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.

Modelling resilient fashion retail supply chain strategies to mitigate the COVID-19 impact

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
P.R.S. Sarma ◽  
Aalok Kumar ◽  
Nishat Alam Choudhary ◽  
Sachin Kumar Mangla
Keyword(s):  
Supply Chain ◽  
Capacity Constraints ◽  
Content Type ◽  
Online Platforms ◽  
Aspiration Levels ◽  
The Future ◽  
Optimisation Model ◽  
Fashion Retail ◽  
Online Stores ◽  
The Impact

PurposeThis paper aims to develop supply chain strategies for the fashion retail supply chain (FRSC), likely to be disrupted by the current pandemic (COVID-19) under physical and online retail stores. The resilient retail supply chain design is proposed under budget allocation and merchandise capacity constraints.Design/methodology/approachThis paper utilises the theory of constraint (ToC) and goal programming (GP) to address the COVID-19 impact on FRSC. The budgetary and capacity constraints are formulated with a constraint optimisation model and tested with six different priorities to deal with the physical and online stores. Next, all priorities are developed under different FRSC business scenarios. The ToC-GP-based optimisation model is validated with one of the Indian fashion retail supply chains.FindingsThe proposed optimisation model presents the optimal retailing strategies for selling fashion goods over physical and online platforms. The multiple scenarios are presented for developing trade-offs among different strategies to maximise the retailer's merchandise performance. This paper also highlighted the strategic movement from high merchandise density stores to low merchandise density stores. This implies a reduction of sales targets and aspiration levels of both online and physical fashion stores.Research limitations/implicationsThe proposed model is validated with one of the fashion retailers in India. Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.Practical implicationsThis research helps fashion retail supply chain managers deal with consumer demand uncertainty over physical and online stores in pandemic times. Limitation: Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.Originality/valueThis is the first study that considered the impact of COVID-19 on the retail fashion supply chain. The effect of physical and online platforms is mainly discussed from consumer marketing perspectives, but an inventory and resilience perspective is missing in earlier studies. The role of merchandise planning is highlighted in this study.

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 Standardisation of Naval Containerised Modules - Enabling Interoperability Across NATO

2019 ◽  
Author(s):  
H. K. Cole ◽  
J. Alvarez-Mari
Keyword(s):  
Supply Chain ◽  
Design Process ◽  
North Atlantic ◽  
Platform Design ◽  
Certification Standards ◽  
The Future ◽  
Future Work ◽  
The Impact

This paper explores the work carried out by the Naval Design Partnering (NDP) team to propose a new North Atlantic Treaty Organisation (NATO) standard covering naval containerised modules outfitted with mission systems. The purpose of the Standard is to facilitate interoperability of naval modules across NATO navies, and to future proof the design process both for supply-chain and vessel designers. Drawing upon the experience of the offshore world in using containerised modules, as embodied in the current certification standards, the Standard provides a uniform set of safety-related construction requirements, standardised interfaces, and guidance for platforms to enable the deployment of naval modules. The paper covers the overall philosophy behind the Standard, the background research conducted, and the rationale behind the requirements chosen. Finally, it explores the impact of the Standard on platform design, the key enablers for the deployment of naval modules, and the future work to develop international inter-operability.

Artificial Intelligence Applications in Tourism

2011 ◽  
pp. 241-247 ◽  
Author(s):  
Carey Goh ◽  
Henry M.K. Mok ◽  
Rob Law
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Demand Forecasting ◽  
Tourism Industry ◽  
Tourism Demand ◽  
Investment Planning ◽  
Data Mining Techniques ◽  
Travel And Tourism ◽  
The Future ◽  
Multiplier Effects

The tourism industry has become one of the fastest growing industries in the world, with international tourism flows in year 2006 more than doubled since 1980. In terms of direct economic benefits, United Nations World Tourism Organization (UNWTO, 2007) estimated that the industry has generated US $735 billion through tourism in the year of 2006. Through multiplier effects, World Travel and Tourism Council (WTTC, 2007) estimated that tourism will generate economic activities worth of approximately US $5,390 billion in year 2007 (10.4% of world GDP). Owing to the important economic contribution by the tourism industry, researchers, policy makers, planners, and industrial practitioners have been trying to analyze and forecast tourism demand. The perishable nature of tourism products and services, the information-intensive nature of the tourism industry, and the long lead-time investment planning of equipment and infrastructures all render accurate forecasting of tourism demand necessary (Law, Mok, & Goh, 2007). Past studies have predominantly applied the well-developed econometric techniques to measure and predict the future market performance in terms of the number of tourist arrivals in a specific destination. In this chapter, we aim to present an overview of studies that have adopted artificial intelligence (AI) data-mining techniques in studying tourism demand forecasting. Our objective is to review and trace the evolution of such techniques employed in tourism demand studies since 1999, and based on our observations from the review, a discussion on the future direction of tourism research techniques and methods is then provided. Although the adoption of data mining techniques in tourism demand forecasting is still at its infancy stage, from the review, we identify certain research gaps, draw certain key observations, and discuss possible future research directions.

The impact of information and communication technologies in fish consumption in Portugal: building a support for the coming generations

2020 ◽  
pp. 855-872
Author(s):  
Valentina Chkoniya ◽  
Ana Oliveira Madsen ◽  
Teresa Coelho
Keyword(s):  
Supply Chain ◽  
Information And Communication Technologies ◽  
Fish Consumption ◽  
Communication Technologies ◽  
Point Of Sale ◽  
The World ◽  
The Future ◽  
Shopping Experience ◽  
Information And Communication ◽  
The Impact

Information and Communication Technologies (ICTs) are powerful tools in an extremely unstable and turbulent world, being a vital weapon in an era of electronic communication, full of unpredictability and entropy. Portugal is Europe's leader in consumption of fish and ranks third in the world. It is with this historical, economic and food galaxy interpretation of the world that we have decided to study the fish consumption supply chain in Portugal, distributing and analyzing 2 different surveys on purchase and consumption behaviors. 1393 respondents participated in surveys, one survey being for the general of the population and another one specifically for generations Y and Z. Being ICTs so vital to young people and being generation Y and Z the future in consumption, we have focused our study on how to build a support for the coming generations. To do that we have design communication strategies for fishing sector, including point-of-sale communication, underlining the issue of shopping experience, which in the case of young consumers is, very closely linked to ICT, based on creating sustained value for society underpinned by a relation of cooperation and proximity. Following Toffler's permanent adaptation survival rule, it is vital to the food supply chain (in our case, fish) to rapidly understand that the future of consumption is in the hands of the "Z/Post-Millennials" generation, meaning that product, place, price and promotion have been replaced by Ettenson's Solutions, Access, Value and Education.

Patterns of seasonality and tourism demand forecasting

2016 ◽  
Vol 23 (5) ◽  
pp. 1011-1027 ◽  
Author(s):  
Anna Serena Vergori
Keyword(s):  
Demand Forecasting ◽  
Tourism Demand ◽  
Economic Activities ◽  
Negative Effects ◽  
Tourist Destinations ◽  
Points Of View ◽  
The Impact ◽  
Tourism Flows ◽  
Overnight Stays

Seasonality is a phenomenon that affects the vast majority of tourist destinations. The negative aspects of seasonality have been widely discussed from economic, social and environmental points of view. On the contrary, the unreliability of tourism demand forecasts is rarely listed among the negative effects of seasonality. This is despite the importance of the quality of forecasts for the planning of economic activities. This article evaluates the impact of different patterns of seasonality on tourism demand forecasting in the light of different volume of tourism flows. With this aim in mind, the monthly tourist overnight stays in four European countries – namely Austria, Finland, Portugal and Netherlands – are analysed for the period January 1990–December 2014. Data show both one-peak and two-peak seasonality. Results highlight that the stronger seasonality is, the less reliable forecasts are.

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