Search results for: sales demand forecasting

Authors: Ghaleb Y. Abbasi, Israa Abu Rumman

Abstract:

This paper modeled sales history archived from 2012 to 2015 bulked in monthly bins for five products for a medical supply company in Jordan. The sales forecasts and extracted consistent patterns in the sales demand history from the Enterprise Resource Planning (ERP) system were used to predict future forecasting and generate sales demand forecasting using time series analysis statistical technique called Auto Regressive Integrated Moving Average (ARIMA). This was used to model and estimate realistic sales demand patterns and predict future forecasting to decide the best models for five products. Analysis revealed that the current replenishment system indicated inventory overstocking.

Keywords: ARIMA models, sales demand forecasting, time series, R code

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Authors: Watchareeporn Chaimongkol

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In this paper, we compare the statistical measures accuracy of composite forecasting model to estimate automobile customer demand in Thailand. A modified simple exponential smoothing and autoregressive integrate moving average (ARIMA) forecasting model is built to estimate customer demand of passenger cars, instead of using information of historical sales data. Our model takes into account special characteristic of the Thai automobile market such as sales promotion, advertising and publicity, petrol price, and interest rate for loan. We evaluate our forecasting model by comparing forecasts with actual data using six accuracy measurements, mean absolute percentage error (MAPE), geometric mean absolute error (GMAE), symmetric mean absolute percentage error (sMAPE), mean absolute scaled error (MASE), median relative absolute error (MdRAE), and geometric mean relative absolute error (GMRAE).

Keywords: composite forecasting, simple exponential smoothing model, autoregressive integrate moving average model selection, accuracy measurements

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Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Xian Li, Qing-Guo Wang, Jiangshuai Huang, Jidong Liu, Ming Yu, Tan Kok Poh

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In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly.

Keywords: power industry, electricity demand, modeling, forecasting

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Authors: Nady Farag Faragalla Hanna

Abstract:

In the car dealership industry in Japan, the sales specialist is a key factor in the success of the company. I hypothesize that when a company understands the characteristics of sales professionals in its industry, it is easier to recruit and train salespeople effectively. Lean human resources management ensures the economic success and performance of companies, especially small and medium-sized companies.The purpose of the article is to determine the characteristics of sales specialists for small and medium-sized car dealerships using the chi-square test and the proximate variable model. Accordingly, the results show that career change experience, learning ability and product knowledge are important, while university education, career building through internal transfer, leadership experience and people development are not important for becoming a sales professional. I also show that the characteristics of sales specialists are perseverance, humility, improvisation and passion for business.

Keywords: electronics engineering, marketing, sales, E-commerce digitalization, interactive systems, sales process ARIMA models, sales demand forecasting, time series, R codetraits of sales professionals, variable precision rough sets theory, sales professional, sales professionals

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Authors: Dimitrios I. Tselentis, Simon P. Washington

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Considerable literature has been focused over the last few decades on forecasting the consumer demand of Energy Efficient Vehicles (EEVs). These methodological issues range from how to capture recent purchase decisions in revealed choice studies and how to set up experiments in stated preference (SP) studies, and choice of analysis method for analyzing such data. This paper reviews the plethora of published studies on the field of forecasting demand of EEVs since 1980, and provides a review and annotated bibliography of that literature as it pertains to this particular demand forecasting problem. This detailed review addresses the literature not only to Transportation studies, but specifically to the problem and methodologies around forecasting to the time horizons of planning studies which may represent 10 to 20 year forecasts. The objectives of the paper are to identify where existing gaps in literature exist and to articulate where promising methodologies might guide longer term forecasting. One of the key findings of this review is that there are many common techniques used both in the field of new product demand forecasting and the field of predicting future demand for EEV. Apart from SP and RP methods, some of these new techniques that have emerged in the literature in the last few decades are survey related approaches, product diffusion models, time-series modelling, computational intelligence models and other holistic approaches.

Keywords: demand forecasting, Energy Efficient Vehicles (EEVs), forecasting methodologies review, methodological approaches

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Authors: Abdulaziz Afandi

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: neural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Huey-Kuo Chen

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Travel demand forecasting including four travel choices, i.e., trip generation, trip distribution, modal split and traffic assignment constructs the core of transportation planning. In its current application, travel demand forecasting has associated with three important issues, i.e., interface inconsistencies among four travel choices, inefficiency of commonly used solution algorithms, and undesirable multiple path solutions. In this paper, each of the three issues is extensively elaborated. An ideal unified framework for the combined model consisting of the four travel choices and variable demand functions is also suggested. Then, a few remarks are provided in the end of the paper.

Keywords: travel choices, B algorithm, entropy maximization, dynamic traffic assignment

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Authors: Amir Andria Gad Shehata

Abstract:

Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenanceneural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Firas M. Tuaimah

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This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Soojin Kim, Jiwon Yang, Sungzoon Cho

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As a seasonal product is only in demand for a short time, inventory management is critical to profits. Both markdowns and stockouts decrease the return on perishable products; therefore, researchers have been interested in the distribution of seasonal products with the aim of maximizing profits. In this study, we propose a data-driven seasonal product sales pattern analysis method for individual retail outlets based on observed sales data clustering; the proposed method helps in determining distribution strategies.

Keywords: clustering, distribution, sales pattern, seasonal product

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Authors: Daham Owaid Matrood, Naqaa Hussein Raheem

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Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima.

Keywords: artificial neural network, demand forecasting, particle swarm optimization, weight optimization

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Authors: Carolina Senabre, Sergio Valero, Miguel Lopez, Antonio Gabaldon

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This paper presents a comprehensive survey of the short-term load forecasting (STLF). Since the behavior of consumers and producers continue changing as new technologies, it is an ongoing process, and moreover, new policies become available. The results of a research study for the Spanish Transport System Operator (REE) is presented in this paper. It is presented the improvement of the forecasting accuracy in the Balearic Islands considering the introduction of meteorological variables, such as temperature to reduce forecasting error. Variables analyzed for the forecasting in terms of overall accuracy are cloudiness, solar radiation, and wind velocity. It has also been analyzed the type of days to be considered in the research.

Keywords: short-term load forecasting, power demand, neural networks, load forecasting

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Authors: Nazaitul Idya Hamzah, Muhammad Syafiq Mazli, Maszatul Akmar Mustafa

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The peak demand forecast is crucial to identify the future generation plant up needed in the long-term capacity planning analysis for Peninsular Malaysia as well as for the transmission and distribution network planning activities. Currently, peak demand forecast (in Mega Watt) is derived from the generation forecast by using load factor assumption. However, a forecast using this method has underperformed due to the structural changes in the economy, emerging trends and weather uncertainty. The dynamic changes of these drivers will result in many possible outcomes of peak demand for Peninsular Malaysia. This paper will look into the independent model of peak demand forecasting. The model begins with the selection of driver variables to capture long-term growth. This selection and construction of variables, which include econometric, emerging trend and energy variables, will have an impact on the peak forecast. The actual framework begins with the development of system energy and load shape forecast by using the system’s hourly data. The shape forecast represents the system shape assuming all embedded technology and use patterns to continue in the future. This is necessary to identify the movements in the peak hour or changes in the system load factor. The next step would be developing the peak forecast, which involves an iterative process to explore model structures and variables. The final step is combining the system energy, shape, and peak forecasts into the hourly system forecast then modifying it with the forecast adjustments. Forecast adjustments are among other sales forecasts for electric vehicles, solar and other adjustments. The framework will result in an hourly forecast that captures growth, peak usage and new technologies. The advantage of this approach as compared to the current methodology is that the peaks capture new technology impacts that change the load shape.

Keywords: hourly load profile, load forecasting, long term peak demand forecasting, peak demand

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Authors: Watcharin Sangma, Onsiri Chanmuang, Pitsanu Tongkhow

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A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.

Keywords: forecasting model, steel demand uncertainty, hierarchical Bayesian framework, exponential smoothing method

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Authors: Ulrike Dowie, Ralph Grothmann

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Rational planning decisions are based upon forecasts. Precise forecasting has, therefore, a central role in business. The prediction of customer demand is a prime example. This paper introduces recurrent neural networks to model customer demand and combines the forecast with uncertainty measures to derive decision support of the demand planning department. It identifies and describes the keys to the successful implementation of an AI-based solution: bringing together data with business knowledge, AI methods, and user experience, and applying agile software development practices.

Keywords: agile software development, AI project success factors, deep learning, demand forecasting, forecast uncertainty, neural networks, supply chain management

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Authors: Andreas Gohs, Reinhold Kosfeld

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This study should be of interest for practitioners who want to predict precisely the sales numbers of vehicles equipped with an innovative propulsion technology as well as for researchers interested in applied (regional) time series analysis. The study is based on the numbers of new registrations of pure electric and hybrid cars. Methods of time series analysis like ARIMA are compared with the Bass Diffusion-model concerning their forecasting performances for new registrations in Germany at the national and federal state levels. Especially it is investigated if the additional information content from regional data increases the forecasting accuracy for the national level by adding predictions for the federal states. Results of parameters of the Bass Diffusion Model estimated for Germany and its sixteen federal states are reported. While the focus of this research is on the German market, estimation results are also provided for selected European and other countries. Concerning Bass-parameters and forecasting performances, we get very different results for Germany's federal states and the member states of the European Union. This corresponds to differences across the EU-member states in the adoption process of this innovative technology. Concerning the German market, the adoption is rather proceeded in southern Germany and stays behind in Eastern Germany except for Berlin.

Keywords: bass diffusion model, electric vehicles, forecasting performance, market diffusion

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Authors: V. Churkin, M. Lopatin

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The purpose of the paper is to estimate the US small wind turbines market potential and forecast the small wind turbines sales in the US. The forecasting method is based on the application of the Bass model and the generalized Bass model of innovations diffusion under replacement purchases. In the work an exponential distribution is used for modeling of replacement purchases. Only one parameter of such distribution is determined by average lifetime of small wind turbines. The identification of the model parameters is based on nonlinear regression analysis on the basis of the annual sales statistics which has been published by the American Wind Energy Association (AWEA) since 2001 up to 2012. The estimation of the US average market potential of small wind turbines (for adoption purchases) without account of price changes is 57080 (confidence interval from 49294 to 64866 at P = 0.95) under average lifetime of wind turbines 15 years, and 62402 (confidence interval from 54154 to 70648 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 90,7%, while in the second - 91,8%. The effect of the wind turbines price changes on their sales was estimated using generalized Bass model. This required a price forecast. To do this, the polynomial regression function, which is based on the Berkeley Lab statistics, was used. The estimation of the US average market potential of small wind turbines (for adoption purchases) in that case is 42542 (confidence interval from 32863 to 52221 at P = 0.95) under average lifetime of wind turbines 15 years, and 47426 (confidence interval from 36092 to 58760 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 95,3%, while in the second –95,3%.

Keywords: bass model, generalized bass model, replacement purchases, sales forecasting of innovations, statistics of sales of small wind turbines in the United States

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Authors: Migael van Zyl, Stefanie Visser, Awelani Phaswana

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The electricity landscape in South Africa is characterized by frequent occurrences of loadshedding, a measure implemented by Eskom to manage electricity generation shortages by curtailing demand. Loadshedding, classified into stages ranging from 1 to 8 based on severity, involves the systematic rotation of power cuts across municipalities according to predefined schedules. However, this practice introduces distortions in recorded electricity demand, posing challenges to accurate forecasting essential for budgeting, network planning, and generation scheduling. Addressing this challenge requires the development of a methodology to quantify the impact of loadshedding and integrate it back into metered electricity demand data. Fortunately, comprehensive records of loadshedding impacts are maintained in a database, enabling the alignment of Loadshedding effects with hourly demand data. This adjustment ensures that forecasts accurately reflect true demand patterns, independent of loadshedding's influence, thereby enhancing the reliability of electricity supply management in South Africa. This paper presents a methodology for determining the hourly impact of load scheduling and subsequently adjusting historical demand data to account for it. Furthermore, two forecasting models are developed: one utilizing the original dataset and the other using the adjusted data. A comparative analysis is conducted to evaluate forecast accuracy improvements resulting from the adjustment process. By implementing this methodology, stakeholders can make more informed decisions regarding electricity infrastructure investments, resource allocation, and operational planning, contributing to the overall stability and efficiency of South Africa's electricity supply system.

Keywords: electricity demand forecasting, load shedding, demand side management, data science

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Authors: Ari Alamäki, Timo Kaski

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The value of co-creation has gained much attention in sales research, but less is known about how salespeople and customers interact in the authentic business to business (B2B) sales meetings. The study presented in this paper empirically contributes to existing research by presenting authentic B2B sales meetings that were video recorded and analyzed using observation and qualitative content analysis methods. This paper aims to study key elements of successful sales interactions between salespeople and customers/buyers. This study points out that salespeople are selling value rather than the products or services themselves, which are only enablers in realizing business benefits. Therefore, our findings suggest that promoting and easing open discourse is an essential part of a successful sales encounter. A better understanding of how salespeople and customers successfully interact would help salespeople to develop their interpersonal sales skills.

Keywords: personal selling, relationship, sales management, value co-creation

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Authors: Yamini Raju, Parminder S. Kang, Adam Moroz, Ross Clement, Alistair Duffy, Ashley Hopwell

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Accurate prediction of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. Current research in this area focused on limited number of factors specific to a single product or a business type. This paper gives an overview of the current literature on the variability factors used to predict demand and the existing forecasting techniques of short shelf life products. It then extends it by adding new factors and investigating if there is a time lag and possibility of noise in the orders. It also identifies the most important factors using correlation and Principal Component Analysis (PCA).

Keywords: demand forecasting, deteriorating products, food wholesalers, principal component analysis, variability factors

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Authors: Hui Hsin Huang

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The downstream manufactures will order their materials from different upstream suppliers to maintain a certain level of the demand. This paper proposes a bivariate model to portray this phenomenon of material demand. We use empirical data to estimate the parameters of model and evaluate the RMSD of model calibration. The results show that the model has better fitness.

Keywords: recency, ordering time, materials demand quantity, multi-source ordering

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Authors: Shahin Mirshekari, Mohammadreza Moradi, Hossein Jafari, Mehdi Jafari, Mohammad Ensaf

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This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.

Keywords: Gaussian process regression, ensemble kernels, bayesian optimization, pharmaceutical sales analysis, time series forecasting, data analysis

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Authors: Tommi Mahlamaki, Mika Ojala

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Digitalization is changing our lives in many ways. As consumers, we are accustomed using different online interactive sales systems. However, while many online selling sites offer systems that have evolved from simple selling functions, the change has not been as rapid in business-to-business (B2B) markets. This is because many B2B companies rely on personal sales and personal communication. The main objective of this research is to clarify perceptions towards digital interactive sales systems and, more specifically, sales configurators. It also aims to identify trends towards the use of sales configurators. To reach these objectives, an online questionnaire was created that targets Finnish B2B distributors who are, by definition, part of B2B markets. The questionnaire was sent to 340 distributors, and it was returned by 150 respondents. The results showed that 82% of respondents had heard about sales configurators, and 64% had used them. The results also showed that 48% of respondents felt that the use of sales configurators would increase in the future, while only 2% felt they would be used less. The future use of sales configurators was not seen as affecting the level of personal sales. In light of the results, we recommend that B2B companies create marketing strategies that integrate and complement traditional sales processes with digital interactive systems.

Keywords: digitalization, interactive systems, marketing, sales process

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Authors: Tehreem Fatima, Enjun Xia

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The research investigates the relationship between total oil consumption and transport oil consumption, GDP, oil price, and oil reserve in order to forecast future oil demand in China. Annual time series data is used over the period of 1980 to 2015, and for this purpose, an oil demand function is estimated by applying structural time series model (STSM). The technique also uncovers the Underline energy demand trend (UEDT) for China oil demand and GDP, oil reserve, oil price and UEDT are considering important drivers of China oil demand. The long-run elasticity of total oil consumption with respect to GDP and price are (0.5, -0.04) respectively while GDP, oil reserve, and price remain (0.17; 0.23; -0.05) respectively. Moreover, the Estimated results of long-run elasticity of transport oil consumption with respect to GDP and price are (0.5, -0.00) respectively long-run estimates remain (0.28; 37.76;-37.8) for GDP, oil reserve, and price respectively. For both model estimated underline energy demand trend (UEDT) remains nonlinear and stochastic and with an increasing trend of (UEDT) and based on estimated equations, it is predicted that China total oil demand somewhere will be 9.9 thousand barrel per day by 2025 as compare to 9.4 thousand barrel per day in 2015, while transport oil demand predicting value is 9.0 thousand barrel per day by 2020 as compare to 8.8 thousand barrel per day in 2015.

Keywords: china, forecasting, oil, structural time series model (STSM), underline energy demand trend (UEDT)

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Authors: Korpapa Srisamai, Pawee Siriruk

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The purpose of this study is to forecast product demands and develop appropriate and adequate procurement plans to meet customer needs and reduce costs. When the product exceeds customer demands or does not move, it requires the company to support insufficient storage spaces. Moreover, some items, when stored for a long period of time, cause deterioration to dead stock. A case study of the wholesale company of electronic equipment and components, which has uncertain customer demands, is considered. The actual purchasing orders of customers are not equal to the forecast provided by the customers. In some cases, customers have higher product demands, resulting in the product being insufficient to meet the customer's needs. However, some customers have lower demands for products than estimates, causing insufficient storage spaces and dead stock. This study aims to reduce the loss of sales opportunities and the number of remaining goods in the warehouse, citing 30 product samples of the company's most popular products. The data were collected during the duration of the study from January to October 2022. The methods used to forecast are simple moving averages, weighted moving average, and exponential smoothing methods. The economic ordering quantity and reorder point are used to calculate to meet customer needs and track results. The research results are very beneficial to the company. The company can reduce the loss of sales opportunities by 20% so that the company has enough products to meet customer needs and can reduce unused products by up to 10% dead stock. This enables the company to order products more accurately, increasing profits and storage space.

Keywords: demand forecast, reorder point, lost sale, dead stock

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Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

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The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, gain

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Authors: S. F. Wong, W. I. Ho, B. Lin, Q. Huang

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ERP has been used in many enterprises for management, the accuracy of the production forecasting module is vital to the decision making of the enterprise, and the profit is affected directly. Therefore, enhancing the accuracy of the production forecasting module can also increase the efficiency and profitability. To deal with a lot of data, a suitable, reliable and accurate statistics model is necessary. LSSVM and Grey System are two main models to be studied in this paper, and a case study is used to demonstrate how the combination model is effective to the result of forecasting.

Keywords: ERP, grey system, LSSVM, production forecasting

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Authors: Yuichi Morita, Yoshiteru Nakamori

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In car dealer business of Japan, a sale professional is a key factor of company’s success. We hypothesize that, if a corporation knows what is the sales professionals’ trait of its corporation’s business field, it will be easier for a corporation to secure and nurture sales persons effectively. The lean human resources management will ensure business success and good performance of corporations, especially small and medium ones. The goal of the paper is to determine the traits of sales professionals for small-and medium-size car dealers, using chi-square test and the variable rough set model. As a result, the results illustrate that experience of job change, learning ability and product knowledge are important, and an academic background, building a career with internal transfer, experience of the leader and self-development are not important to be a sale professional. Also, we illustrate sales professionals’ traits are persistence, humility, improvisation and passion at business.

Keywords: traits of sales professionals, variable precision rough sets theory, sales professional, sales professionals

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