Essilor International, a leading ophthalmic optics company, was facing the challenge of improving the processes and performance of their surfacing machines to significantly enhance their production. The surfacing step in lens creation is complex and delicate, as it gives the lens its optical function. The company aimed to optimize this step to correspond to each person’s individual prescription and personal parameters. However, they were dealing with large, heterogeneous datasets from the surfacing machines and needed a scalable way to work with this data. The company was already using continuous monitoring technologies like IoT connected devices, but they wanted to take a step further by employing advanced algorithms and machine learning to take action from real-time insights.

Showroomprivé, an e-commerce retailer specialized in flash sales, faced challenges in targeting their marketing emails. Until 2016, the team selected the target audience for these marketing emails manually based on what they know about the brand. However, this approach presented several challenges. Brands often have overlapping or broad audiences, which meant touching some prospective buyers multiple times, while others not at all. This also meant casting out a wide net, potentially sending emails to people who were not interested in that particular brand. The ultimate goals of the project was for the marketing team to be completely autonomous in targeting and sending these emails.

The Law Society of British Columbia, a non-profit organization that regulates lawyers in British Columbia, was looking to increase the efficacy of their trust assurance audit program. The organization regulates 3,800 law firms and audits approximately 550 firms per year, which means that each firm is audited at least every four to six years. The Law Society has three decades of historical data, which enables them to categorize law firms according to their risk level: low, neutral, or high risk. The organization made the decision to focus on risk factors and, from there, work to adjust the audit schedule based on the risk category of each firm. The senior management team at The Law Society of BC firmly believes that AI and machine learning will play an important role in their responsibilities in the near future. They knew it was time to take advantage of their collected data and leverage technology to identify patterns and behaviors and increase effectiveness and efficiencies within Law Society programs.

Malakoff Humanis, the leading non-profit group health insurer in France, was facing growing challenges in keeping up with customer demands and providing quality customer service. The company offers supplementary health, welfare, and pension contracts to companies, employees, self-employed individuals, and single-payer individuals. It covers healthcare reimbursements in addition to the French social security and guides clients in their choice of care establishments. The company has a dedicated data science and analytics department led by a Chief Data Officer. The data department is comprised of four main branches, each in charge of Data Science and Analytics, Data Governance, Data Architecture and Cloud, and AI and Data Visualization. However, the company was struggling to effectively manage customer claims and improve telephone customer assistance.

Heetch, a French company founded in 2013, has grown quickly to 250 employees united around one goal: making mobility more accessible by offering a smooth user experience. The company has gathered troves of data from drivers, passengers, global operations, and more since its launch, yet they struggled to scale their ability to actually leverage that data. Five years in, data warehouse costs were spiraling out of control, and performance was suffering as the amount of data grew. The company needed to find a solution that would allow anyone across the organization to work with large amounts of data while also ensuring optimized resource allocation.

La Mutuelle Générale, a French insurance company with over 70 years of experience in the market, serving over 1.4 million customers and 8,000 enterprise clients, and generating more than €1.1 billion in turnover annually, was facing a challenge in customer acquisition. The competition in the insurance industry is fierce, with organizations all vying to capture the same type of customer. The cost of acquiring a new customer has significantly increased in recent years. To address this, La Mutuelle Générale sought to develop a decision support tool for sales to aid their understanding and prioritization of prospects based on their likelihood to convert and their potential value compared to their cost of acquisition.

MandM Direct, one of the largest online retailers in the United Kingdom, faced a significant challenge as they grew rapidly. With over 3.5 million active customers and seven dedicated local market websites across Europe, the company delivers more than 300 brands annually to 25+ countries worldwide. Their accelerated growth meant more customers and, therefore more data, which magnified some of their challenges and pushed them to find more scalable solutions. The two main challenges were getting all the available data out of silos and into a unified, analytics-ready environment and scaling out AI deployment in a traceable, transparent, and collaborative manner. Initially, the company's first machine learning models were written in Python (.py files) and run on the data scientist’s local machine. However, as the number of models in production increased, the team quickly realized the burden involved in maintaining models.

Coyote, a European leader in real-time road information, uses IoT-based devices and mobile applications to warn drivers of traffic hazards and conditions. The company collects extensive data on the different uses of its community, such as mileage, time spent on the road, or the number of alerts issued by the community members. Initially, Coyote started with predictive analytics for improving their customer retention. However, they wanted to leverage the value of their vast data sources and implement a data-driven strategy at the heart of their core products and services. They aimed to improve road safety using IoT devices.

Finexkap, a leading fintech providing digital solutions for B2B operators, marketplaces, and e-commerce in western Europe, was facing a challenge with its data science team. The team, consisting of only three data scientists, was using Python in notebooks and a bit of C# to automate processes, but they didn’t have any visual tools for building data pipelines or to conduct on-the-fly data analysis. This method was functional but extremely tedious, and in the long run, they realized it was not sustainable, especially with the company’s growth and plans for future products and expansions.

Provincie Noord-Holland (PNH), a province in the Netherlands, embarked on an initiative to become a more data-driven organization. However, they faced challenges in determining the necessary steps to achieve this goal, including the required technology and expertise, setting up experiments, and implementing new processes. They also faced unique challenges as a public sector organization, such as the need to consider regulations and societal impact when conducting experiments and working with data. Additionally, they had to work within a closed IT environment, limiting their access to data science tools. They also realized the need for data scientists and technology to help them succeed with their data science initiatives, and the importance of being both data and business-driven to generate positive performance and encourage buy-in among organization-wide stakeholders.

Buildertrend, a leading construction project management software company, was looking to disrupt the residential construction industry by leveraging data science to improve business operations and make residential contractors more efficient. They were seeking a data science platform that could enhance speed and agility in their data-to-insights process, enable company-wide collaboration on data projects, and empower their data scientists with the right tools and resources. The company was also keen on automating repetitive tasks, improving documentation practices, and increasing the amount of data included in their models. One of their key use cases was churn reduction, where they aimed to efficiently target at-risk accounts to drastically reduce churn.

Ngozi Dozie and his brother Chijioke identified a significant gap in the Nigerian financial landscape, particularly in the areas of consumer lending and credit infrastructure. Out of 100 million adults in Nigeria, over 40 million of them did not have bank accounts, and there were only about 200,000 distributed credit cards in the entire country. Commercial banks were hesitant to offer consumer loans due to the high risk associated with lending to consumers without credit. Building a credit score in a market like Nigeria is a huge challenge, with little documented financial history or asset ownership. This presented an opportunity for Carbon, the fintech company started by Ngozi and his brother, to help serve the underbanked population of Nigeria.

Trupanion, a leading provider of medical insurance for cats and dogs, was dealing with a lot of data from different aspects of their business; pricing, sales, claims projection, customer retention, and more. They did a good job of reporting metrics, but they did not yet have the technical capability to analyze that data on a deeper level for optimal decision-making. This required more sophisticated technology and a lot of time. Trupanion was looking for fast and accurate predictive modeling software that is robust enough to support all their different data and information from different functions of their business.

Catholic Education Diocese of Parramatta (CEDP) is an educational institution with 80 schools and 44,500 students across New South Wales. The institution holds a wealth of data on its students, from performance to attendance to demographics. However, CEDP lacked the internal resources to mine this data to improve student performance and advance operational goals. They sought a solution that could help them leverage this data to enhance student success and operations.

NTUC Income, a top composite insurer in Singapore, was facing rising claims costs across the insurance industry. As the cost of doing business increased, the company needed to understand the factors driving up claims costs, who was affected, and what actions to take. Furthermore, with insurance increasingly becoming a commodity, accurate price setting became more critical than ever. However, pricing analysis in insurance can be complex, repetitive, and time-consuming. The traditional method of using Generalized Linear Models (GLMs) for pricing analysis was not ideal due to several limitations. These included assumptions of a straight-line relationship between a rating factor and claim costs, time-consuming processes, and inability to analyze text in claim descriptions. The company needed a solution that could address their pricing analysis challenges and scale with their team.

DemystData, a New York-based software company, aims to 'demystify' data by providing a platform that helps clients discover, explore, and access the vast world of data. However, as datasets get larger and data sources more varied, the complexity increases, leading to more time-consuming work for the company's limited pool of data science resources. The company's clients, particularly financial institutions, are underutilizing data, leading to business decisions being made based on suboptimal or incomplete information. DemystData aims to close this gap by increasing their clients' access to new and more data.

Steward Health Care, the largest for-profit private hospital operator in the United States, was faced with the challenge of how to use predictive analytics, artificial intelligence (AI) and machine learning to derive value from the vast amount of data they are required to collect and maintain. The primary task was to improve operational efficiency across Steward’s network of 38 hospitals, with a focus on reducing costs. The company decided to address one of the most pressing challenges facing hospital operations — staffing volume. The typical hospital staffing model is set to average census and volume, leading to inefficiencies during peaks and valleys in patient volume. This results in high expenses for on-call staff and overtime pay. Steward Health Care’s CEO, Dr. Ralph de la Torre, challenged his team to find a more proactive approach.

Harmoney, a marketplace lending platform in Australasia, was facing the challenge of keeping pace with the constant innovation required to stay ahead of big banks. The company's small team of data scientists was tasked with the development and deployment of machine learning models to improve the efficiency of the personal loans market. However, the team was finding it difficult to dedicate sufficient time to predictive analytics due to their other responsibilities. Additionally, the traditional tools they were using for modeling were time-consuming and often led to distractions from the main goal of improving the business.

Americold, the world's largest temperature-controlled warehousing and distribution services provider, was facing challenges with its legacy warehouse management and labor management systems. These systems lacked the flexibility to adapt quickly to the fast-evolving marketplace. Americold needed to increase the productivity and efficiency of their logistics personnel to meet growing customer demand. The company also aimed to drive innovation in the cold-chain industry as the hub between food manufacturers and retailers, which required advanced capabilities and efficiencies not available from their legacy solutions. To address evolving customer requirements and develop new and innovative supply chain solutions, including cross-docking capabilities and other value-add products and services specifically designed to complement traditional temperature-controlled supply chain activities, Americold needed to upgrade from its legacy warehouse and labor management solutions.

Lenzing Group, an Austria-based company that supplies high-quality specialty fibers to the global fashion industry, was facing a challenge. The company's botanic cellulose fibers, known for their innovative properties and reduced environmental impact, were in high demand. However, the company was struggling to connect demand forecasting, sales planning, and operations planning through digitalization. This lack of connection was creating inefficiencies in their end-to-end supply chain. The company sought a solution that would create an extremely accurate, efficient end-to-end supply chain.

Gousto, a London-based recipe box service, was facing a significant challenge due to its exponential growth in consumer demand and increasing product offerings. The company buys products like rice and potatoes in bulk, then these are broken down into smaller quantities. These ingredients support the assembly of 40 different weekly recipes. As specific work orders are created, about 60 unique SKUs are assembled into each individual box, which is then shipped to the consumer. Because many ingredients are perishable, or have special handling needs, time is always of the essence. The company's volume has grown considerably since 2012, increasing their warehouse logistics challenges significantly. The global market for meal kits is projected to reach $10 billion in sales by 2020, indicating that Gousto's growth is expected to continue.

Gedimat, a member cooperative with approximately 500 independent dealers in France and Belgium, is the second-largest home improvement player in the French market. The company's success is largely dependent on the in-store retail experience it provides, as customers rely on their local store for trusted advice and information. Speed of delivery is also crucial, especially during busy seasons and when new products are being introduced. However, Gedimat's old sales and merchandising solution lacked the analytical capabilities needed to support their large retail cooperative and build better relationships with suppliers and manufacturers. The company also wanted to automate time-consuming manual tasks to increase productivity and efficiency and better serve their dealers.

Kaufland, a supermarket chain active throughout Europe with about 1,200 stores, offers a range of around 60,000 items to its customers. The main product focus includes fresh food comprised of fruit and vegetables, dairy, meat and fish. The range also includes household goods, electronics, textiles, stationery, toys and seasonal items, as well as weekly promotional merchandise. Kaufland set itself the ambitious goal of automating the replenishment process in its fresh meat division, as their existing supply chain processes had reached their limits.

Ernsting’s family, one of the largest cross-channel retailers, was facing challenges in maintaining consistent sales levels across their varying product ranges. The German-based company was also dealing with the tremendous upheaval in the marketplace due to the rise of online stores and digitization. Classic seasonal cycles gave way to faster trends and short-lived monthly collections, forcing new stock arrivals every two days. With more than 1,800 stores across Germany and Austria, the company needed a more strategic way to optimize pricing and promotions to quickly sell new collections within specified timeframes while increasing margins.

dm, a large retail company, faced several challenges in its operations. The company needed to improve the cooperation between the manufacturer and the distribution center to ensure product availability. It also needed to provide valid predictions for industry partners. The company was dealing with the issue of short-term demand for goods in stores versus long delivery times of industry partners. It needed to make precise sales forecasts, even for exceptional cases like holidays or vacations. The company also wanted to avoid over- and understaffing in its stores.

Tallink Grupp, a leading provider of premium mini-cruise and passenger transport services in the Baltic Sea region, was facing a challenge with its growing customer demand and business expansion. The company's largest revenue stream was on-board purchases, making it crucial to consider ancillary revenues in addition to ticket revenues when making pricing policies and revenue decisions. The company was also dealing with an abundance of last-minute online travel bookings, which made it difficult to make effective pricing decisions. Tallink felt that automation of pricing decisions would free them to focus on new trends that could boost revenue.

Michelin, a leading tire manufacturer, faced increased supply chain complexity due to scarce capacity, a growing portfolio of tire types, and a significant increase in parts resulting from the company’s innovation efforts. The market in which the company operates had also become increasingly volatile and competitive, as well as impacted by seasonal demand. After analyzing its existing S&OP decision-making process, several potential improvements were identified including undetected opportunities, risks, and constraints. Legacy tools and processes used by the Michelin business units were heterogeneous and didn’t have the flexibility necessary to support its S&OP transformation.

PepsiCo Australia & New Zealand, home to globally recognized brands such as Smith’s Chips, Red Rock Deli, Bluebird Chips and Twisties, sought to evolve from category management to category leadership. The company aimed to establish a total macro-snacking view, given the increasing importance of grocery. PepsiCo also wanted to improve its rankings in retail benchmarking surveys as a measure of its performance. The company aimed to better engage with retailers to create a macro-snacking total impulse solution and drive store-of-the-future concepts in order to increase basket value.

Compañía Cervecerías Unidas S.A. (CCU), Chilé’s largest brewery, was anticipating a 30% growth in order volume which would flow through their main distribution center (DC) in Curauma. They wanted to accommodate this growth with existing facilities and personnel. The company wanted full control over all warehousing operations through a single technology solution to drive greater efficiency and throughput. They also wanted to optimize its processes and use of personnel in order to improve its delivery speed and customer service.

Campbell Arnott’s, a player in the fast-moving consumer goods (FMCG) market, faced increasing competition and cost pressures across the supply chain. The company needed to adapt to new consumer trends and competitive strategies implemented by retailers. Amid these dynamic factors, Campbell Arnott’s recognized the need to ensure their teams continue to do their jobs efficiently and have the technology that supports the process of continuous improvement. The company sought to improve forecast accuracy, reduce stock-outs, and decrease inventory levels. They also aimed to create alignment across supply and demand planning to drive greater operational synergies.