ITC Limited, a multi-billion-dollar conglomerate based in India, was facing challenges in managing its supply chain for its fast-growing packaged foods business, particularly biscuits. The biscuit segment was experiencing high demand volatility and materials cost fluctuations. ITC's existing spreadsheet-based tools were not accounting for its continuously changing cost structures, making it difficult for the company to see the macro effects of the supply chain decisions they were making. The frequent manufacturing changeovers required to produce its more than 120 distinct biscuit SKUs across its network of 17 factories and the dramatic price fluctuations in the agricultural sector that affect their materials costs added to the complexity of cost optimization. Furthermore, ITC’s spreadsheet-based planning did not allow them to make effective medium and long-range strategic plans.

The Hershey Company, a global leader in chocolate and confectionery, operates a complex and global supply chain. With eight U.S.-based manufacturing facilities, five finished-goods distribution centers, and over 100,000 outbound refrigerated truckloads per year, the company faced the challenge of reducing transportation costs while dealing with increased fuel charges. The company's supply chain had to handle more than two billion pounds in annual throughput in the U.S. and Canada, and its products were sold in more than 90 countries around the world. As part of Project Greenlight, an overall transportation initiative launched in 2007, Hershey decided to invest in transportation-related process and technology improvements that would deliver a high return on investment.

Grupo Flexi, a leading footwear manufacturer in Mexico, faced increasing complexity in its supply chain as it expanded its product assortment. The company produces over 13 million pairs of shoes annually, with each pair requiring up to 30 different components and several raw materials. One of the most critical materials, leather, has variable lead times ranging from three weeks to three months, depending on the supplier. Flexi sources materials from several countries and distributes them to its manufacturing operations, which include 30 company-owned and subcontracted factories. Eight years ago, Flexi decided to expand its product assortment, introducing new products for different market segments. This initiative increased the complexity of managing more raw materials and stock-keeping units (SKUs), as well as more construction of shoes, which required different specializations and capacities in different factories.

Grupo Damm, a prestigious brewery based in Barcelona, Spain, was facing challenges with its outdated transportation management solutions. The company's supply chain had become more complex, and its customers were farther away. This made it extremely important for the company to tightly control the distribution chain through integrated systems and better meet its customers’ needs. The existing solutions, which were implemented more than a decade ago, were becoming outdated. To reduce manpower and administrative costs — while also leveraging additional capabilities in the software — Grupo Damm decided to upgrade its JDA transportation management solutions.

Fiat Chrysler Automobiles (FCA), the world’s seventh-largest auto manufacturer, faced challenges in increasing production speed and consistency due to the growth of global demand for automobiles. The company was running its plants and suppliers at full capacity, which led to capacity chokepoints. Identifying and managing these constraints became a priority for FCA. The company needed a solution that could help them identify these capacity chokepoints as early as possible and work to resolve them, thereby improving the speed of delivery.

Caterpillar Logistics, a leading manufacturer of construction and mining equipment, diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives, sought to improve its existing processes, produce knowledgeable and effective users, strengthen customer relationships, and establish customized, in-house training based on industry best practices. The company has robust experience in many logistics processes, including inbound logistics, after-sales service support, finished goods distribution and reverse logistics. However, it needed a solution that could help it optimize these processes and increase customer satisfaction.

Delphi Automotive PLC, a key supplier to the world’s leading automakers, faced the challenge of incorporating increased delivery speed and product customization into its operations. The company assembles and delivers critical components to automotive OEM assembly lines, in a predefined sequence at short notice. This is particularly challenging due to the complexity of Delphi’s products. For instance, a single electrical harness may be composed of hundreds of miles of wire, connecting to thousands of points within the vehicle. Delphi recognized the need for an automated decision-support tool to help it support these increasingly complex build-to-order requirements.

Briggs & Stratton, the world’s largest producer of air-cooled gasoline engines for outdoor power equipment, was facing a challenge in its warehouse labor management. The company's replacement parts division ships aftermarket and service parts worldwide from two facilities. It handles between 70,000 to 75,000 SKUs representing $40 million to $45 million in inventory. Prior to 2003, the Menomonee Falls Distribution Center (MFDC) had no standardized way for associates to do their jobs or to measure productivity. The MFDC management team felt they could get greater productivity if they standardized work methods, set goals and measured results. To help research this hypothesis the University of Wisconsin Supply Chain Consortium conducted a study that suggested there was significant opportunity for return on investment if the MFDC deployed labor management technology.

Edwards Lifesciences Corporation, a global leader in the science of heart valves and hemodynamic monitoring, was struggling to understand and meet demand. The company sells roughly 5,500 SKUs for the hemodynamic monitors and critical care products, and speed of delivery is an essential part of its business. When a customer orders a product, they are expecting to perform a surgery that’s been scheduled. If Edwards does not have the product or cannot deliver it quickly enough, surgeries have to be postponed or delayed. In the case of heart valves, not having the product or not being able to deliver it quickly could pose health risks to the patient who needs the heart valve. Prior to 2009, the company was struggling with forecast accuracy. The commercial team had a forecast that was 'aspirational,' and the supply chain side had one that tended to be statistically based. They initially couldn’t come to an agreement about which forecast to use, and as a result, they manufactured a lot of incorrect product mix.

L’Oreal, the world’s largest cosmetics company, wanted to optimize the effectiveness of its teams worldwide by improving knowledge transmission at all levels of the group. To achieve this, L'Oreal deployed 'Yammer,' a social web platform developed by Microsoft, for its employees in 2012. Three years later, 23,000 L’Oreal employees were using the internal social network on a voluntary basis. However, to intensify the qualitative aspect of conversations within Yammer, L’Oreal Operations wished to identify conversation leaders and incite actions for business knowledge transmission.

PagesJaunes.fr, the French equivalent of the YellowPages, is a leader in local advertising and information on web, mobile, and print, generating hundreds of millions of queries each year. The quality and relevance of results is a top priority for PagesJaunes. Category managers are responsible for maintaining the quality and relevance of the directory by creating the pertinent associations between terms and categories. The challenge was to improve user experience without increasing workload. The client wanted a solution that would help them measure and improve customer satisfaction, help Category Managers automatically detect and correct problematic queries, and optimize the quality of results to improve customer satisfaction.

Insurance organizations are constantly exposed to fraud risks, including false claims, false billings, unnecessary procedures, staged incidents, and withholding of information. Santéclair, a subsidiary of several supplementary health insurance companies, was struggling with fraudulent reimbursements from both opticians and patients. They lacked a system that could effectively analyze the right data and adapt to increasingly sophisticated fraudsters. Instead, they relied on “if-then-else” business rules to identify likely fraud cases, which resulted in the manual audit team spending their time on too many low-risk cases. With the increase of reimbursement volume (more than 1.5M a year), they needed to improve their efficiency and productivity.

Showroomprive, a leading e-commerce player in Europe, was facing a challenge with customer churn. The company was using static rules to trigger marketing actions, which were common to all customers and did not take into account the individual value of each client. This approach was not effective in preventing churn and improving customer loyalty. Showroomprive wanted to refine its client qualification process to anticipate, prevent, and reduce churn rates. The company aimed to detect clients with a high potential of no longer buying from the website based on individual purchase rates and refine the targeting of marketing campaigns for each potential churner to improve customer loyalty.

Trainline, Europe’s leading independent train travel platform, was facing a challenge in monitoring and improving their marketing acquisition. With paid campaigns running 24/7 and users interacting with those ads around the clock, static dashboards were no longer sufficient. The company needed a dynamic, real-time data tool for accurate marketing insights. They had invested in many different services and solutions to sustain their growth, but these were not always easy to manage. The company decided to build a centralized, global, real-time dashboard to get a global understanding of their marketing acquisition. The challenge was to start a big data project from scratch, ensuring that the technical team ended up with a tool that allowed them to improve and upgrade their own skills while also satisfying the marketing department’s requests quickly and efficiently.

Voyage Privé, a boutique vacation retailer, faced the challenge of creating personalized offer displays for its customers. The company needed to expand the range of customer signals that could be captured and analyzed to offer travel options that were appropriate for their members. This required a software solution that could capture and make sense of large amounts of data, develop effective customer segmentation, and implement a new non-rule-based approach for analyzing incoming and historical data. The end goal was to increase customer satisfaction by providing users with personalized offer selections while simultaneously boosting the total transaction value by customer.

The healthcare industry is grappling with a high rate of patient no-shows, with studies indicating that 5-10% of scheduled patients miss their appointments. This has a significant impact on the financial health of healthcare organizations and their ability to care for other patients. Primary care physicians lose an average revenue of $228 for every no-show, and the lost revenue for specialists is even higher. When a patient misses an appointment, overhead costs including staffing, insurance, and utilities are not reimbursed. Cancellations with primary care physicians also impact the number of necessary specialist referrals those physicians can make. Combined, these factors contribute to significant revenue loss for physicians. To help minimize the occurrence of no-shows and thus reduce associated costs, Intermedix decided to develop and operationalize a no-show predictor that would assist office managers in scheduling appointments.

A large national media organization wanted to provide high-quality recommendations for users of their app. Their goal was to target consumers with content that they would actually be interested in based not only on what they previously consumed, but how exactly they interacted with topics in which they previously expressed interest. For example, if someone chose to listen to a report on Topic A but then fast forwarded through much of the piece (as opposed to actually listening to the piece in its entirety), the app should take that activity into account for future recommendations. However, with a very small team and limited resources, the organization wanted to accomplish this in a scalable way. Not only would the system have to be mostly or entirely automated, but the team itself would have to be able to build the recommender easily in a way that would allow for quick tweaks and adjustments in the future.

A leading retailer in Europe with more than 3,500 stores and an e-commerce component was losing money due to being undercut by competitors on price. They also found that their customer base tended to wait until the end of seasons for huge markdowns and would only then buy certain seasonal products, which skewed their predictions for how to stock items in the future and perpetuated the pricing issue. In addition, they struggled to efficiently change prices and keep them consistent across stores and online - often, this resulted in inconsistent pricing, especially when individual store managers made their own decisions on sales. The retailer wanted to improve their pricing strategy by understanding what drove customer purchasing decisions for specific products and what prices would resonate best, easily understanding the price offered by all competitors in real time, and updating pricing consistently across stores and online.

BGL BNP Paribas, one of the largest banks in Luxembourg, had a machine learning model in place for advanced fraud detection. However, the model remained largely static due to limited visibility and limited data science resources. The business team was keen on updating the model but faced challenges due to lack of access to data projects and the data team. The challenge was to harness a data-driven approach across all parts of the organization. The bank needed a solution that would democratize access to and use of data throughout the company, without compromising data governance standards.

Dentsu Aegis is a media buying company that allocates advertisers’ budgets on campaigns across various media using targeted segmentation. When pitching their services to potential customers, the sales staff recommends specific segments that would be the best to target with a particular campaign to maximize return. After they make the sale, the teams need to be able to deliver on those promises and actually maximize return with effective segmentation. However, the department struggled to quickly provide segmentation recommendations to the sales team. The teams built a data lake to collect data from multiple sources, but actually using the data meant embarking on the painful process of writing new code (Python, Spark, or SQL) every time. Every time they had a project, team members had to write a query, get the results, analyze those results with another tool, and write more code to reprocess and use the data. Without an easy way to replicate past work, each project required them to start their process from scratch, no matter how similar two prospects’ or customers’ use cases were.

In 2017, LINK Mobility, Europe’s leading provider of mobile communications, decided to scale up their data efforts for handling internal requests and externally with customers. Their primary offering is mobile messaging services, sending more than 6 billion messages a year worldwide carrying invoices, payments, and vouchers, associated with various services. They produce a lot of data and saw an opportunity to expand their offerings to provide more data-driven insight to customers surrounding the delivery and performance of their messages and services. They were looking to expand to customer dashboards as well as the ability to take action based on that data. However, with just a one-man data science team at the beginning of the project, they needed to be able to get up and running quickly and easily. They also needed to find a tool that would allow them to scale up data requests coming from inside the company as well as to be flexible enough to provide data insights to customers without having to use two different tools or platforms to cover their various needs, use cases, and data types.

Coyote, a French leader in real-time road information, was facing a challenge in retaining its customer base and enhancing its service quality. The company wanted to optimize its loyalty program to encourage customers to increase device use. To achieve this, Coyote needed a technical solution that would enable them to segment its customer base by user profile, qualify incoming data, and quantify device use through anonymous data analysis. The company understood that the more data it collected, the better its service would be. Therefore, improving retention rates was crucial to enhance the service quality and acquire more users.

DAZN, a subscription sports streaming service, was looking to grow their business in existing and new markets. They wanted to enable their small data team to run predictive analytics and machine learning projects at scale. They also wanted to find a way to allow data analysts who were not necessarily technical or experienced in machine learning to contribute in meaningful ways to impactful data projects. The goal was to support an underlying data culture with advanced analytics and machine learning at the heart of the business.

A major healthcare provider in the UK was struggling with staffing inefficiencies, leading to physician overwork, patient dissatisfaction, and high costs. The hospital's staffing process was largely manual and based on the number of available beds, which did not allow for efficient allocation of staffing hours. This lack of data-driven decision making was impeding the hospital's ability to deliver optimal care and retain the best doctors. The hospital sought a technical solution that would enable it to model patient inflows on a small scale and recommend staffing schedules based on patient demand forecasting.

Infopro Digital, a crossmedia company, wanted to offer more advanced targeting options to its advertising customers. Instead of basic category targeting, they wanted to leverage the user’s navigation path and behavior to more accurately target those who may be interested in a particular ad. This advanced targeting required experienced technical teams to handle a vast data lake. However, Infopro Digital’s marketing teams needed to be able to handle the queries and most of the day-to-day work themselves without the help of IT every time. The marketing teams had some prior knowledge of processing data using Microsoft Excel, but they were frustrated by its computing and speed limitations. Infopro Digital also wanted to develop any new processes and skills within the company to keep costs and production delays low.

OVH, a global provider of hyperscale cloud services, was facing challenges with its dashboarding system. The business analysts responsible for disseminating data and insights to inform the commercialization and optimization of the website were spending more than 80% of their time on data preparation for the dashboard. The existing dashboard only provided basic, high-level metrics and did not combine different data sources for a complete view. This necessitated ad-hoc analysis, for which the analysts had little time. Additionally, the ETL process for the dashboard presented concerns for the data architects around data and insights quality, as there was a lack of transparency around exactly what data was being transformed and how.

PriceMoov, a service that delivers optimal pricing suggestions and solutions to its customers, was facing a challenge with data originating from old SI systems, Oracle, or MySql. The data was dirty and required a fulltime developer to perform long ETL steps in PHP for cleaning. Once cleaned, the datasets were painfully entered into a model, as they were custom-built pipelines. And once finished, the replication and deployment process for the next customer was taking weeks. This long and arduous data preparation process was causing stale pricing recommendations.

SendinBlue, a relationship marketing SaaS solution, faced a significant challenge in validating new customers and ensuring the quality of their databases. The company had to ensure that all contacts on the list were opted in, which required manual validation. This process was not only time-consuming and required a large workforce, but it also severely delayed account validation for customers, damaging SendinBlue’s reputation. As the customer base grew, manual validation became increasingly unfeasible. The company needed a solution that could automate the validation process and scale with the growing demand.

Technical Safety BC, an independent, self-funded organization, oversees the safe installation and operation of technical systems and equipment across British Columbia, Canada. Conducting physical assessments is costly, and false positive inspections can result in significant opportunity costs each year. Those same resources could be better allocated within the safety system; therefore, finding a way to more accurately predict hazards is of high strategic value to the organization, and it creates greater safety benefit to the public. Technical Safety BC was looking to find more high-hazard sites while operating at the same resourcing level by introducing more sophisticated machine autonomy in the risk assessment process. Some of the challenges faced included: uncoordinated heterogeneous data sources; data quality; speed of collaboration; and training challenges in the use of machine-recommended predictions.

The customer, a major hospital in Western Europe, was facing challenges in accurately measuring physician and healthcare organizations' performances due to uncoordinated heterogeneous data sources, irregular and poor quality data, insufficient risk-adjustment of results, and lack of automation in physician profiling processes. They were seeking to embrace an Accountable Care Organization (ACO) model to improve clinical outcomes and compete on cost. Some clinical processes, like prescribing expensive or unnecessary drugs or recommending longer hospital stays than needed, were costly and detrimental to patient care. The customer estimated that administering the wrong care at the wrong time represented upward of $1.6M loss per year, a problem that they believed could be solved with accurate physician profiling.