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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.

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.

International fashion retailer bonprix was struggling with outdated pricing and promotion systems, using rigid price-conversion tables. The company was facing high prices for many products in the highly competitive Russian market, leading to rising costs and falling profits. To modernize internal processes and meet the complex and changing market demands, the online German shop needed an automated solution to achieve consistent and granular price optimization with varying parameters for different countries. With five house brands in 30 countries, it was imperative the solution be seamless and effective.

UK-based supermarket chain Morrisons wanted to increase on-shelf availability through improved demand planning and replenishment based on analysis of customer behavior at every store. The goal is to put the customer at the heart of every decision. Store replenishment based on manual ordering by in-store teams proved time consuming, created inconsistencies between stores and was not always accurate. Morrisons wanted any new planning solution to easily integrate with, and streamline, its complex IT infrastructure, as well as be capital light.

SELGROS, a wholesale company, was facing a challenge with its advertising strategy. Every two weeks, the company would mail up to one million brochures to its customers. The decision on which customer should receive a catalog was a manual process using segmentation and decision trees. This process was not only time-consuming but also inefficient as it did not consider all the elements that influence customer spending. As a result, the company acknowledged that its marketing budget was not being used efficiently. SELGROS sought a solution to automate the process, reduce advertising costs, and improve customer targeting.

Globus CR, a part of the Globus Group, operates in three countries with a total of 47 hypermarkets, 91 DIY stores, and six electronics stores in Germany, 15 full-range stores in the Czech Republic, 15 in Russia, and two DIY stores in Luxembourg. The company was facing challenges with its supply chain management strategy, particularly in the areas of promotion and inventory replenishment. The market landscape they operate in is extremely promotion and price-driven, and their promotion planning processes were manual and cumbersome. This resulted in high leftover stocks after promotions and high planning and handling costs. The increasing labor costs were driving the need for automation. The company was also struggling with maintaining both promotion leftovers and out-of-stocks at a reasonable level due to data insights and planning activities being housed across different parts of the business.

OTTO, a German multichannel retailer, faced challenges in its competitive environment characterized by low margins, high competitive pressure, and rapidly changing market conditions and customer demands. The company needed to balance product availability and pricing for every single article in its extensive product portfolio. One of the greatest challenges was predicting the sales of an article at an early stage, as the profitable purchase of goods determines overall success. OTTO also faced challenges in reducing delivery times for partner products, which were longer than for OTTO’s own brands due to more complicated logistics processes. The retailer needed to know which articles would sell, how frequently, and in what sizes and amounts to order the right articles in advance based on the forecasts and expedite delivery to the customer.

Ariztía, the second largest chicken and poultry producer in Chile, was facing challenges in managing its short shelf life products. The company's products, which are shipped either fresh or frozen to customers, required tight inventory control to assure freshness. This was of paramount importance to Ariztía as it directly impacted their mission to provide quality products and excellent service to their customers. The company also strongly believed in the value of training and education to improve employee productivity. However, assuring freshness mandated strict adherence to first expired/first out inventory turnover practices, which was proving to be a challenge.

Campbell’s Snacks team advises on a robust bakery product line, with 750 distinct items sold under 100 brands. The advisor team must routinely produce and update more than 4,500 planograms to cover this complex product line, especially as marketing strategies and demand patterns shift. Historically, it took up to 10 weeks to create these planograms manually, and there was frequent employee overtime. The challenge was to find a solution that could automate and accelerate the process of producing thousands of customized space plans.

Harris Teeter, a North Carolina-based subsidiary of The Kroger Co., operates over 265 retail locations, 52 fuel centers, three distribution centers, and a dairy manufacturing operation. The company needed an enterprise-wide workforce management platform to drive associate engagement, efficiency, and customer satisfaction. The solution had to reflect and support the culture of diversity and inclusion that is a foundation of their business model. Harris Teeter wanted a workforce management solution that not only supported their continued push for innovation and inclusion but also brought innovative capabilities to the table. The company continuously strives to innovate in the way they serve their customers, which includes having Starbucks locations in stores, offering “Hot Bars” serving pizza by the slice and other chef-prepared foods, omelet stations, sushi stations, burger bars, and beer and wine bars. Their stores also support buy online/pickup in-store (BOPUS) convenience for shoppers.

Mahindra & Mahindra Farm Equipment, part of the $20 billion Mahindra Group, is the world’s number-one tractor company by volume. Its automotive business competes in almost every segment of the industry. The Spares Business Unit (SBU) provides genuine vehicle and tractor spare parts via advanced capabilities in sourcing, assembling, warehousing and distribution. To maximize supply chain efficiencies and service, Mahindra & Mahindra constantly evaluates scientific methods to tweak demand forecasting, inventory management and replenishment planning strategies to ensure that the right parts are available at the right place and time. However, their traditional, manually driven segmentation processes and tools often resulted in inefficient allocation, high safety inventory levels and less-than-optimal service levels.

Tesco, the world’s third-largest retailer, operates over 6000 stores across Europe and Asia, offering a wide product range, from groceries to clothing and electronics. The company produces one million planograms and 125,000 store floorplan changes annually. For over 20 years, Tesco has relied on Blue Yonder solutions to ensure the fast, accurate generation of these planograms. More than 100 colleagues at Tesco use Blue Yonder solutions. However, the company faced a challenge in maintaining software knowledge and best practices as the workforce evolved. The traditional on-the-job learning and peer training methods were not efficient enough to capture best practices that lead to higher-quality plans.

Wegmans, a family-owned supermarket chain with over 100 stores across the northeast and midAtlantic states, operates a private fleet to transport fresh, frozen, and dry foods and ingredients to their network of stores. However, with a network of this size and complexity, Wegmans wondered if they were getting the most utility possible from their fleet investments. The challenges they faced included tight store delivery windows, which limited opportunities for order consolidation or routing options, routing restrictions by type of commodity being shipped, and virtually no visibility to cost savings available from the use of backhauls.

Western Digital Corporation, a global leader in flash memory storage solutions, was facing challenges with its resource-intensive spreadsheet-based planning process. The process involved four-week planning cycles with only basic planning assumptions. The company wanted a daily integrated supply chain plan that includes demand, material supply and capacity constraints and utilization, allocation and execution objectives. They also wanted visibility into demand and supply across the entire organization, including the inventory in each segment and channel, promotion plans and supply and production plans. Furthermore, they wanted to use customer segmentation and a multilayered postponement strategy to reduce overall inventory levels while better positioning products and materials to support improved customer service.