. The visualization is based on the forecasts of labor mobility with public transport made by the Swedish national mobility transport forecasting tool, SAMPERS, for two alternative high-speed rail scenarios. The analysis, not surprisingly, suggests that the largest impact of high-speed trains results in the area where the future high speed rail tracks are planned to be built. This expected effect on local labor market regions of high-speed trains could mean that possible regional economic development effects also are to be expected in this area. However, the results, in general, from the SAMPERS forecasts indicaterelatively small increases in local labor market potentials.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Energy demand forecasting [microform] : hearings before the Subcommittee on Investigations and Oversight of the Committee on Science and Technology, U.S. House of Representatives, Ninety-seventh Congress, first session, June 1, 2, 1981.

"No. 14."

Forecasting tourism demand using consumer expectations

Abstract Purpose- There is a lack of studies on tourism demand forecasting that use non-linear models. The aim of this paper is to introduce consumer expectations in time-series models in order to analyse their usefulness to forecast tourism demand. Design/methodology/approach- The paper focuses on forecasting tourism demand in Catalonia for the four main visitor markets (France, the UK, Germany and Italy) combining qualitative information with quantitative models: autoregressive (AR), autoregressive integrated moving average (ARIMA), self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. The forecasting performance of the different models is evaluated for different time horizons (one, two, three, six and 12 months). Findings- Although some differences are found between the results obtained for the different countries, when comparing the forecasting accuracy of the different techniques, ARIMA and Markov switching regime models outperform the rest of the models. In all cases, forecasts of arrivals show lower root mean square errors (RMSE) than forecasts of overnight stays. It is found that models with consumer expectations do not outperform benchmark models. These results are extensive to all time horizons analysed. Research limitations/implications- This study encourages the use of qualitative information and more advanced econometric techniques in order to improve tourism demand forecasting. Originality/value- This is the first study on tourism demand focusing specifically on Catalonia. To date, there have been no studies on tourism demand forecasting that use non-linear models such as self-exciting threshold autoregressions (SETAR) and Markov switching regime (MKTAR) models. This paper fills this gap and analyses forecasting performance at a regional level. Keywords Tourism, Forecasting, Consumers, Spain, Demand management Paper type Research paper

Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study

This study is aimed to model and forecast the tourism demand for Mozambique for the period from January 2004 to December 2013 using artificial neural networks models. The number of overnight stays in Hotels was used as representative of the tourism demand. A set of independent variables were experimented in the input of the model, namely: Consumer Price Index, Gross Domestic Product and Exchange Rates, of the outbound tourism markets, South Africa, United State of America, Mozambique, Portugal and the United Kingdom. The best model achieved has 6.5% for Mean Absolute Percentage Error and 0.696 for Pearson correlation coefficient. A model like this with high accuracy of forecast is important for the economic agents to know the future growth of this activity sector, as it is important for stakeholders to provide products, services and infrastructures and for the hotels establishments to adequate its level of capacity to the tourism demand.

Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

The demand-supply balancing process in supply chain management - case mining & metals industry equipment

The aim of this thesis is to search how to match the demand and supply effectively in industrial and project-oriented business environment. The demand-supply balancing process is searched through three different phases: the demand planning and forecasting, synchronization of demand and supply and measurement of the results. The thesis contains a single case study that has been implemented in a company called Outotec. In the case study the demand is planned and forecasted with qualitative (judgmental) forecasting method. The quantitative forecasting methods are searched further to support the demand forecast and long term planning. The sales and operations planning process is used in the synchronization of the demand and supply. The demand forecast is applied in the management of a supply chain of critical unit of elemental analyzer. Different meters on operational and strategic level are proposed for the measurement of performance.

Evaluating and improving demand forecast practices for B2B and B2C sectors

Demand forecasting is one of the fundamental managerial tasks. Most companies do not know their future demands, so they have to make plans based on demand forecasts. The literature offers many methods and approaches for producing forecasts. Former literature points out that even though many forecasting methods and approaches are available, selecting a suitable approach and implementing and managing it is a complex cross-functional matter. However, it’s relatively rare that researches are focused on the differences in forecasting between consumer and industrial companies. The aim of this thesis is to investigate the potential of improving demand forecasting practices for B2B and B2C sectors in the global supply chains. Business to business (B2B) sector produces products for other manufacturing companies. On the other hand, consumer (B2C) sector provides goods for individual buyers. Usually industrial sector have a lower number of customers and closer relationships with them. The research questions of this thesis are: 1) What are the main differences and similarities in demand planning between B2B and B2C sectors? 2) How the forecast performance for industrial and consumer companies can be improved? The main methodological approach in this study is design science, where the main objective is to develop tentative solutions to real-life problems. The research data has been collected from a case company. Evaluation and improving in organizing demand forecasting can be found in three interlinked areas: 1) demand planning operational environment, 2) demand forecasting techniques, 3) demand information sharing scenarios. In this research current B2B and B2C demand practices are presented with further comparison between those two sectors. It was found that B2B and B2C sectors have significant differences in demand practices. This research partly filled the theoretical gap in understanding the difference in forecasting in consumer and industrial sectors. In all these areas, examples of managerial problems are described, and approaches for mitigating these problems are outlined.