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 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 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 Analysis of Variance Amplification and Service Level in a Supply Chain with Correlated Demand

2020 ◽  
Vol 12 (16) ◽  
pp. 6470 ◽  
Author(s):  
Ahmed Shaban ◽  
Mohamed A. Shalaby ◽  
Giulio Di Gravio ◽  
Riccardo Patriarca
Keyword(s):  
Supply Chain ◽  
Performance Measures ◽  
Bullwhip Effect ◽  
Service Level ◽  
Operating Conditions ◽  
Supply Chain Performance ◽  
Chain Model ◽  
Ordering Policy ◽  
Smoothing Parameters ◽  
The Impact

The bullwhip effect reflects the variance amplification of demand as they are moving upstream in a supply chain, and leading to the distortion of demand information that hinders supply chain performance sustainability. Extensive research has been undertaken to model, measure, and analyze the bullwhip effect while assuming stationary independent and identically distributed (i.i.d) demand, employing the classical order-up-to (OUT) policy and allowing return orders. On the contrary, correlated demand where a period’s demand is related to previous periods’ demands is evident in several real-life situations, such as demand patterns that exhibit trends or seasonality. This paper assumes correlated demand and aims to investigate the order variance ratio (OVR), net stock amplification ratio (NSA), and average fill rate/service level (AFR). Moreover, the impact of correlated demand on the supply chain performance under various operational parameters, such as lead-time, forecasting parameter, and ordering policy parameters, is analyzed. A simulation modeling approach is adopted to analyze the response of a single-echelon supply chain model that restricts return orders and faces a first order autoregressive demand process AR(1). A generalized order-up-to policy that allows order smoothing through the proper tuning of its smoothing parameters is applied. The characterization results confirm that the correlated demand affects the three performance measures and interacts with the operating conditions. The results also indicate that the generalized OUT inventory policy should be adopted with the correlated demand, as its smoothing parameters can be adapted to utilize the demand characteristics such that OVR and NSA can be reduced without affecting the service level (AFR), implying sustainable supply chain operations. Furthermore, the results of a factorial design have confirmed that the ordering policy parameters and their interactions have the largest impact on the three performance measures. Based on the above characterization, the paper provides management with means to sustain good performance of a supply chain whenever a correlated demand pattern is realized through selecting the control parameters that decrease the bullwhip effect.

A new model development for demand forecasting of supply chain from data fusion perspective

2021 ◽  
pp. 1-7
Author(s):  
Zhonghuai Wang ◽  
Guoping Cheng ◽  
Yu Xiong
Keyword(s):  
Supply Chain ◽  
Data Fusion ◽  
Conceptual Model ◽  
Predictive Analytics ◽  
Demand Forecasting ◽  
Model Development ◽  
Bullwhip Effect ◽  
Profit Center ◽  
Source Data ◽  
Tremendous Impact

In the increasingly competitive society, the supply chain, as the third profit center of the enterprise, makes scholars and industry-related with supply chain more and more interesting in the study of relevant issues. Exiting studies believed that an application of predictive analytics could be a tremendous impact on supply chain management. The uncertainty of supply chain demand and bullwhip effect is a challenge in the supply chain. Data fusion can effectively reduce the uncertainty of demand and the amplification effect. In this study, a new conceptual model was established on the traditional supply chain based on data fusion. Results show that the conceptual model refers to data fusion for solving the uncertain and inconsistent multi-source data by Bayesian estimation and to providing reasonable decision information for supply chain managers.

System dynamics modeling with fuzzy logic application to mitigate the bullwhip effect in supply chains

2019 ◽  
Vol 14 (3) ◽  
pp. 610-627 ◽  
Author(s):  
Mehdi Poornikoo ◽  
Muhammad Azeem Qureshi
Keyword(s):  
Fuzzy Logic ◽  
Supply Chain ◽  
System Dynamics ◽  
Dynamic Models ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Automated System ◽  
Supply Chain Performance ◽  
Inference System ◽  
Content Type

Purpose A plethora of studies focused on the cause and solutions for the bullwhip effect, and consequently many have successfully experimented to dampen the effect. However, the feasibility of such studies and the actual contribution for supply chain performance are yet up for debate. This paper aims to fill this gap by providing a holistic system-based perspective and proposes a fuzzy logic decision-making implementation for a single-product, three-echelon and multi-period supply chain system to mitigate such effect. Design/methodology/approach This study uses system dynamics (SD) as the central modeling method for which Vensim® is used as a tool for hybrid simulation. Further, the authors used MATLAB for undertaking fuzzy logic modeling and constructing a fuzzy inference system that is later on incorporated into SD model for interaction with the main supply chain structure. Findings This research illustrated the usefulness of fuzzy estimations based on experts’ linguistically and logically defined parameters instead of relying merely on the traditional demand forecasting based on time series. Despite the increased complexity of the calculations and structure of the fuzzy model, the bullwhip effect has been considerably decreased resulting in an improved supply chain performance. Practical implications This dynamic modeling approach is not only useful in supply chain management but also the model developed for this study can be integrated into a corporate financial planning model. Further, this model enables optimization for an automated system in a company, where decision-makers can adjust the fuzzy variables according to various situations and inventory policies. Originality/value This study presents a systemic approach to deal with uncertainty and vagueness in dynamic models, which might be a major cause in generating the bullwhip effect. For this purpose, the combination between fuzzy set theory and system dynamics is a significant step forward.

Investigating the value of reducing errors in inventory information from a supply chain perspective

Kybernetes ◽  
2015 ◽  
Vol 44 (2) ◽  
pp. 176-185 ◽  
Author(s):  
Kazim Sari
Keyword(s):  
Supply Chain ◽  
Customer Service ◽  
Lead Time ◽  
Service Level ◽  
Supply Chain Collaboration ◽  
Content Type ◽  
Useful Knowledge ◽  
Lead Time Reduction ◽  
Customer Service Level ◽  
Time Reduction

Purpose – The purpose of this paper is to investigate the value of reducing errors in inventory information from a supply chain perspective. To this end, the benefits of reducing errors in inventory information are compared with those of lead time reduction and supply chain collaboration. Design/methodology/approach – A simulation model is constructed to perform the analysis. Findings – Results show that lead time reduction is the most important strategy for a supply chain in reducing total supply chain cost. In terms of customer service level, on the other hand, strategy of reducing errors in inventory information is observed as the most considerable strategy. However, the results for supply chain collaboration are somewhat unexpected. Namely, in spite of its popularity, supply chain collaboration provides very limited contribution to the supply chain. Practical implications – This research provides useful knowledge for the managers of a business enterprise in prioritizing various supply chain strategies. Originality/value – In supply chain management literature, greater emphasis is given to lead time reduction and supply chain collaboration than dealing with errors in inventory information. This research makes it clear that errors in inventory information should not be underestimated.

scholarly journals CoVid Key Figures and New Challenges in the HoReCa Sector: The Way towards a New Supply-Chain

2021 ◽  
Vol 13 (12) ◽  
pp. 6884
Author(s):  
Miguel-Ángel García-Madurga ◽  
Miguel-Ángel Esteban-Navarro ◽  
Tamara Morte-Nadal
Keyword(s):  
Supply Chain ◽  
Demand Forecasting ◽  
Competitive Intelligence ◽  
Tourism Demand ◽  
Social Trends ◽  
The Future ◽  
Global Tourism ◽  
Tourism Activity ◽  
Hospitality Sector ◽  
The Impact

The profound impact of the coronavirus pandemic on global tourism activity and the hospitality industry has rendered statistical approaches on tourism-demand forecasting obsolete. Furthermore, literature review shows the absence of studies on the supply chain in the HoReCa (hotel, restaurant, catering) sector from a sustainability perspective that also addresses economic and social aspects, and not only environmental ones. In this context, the objective of this article is to carry out a prospective analysis on how the changes in the behaviour of consumers during the pandemic and the uncertainties regarding the exit from the health emergency can give rise to social trends with a high impact on the HoReCa sector in the coming years and, specifically, how they will affect the HoReCa supply chain. In the absence of investigations due to the proximity of what has happened, public sources and reports of international relevance have been identified and analysed from the future studies and strategic and competitive intelligence disciplines. The HoReCa sector in Spain has been chosen as field of observation. This analysis draws the future of the HoReCa sector, describes the changes in customer behaviour regarding food and beverages, explains the changes in distribution chains, and reflects on the impact of potential scenarios on the sector. The confluence of all these changes and trends can even configure a new supply chain in the hospitality sector with the emergence of new actors and the increase of access routes to a new final customer for whom security prevails in all its dimensions: physical, emotional, economic, and digital.

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