X

Marketing Investment Group (MIG), a leading retailer of footwear and clothing in Central and Eastern Europe, was struggling with the complexity of optimally pricing thousands of items across multiple countries, currencies, and channels. The company operates more than 400 stores and over 20 ecommerce platforms, with multiple retail brands, including regular-price stores and outlets, in 11 countries. The manual methods and consumer-grade tools they were using were not sufficient to optimize pricing across all these variables. The process was complex, tedious, and error-prone, leading to a lot of markdowns and inability to change prices frequently.

Sally Beauty Holdings, a retailer and distributor of professional beauty supplies, was facing the challenge of managing the complexity of demand planning, fulfillment execution, and category management across two very different markets. The company operates two business units: Sally Beauty Supply, aimed at consumers, and Beauty Systems Group, which targets professional stylists. With 20,000 SKUs, 5,000 stores, and annual revenues of over $3.9 billion, the company needed to increase visibility, responsiveness, and revenues while also managing costs. The pandemic further complicated matters by causing a rapid shift in consumer behaviors and a 30% increase in the rate of buy online/pickup in store (BOPIS) orders.

Amway, a global company selling health, beauty, and home care products in over 100 countries, was facing inconsistencies in its supply chain and logistics processes as it expanded into new regions. The company's annual sales exceed $8 billion, and managing the supply chain for such a vast operation was becoming increasingly complex. The company had a long-standing partnership with Blue Yonder, which had helped unify the global supply chain and deliver more consistent results. However, Amway was looking to further improve its operations by migrating its Blue Yonder solutions to a software-as-a-service (SaaS) delivery model. This move was aimed at maximizing speed, capacity, and agility, while minimizing Amway’s total cost of ownership.

ThaiNamthip (TNT), Coca-Cola’s bottling partner in Thailand, was facing challenges in optimizing its warehouse operations. The company aimed to increase the productivity of both human workers and physical assets while minimizing errors. TNT's goal was to replace time-consuming, paper-based manual processes with speed and automation. As part of a long-term effort to digitalize its entire supply chain, TNT sought a solution that could help it maintain its market leadership position in the carbonated beverages market. The company recognized that continued investment in new technologies was crucial for its long-term success.

SuperFrio, South America’s leader in refrigerated logistics, operates 22 distribution centers across Brazil and has five more under construction. To support its ambitious growth plans, the company decided to replace its legacy warehouse software and manual processes with a new level of speed and automation. The aim was to standardize processes and improve quality, accuracy, efficiency, and customer responsiveness. SuperFrio's warehouse operations are complex, with 10,000 stored SKUs, 300,000 pallet positions, and 15,000 vehicles dispatched monthly across 22 distribution centers.

PVH, a U.S.-based retailer with a diversified portfolio of brands including CALVIN KLEIN, Tommy Hilfiger, Van Heusen, and IZOD, aimed to enable end-to-end global planning across multiple brands. As one of the largest global apparel companies reporting $8.2 billion in 2016 revenues, visibility of products from creation through the end consumer’s purchase was crucial. The company needed to ensure they were using their data properly, sending out the right demand, and contracting correctly with their factories without overcapacity.

A leading cloud-based communications technology company that offers hosted contact center services needed a way to improve performance metrics, eliminate the guessing game of problem resolution and dramatically increase customer satisfaction. To attain this, they wanted a unified view into their infrastructure that would allow them to monitor calls in real-time. In the battle for consumer loyalty, the contact center is at the heart of customer care strategies. It is the central hub of communications and customer service for enterprises and is responsible for the vast majority of consumer interactions and service-related transactions in today's market. The customer service touch points—such as resolving a complaint, taking an order, renewing a warranty or up-selling a product—are pivotal in accomplishing strategic business objectives.

The customer, a cable TV and telecom provider operating in over nine US states and serving nearly 5 million customers, was facing intense competition from traditional players and new digital players like Netflix, Amazon Prime, Roku, and more. These digital players were using predictive analytics and machine learning to deliver highly personalized, contextual, and content-driven interactions. The customer was experiencing a steady decline in demand and high churn rates. They lacked proactive and contextualized customer service, with their data analytics restricted to a historical analysis of a limited set of monthly calls. The absence of real-time dashboards and lack of customer data enrichment prohibited contextualization. Their technology stack was not equipped to analyze large volumes of disparate data in real-time.

Enterprises need to analyze large volumes of data from various sources in real-time to make strategic business decisions. They often create custom frameworks to process these large data sets, which can lead to technical debt and dependency on IT teams who understand the historical choices made during the initial platform designs. This can risk impacting businesses and increase customization costs. The customer, a leading security and intelligence software provider, wanted to modernize their existing big data applications. They were looking for an easy-to-use and scalable solution that could process 1.5 billion transactions generated per day from multiple real-time feeds. They needed a near-zero-code solution for ETL processing jobs that could perform real-time ingestion and complex processing, ensure high throughput while indexing and storing, and detect anomalies in transactions.

The auto insurance industry is increasingly investing in connected car solutions to offer simplified, transparent, and flexible products and pricing options. Usage-based insurance is a voluntary, behavior-based insurance program that uses analytics to create highly personalized and dynamic plans based not only on the driver’s age and other demographics, but also accounts for the driver’s behavior, risks related to a vehicle, and external factors such as driving conditions and weather. A leading auto insurance provider chose Gathr to ingest, transform, enrich, analyze and store automotive telematics data in real-time to build an end-to-end analytics application for driver profiling & individual risk assessment, and subsequently offer dynamic, usage-based, plans to its customers.

Insider threats are a significant cybersecurity risk to banks, becoming more frequent, harder to detect, and more complex to prevent. These threats can include employees mishandling user credentials and account data, lack of system controls, responding to phishing emails, or regulatory violations. The bank's traditional threat detection relied on setting static rule-based alerts on users' activities, which resulted in a high number of irrelevant flags when applied to thousands of users. The bank's current relational technology stack was proving to be too expensive and inflexible, limiting the bank to processing data from only 15-20% of hundreds of sensitive customer-facing and operational applications. It took almost 2 years for the solution to move a single use case to production, making it difficult for the bank to scale out.

The company, a leading IT services and consulting provider catering to B2B sales, marketing, and customer success departments, was facing difficulties in optimizing and making the most of its cloud investments. The challenges included expensive VM sprawl, limited visibility into resource consumption and costs, and a lack of readiness for migration to a containerized environment. The company has a significant presence, with over 3000 employees and operations in over 170 countries. However, these challenges were hindering its ability to fully leverage its cloud infrastructure and achieve cost-efficiency, scalability, and availability goals.

The company, a global leader in insurance broking and risk advisory with a presence in 130 countries and a headcount of 45,000, was facing difficulties in monitoring its cloud resources. The challenges included a lack of centralized cloud cost monitoring, control over dangling and idle resources, visibility into cloud costs for development teams, and anomaly detection and predictive capabilities.

The airline was experiencing a massive growth of high-speed data coming in from various online and offline customer touch points and operational systems; nearly 5TB of data was coming into its systems every day at an input data velocity of 7,000 events/second. The massive volume of data limited data searches to only two days of data logs; preventing analysis of customer behavior patterns and anomaly detection based on a longer and more relevant time window. The traditional technology stack was unable to manage the rapidly growing volume of high-speed data.

A Fortune 500 American multinational automaker was seeking a solution to predict faults in their auto parts to proactively ensure fault-free production, thereby saving maintenance time and improving the customer experience. The company faced several challenges. Data was being generated from multiple discrete systems, all of which had to be processed simultaneously to get a complete picture. The data was in different formats like JSON, CSV, and other proprietary formats. The cutting tools had to be replaced before they reached end-of-life, affecting the production quality. Therefore, the automaker was looking for a solution that would predict in real-time, giving them enough time to replace the waned cutting tools. The data collected from multiple systems had several quality issues and missing records. This data had to be formatted, cleansed, and prepared before it could be fed into the predictive analytics models. The manufacturing unit had thousands of machines generating millions of events every minute. The automaker needed to process this massive amount of data in real-time using a single solution and shared infrastructure. Real-time alerts to floor operators and the downstream application was a crucial component. Any failure or delay in these alerts had a direct impact on the quality of parts produced.

Payoneer, a digital payment platform, needed a way to serve its AI/ML fraud predictive/preventative models against fresh, real-time data to provide their customers with a safer payment experience. The company was using a retroactive approach that detected fraud attempts after the fact, which meant customers could only block users after a (possibly successful) fraud attempt. This approach had several limitations including the inability to track fraud attempts across complex networks, lack of advanced analytics and log correlation to identify anomalies, and a negative impact on customer experience and satisfaction. Payoneer needed a solution that leveraged sophisticated algorithms to track multiple parameters and detect fraud within complex networks. While Payoneer had built sophisticated machine learning models, these only worked offline and could not be used against real-time threats.

NetApp, a leading provider of hybrid cloud data services, needed to enhance its Active IQ solution to incorporate an AI-driven digital advisor. The goal was to use AI to gain intelligent insights into its customers’ storage controllers and deliver prescriptive guidance, as well as automate “best actions” to achieve predictive maintenance on said devices. The company was dealing with the challenge of analyzing 10 trillion data points per month from storage sensors worldwide. The existing infrastructure of Active IQ, built on Hadoop, was unable to cost-effectively enable real-time predictive AI, run large-scale analytics, or deploy new AI services at scale. The traditional data warehouse and Hadoop-based data lake were unable to efficiently process the trillions of data points collected from storage controllers at the speed required to derive actionable intelligence needed for real-time predictive maintenance.

International fashion retailer bonprix, which operates an online shop with five house brands in 30 countries, was forced to reevaluate their pricing strategy due to the COVID-19 pandemic. Since 2014, bonprix has employed Blue Yonder’s pricing capabilities as a purely market-driven pricing tool. However, the sudden and drastic shift in the marketplace due to the pandemic required them to take more factors into account regarding individual stock and demand. The supply chain was heavily disrupted, and the supply of goods was no longer guaranteed. As a result, bonprix faced both overstocking and understocking of products due to delayed and canceled orders.

Accel Logistics, a leader in the Mexican logistics market, has been using Blue Yonder’s warehouse management solution to streamline processes in its 19 distribution centers. This has resulted in cost reductions, productivity growth, and superior customer service. However, the recent surge in market demand for logistics has also brought about an increase in demand volatility. Accel needed to improve its speed and agility to keep up with this unpredictable demand.

Bayer Crop Science, a company with annual revenues exceeding $23 billion US, delivers agricultural products in more than 70 countries. The company operates more than 350 facilities and was challenged to adopt consistent technology solutions, analytical tools, and best practices across all these locations. Logistics managers at Bayer needed standardized decision-making practices and shared values that would enable them to act in the best financial interests of the company. The challenge was to implement a single logistics platform with shared data, metrics, workflows, and best practices across all locations, supporting the company’s goal for global standardization.

Henkel, a German multinational company and a leader in both the consumer goods and industrial sectors, operates hundreds of distribution facilities of all sizes around the world. These facilities were relying on time-consuming, error-prone manual processes and a variety of outdated technology solutions. To improve customer service while driving down costs, Henkel needed to adopt best practices, advanced digital capabilities and process standardization across its worldwide network. The company was looking for a solution that could provide improved process speed, accuracy, and efficiency, higher service at a reasonable cost, and increased visibility and control.

Retailer CR Vanguard (CRV), part of the CR Group, operates more than 3,240 stores across China, with total sales revenue of nearly 90 billion yuan. As one of the largest retailers in China, CRV must strategically orchestrate planning and merchandising across these stores in multiple formats, including hypermarkets, supermarkets, and convenience stores. The company's objective is to be customer-centric by leveraging best-in-class technology to understand and meet shopper needs, even as demand volatility increases. However, CRV's demand planning was accomplished by a homegrown solution that failed to capture the complexity of 3,000 stores, multiple formats, and localized shopper needs.

Lenzing Group, a supplier of high-quality botanic cellulose fibers to the global fashion industry, is committed to sustainability. The fashion industry is responsible for 10% of global carbon emissions and 20% of the world’s waste water. Lenzing aims to minimize its environmental impacts and become carbon-neutral by 2050. However, the company lacked the transparency to achieve this goal. Five years ago, Lenzing was struggling with an uncontrolled propagation of Excel planning spreadsheets. The lack of transparency and agreement was a significant problem, especially across the fragmented, large, extended fashion supply chain. When conditions changed, it took Lenzing three or four months to respond, leading to significant environmental impacts such as waste from overproduction.

PepsiCo Latin America, a long-time Blue Yonder customer, was seeking a customer-focused and perfectly synchronized value chain, supported by standardized usage of its supply planning and execution solutions across 16 countries. As demand variability, materials costs and transportation expenses increased, this value chain would enable greater speed, accuracy and responsiveness. The company was looking for a solution that could integrate and roll out its solutions in demand planning, supply planning, inventory optimization, shipment scheduling and promotions across 34 countries.

Microsoft manages more than 200 data centers in 34 countries, and it’s adding 50 to 100 new data centers every year. The global supply chain also includes traditional functions such as manufacturing and transportation, provided by third parties. Across this geographically distributed network, Microsoft needs to ensure uninterrupted, high-quality, reliable service. The COVID-19 pandemic revealed the weaknesses in the world’s supply chains, as well as the need for a rapid, coordinated response when the unexpected happens.

Armada, a supply chain solutions provider with approximately $4.0 billion in revenues, was focused on enabling next-generation supply chain orchestration solutions. The company wanted to create a digital thread across the network that would enhance real-time visibility and connectivity of network stakeholders, leading to greater agility and responsiveness in the face of inevitable disruptions. Armada moves nearly 100 million cases annually and approximately 450,000 truckloads with speed and agility. One in eight US consumers benefits from Armada’s services each day. The company was looking for a solution that could help them maximize the value of its end-to-end solutions for existing clients while also attracting new ones.

HERBL Solutions, California’s largest cannabis distributor and supply chain solutions company, experienced rapid growth as demand for cannabis products exploded. The company grew from $20 million in revenues to $200 million in just three years. This rapid growth presented a challenge for HERBL to serve the growing demand profitably while also meeting strict regulatory requirements and supporting its retail partners. The cannabis industry is strictly regulated, which places huge pressures on all supply chain processes. HERBL needed a solution that could easily meet regulatory requirements and support the level of visibility and transparency needed to track and trace all their products in real-time.

Founded in 2014, HappyFresh is the fastest-growing online grocery platform in Southeast Asia, with its own delivery fleet and network of darkstore centers. The company serves millions of consumers in 14 cities, partners with over 300 supermarkets, and employs over 500 associates across Indonesia, Malaysia and Thailand. As demand increased for online grocery delivery, HappyFresh needed to digitally transform its warehouse operations to maintain its high level of real-time responsiveness, including customized order handling and delivery in as little as one hour.

Smile Savvy, a dental digital marketing company, was facing challenges with their SEO reporting process. With over 900 clients, manual reporting was time-consuming and prone to errors. The company was spending more time reporting SEO issues than resolving them. Additionally, the tool they were using for building citations and business listings was becoming too expensive, with costs tripling. The company needed a solution that could automate their reporting process, ensure accuracy and consistency, and provide cost-effective citation building.

Kumon, a tutoring organization with over 650 locations in the UK and Ireland, was facing a challenge with their online visibility. Each of their education centers operates in a local catchment area, making local search crucial for their visibility. However, the company did not have a centrally managed approach for local citations for their centers. This led to them missing out on local search traffic and inconsistent citations, which resulted in parents often not knowing how to contact their nearest center. These were significant problems for a company like Kumon, which relies heavily on local visibility and accessibility.