Impact of crisis and risk management strategies on sales and costs (the case of COVID-19)

by | Nov 16, 2020 | 0 comments

Application Track:

Theme Driven

Code:

REACH-2020-THEMEDRIVEN-ITI_7.2

Domain:

Proposed by:

ITI

Entity Logo:

Summary of the entity:

Instituto Tecnológico de Informática (ITI) is a Research & Technology centre specialized in ICT, located in Valencia, Spain. ITI research and innovation activity is developed by a team of 200 highly-skilled researchers and technicians, focused on 6 key digital enablers around the Data Cycle: Cyber connectivity, Cyber-Physical Systems, Computing Infrastructures & Platforms, Big Data Analytics, Optimization Technologies, and Artificial Intelligence. All this knowledge and experience is brought to the industrial and public sector through a range of services: Access to infrastructure and technology platforms, Access to specialist expertise on digitisation & applications, Collaborative research for Industry needs, Demonstration of best practices, Training and Education, Showcase technologies in pilot factories, and Support experimentation in real-life environments.

ITI provides a Big Data Space offering infrastructure, tools and data for research and experimentation with Big Data Technologies, which has been awarded with the BDVA i-Space Silver label and is a key infrastructure for the Data Cycle Hub. The Data Cycle Hub (DCH, https://thedatacyclehub.com) is the reference one-stop shop DIH in the Valencia region to foster data driven and artificial intelligence-based innovation. The objective is to bridge the gap between research and industry, specifically SMEs, providing innovative solutions and services that require advanced data analytics, automatic learning and artificial intelligence. The DCH is one of the 3 Spanish members of the European Network of Artificial Intelligence DIHs (https://ai-dih-network.eu), and was one of the 5 finalists to the DIHNET DIH Champions Challenge 2019.

Summary of the challenge:

The challenge is to develop a crisis and risk management tool able to simulate the impact of the safety measures, protocols and risks on expected sales and costs, over historical sales and inventory data and expected consumption trends.

Description:

Stakeholders: Retailers, Wholesalers, Customers

Description

The challenge is to develop a data-based decision-making tool for analysing the impact of crisis and risk management strategies in the value chain of a retailer, so that the most cost-effective ones could be identified and/or adequate provisions could be prepared. For instance, in the case of the COVID19 crisis, this tool should enable retail managers: (1) to characterise the different safety measures and protocols that are impacting their value chain processes (e.g. logistics restrictions, in-store cleaning protocols, protective equipment and/or disinfectant for staff and customers, ventilation requirements, new protective package for items, etc.); (2) to identify the potential risks that could emerge during the crisis (e.g. increased absenteeism due to health issues or restrictions, supplier closure or temporal lockdown, inventory, sales reduction in non-essential items, etc.); (3) map and quantify the impact of these safety measures and risks to different areas or variables (e.g. store operation costs, product cost structure, product inventory, logistics costs, staff costs/availability, expected sales, etc.); (4) with this information, the tool should enable to simulate different scenarios to evaluate how the selection of strategies and variation of parameters would impact incomes/costs. Relevant data that could be considered for simulating the scenarios could be: sales and inventory data from normal operation periods, consumption statistics, consumer buying patterns during crisis (key product demands, see sub-challenge 1), etc.  For retailers, this tool will be a key crisis and risk management tool that will facilitate better costs planning and adaptation when facing high uncertainties.

Data:

  • REACH Data Providers
    • MIGROS: Sales data. Provides aggregated sales transaction data from MIGROS stores in Turkey, including fresh produces (store, product, sales amount, sales quantity, date).
    • SONAE: Customer transactions, Customer DNA. Two datasets from SONAEMC stores in Portugal, that provide sales transaction data and relevant segmentations on customer level for the identification of different profiles.
  • Other datasets: Other datasets could be used if needed to improve the building of the predictive model. For instance:
    • Statistics on consumption demand, at National or Global level, focusing on crisis or emergency periods, like COVID19 or previous ones.
    • Social media (identification of major buying keywords)
    • Google Mobility index (correlation with sales)
    • Historical Weather conditions (correlation with seasonal trends).

Expected outcomes:

  • Improve decision-making of the retailers in case of crisis situations
  • Tool for defining and quantifying the impact of COVID19 crisis management strategies and potential risks on retailer processes
  • Data-based simulator of the impact of crisis management strategies on sales and inventory

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