scholarly journals Demand forecasting and information platform in tourism

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 247-252 ◽  
Author(s):  
Yue Li ◽  
Qi-Jie Jiang
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Information Processing ◽  
Information Asymmetry ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Information Platform ◽  
On Demand ◽  
Processing Ability ◽  
Tourism Supply Chain

AbstractInformation asymmetry and the bullwhip effect have been serious problems in the tourism supply chain. Based on platform theory, this paper established a mathematical model to explore the inner mechanism of a platform’s influence on stakeholders’ ability to forecast demand in tourism. Results showed that the variance of stakeholders’ demand predictions with a platform was smaller than the variance without a platform, which meant that a platform would improve predictions of demand for stakeholders. The higher information-processing ability of the platform also had other effects on demand forecasting. Research on the inner logic of the platform’s influence on stakeholders has important theoretical and realistic value. This area is worthy of further study.

scholarly journals CPN Based Modeling of Tourism Demand Forecasting

2016 ◽  
Vol 12 (1) ◽  
pp. 28
Author(s):  
Hua Bai ◽  
Haoyuan Zhang
Keyword(s):  
Supply Chain ◽  
Good Practice ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Service Level ◽  
Supply Chain Collaboration ◽  
Tourism Demand ◽  
High Volatility ◽  
Low Efficiency ◽  
Tourism Supply Chain

The tourism demand has become more and more diversified and sensitive to traveling environment, resulting in the high volatility of tourism market. Travel agencies, scenic spots, hotels and other tourism businesses in the tourism supply chain (TSC) need a tight collaboration in order to minimize cost and improve responsiveness and service level. The existence of the bullwhip effect will cause the waste of resources and low efficiency, thus collaborative demand forecasting becomes a good practice to enhance sharing of information and resources, and as a result improving the efficiency and effectiveness of tourism demand forecasting. This paper proposes a collaborative tourism demand forecasting framework based on Colored Petri Net (CPN), which can simulate and examine the effectiveness of tourism supply chain collaboration.

scholarly journals Mathematical analysis of the impact mechanism of information platform on agro-product supply chain and agro-product competitiveness

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 108-120 ◽  
Author(s):  
Qi-Jie Jiang ◽  
Mao-Zhu Jin ◽  
Pei-Yu Ren
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Information Acquisition ◽  
Bullwhip Effect ◽  
Agricultural Products ◽  
Operating Efficiency ◽  
Market Demand ◽  
Information Platform ◽  
The Operating Mechanism ◽  
The Impact

AbstractHow to optimize agro-product supply chain to promote its operating efficiency so as to enhance the competitiveness of regional agricultural products has posed a problem to academic circles, business circles and governments of various levels. One way to solve this problem is to introduce an information platform into the supply chain, which this essay focuses on. Firstly, a review of existing research findings concerning the agro-product competitiveness, agro-product supply chain (ASC) and information platform was given. Secondly, we constructed a mathematical model to analyze the impact of information platform on the bullwhip effect in ASC. Thirdly, another mathematical model was constructed to help compare and analyze the impact of information platform on information acquisition of members in ASC. The research results show that the implantation of information platform can mitigate the bullwhip effect in ASC, and members can determine order amount or production more close to the actual market demand. And also the information platform can reduce the time for members in ASC to get information from other members. Besides, information platform can help ASC to alleviate information asymmetry among upstream and downstream members. Furthermore, researches about the operating mechanism and pattern, technical feature and running structure of the information platform, along with their impacts on agro-product supply chain and the competitiveness of agricultural products need to be advanced.

Optimization Control of the Bullwhip Effect in Supply Chain Inventory

2014 ◽  
Vol 945-949 ◽  
pp. 3187-3190
Author(s):  
Hai Dong ◽  
Jin Hua Liu ◽  
Liang Yu Liu
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Control Theory ◽  
Inventory Control ◽  
Pid Controller ◽  
Bullwhip Effect ◽  
Control Gain ◽  
Optimization Control ◽  
Suitable Combination ◽  
The Mathematical Model

The bullwhip effect was caused by fuzzy demand among the enterprises. In order to reduce this effect, control theory was applied to solve the inventory in supply chain. Firstly, inventory control in supply chain and the bullwhip effect was researched. Secondly, a kind of proportional integral differential (PID) controller was developed for inventory control in a three-level supply chain, and the mathematical model of the PID controller for inventory control was presented. Finally, the results show that the PID controller can evidently alleviate the bullwhip effect and inventory fluctuations under the suitable combination of control gain.

A Mathematics Model for Quantitative Analysis of Demand Disruption Caused by Rumor Spreading

2014 ◽  
Vol 13 (03) ◽  
pp. 585-602 ◽  
Author(s):  
Haifeng Zhao ◽  
Bin Lin ◽  
Chongqing Guo
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Quantitative Analysis ◽  
New Method ◽  
Mathematics Model ◽  
Rumor Spreading ◽  
On Demand ◽  
Spreading Model ◽  
Internet Age ◽  
Purchasing Intention

Rumors greatly impact consumers' attitudes and purchasing intention. Rumor spreading can disrupt supply chain demand, particularly in today's Internet age. We propose a mathematical model for the quantitative analysis of demand disruption caused by rumor spreading based on the susceptible-infective-isolated-immune (SI2I) rumor spreading model, which extends the susceptible-infective-recovered (SIR) rumor spreading model by dividing stiflers into isolators and immunes. Both groups represent individuals who do not propagate a rumor, but the former believes the rumor while the latter does not. From the firms' perspective, only ignorants and immunes will still purchase their products and services after a rumor has spread. Hence, the influence of rumors on demand can be quantitatively reflected by the proportion of ignorants and immunes in the population. This study offers a new method for company managers to predict the variation trend of demand and estimate demand loss when a firm is attacked by rumors.

Impact of Auto-regressive (AR) Process in Bullwhip Analysis in a Multi-location Supply Chain Network

2019 ◽  
Vol 6 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Prabhat Mittal
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Network Structure ◽  
Bullwhip Effect ◽  
Supply Chain Network ◽  
Risk Pooling ◽  
On Demand ◽  
Forecasting Methods ◽  
Auto Regressive ◽  
Ar Process

The present study is an attempt to quantify the Bullwhip Effect (BWE) -the phenomenon in which information on demand is distorted in moving up a supply chain. Assuming that the retailer employs an order-up-to level policy with auto-regressive process (AR), the paper investigates the influence of forecasting methods on bullwhip effect. Determining the order-up-to levels and the orders for the retailers’ demands in an isolated manner neglects the correlation of the demands and the relevant risk pooling effects associated with the network structure of the supply chains are disregarded. It is illustrated that the bullwhip effects are significantly reduced with consideration of potential correlation between the retailers’ demand.

scholarly journals An Empirical Study of Supply Chain Frangibility and Risk Management in Indian Automobile Industry

2021 ◽  
Author(s):  
Arora Ankit ◽  
Rajagopal Rajesh
Keyword(s):  
Risk Factors ◽  
Supply Chain ◽  
Automobile Industry ◽  
Lead Time ◽  
Risk Mitigation ◽  
Transportation Network ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
Supply Chain Efficiency

Abstract The automobile sector in India is one the key segment of Indian economy as it contributes to 4% of India’s GDP and 5% of India’s Industrial production. The supply chain of any firm is generally dependent on six driving factors out of which three are functional (information, inventory, and facilities) and 3 are logistic (sourcing, pricing, and transportation). The risk causing factors in supply chains consists of various levels of sub-factors under them. Say for instance, under supply risk, the sub-factors can be poor logistics at supplier end, poor material quality etc., under demand risk, the sub-factors can be inaccurate demand forecasting, fluctuating demand, bullwhip effect, and under logistics risk, the sub-factors can be poor transportation network, shorter lead time, stock outs. Through this study, we observe to find the effect of these factors in the supply chain. We use Failure Mode and Effect Analysis (FMEA) technique to prioritize the various types of risk into zones namely high, medium and low risk factors. Also, we use the Best Worst Method (BWM), a multi-criteria decision-making technique to find out the overall weightings of different risk factors. The combination of these methods can help an organization to prioritize various risk factors and proposing a proper risk mitigation strategy leading to increase in overall supply chain efficiency and responsiveness.

scholarly journals Alternative Forecasting Techniques that Reduce the Bullwhip Effect in a Supply Chain: A Simulation Study

1970 ◽  
Vol 25 (2) ◽  
pp. 177-188 ◽  
Author(s):  
Francisco Campuzano-Bolarín ◽  
Antonio Guillamón Frutos ◽  
Ma Del Carmen Ruiz Abellón ◽  
Andrej Lisec
Keyword(s):  
Supply Chain ◽  
Moving Average ◽  
Management Strategies ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Good Alternative ◽  
Critical Problems ◽  
Simple Exponential Smoothing ◽  
Demand Patterns ◽  
Forecasting Techniques

The research of the Bullwhip effect has given rise to many papers, aimed at both analysing its causes and correcting it by means of various management strategies because it has been considered as one of the critical problems in a supply chain. This study is dealing with one of its principal causes, demand forecasting. Using different simulated demand patterns, alternative forecasting methods are proposed, that can reduce the Bullwhip effect in a supply chain in comparison to the traditional forecasting techniques (moving average, simple exponential smoothing, and ARMA processes). Our main findings show that kernel regression is a good alternative in order to improve important features in the supply chain, such as the Bullwhip, NSAmp, and FillRate.

scholarly journals A Comprehensive Study on Demand Forecasting Methods and Algorithms for Retail Industries

2021 ◽  
Vol 23 (06) ◽  
pp. 409-420
Author(s):  
Udbhav Vikas ◽  
◽  
Karthik Sunil ◽  
Rohini S. Hallikar ◽  
Pattem Deeksha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Performance Comparison ◽  
Supply Side ◽  
Learning Approaches ◽  
Time Series Regression ◽  
On Demand ◽  
Forecasting Methods ◽  
Comprehensive Study

Without a doubt, demand forecasting is an essential part of a company’s supply chain. It predicts future demand and specifies the level of supply-side readiness needed to satisfy the demand. It is imperative that if a company’s forecasting isn’t reasonably reliable, the entire supply chain suffers. Over or under forecasted demand would have a debilitating impact on the operation of the supply chain, along with planning and logistics. Having acknowledged the importance of demand forecasting, one must look into the techniques and algorithms commonly employed to predict demand. Data mining, statistical modeling, and machine learning approaches are used to extract insights from existing datasets and are used to anticipate unobserved or unknown occurrences in statistical forecasting. In this paper, the performance comparison of various forecasting techniques, time series, regression, and machine learning approaches are discussed, and the suitability of algorithms for different data patterns is examined.

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