#NordicDAIRAwards
INNOVATE | SHARE | INSPIRE
An award presented by the Nordic community to nominated individuals and organisations for their exceptional work done to innovate through data, drive data and AI transformation forwards, foster talent, promote diversity and inclusion, selflessly share their knowledge with others and inspire young generations and other practitioners to follow the same path.
The award will go to an organization that has shown a great example of moving their business to the next level and disrupting their industry by identifying new technologies, using AI, automation and machine learning, applying new skills, and operationalizing processes.
The logistics industry is experiencing a paradigm shift, moving from a physical problem-solving to a digital opportunity-seeking mindset. A.P. Moller – Maersk is playing a critical role in transforming the industry through its digital native technology strategy to connect and simplify global supply chains. Historically, logistics professionals have experienced a lack of visibility and reliability across their fragmented supply chains. Maersk NeoNav⢠addresses this major pain point by breaking down the siloes across all supply chain participants, enabling execution through a single platform that is accountable for all mission-critical supply chain functions. It is the first-of-its-kind integrated solution that brings together data across the 4 core supply chain domains: Logistics, Demand, Supply and Inventory Management, converging the physical and digital to orchestrate supply chains end-to-end.
Data and AI firmly sit at the heart of Maerskâs innovation strategy, and Maersk NeoNav⢠takes advantage of a data harmonization engine, advanced analytics, machine learning and AI to provide users with the insights to make objective trade-off decisions and optimize their supply chains end-to-end. Through its unique approach, Maersk NeoNav⢠enables logistics professionals to reduce supply chain costs, increase reliability and predictability as well flexibility and agility.
How Maersk NeoNav⢠works
⢠Advanced analytics enable actionable insights, directly supporting strategy and commercial objectives
⢠Planning and sensing capabilities prevent stock-outs and enable better management of inventory
⢠Access to industry-wide best practice and technology eliminate the need for large investments
⢠A single source of truth across internal and external parties improves visibility and breaks down silos
⢠Insights-powered smart decision making reduces waste in global supply chains
The award will go to an organisation that has deployed use cases for enterprise-scale machine learning and has industrialized AI to automate, secure, and optimize data-driven decision making and/or applications.
The “Computer Vision Platform” is an intuitive platform service to scale computer vision models across Stora Enso effectively. You can share existing computer vision models, do quick Proof of Concepts (PoCs), finetune existing models for your needs, train new models using your own data, and deploy solutions to your local environment. Herewith, we avoid double work, reduce time and costs for upscaling and allow non-data scientists to benefit of computer vision by using the platform.
Project Background and Overview, Strategic Objectives, Key Challenges, Innovative Solution, Positive Impact or Value:
Every control room in every mill is full of screens that must be observed 24/7. Today´s operators are overwhelmed by a high number of displays and the information they provide from all different parts of the process. The need for manual observation forces the operators to stay in the control room and limits their mobility. It also hinders the development towards more autonomous processes, especially as the video display monitoring isnât automated yet, at least not in a coordinated way and in large scale.
Noticing problems in video feeds is very difficult when monitoring multiple screens in the control rooms. Machine vision applications improve this greatly, but until now, cooperation within Stora Enso is limited.â
Many of Stora Ensoâs operational activities already benefit from having digitalisation and automation solutions. To continue to increase business value and ensure we donât limit improvements only to a specific mill operation, we intend to enable and scale computer vision across the whole organisation.
We have recognized that a lot of computer vision use cases are highly scalable but still need additional adaptation to be able to be scaled from one location to the other. This is time consuming and costly, when done fully manual by data-scientists.
The “Computer Vision Platform” is an intuitive platform service to scale computer vision models across Stora Enso effectively.
Users can easily deploy or fine-tune the existing models from the solution library of the platform to their own environment. They can also onboard and share the models that are already running at their site to the solution library of the platform. This onboarding of models allows fast and cost-efficient development and scale-up of computer vision applications.
At the same time it allows Stora Enso to maintain computer vision models centrally and allows non-data scientists to use computer vision as a self-service.
The service is available to all Stora Enso employees.
The platform runs centrally on Stora Ensoâs Azure cloud, which reduces the overall costs and makes the applications easier to develop and use.
The Computer Vision Platform helps to coordinate the development and scaling of computer vision models. It leads to fast and cost-effective development and use.
The standardized approach of applying AI to mill camera systems enables us to share already developed algorithms more efficiently.
Owning the platform and the developed models reduces the license costs for our mills and helps to widen Stora Enso´s competitive advantage in digitalization.
Computer Vision and its development is well in line with Stora Enso´s roadmap towards the âDigital mill 2030â.
This award will go to an organisation that can demonstrate a truly industry-leading and ROI-driven case study that brings something new to its users, has enhanced customer experience, improved operational efficiency or changed business models by developing and deploying AI/ML products.
IKEA is doing a tremendous work with data and analytics. From advancing its personalized recommendations, thus increasing the order value by 2%, to implementing 3D AI imaging allowing the customers to design their homes online. It also keeps working on trying to make privacy settings more intuitive and explainable to its end users.
Project Background and Overview, Strategic Objectives, Key Challenges, Innovative Solution, Positive Impact or Value:
In a future connected home security is paramount. IKEA does not only use AI to provide seamless experience to its users, allowing them to design their home online, but works a lot with the privacy and explainability of different security settings. Finally it keeps exploring various AI projects and ethics implications on AI used at a connected home (like the Digital Buddy by FIELD.SYSTEMS a 3D AI/AR avatar that would help protect your interests online) through Space10, its innovation hub.
This award will go to an organisation that can demonstrate a truly industry-leading and ROI-driven case study of deploying predictive and prescriptive analytics/statistical models to gain valuable insights from data and improve operational efficiency and results.
The logistics industry is experiencing a paradigm shift, moving from a physical problem-solving to a digital opportunity-seeking mindset. A.P. Moller – Maersk is playing a critical role in transforming the industry through its digital native technology strategy to connect and simplify global supply chains. Historically, logistics professionals have experienced a lack of visibility and reliability across their fragmented supply chains. Maersk NeoNav⢠addresses this major pain point by breaking down the siloes across all supply chain participants, enabling execution through a single platform that is accountable for all mission-critical supply chain functions. It is the first-of-its-kind integrated solution that brings together data across the 4 core supply chain domains: Logistics, Demand, Supply and Inventory Management, converging the physical and digital to orchestrate supply chains end-to-end.
Connected to the largest supply chain ecosystem and data set, Maersk NeoNav⢠empowers users to ship smarter, cheaper and greener through the application of predictive analytics and machine learning. It provides users with the ability to consume inventory, capacity, production, emission and consumption data to assess true supply chain impacts of transportation events by filtering out the noise across signals. Maersk NeoNav⢠even has the ability to run automatic resolutions to identified supply chain problems.
Maersk NeoNav⢠is a unique platform solution to digitise, manage, automate and optimise supply chains, designed for companies with international and often complex supply chains. It brings the physical and digital part of supply chains together in a 4PL+ solution converging best-in-class logistics service, technology and network through one partner to create more transparency and resilience and orchestrate supply chains end-to-end. Driven by control tower professionals, process expertise and a large crowd-source ecosystem, Maersk NeoNav⢠is an open, neutral software platform to integrate all trading partners and data in one closed-loop to drive inventory optimization, predictive visibility and traceability. This 4PL+ solution allows companies to connect all existing business processes and relevant supply chain systems.
This award will go to an organisation that has made the most advancement in adding transparency to their AI processes.
We see a general decline in what consumers think about data collection. Consumers are sceptical and hesitant towards sharing data, and peopleâs resignation harms the market in the long-term. Traditionally, trust is a fundamental mechanism in trade and in retail specifically. Regulations in this area have been very focused on the individual, giving the individual consumer more rights. But we also need a structural approach. At ICA we work with how we can build that trust towards our customers.
Project Background and Overview, Strategic Objectives, Key Challenges, Innovative Solution, Positive Impact or Value:
At ICA we work continuously with building trust towards our customers. We have a structured approach to this through data governance. That starts with defining accountability for data i.e. conscious decisions around who is allowed to do what with which data. There is also orchestration of resources, establishing standardized processes so that we can utilize technology to raise the efficiency of our governance.
Another part is by ICAâs guiding principles of data governance that go beyond formal laws and regulations and builds on our principles and values which are very focused on ensuring that we always keep a customer focus, protecting our customers integrity and working our hardest to always provide them with the best service on the market.
Our customer surveys indicate that we are quite successful with this. 63% of our customers are generally positive to us collecting data about them so we can offer more relevant service and FĂśrtroendebarometern 2021 puts ICA at top 3 position out of all Swedish companies.
We develop our business through cross-functional teams with representation from IT, Data Management, Analytics and of course the business in their role as the data owners! As an example we have a team focusing on customer communication that is a fantastic example of collaboration between business and IT.
The business oversees the customer master data and the definition of new customer facing communication. We have a recommendation engine analyzing the customers purchasing behavior and making recommendations based on this. The decisions of what recommendations are used is taken care of by the business, whereas the technology is built by a technical team with data engineers, data architects and data scientists.
Our information security department is heavily involved in daily business ensuring that our customers integrity is protected at all times. We have specific roles called Data Protection Managers or Guardians that are an active resource tied to all development projects involving use of customer data, thereby not only ensuring compliance but making sure that the customer focus is maintained throughout the process.
According to GDPR the customer has always the right to demand an extract of all data that we keep on them and they also have the right to demand that we remove all records of them. We believe that this transparency is an important factor in our customers continued will to share their data with us. But we believe that an even more important factor is the level of improved service that our loyalty customers receive as a result of sharing data. As an example of that, our loyalty customers get concrete payback through discounts on the products that they purchase the most!
In fact, our customers are sometimes requesting whether we might be able to handle even further information about them (such as allergies or dietary preferences), so we could be providing even better service going forward. This is however sometimes conflicting with the overall laws and regulations, even if the individual customers might wish for that. This is also where we as a business need a capable information security department that can help us balance the risk of conflicts with the customers will and the valid laws.
These are questions we discuss at ICA in trying to make responsible data-driven work and AI happen in the real world. We know that AI will never be responsible if itâs just a set of principles decided in the beginning of a process or a check-list before deployment. Instead, efforts for discussing and ensuring responsibility measures for the models must be a continuous process taking place alongside the whole development process. Furthermore, responsible AI needs to be a collaborative process. To ensure transparency and accountability, we require many different perspectives to come together. Decisions around data usage and modelling cannot be made solely by the developer or data scientist, neither can it be made by the business operations alone. Instead accountability requires bringing legal, social and technical perspectives together. In practice that means people with different professional backgrounds need to work together so that risks can be discovered, analyzed and mitigated.
There is a reason to why the trust among consumers is so high for ICA as a company, and part of this is because our work in this field.
This award will go to a public sector organisation that has successfully implemented advanced analytics and AI projects in production to create a positive impact to society by creating, transforming or improving functions and services within the country in the interest of citizens.
When the corona pandemic hit Norway, the health department in our capital, Oslo, had to make high stakes decisions in a very complex situation. As we all know, having the right data and being able to make sound analyses is key in situations like these. A team in the health department was scrambled on short notice and with support of teams in other departments, they built a data solution from the ground up while delivering valuable data and insights to decision makers along the way.
Project Background and Overview, Strategic Objectives, Key Challenges, Innovative Solution, Positive Impact or Value:
The team at the department of health, together with people from other departments and a small number of consultants, worked in a true agile way where they focused on delivering value from the start through MVPs and then continuously improving through the whole pandemic (they are still doing that).
In the beginning, there were no data solutions suitable for the task but decisions had to be made by politicians. A small group of people started out with data collected manually from public sources and data in Excel sheets provided from various departments.
This very rough MVP had to be improved over time, and in a joint effort with other teams in the health department and other departments, they combined APIs (both public and internal to the municipality), custom Python scripts, SPSS scripts and MS Excel sheets to build dashboards and analysis tools. In the autumn of 2020 they extended their data collection abilities, for example by rolling out forms filled out by all test stations in Oslo. The MVP provided value, but was cumbersome to maintain over time as the analysis needs grew and the number of data sources increased. In the beginning Excel was chosen since it enabled them to move very fast on getting analyses out to decision makers, but it was clear that they needed something less brittle over time. The solution was also highly reliant on manual work and quality control which was taking a toll on the people maintaining the solution and doing the analysis.
In the end of October 2020 the decision was made to rebuild the solution using PowerBI and the new solution was in place before Christmas. The team migrated the old solution to PowerBI and built advanced dashboards used by the top decision makers, all while keeping up with requests for ad-hoc analyses as new decision problems arose. In 2021 the solution has been further developed and is now used to track the progress of the vaccination program making detailed analysis of vaccination rates across segments of the population possible.
What makes this project even more impressive, is a large proportion of the work was done by internal employees who had to learn the tools as they were building the solution.
Impact: When the pandemic hit Norway, Oslo was among the hardest hit. Furthermore, as Norwayâs largest city, handling the pandemic was more difficult than in smaller communities and failure to manage the pandemic in Oslo could have severe implications for the rest of the country. As we all know, having the right data and being able to make sound analyses is key in situations like these. The provided dashboards and supporting analyses was used by the municipalityâs top politicians and health officials when making decisions on restrictions and other policies throughout the pandemic. To me, it is obvious that this project has provided great value to the entire population of Oslo by enabling the municipality to make the best decisions possible through the pandemic. Given the small size of the team and the pressure to deliver value while building and improving the solution, I think that this is very impressive. As of today that total number of Covid related deaths in Norway in total is about 900.
This award will be given to an organization that has presented a significant impact with its data, analytics and AI efforts to improve social diversity, equity, inclusion and sustainability, and is dedicated to solving global challenges to the environment, humanitarian issues, accessibility, health, and cultural heritage.
Covid-19 had governments put restrictions & recommendations in place which rapidly changed how people moved about and behaved in society, limiting for example unnecessary travelling. But did people adhere to the restrictions? Telia Crowd Insights gives data sets based on Telia subscribers across the Nordics, with real-world accuracy on movement patterns representative of populations with extremely high statistical confidence. This data is accountable, explainable and transparent and always anonymous and aggregated. Utilizing this kind of data provided unique and valuable insights to municipalities, Public Health Agencies and governments to better manage the crisis.
Project Background and Overview, Strategic Objectives, Key Challenges, Innovative Solution, Positive Impact or Value:
As national authorities around the world took measures to handle the Coronavirus pandemic when it hit, they faced several challenges when trying to make the best possible public health decisions – based on evidence and data. One measure taken everywhere was to limit travelling and to urge people to stay in or close to their local area. In most countries, travel restrictions were put in place early. Data on movement patterns is traditionally collected by manually. It’s expensive, time-consuming and not very precise. When following up on the effects of the restrictions, national authorities across the Nordics could use our travel pattern data and adjust its strategies to limit the spread of the pandemic. It enabled them to make positive or sustainable changes, and to encourage the public to make positive changes.
In addition to the above use of Crowd Insights, Telia wanted to be transparent with the data as the public and general interest in the data was big. By simplifying the dashboards, Telia could present the data in a way that was easy to grasp for anyone with an interest.
In Sweden the insights were delivered to Folkhälsomyndighten, (Swedish Public Health Authority) from the beginning of April 2020 and then on a weekly basis. It is still ongoing. Because the dashboards visualized changes in travelling behavior and could be set in relation to the pandemic development, engagement increased among both authorities and the public about the best sustainable behavior during the current situation. By measuring the activity on our web page where the dashboards are available and updated weekly, we also see that the information engaged people, as the number of visitors to the website increased after every new publication. The information from our analyses quickly became sought-after among the media and the material was, and still is, widely and regularly covered. This further increased public and media engagement in the seriousness of and interest in the current situation.
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