Product Demand Forecasting Research Articles

A Study on Determining Factors Influence Pricing Competitive for Airlines in Malaysia

The purpose of this study is to determine the factors influence pricing competitive dynamic airlines by passenger for the specific routes of Kuala Lumpur to Singapore between the Prime Carrier and Low-Cost Carrier. This research was derived in response of World’s Busiest International Air Routes report from OAG Aviation Worldwide that shows more than 30,000 flights frequency in both directions for international route Kuala Lumpur to Singapore during 12 months in 2018. This study will be understanding the influence in pricing competitive between forecast on demand, and product quality. A framework model has been proposed to determine the factors on competitive airlines pricing in Malaysia and the variables are Market Segmentation, Product Quality and Demand Forecast. This research used a quantitative data collection method to gather or to prompt information from respondents, in this case passenger who is using flights from Kuala Lumpur to Singapore. A total of 424 passengers was sampled and the set of questionnaires were distributed using the non- probability sampling technique which is convenience sampling to obtain sample data to be analyzed. The analysis in this study was conducted using descriptive statistical analysis tools. – SPSS – version 23. Results from the multiple analysis demonstrate that product quality and demand forecast as factors influencing pricing competitive.

Peramalan Permintaan Toza Juice Strawberry Sebagai Dasar Penentuan Kebutuhan Persediaan Bahan Baku

Fruit juice is one of the favorite soft drink products in Indonesia. PT. Amanah Prima Indonesia is an agroindustry company that produces the fruit juice RTD for the HORECA market. In the company’s activities, the company will not be separated from a plan. One of the planning activities is forecast demand. The results of product demand forecast can assist in the decisions making to determine the amount of raw material inventory needs. The purpose of the research are: 1) to find out the forecast method used by the company, 2) to analyze the best demand forecast method, and 3) to analyze the optimal amount of the raw material inventory requirements based on the best forecast method results. This research used product sales data in the period of May 2016-April 2018. Demand forecast analysis used the ARIMA method and Exponential Smoothing method. Then inventory analysis used the Min-max Stock method. The result of this research shows that the forecasting system used by the company is currently unrealistic because it has big significant of difference with the actual data. The best time series model of forecast analysis acquires ARIMA model with value of MSE was smaller than DES (Holt) model. Analysis of the raw material inventory requirements with Min-Max Stock results obtain supplies the raw material of strawberry puree is 1688 kilograms, with safety stock of 106 kilograms puree, minimum stock of 830 kilograms puree, maximum stock of 1447 kilograms puree, and reorder point for the next purchase of strawberry as much as 562 kilograms.

Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey

Crowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI. INDEX TERMS Crowdsourced business intelligence, consumer behaviors, competitive intelligence, crowd intelligence, commercial site recommendation, brand trending.

Demand forecasting in pharmaceutical supply chains: A case study

Demand forecasting plays a critical role in logistics and supply chain management. In the paper, state-of-art methods and key challenges in demand forecasting for the pharmaceutical industry are discussed. An integrated procedure for in-market product demand forecasting and purchase order generation in the pharmaceutical supply chain is described. A case study for supply of pharmaceutical products from a wholesaler to a distribution company located in an emerging market is presented. Alternative forecasting scenarios for thebaseline demand calculations using the SMA model, multiple linear regressions and symbolic regression with genetic programming are experimentally investigated, and their practical implicationsare discussed.

Policy uncertainty and dynamics of international trade

PurposeThis study aims to empirically examine how economic policy uncertainty emanating from three major global economic blocks (the US, the Chinese and the European Union) and volatility in global oil prices influence international trade.Design/methodology/approachThe study uses quarterly data spanning the period between 1995 and 2014 in an autoregressive distributed lag framework.FindingsThis study finds that economic policy uncertainty conditions associated with the US and the Chinese economies tend to have significant negative or constraining impact on key components of international trade. Further analysis suggests that between the two leading economies (the US and the Chinese economies), economic policy uncertainty emanating from the US economy tend to have much more constraining impact on dynamics of international trade than the Chinese economy all things being equal.Practical implicationsThis study’s findings carry significant strategic planning and policy implications for international trade dependent firms or corporations and economies. For instance, for multi-national corporations or firms whose products and services depend heavily on cross-border trade, understanding and taking into consideration prevailing economic policy dynamics emanating from the US and the Chinese economies in product and services demand forecast, and other strategic moves could be critical in minimizing potential adverse effects on projected performance or growth targets.Originality/valueThe uniqueness of this study’s approach stems from its assessment of how perception of uncertainty among economic agents about economic policies originating from three noted global economic blocks impacts international trade. In other words, instead of traditional factors or conditions surmised to influence variability in trend associated with international trade found in related studies, this study rather examines how perceptions of uncertainty about prevailing or yet to be enacted economic policy within specific global economic block impacts international trade.

When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions

This paper examines the accuracy of judgmental forecasts of product demand and the quality of subsequent production level decisions under two different conditions: (i) the availability of only time series information on past demand; (ii) the availability of time series information together with scenarios that outline possible prospects for the product in the forthcoming period. An experiment indicated that production level decisions made by participants had a greater deviation from optimality when they also received optimistic and pessimistic scenarios. This resulted from less accurate point forecasts made by these participants. Further analysis suggested that participants focussed on the scenario that was congruent with the position of the latest observation relative to the series mean and discounted the opposing scenario. This led to greater weight being attached to this observation, thereby exacerbating the tendency of judgmental forecasters to see systematic changes in random movements in time series.

Analogue-based demand forecasting of short life-cycle products: a regression approach and a comprehensive assessment

In several industries, global competition, increasing customer expectations and technological innovations tend to accelerate product life-cycles. In this changing environment, traditional forecasting methods tend to be ineffective as a consequence of the transient and highly uncertain demand of short life-cycle products (SLCP), and the scarcity of sales data. To address this challenge, we present a methodology to forecast SLCP demand using time series of similar products referred to as analogies. Linear regression and clustering techniques are used for the selection and weighting of suitable analogies. The proposed methodology is tested against seven analogue-based forecasting methods, including two implementations of non-linear regression methods. In different sets of time series, our methodology attained more accurate forecasts with short processing times compared with state-of-the-art methods. Such results reveal promising applications of combined regression and clustering techniques as simple and effective forecasting tools for supporting replenishment decisions for SLCP.

Perencanaan Produksi Agregat Dengan Metode Goal Programing

A Production planning is very important to all activities in manufacturing company. For a company producing a variety of product the production planning will be complicated. In this case it’s necessary to make an aggregate production planning. A Company has goals to achieve the products, althought there differenciate between each other. Other wise, if needs to make a production planning that accommodate such goals. Goal programming model can be used for aggregate production planning. This multi objective model can accommodate more than one goal which will be achieved by the company. Product demand forecasting is input for aggregate production planning. From this aggregate production planning, futhermore, disaggregation must be conducted, then it will be continued by raw material planning. The raw material planning is made by lot size each order economically. The aim of goal programming model is to maximize profits, to minimize subcontract products amount, to minimize overtime labours cost and to minimize inventory amount. The results show that the goal programming model could accommodate three goals that will be achieved by the company, except that goal to maximize profit high goal is to minimize inventory amount. The comparison between planning and realitation show that the goal programming model could reduce the minimize overtime labours cost.

A Study on the Chain Restaurants Dynamic Negotiation Games of the Optimization of Joint Procurement of Food Materials

In the era of meager profit, production costs often become an important factor affecting SMEs’ operating conditions, and how to effectively reduce production costs has become an issue of in-depth consideration for the business owners. Especially, the food and beverage (F&B) industry cannot accurately predict the demand. It many cause demand forecast fall and excess or insufficient inventory pressure. Companies of the F&B industry may be even unable to meet immediate customer needs. They are faced great challenges in quick response and inventory pressure. This study carried out the product inventory model analysis of the most recent year’s sales data of the fresh food materials for chain restaurants in a supply chain region with raw material suppliers and demanders. Moreover, this study adopted the multi-agent dynamic strategy game to establish the joint procurement decision model negotiation algorithm for analysis and verification by simulation cases to achieve the design of dynamic negotiation optimization mechanism for the joint procurement of food materials. Coupled with supply chain management 3C theory for food material inventory management, we developed the optimization method for determining the order quantities of the chain restaurants. For product demand forecast, we applied the commonality model, production and delivery capacity model, and the model of consumption and replenishment based on market demand changes in categorization and development. Moreover, with the existence of dependencies between product demands as the demand forecast basis, we determined the appropriate inventory model accordingly.

Product Demand Forecasting and Dynamic Pricing considering Consumers’ Mental Accounting and Peak-End Reference Effects

We introduce a demand forecasting model for a monopolistic company selling products to consumers with double-entry mental accounting, which means consumers experience pleasure when consuming goods or service and feel pains when paying for them. Moreover, as the monopolist changes prices, consumers form a reference price that adjusts an anchoring standard based on the lowest price that they perceived, namely, the peak-end anchoring. We obtain the steady state prices under three different payment schemes for two- and infinite-period. We also analyze the relationship between these steady prices and maximal profit and compare the steady state prices of different payment schemes by changing the double-entry mental accounting’s parameters through numerical examples. The proposed model is computationally tractable for demand forecasting of realistic size.

Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach

This study proposes a novel approach to the pre-launch forecasting of new product demand based on the Bass model and statistical and machine learning algorithms. The Bass model is used to explain the diffusion process of products while statistical and machine learning algorithms are employed to predict two Bass model parameters prior to launch. Initially, two types of databases (DBs) are constructed: a product attribute DB and a product diffusion DB. Taking the former as inputs and the latter as outputs, single prediction models are developed using six regression algorithms, on the basis of which an ensemble prediction model is constructed in order to enhance predictive power. The experimental validation shows that most single prediction models outperform the conventional analogical method and that the ensemble model improves prediction accuracy further. Based on the developed models, an illustrative example of 3D TV is provided.

Strategic Management of New Products: Ex-Ante Simulation and Market Segmentation

Among various methodologies for demand forecasting of new products, the random-coefficient discrete-choice model using stated preference data is considered to be effective because it reflects heterogeneity in consumer preference and enables the design of experiments in the absence of revealedpreference data. Based on estimates drawn from consumer preference data by structural hierarchical Bayesian logit models, this study develops the overall, strategic, demand-side management for new products by combining market share simulation and a rigorous clustering methodology, the Gaussian mixture model. It then applies the process to the empirical case of electronic payment instruments.

Demand forecasting of perishable farm products using support vector machine

This article presents a new algorithm for forecasting demand for perishable farm products, based on the support vector machine (SVM) method. Since SVMs have greater generalisation performance and guarantee global minima for given training data, it is believed that support vector regression will perform well for forecasting demand for perishable farm products. In order to improve forecasting precision (FP), this article quantifies the factors affecting the sales forecast of perishable farm products based on the fuzzy theory, which is suitable for real situations. Numerical experiments show that forecasting systems with SVMs and fuzzy theory outperform the radial basis function neural network, based on the criteria of day absolute error, relative mean error and FP. Since there is no structured way to choose the free parameters of SVMs, the variational range of free parameters and the effects of the parameters on prediction performance are discussed in this article. Analysis of experimental results proves that it is advantageous to apply SVMs forecasting system in perishable farm products demand forecasting.

General Electric Uses an Integrated Framework for Product Costing, Demand Forecasting, and Capacity Planning of New Photovoltaic Technology Products

General Electric (GE) Energy's nascent solar business has revenues of over $100 million, expects those revenues to grow to over $1 billion in the next three years, and has plans to rapidly grow the business beyond this period. GE Global Research (GEGR), in partnership with GE Energy's solar platform team, is pursuing a number of technological alternatives to bring new low-cost solar products to the market. However, the GE solar business is facing a challenge—making optimal investment decisions to realize its growth objectives in the presence of major uncertainties in technology, costs, demands, and energy policy. We have developed analytical decision support tools with embedded mathematical models to estimate product costs and demands, and to support capacity planning decisions under cost and demand uncertainties. In this paper, we outline our algorithmic approach and system implementation, which help to support strategic decisions at GE.

Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle

Semiconductor industry is capital intensive in which capacity utilization significantly affect the capital effectiveness and profitability of semiconductor manufacturing companies. Thus, demand forecasting provides critical input to support the decisions of capacity planning and the associated capital investments for capacity expansion that require long lead-time. However, the involved uncertainty in demand and the fluctuation of semiconductor supply chains make the present problem increasingly difficult due to diversifying product lines and shortening product life cycle in the consumer electronics era. Semiconductor companies must forecast future demand to provide the basis for supply chain strategic decisions including new fab construction, technology migration, capacity transformation and expansion, tool procurement, and outsourcing. Focused on realistic needs for manufacturing intelligence, this study aims to construct a multi-generation diffusion model for semiconductor product demand forecast, namely the SMPRT model, incorporating seasonal factor (S), market growth rate (M), price (P), repeat purchases (R), technology substitution (T), in which the nonlinear least square method is employed for parameter estimation. An empirical study was conducted in a leading semiconductor foundry in Hsinchu Science Park and the results validated the practical viability of the proposed model. This study concludes with discussions of the empirical findings and future research directions.

Enhancing the product development process through a sequential approach. Part I: product design

A structured and systematic approach to product design has been presented in this three part paper. This approach constitutes a logical framework for designing consumer products by taking into consideration various X's such as functionality, cost, assembly, usability and disassembly. The primary and most important 'X' in product design is functionality. Since every product exists to perform a specific function, the design process essentially starts with this important attribute. This paper focuses on achieving efficient product design consistent with its intended function. Part 2 of this paper will deal with product assembly/disassembly and product disposal. Part 3 of the paper will address factors related to product manufacture such as consideration of materials and processes, designing for functionality, design review and designing for usability such as ease of use and maintenance. Part 3 will also deal with management issues such as product demand forecasting and facilities design.

Enhancing the product development process through a sequential approach part III: manufacturing

This is the final and concluding part of this series dedicated to presenting a comprehensive approach to consumer product design using DfX principles. This paper focuses on dealing with design principles that enhance the usability of a product in general. In this context, it deals with factors such as designing for maintainability and designing for ease of use. It also addresses the all important concept of design review, by means of which quality can be engineered into the product at the design stage itself. Finally, the paper also addresses the issue of paramount importance: cost in order to maximize profits. The issue of demand forecasting in order to enable product managers to predict future product demand is also addressed at some length towards the end of this paper. This paper serves to act as an all important link between product design, demand forecasting and facilities design.

The impacts of a GTL plant on Brazil's oil products supply and refinery expansion

This paper assesses the impacts of a GTL plant on the expansion of Brazil's oil refining segment. The GTL plant (50,000 bpd) was sized to start up operations in 2015, producing diesel and naphtha through the indirect route (FT-synthesis). This plant will consume the non-associated natural gas production from the recent discoveries at the Santos Basin (around 419 Bm 3), and the associated gas production from the Campos Basin. Both basins are located in the Southeast of Brazil, the most populated, rich and industrialized region of the country. Two different criteria for refinery expansion were simulated in order to meet oil product demand scenarios. Findings show that depending on the refinery expansion criteria considered GTL will play a fundamental hole to meet the oil product demand forecast to Brazil in the next 10 years.

Fuzzy logic-based forecasting model

In this paper a fuzzy logic-based software tool, fuzzy logic advisory tool (FLAT), for demand forecasting of signal transmission products is presented. The FLAT was developed for the prediction of demand of about 1000 different products in order to aid materials purchasing process of about 14,000 different components in the electronics manufacturing processes of Nokia Network Systems's Haukipudas factory. The prediction values of different products are inferred by starting from a set of eight input values. Each input value is fuzzied by the FLAT. Thereafter, fuzzy results are inferred in three sequential phases. In each phase the number of variables is split due to hierarchical structure of the inference module. A data base and a rule base are divided accordingly into three hierarchical levels. Rules are represented by linguistic relations changed into matrix equations form in order to apply linguistic equations framework technique (LE). Fuzzy membership functions for input values are determined on-line from earlier input values of the products. Fuzzy rules were inferred by analyzing behavior of the products together with market experts and product experts of the company. The model is able to produce more accurate decision-making support than more traditional approaches. This is probably due to the model-based approach and systematic data management.

A fuzzy set model for market share and preference prediction

This paper presents a new method of modelling preferences using fuzzy sets. A fuzzy set conjoint model is developed by incorporating fuzzy measurement of evaluations into the vector preference model. A comparison test of rank prediction is conducted on data gathered from consumer subjects by an adaptive software program. A methodology is developed to generate aggregate crisp market share estimates from individual fuzzy set preference predictions. The result is a substantial improvement in individual predictive validity using the fuzzy set model and more accurate aggregate market share prediction for use in product design, demand forecasting and other management functions.