scholarly journals The impact of implementing a demand forecasting system into a low-income country's supply chain

Vaccine ◽  
2016 ◽  
Vol 34 (32) ◽  
pp. 3663-3669 ◽  
Author(s):  
Leslie E. Mueller ◽  
Leila A. Haidari ◽  
Angela R. Wateska ◽  
Roslyn J. Phillips ◽  
Michelle M. Schmitz ◽  
...  
Keyword(s):  
Supply Chain ◽  
Low Income ◽  
Demand Forecasting ◽  
Forecasting System ◽  
The Impact

scholarly journals Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion

2020 ◽  
Vol 142 ◽  
pp. 106380 ◽  
Author(s):  
Mahdi Abolghasemi ◽  
Eric Beh ◽  
Garth Tarr ◽  
Richard Gerlach
Keyword(s):  
Supply Chain ◽  
Demand Forecasting ◽  
Demand Volatility ◽  
The Impact

scholarly journals Data analytics in supply chain management

2020 ◽  
Author(s):  
Hendro Wicaksono
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Demand Forecasting ◽  
Chain Management ◽  
Strategic Sourcing ◽  
Management Processes ◽  
The Impact

The presentation discussed the impact of the technologies related to the 4th industrial revolution on big data. The 4th industrial revolution ecosystem is characterized by the presence of smart PPR (Product, Process, and Resource) who generates data. It transforms the product-based business model to product-data-driven service model. Big data also exist due to the digital transformation of supply chain management processes. Data analytics and machine learning can improve the supply chain management processes, such as demand forecasting, production, strategic sourcing, etc. Finally, the presentation gives some examples of the application of data analytics in real companies.

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.

Bullwhip Effect Analysis in Two-Level Supply Chain Distribution Network Using Different Demand Forecasting Technology

2016 ◽  
Vol 33 (03) ◽  
pp. 1650016 ◽  
Author(s):  
Xi Gang Yuan ◽  
Nan Zhu
Keyword(s):  
Supply Chain ◽  
Moving Average ◽  
Minimum Mean Square Error ◽  
Distribution Network ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
First Order ◽  
Basic Work ◽  
The Impact

Following the basic work conducted by Lee et al. [(1997a), The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93–102; (1997b), Information distribution in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558] and using two first-order autoregressive AR(1) models, respectively, this paper provides three quantitative models of the bullwhip effect of the two-level supply chain distribution network consisting of a single manufacturer and two retailers. The paper assumes that two retailers adopt the order point method, uses three kinds of demand forecasting technology, i.e., moving average, exponential smoothing and minimum mean square error methods, respectively, provides three corresponding models for analyzing the impact of bullwhip effect of two-level supply chain distribution network. At the same time, this paper compares and analyzes the results of the three models through simulation.

Demonstrating Nutrient Cost Gradients: A Brooklyn Case Study

2014 ◽  
Vol 84 (5-6) ◽  
pp. 244-251 ◽  
Author(s):  
Robert J. Karp ◽  
Gary Wong ◽  
Marguerite Orsi
Keyword(s):  
Low Income ◽  
Energy Content ◽  
Food Selection ◽  
The United States ◽  
United States Department ◽  
Micronutrient Deficiencies ◽  
Caloric Density ◽  
Low Income Families ◽  
The Cost ◽  
The Impact

Abstract. Introduction: Foods dense in micronutrients are generally more expensive than those with higher energy content. These cost-differentials may put low-income families at risk of diminished micronutrient intake. Objectives: We sought to determine differences in the cost for iron, folate, and choline in foods available for purchase in a low-income community when assessed for energy content and serving size. Methods: Sixty-nine foods listed in the menu plans provided by the United States Department of Agriculture (USDA) for low-income families were considered, in 10 domains. The cost and micronutrient content for-energy and per-serving of these foods were determined for the three micronutrients. Exact Kruskal-Wallis tests were used for comparisons of energy costs; Spearman rho tests for comparisons of micronutrient content. Ninety families were interviewed in a pediatric clinic to assess the impact of food cost on food selection. Results: Significant differences between domains were shown for energy density with both cost-for-energy (p < 0.001) and cost-per-serving (p < 0.05) comparisons. All three micronutrient contents were significantly correlated with cost-for-energy (p < 0.01). Both iron and choline contents were significantly correlated with cost-per-serving (p < 0.05). Of the 90 families, 38 (42 %) worried about food costs; 40 (44 %) had chosen foods of high caloric density in response to that fear, and 29 of 40 families experiencing both worry and making such food selection. Conclusion: Adjustments to USDA meal plans using cost-for-energy analysis showed differentials for both energy and micronutrients. These differentials were reduced using cost-per-serving analysis, but were not eliminated. A substantial proportion of low-income families are vulnerable to micronutrient deficiencies.

Monitoring the Impact of Family Health Benefits for Low-Income Families

2001 ◽  
Author(s):  
Trish Livingstone ◽  
Lisa Lix ◽  
Mary McNutt ◽  
Evan Morris ◽  
William Osei ◽  
...  
Keyword(s):  
Low Income ◽  
Health Benefits ◽  
Family Health ◽  
Low Income Families ◽  
The Impact

THE IMPACT OF ZONATION SYSTEM POLICY IN THE PPDB ON STUDENTS' OBTAINING AT SMA NEGERI 8 SURABAYA

2020 ◽  
Vol 4 (3) ◽  
pp. 525
Author(s):  
Idawati Idawati
Keyword(s):  
Junior High School ◽  
Low Income ◽  
Educational Background ◽  
Quantitative Approach ◽  
National Examination ◽  
New Students ◽  
The Impact ◽  
New Student ◽  
Academic Year ◽  
Descriptive Method

This research was conducted by using a descriptive method with a quantitative approach. The quantitative approach was chosen to be tested theories by examining and measuring variables in the form of relationships, differences, influences, contributions, and the others. The research was carried out by describing the students acquisition data on the new student admission (PPDB) using zoning system based on the academic year 2019-2020 and the student acquisition data on the academic year PPDB 2018-2019 as a comparison. Based on the results of the study, the new students of PPDB using zoning system was considered lower in terms of economic and educational background of parents. There were more parents with less education (elementary & junior high school) in the zoning system than in the rayon system, whereas parents with higher education in the zoning system were fewer than the rayon system.  Likewise, in terms of income, there were more people with the low income in the zoning system than in the rayon system, and those having high income were fewer than in the rayon system. The study showed that the intelligence and the result of National Examination Score (NUN) in the zoning system is lower than in the rayon system. The intelligent level of the students in the zoning system is mostly dominated by the scores under 90-109, while in the rayon system were dominated by the scores above 90-109.  The National Examination Scores (NUN) in the zoning system were evenly distributed from a range of scores 0 to 30, while in the rayon system the scores were dominated by a range of scores 28-30, with the lowest score 24.

The Impact of a Potential Public/Private Initiative on a Defined Region of the South Suburban Chicago Metropolitan Area

2019 ◽  
pp. 14-32
Keyword(s):  
Supply Chain ◽  
Economic Impact ◽  
Impact Analysis ◽  
Census Data ◽  
Economic Benefits ◽  
Local Economy ◽  
Market Area ◽  
State And Local ◽  
The University ◽  
The Impact

The university is considered one of the engines of growth in a local economy or its market area, since its direct contributions consist of 1) employment of faculty and staff, 2) services to students, and supply chain links vendors, all of which define the University’s Market area. Indirect contributions consist of those agents associated with the university in terms of community and civic events. Each of these activities represent economic benefits to their host communities and can be classified as the economic impact a university has on its local economy and whose spatial market area includes each of the above agents. In addition are the critical links to the University, which can be considered part of its Demand and Supply chain. This paper contributes to the field of Public/Private Impact Analysis, which is used to substantiate the social and economic benefits of cooperating for economic resources. We use Census data on Output of Goods and Services, Labor Income on Salaries, Wages and Benefits, Indirect State and Local Taxes, Property Tax Revenue, Population, and Inter-Industry to measure economic impact (Implan, 2016).

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