Dorman Products, a leading supplier of automotive and heavy-duty replacement parts, was facing challenges in project and support testing cycles due to the large orders they were processing. The Quality Assurance (QA) team was responsible for ensuring optimal support for new products, enhancements, and bug fixes. However, they were struggling with addressing order-related issues in a timely manner. Each order could have several thousands of order lines, making it difficult and time-consuming to recreate and debug issues. The process to replicate and debug production problems was highly manual and involved many steps. The team recognized the need for a solution to streamline this multi-step process.

VanEck 是一家以其创新金融解决方案而闻名的资产管理公司，正在寻求将其业务扩展到国际市场。该公司的理念是为投资者提供增强其投资组合的机会，这使他们成为首批为客户提供进入国际市场的资产管理公司之一。然而，挑战在于与当地投资者就全球增长机会进行有效沟通。该公司需要将其网站内容、监管文件和营销材料翻译成多种语言，以满足不同地区的需求。此外，快速增长的交易所交易基金 (ETF) 和交易所交易票据 (ETN) 市场给向广大受众营销这些产品带来了挑战。该公司还面临着保持平衡沟通的挑战，强调其产品的优势和风险。

Understanding issues in a web application may be a challenge, but getting the same understanding from a mobile app is a flat-out guessing game. Just ask Pierre-Luc Simard, CTO at Mirego. “If you’re developing a web app, you can test in every single condition from your computer,” he says. “You can try every version of each browser. You can simulate any kind of network connection. With mobile applications, you can’t do it all. You make your best guess based on the limited evidence at hand, then you release a new version to every single user through the AppStore or Google Play. You can’t release it to a subset of users — it goes out to every single user. When working with the AppStore you may have to wait two weeks for the review, and then, at long last, you find out if you actually fixed the problem. It’s like driving blind, except you don’t know if you’ve wrecked the car until weeks afterward.” In many cases, an issue emerges only after users have complained — which usually means a crop of one-star reviews in the various stores. “It can be very difficult to recover from a lot of bad reviews,” says Simard. “For every one-star review, you need a couple five-star reviews to get back on track. In other words, waiting for users to complain can be a very damaging approach to testing a mobile application.”

CanvasPop, an online retail business, experiences major spikes in traffic, some more predictable than others. Most of the site’s traffic appears during the fourth-quarter holiday season, when average pageviews rise from about 25,000 per day to as many as 70,000 per day. At other times of the year, heavy media coverage may cause similar jumps in demand. Another challenge arises in the CanvasPop manufacturing facility, where order fulfillment requires a complex interchange between digital and physical processes. When a customer creates an order, he or she will upload a digital image and select from a variety of canvas sizes, frame designs, and image effects. Then the CanvasPop system delivers the image to an in-house graphic artist who can work one-on-one with the customer to recommend adjustments or enhancements. After printing on canvas, the customer’s digital image becomes a physical product, with CanvasPop employees laminating and then manually framing each piece of artwork prior to shipping. The interplay between digital image and physical product can make order fulfillment very tricky. If they’re not careful in navigating those transitions, an order can simply disappear from the system. That’s why the performance of their backend manufacturing application is so important — without it, they essentially don’t have a business.

Next Update, a small company founded in 2008, developed a SaaS-based bug and issue tracking application called Sifter. The application was designed to simplify the bug tracking process and make it accessible to both technical and non-technical team members. However, the entire Sifter application and database initially ran on a single virtual server. This meant that even minor issues could impact performance or take the application offline. With customers in almost every time zone, this was unacceptable. The team’s initial objective was to improve availability, avoid downtime, and have more time and resources to enhance the application.

RimuHosting, a New Zealand-based hosting provider, was experiencing performance degradation and unexplained issues that they needed to diagnose. The company's open, non-hierarchical culture meant that everyone was encouraged to handle any issue and go the extra mile for a customer. However, the tools they were using to track down bugs and errors were proving to be slow and painful. They were also finding that the ongoing performance monitoring was becoming a more substantial task. As a hosting provider, it was critical that their webserver was up and running 24/7 as it contained the control panel for both the host servers and the virtual servers (VPS) running on them.

Zendesk, a cloud-based customer service help desk system, was facing challenges in ensuring an excellent user experience for its customers. The system needed to be accessible, intuitive, and consistently high-performing. With development teams in California and Denmark, the company was rapidly rolling out new features and integrations, which added to the complexity of maintaining performance. The company had a published SLA of 99.9% uptime per month, often achieving 100% uptime, so visibility into performance issues and the ability to quickly resolve them was crucial. The company was also outgrowing its initial hosting environment and needed a solution that could provide the level of performance, scalability, and security they required.

Lufthansa Systems, a subsidiary of Europe’s largest airline, provides IT services to the airline industry. Its BoardConnect product powers in-flight entertainment and connectivity for millions of airline passengers. The product requires a robust and scalable IT infrastructure that can meet customer demands for flexibility, robustness, modularity, and ease of use. As the company increasingly built its infrastructure around microservices and containerization, it identified a missing link – data persistence. The company needed a solution that would allow data persistence to catch up to the flexibility, ease of use, and speed that they needed to achieve in a containerized environment. The company also needed a solution that would allow it to auto-instantiate CMS environments on developer-committed projects and manage customers’ individual CMS production instances.

Spotlight Therapeutics faced several challenges in their lab management. Their inventory management system was prone to human error and created bottlenecks as it was managed by a single person using spreadsheets. This was inefficient and led to errors. Additionally, they lacked a data infrastructure that would allow them to run big data analysis, which was crucial for their discovery platform. Their data lacked the structure and standardization necessary for big data analytics. Furthermore, their unconnected note-taking, lab management, and data interpretation systems encouraged siloed work rather than collaborative work.

Zymergen, a high-growth biotechnology company, was facing organizational challenges due to its rapid growth. The company was using many custom software systems and algorithms to push research boundaries. However, as the company grew, it struggled to organize its rapidly growing R&D team, ensure streamlined collaboration among scientists working across different labs and teams, scale its R&D organization without losing institutional knowledge, and integrate custom software platforms and algorithms with best-in-class software tools.

Synlogic, a company creating a novel class of living medicines, was facing challenges with its legacy software. The software couldn't keep up with Synlogic's need for rapid process iteration, which resulted in scientists having to manage manual data entry across disparate systems. This hindered analysis and reporting, delaying critical business decisions. Furthermore, without a central place to store the data produced by their bioreactors, scientists spent significant time tracking people down and sending emails.

Intellia was facing several challenges in their registration and request process for their CRISPR/Cas9-based gene editing therapeutics. The previous registration process was scattered across SharePoint spreadsheets, emails, and paper, leading to unreliable data and a significant amount of time spent piecing together lineages. Their plasmid repositories couldn’t be tracked and kept up to date. Furthermore, the lack of a formal request system led to lost requests and insufficient detail in requests. Without a reliable plasmid inventory, certain requests were difficult to complete.

The Cancer Research UK Cancer Therapeutics Unit (CTU) at The Institute of Cancer Research, the largest academic cancer drug discovery and development group worldwide, was facing a challenge in managing its drug discovery projects. There was no cheminformatics platform in place at the CTU to consolidate and provide quick and easy access to data, which had led to only certain people having full access to the project data landscape. This was limiting the ability of the team to freely access data, analyze it, see the correlations, and then being able to use those correlations to determine what direction to head in – all in real time. Moreover, it was hindering the opportunity for everyone to review the data, challenge discrepancies, spot outliers and have input into what that next step should be.

Before the deployment of Vortex, scientists at The Cancer Therapeutics Unit used an Excel spreadsheet to copy and paste raw values from Thermal Shift Assay readers output before evaluating the plots to set the minimum/maximum. They would then copy and paste a subset of that data into GraphPad Prism, before copying the results back into the Excel spreadsheet for further analysis. This process was laborious and prone to errors due to manual data entry. The half a day taken to process that data had become a bottleneck for the Unit. The solution was to define and implement a standardised method that would eliminate errors and streamline the workflow.

Just Biotherapeutics faced several challenges in analyzing biotherapeutics using mass spectrometry-based methods. The large amount of data generated by comprehensive in-depth characterization was a major challenge, as it required frequent analysis of various samples and large-scale studies. Current solutions often relied on multiple software packages and manual transfer, making data processing and analysis a major bottleneck in the biopharmaceutical development process. Additionally, the richness of MS data often led to the generation of large numbers of false-positive identifications, which had to be manually investigated, a process that was typically laborious and time-consuming. MAM monitoring systems also needed to deliver efficient, reproducible, and reliable detection of new or unexpected peaks. Lastly, the development and manufacturing of biopharmaceutical molecules generated data from a multitude of sources, requiring a data system that could manage and collate large amounts of disparate data.

Sartorius Stedim Biotech, a leading provider of instruments, technologies, and expertise for the development and production of biopharmaceuticals, was facing a major data processing bottleneck due to its rapid growth. The company was primarily using Excel and paper for data management, which became increasingly challenging as the number of cell lines they were handling per year grew. The manual integration and correlation of data for in-project and cross-project analyses were extremely labor-intensive and time-consuming. Sartorius needed a central platform to streamline and digitalize cell line and process development, expand automation, and improve throughput and efficiency. They were looking for a solution that could handle their specific workflow requirements, automate laboratory operations, enable a transition to a fully paperless laboratory, and enhance scalability to support their continued growth.

The biopharmaceutical industry is constantly seeking strategies to improve the quality and effectiveness of their products. One such strategy is the characterization and control of glycosylation patterns. However, the current gold standard for N-glycan mapping, ultrahigh pressure liquid chromatography (UHPLC) with a fluorescence-based detection method, presents several limitations. It loses site-specific information for molecules with multiple glycosylation sites, has issues with co-elution and new peak appearance, and requires laborious data processing, analysis, and reporting. The need for a more robust and high-throughput glycan analysis methodology led to the exploration of LC-MS glycopeptide mapping.

Amogy, a clean energy company, is developing an ammonia-based power system to fuel heavy-duty transportation vehicles as a solution to reduce greenhouse gas emissions. The company relies heavily on computational research using high-performance computing to test multiple models under varying conditions. However, as the volume and fidelity of their simulations increased, they faced challenges in meeting their aggressive R&D deadlines due to long queue and solve times. They needed a high-performance computing solution that could be quickly implemented, did not require extensive IT support, and could support a variety of use cases.

JTEKT Thermo Systems, a leading manufacturer of industrial heating systems, is facing the challenge of making its products more energy-efficient and environmentally friendly. This is in response to the growing demand for greener industrial processes and the need to reduce greenhouse gas emissions. At the same time, the company needs to continue reducing its engineering and manufacturing costs while improving the effectiveness of its heating systems. The complexity of modern digital simulation and analysis models that guide research is increasing, requiring highly parallel calculations on the fastest computing architectures possible. JTEKT conducts analysis in four areas: thermo-fluid analysis, structural analysis, heat treatment analysis, and induction heating analysis, all of which require supercomputing capabilities.

Dyax Corporation, a Massachusetts-based biopharmaceutical company, was struggling to accurately identify and engage key customer segments across channels due to rapidly changing healthcare professional (HCP) and healthcare organization (HCO) information. The company's field representatives were updating HCP data directly in the company’s CRM system, which resulted in duplicate accounts, outdated information, and reps spending their time sorting out inconsistencies prior to meeting with HCPs. Dyax had 2,000 duplicate or incomplete HCO records representing more than 15% of targeted accounts, several hundred duplicate HCP records, thousands of missing phone numbers and addresses, and few unique identifiers like NPI numbers. The company wanted an innovative way to get accurate customer data quickly, without the cost and complexity of traditional master data management (MDM) tools.

Medicines360, a nonprofit pharmaceutical company, was facing challenges in controlling and securely sharing regulated content among its geographically dispersed teams and partners. The company was heavily reliant on email, file share, and a paper-based system, which made it difficult for remote employees or contractors to access standard operating procedures (SOPs) or other regulated documents. As the company was transitioning from research and development to launching and commercializing its first product, it was struggling with complex and inefficient processes for developing, reviewing, approving, and tracking training of controlled documents. Without a single document management system that was easily accessible from anywhere, the company lacked a consistent and efficient way to distribute controlled content and track SOP training to ensure compliance.

PDI, a leading contract sales organization (CSO) in the pharmaceutical industry, faced the challenge of providing its customers with an adaptable sales support system as quickly as it could provide sales personnel. The company was using an inflexible, client/server, on-premise CRM system that took months and often hundreds of thousands of dollars to customize for any new system change. PDI wanted to offer its customers a breakthrough sales support system that integrated all of the many different communication channels emerging today – from face-to-face selling and telesales to digital detailing through online videos, web portals and more. To make this vision a reality, PDI needed a foundation for the new solution that would enable seamless integration with other systems, was rapidly scalable and would easily adapt to its customers’ unique selling process.

Accera, a biotechnology company, initially invested in a generic customer relationship management (CRM) system, Microsoft Dynamics® CRM, which proved inadequate for their growing needs. The application was sufficient for basic contact management, but lacked life sciences-specific functionality. Less than nine months later, Accera started looking for a replacement. They needed a system designed for their industry and backed by a vendor who understands life sciences. They also wanted to upgrade the technology to gain something more flexible and that would deliver stronger reporting capabilities, a more intuitive interface for the field force, and generally more industry-specific functionality.

Comar, a manufacturer of plastic packaging products and components for the medical, pharmaceutical, healthcare, personal and home care, food and beverage, and niche markets, needed a system that would provide real-time product and processing insights and the traceability to efficiently complete audits. The company serves highly regulated industries and competes on both quality and agility. The challenge was to deliver products that meet both customers’ deadlines and quality requirements, including compliance with the Food and Drug Administration (FDA) and International Standards Organization (ISO) standards. The company also needed to handle short-notice production runs when their customers need them and consolidate financial reports across all nine Comar manufacturing plants for greater financial visibility and control.

Athea Labs was operating with an outdated ERP solution that had become so specialized for their needs that updates to current releases were no longer possible. The old ERP solution lacked key functions such as MRP and MPS, which were needed to drive Inventory and Manufacturing requirements. This led Athea to develop many of their own functions outside the old ERP solution. The old ERP solution maintained Production Work Orders and routings, but without Planning. The old ERP solution did not maintain a Lot Number/Location relationship for their items and since they inventory all their chemicals and finished goods by lot number, the process of locating the First Expired, First Out (FEFO) quantity of their chemicals could sometimes end up being a long and tedious process.

Nu-Well Physicians, a new business venture in the organic cold-pressed juice manufacturing and distribution industry, was in need of an ERP software solution that could be implemented in tandem with the construction of their manufacturing facilities. The company had a tight schedule to adhere to, with the aim of having their ERP software ready to go as soon as their facilities were operational. The challenge was to find a flexible solution that could accommodate their unique needs and circumstances, including the need for remote training and consulting.

Richard Childress Racing (RCR) faced the challenge of managing their manufacturing process efficiently to meet tight deadlines and adapt to product changes. Initially, they used JobBOSS for scheduling their machines to quickly finish parts and prototypes for testing before race day. However, as the complexity of their operations increased, they needed to schedule programming as well. They were also struggling with communication issues, especially regarding materials needed for building a racecar. The team had to complete a working chassis in less than a week, which required efficient and transparent information relay. The short turnaround time and the delay in getting material requirements to the materials planner were other challenges they faced.

When Scott Elgin became the IT Director at CleanCut Technologies the company had 22 users in JobBOSS, but the software was underutilized. JobBOSS and Synergy were primarily used to keep track of vacation requests, while CleanCut still relied primarily on paper and excel for daily operations. Relying on people and paper proved troublesome - communication was slow, data was easily misplaced and difficult to find, and it was near impossible to track job profitability or timing. Scott, who was new to JobBOSS, knew there needed to be a change.

Quality Production Ltd (QPL) had been using a paper-based manufacturing system which became unsatisfactory for a number of reasons. The system was not an integrated solution, with word processing, spreadsheet, database and drawing functions each obtained from separate, standard PC software products. Combining these into a usable documentation package was very difficult and labor intensive. Document version control problems also arose during use. Additionally, extracting timely quality data presented substantial difficulties. In reviewing and considering improvements for this process, QPL identified and prioritized a number of requirements: Increased speed and accuracy in BOM scrubbing and creating documentation packages, better efficiency in communicating ECO information to the shop floor, reduced machine programming times, extraction of real time data to drive QPL’s quality improvement initiative, and implementation of these improvements within a completely paperless environment.

Late in 2010, a Surmotech customer with a highly complex, dense PCBA required complete product, materials, and process traceability. While it was possible to fulfill these requirements using the existing systems, it would have adversely impacted throughput. Surmotech began investigating an integrated solution. The challenge was to find a system to manage the setup of their machines, track the components and units, and finally compile data from each point on the manufacturing floor to meet the end customer requirements for traceability and historical genealogy. Surmotech explored several alternative systems but most of these lacked the depth and scope to integrate the multiple machines and data points of their process.