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Hackathon Retail 4.0

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What is Hackathon Retail 4.0? It is an initiative sponsored by Upcoming Energies powered by Galp in partnership with EIT InnoEnergy and Microsoft and built by imatch to display the role of technology in solving global challenges concerning the reduction of carbon emissions. It is an online competition where innovators will compete to create a tech solution.   How much does it cost? Hackathon Retail 4.0 is free for anyone who wants to participate.   Who can participate in the challenge? The Hackathon is open to anyone with a creative or technical profile. People from different fields and with experience in data related fields are more than welcome to participate. Diverse teams usually present more robust solutions.   How do I form a team? All your team members should register individually as innovators in the hackathon page. You must first create a project and afterwards you can add your team members as project members using their username or registered email. If you don't have a team and would like to be part of a project you can use the Matchmaking feature to find a team. Find out more in the "Resources" section.   What is the size of each team? If you have a team, it should have a maximum size of 5 participants to be eligible to participate.   Is there any mentorship available? Yes. There will be mentors available to provide feedback throughout the challenge. We will provide you further information on how to contact them.  Are the other participants apart from my team able to see my project details? Not during the competition. The published projects and draft projects are only visible to project members and judges, thus no one is able to copy or clone your project resources to use on their submission. After the competition is finished and the results are revealed, the project details become visible to anyone who visits the platform.   How can I submit a project? After your registration, you'll find a button to “Create a project”. Add a name, a description and who will join you on your team. It will also be on your project page where you will showcase some of the deliverables required throughout the Hackathon. Don't forget to click on "Publish" to have your project visible to the jury and organization. You can do as many changes as you need until the submission deadline.   Is English the official language? All the communication for the Hackathon is done in English and the challenge is indeed a global initiative so it is strongly recommended to use English in your project. However, we won't let language barriers stop you in developing a great solution. We will grant exceptions to present your project and pitch it to the jury in Portuguese. However, all exceptions will need to be approved by the organization. Email us at: bb@imatch.pt  How are the winners selected? The w winners are selected by a group of judges selected by the organization. They will evaluate the project submissions based on the criteria defined and on the project pitch session. Each judge will have a limited amount of KAI tokens to invest and back the eligible projects. The final result is going to be produced based on the amount of KAIs received by each project. They will consider: CRITÉRIOS DE AVALIAÇÃO POR DEFINIR  Who owns the project's intellectual property? The team which develops the project is the owner of the project and its IP. We do encourage sharing of code with others in the community or making code open source but it is not required to participate or be eligible for the prizes.   Are there any prizes in Hackathon Retail 4.0? Yes. The winners of the 2 categories will win a money prize: 10,000€ to each.  How will the prize be paid? Winners will be informed by the Organization about their Prize via email, at the latest, 15 days after the challenge, to their accounts communicated upon Registration on the taikai.network.   When will the winners be announced? Hackathon Retail 4.0 winners will be announced on the 1st of June of 2022.   Is there a dataset available?Galp will not provide a specific data set, you can use all available public information, including this one <a href="http://ai.stanford.edu/~jkrause/cars/car_dataset.html" target="_blank">http://ai.stanford.edu/~jkrause/cars/car_dataset.html</a>  Still have questions? If you have any technical questions about the TAIKAI platform, please reach out to: support@taikai.network If you have any questions about the challenge or its rules or want to get in touch with the organization, please reach out to: bb@imatch.pt

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<h2 style="text-align:center">Welcome to Hackathon Retail 4.0!</h2> <h3 style="text-align:center">We're looking for innovators that will shape the future of retail.</h3> Galp operates a total of 1500 service stations in Portugal and Spain, most of them with a convenience store that services drivers on the road. It’s part of our purpose to continuously improve our business and provide better experiences to the customers.  <h2 style="text-align:left"> </h2><h2 style="text-align:left">  The Goal   </h2> With this Hackaton, we are challenging the community to propose solutions to specific problems and use cases that we think could improve our typical customer journey in the service stations. We have 2  challenges that will be further detailed in the following sections.  To tackle these challenges, participants can expect support from Galp’s Commercial BU, Retail 4.0 team, IT&amp;Digital and Data Office, as well as a series of benefits and prizes supported by our Open Innovation program <a href="http://upcomingenergies.galp.com" target="_blank">Upcoming Energies</a>. <h2> </h2><h2>💡 The Challenges</h2> Challenge #1: Car Wash services proposal based on AI, weather rules and computer visionLeverage Artificial Intelligence algorithms to visually identify the level of dirtiness of the customer’s cars (one or more) to grade a vehicle so Galp can suggest the customer car wash services. <img src="https://taikai.azureedge.net/XZEcQ6m_ZX9KaL_nCPMW-NfuFnhzqmUI9qhGsaEOc7c/rs:fit:800:0:0/aHR0cHM6Ly9zdG9yYWdlLmdvb2dsZWFwaXMuY29tL3RhaWthaS1zdG9yYWdlL2ltYWdlcy82MmIyZTgxMC1iMDM1LTExZWMtOWI4OC1hNzM5MWM3ZmEzZTFDYXJXYXNoLmpwZw" class="fr-fic fr-dib" style="width:423px" /> Galp has a wide network of service stations in the Iberian Peninsula ( aprox. 1500), out of which several offer car washing services to visiting customers. It is our goal to increase the overall usage of this specific service, so we must find innovative and interactive ways to encourage drivers to wash their cars once they enter the stations. We challenge the hackers community to develop an algorithm based on Machine Learning that would be able to identify if a car is dirty once it enters the forecourt of the station. This algorithm would feed off Computer Vision datasets and prediction / historic weather rules to promote carwash services to the final client.  👉 Find out more details about this challenge on the <a href="https://taikai.network/en/galp/hackathons/retail40/categories" target="_blank">Categories tab</a> Challenge #2: Optimize in-store product assortments and estimate potential sales volumeLeverage Artificial Intelligence algorithms to identify trends, handle historical data and combine various data sets of different origins to optimize in-store product assortments and estimate potential sales volume. <a href="https://taikai.network/en/galp/hackathons/retail40/categories" target="_blank"><img src="https://taikai.azureedge.net/aWc9aF8ZAbHiMGus1_8KrvkW1HLZeY0HR-oI4Xbt3-s/rs:fit:800:0:0/aHR0cHM6Ly9zdG9yYWdlLmdvb2dsZWFwaXMuY29tL3RhaWthaS1zdG9yYWdlL2ltYWdlcy83NzVkZTQ0MC1iMDM1LTExZWMtOWI4OC1hNzM5MWM3ZmEzZTFTdG9yZU9wdCAoMSkuanBn" class="fr-fic fr-dib" style="width:421px" /></a> With such a great deal of service stations in the Iberian Peninsula , it gets hard to understand which products sell the most, at what time and in which shops, where should they be placed in-store, etc. The challenge gets even greater if we try to do successful forecasting considering other external factors such as weather, time of the year, or football matches to optimize planning and dynamically change the products positioning. Teams will need to use publicly available data (e.g.: stocks, sales, product rotation, etc) to tackle this problem. We challenge the hackers community to come up with a solution that is capable of treating inputs from sales data, stocks, and planograms to:<ul><li>Dynamically identify what are the products that sell the most, and correlate these trends with their in-store positioning (planogram), price, and margins;</li><li>Associate periodic sales trends with temporary events such as football matches, time of the year, weather, daytime, and other external factors that might affect sales;</li><li>Predict future sales volumes;</li><li>Based on these inputs, propose planograms and prices that optimize sales at each time;</li><li>Consider the introduction of new products based on external data such as market trends.</li></ul> 👉 Find out more details about this challenge on the <a href="https://taikai.network/en/galp/hackathons/retail40/categories" target="_blank">Categories tab</a><h2> </h2> <h2>🥷 Audience</h2> Anyone that is passionate about solving problems: you can be a coder, a developer, an IT market professional, or a university student.<h2> </h2> <h2>🏆 Prizes</h2> Winner of Challenge #1: 10.000€Winner of Challenge #2: 10.000€ <h2>📁 Deliverables</h2> You must register on the hackathon and start a project page by the 29th April, only at the end of May it is necessary to submit the completed project. By the 23rd May, you will have to publish on the platform, a detailed Project Description and the following deliverables: <ul style="-webkit-font-smoothing:antialiased;box-sizing:border-box;clear:left;list-style-type:disc;color:rgb(36, 36, 36);font-family:&quot;Segoe UI&quot;, system-ui, &quot;Apple Color Emoji&quot;, &quot;Segoe UI Emoji&quot;, sans-serif;font-size:14px;font-style:normal;font-variant-ligatures:normal;font-variant-caps:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:start;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;-webkit-text-stroke-width:0px;background-color:rgb(255, 255, 255);text-decoration-thickness:initial;text-decoration-style:initial;text-decoration-color:initial"><li style="-webkit-font-smoothing:antialiased;box-sizing:border-box;font-style:inherit;font-weight:inherit">All the assets that comprise the submission (code assets, configurations, documentation, etc) must be delivered on a branch of the github repository with a pull request submitted on branch main assigned to the team.</li><li style="-webkit-font-smoothing:antialiased;box-sizing:border-box;font-style:inherit;font-weight:inherit">At least the following information is required:<ul style="-webkit-font-smoothing:antialiased;box-sizing:border-box;clear:left;list-style-type:disc;font-style:inherit;font-weight:inherit"><li style="-webkit-font-smoothing:antialiased;box-sizing:border-box;font-style:inherit;font-weight:inherit">A brief description of the layout of the folders and their purposes / what assets they comprise</li><li style="-webkit-font-smoothing:antialiased;box-sizing:border-box;font-style:inherit;font-weight:inherit">A brief description of the asset itself</li><li style="-webkit-font-smoothing:antialiased;box-sizing:border-box;font-style:inherit;font-weight:inherit">A documentation (markdown, restructured text, etc) of the whole submission detailing what was the approach to solving the challenge</li></ul></li><li style="-webkit-font-smoothing:antialiased;box-sizing:border-box;font-style:inherit;font-weight:inherit">The documentation delivered inside the repository
should present this following topics:1. Delimitation about the problem2. Hypothesis of solution3. Team members4. Team strategy5. Technologic pre requisites6. Used data sets7. How the solution can be used8. Metrics and results 1. Delimitation about the problemThe team vision about the challenge selected and why
the challenge was selected by the team 2. Hypothesis  of solutionDetailed description about how the challenge can be
solved in the team perspective 3. Team membersWho and how the team organize ours roles and
responsibilities 4. Team strategyDetailed description about the plan of resolution of
the challenge 5 .Technologic pre-requisitesWhat kind of technologic tools is needed for the
solution (what galp needs to validate the functionality of de deliverable), all
the tools used should be free of charges to enable Galp to evaluate, if some
tools isn't available the project will be disqualified. 6. Used data setsWhat data sets are used and where they can be picked.
(attention to EULA and legal use about the data, ilegal data set will be
disqualified) 7. How the solution can be usedA manualized application of the final product with use
example. 8. Metrics and resultsHow the team evaluate the solution delivered and how
the metrics can be afford</li></ul><h2> </h2><h2>  Evaluation Criteria</h2><br style="box-sizing:inherit;color:rgb(29, 28, 29);font-family:Slack-Lato, Slack-Fractions, appleLogo, sans-serif;font-size:15px;font-style:normal;font-variant-ligatures:common-ligatures;font-variant-caps:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:left;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;-webkit-text-stroke-width:0px;background-color:rgb(248, 248, 248);text-decoration-thickness:initial;text-decoration-style:initial;text-decoration-color:initial" />1. The project reached the final objective of the challenge?<br style="box-sizing:inherit;color:rgb(29, 28, 29);font-family:Slack-Lato, Slack-Fractions, appleLogo, sans-serif;font-size:15px;font-style:normal;font-variant-ligatures:common-ligatures;font-variant-caps:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:left;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;-webkit-text-stroke-width:0px;background-color:rgb(248, 248, 248);text-decoration-thickness:initial;text-decoration-style:initial;text-decoration-color:initial" />2. The project proves a hypothesis?<br style="box-sizing:inherit;color:rgb(29, 28, 29);font-family:Slack-Lato, Slack-Fractions, appleLogo, sans-serif;font-size:15px;font-style:normal;font-variant-ligatures:common-ligatures;font-variant-caps:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:left;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;-webkit-text-stroke-width:0px;background-color:rgb(248, 248, 248);text-decoration-thickness:initial;text-decoration-style:initial;text-decoration-color:initial" />3. The experiment is reproducible (Galp can execute the code and achieve the same results)?<br style="box-sizing:inherit;color:rgb(29, 28, 29);font-family:Slack-Lato, Slack-Fractions, appleLogo, sans-serif;font-size:15px;font-style:normal;font-variant-ligatures:common-ligatures;font-variant-caps:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:left;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;-webkit-text-stroke-width:0px;background-color:rgb(248, 248, 248);text-decoration-thickness:initial;text-decoration-style:initial;text-decoration-color:initial" />4. The results of the project are measurable?<br style="box-sizing:inherit;color:rgb(29, 28, 29);font-family:Slack-Lato, Slack-Fractions, appleLogo, sans-serif;font-size:15px;font-style:normal;font-variant-ligatures:common-ligatures;font-variant-caps:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:left;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;-webkit-text-stroke-width:0px;background-color:rgb(248, 248, 248);text-decoration-thickness:initial;text-decoration-style:initial;text-decoration-color:initial" />5. The solution can be scalable?

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<h2> </h2><h3>You can use all public data available!</h3><h3> </h3><h3>Here are some examples:</h3>    <a href="https://data.europa.eu/data/datasets?categories=tran&amp;page=1&amp;locale=en" target="_blank">European Data Set</a>     <a href="http://ai.stanford.edu/~jkrause/cars/car_dataset.html" target="_blank">Cars Data Set</a>

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In-Store Optimization

Smart Car Wash Recommendation Service

Hackathon Retail 4.0

Organização

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Challenge #1

Smart Car Wash Recommendation Service

🏆 10.000€ 🏆

Challenge #2

In-Store Optimization

🏆 10.000€ 🏆

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