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

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.

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Quantifying the Bullwhip in Multi-location Supply Chain Network: The Impact of Demand Forecasting

BULMIM Journal of Management and Research ◽

10.5958/2455-3298.2016.00006.4 ◽

2016 ◽

Vol 1 (1) ◽

pp. 55 ◽

Cited By ~ 18

Author(s):

Sunil Sharma ◽

Prabhat Mittal

Keyword(s):

Supply Chain ◽

Demand Forecasting ◽

Supply Chain Network ◽

The Impact

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

Sustainability ◽

10.3390/su13126884 ◽

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

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595916500160 ◽

2016 ◽

Vol 33 (03) ◽

pp. 1650016 ◽

Cited By ~ 4

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.

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

International Journal of Business and Applied Social Science ◽

10.33642/ijbass.v5n2p2 ◽

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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