With an approximately $20M contract from the U.S. Army, SIONYX launched a product program that required not only the team’s innovation and technical competence but also control of all technical data and design for government regulatory compliance. “We needed to demonstrate that we have a formal set of industry-standard controls around how we do things and how we manufacture,” stated Steve Anderson, Vice President of Operations for SIONYX. “We had manual processes early on and our government customers required us to implement a PLM system with embedded best practices and automated workflows—aligned to ISO9000 standards.” The defense contract brought SIONYX the compliance requirement for proper handling of technical data, including access controls, full audit history, and a compliant platform architecture. Arena’s AWS GovCloud platform solution provided a low-overhead solution for SIONYX to meet the business and regulatory needs with significantly reduced risk. An additional hurdle to the project was time, or lack of it. “We kicked off this project in January and we needed to demonstrate to the government by August that we had a compliant setup between QMS and document controls,” shared Anderson. “We had a very tight timeline.”

Opening a new site in South Africa has H2 PowerTech product teams working around the clock. The company has three locations in different time zones. Research and development is located in Bend, Oregon, while manufacturing and test take place in the new facility in South Africa and in Taiwan, which also provides design engineering. Before opening the South Africa location, H2 PowerTech also had a site in Mexico. At that time, collaborating around the latest product design information in real time was a challenge, as product information was managed in spreadsheets and not easily searchable. Designing and tracing items and part number information was performed using an antiquated system and Microsoft file structure that lacked revision control. Adding to the complexity, a single H2 PowerTech fuel cell product can contain over 2,000 parts with multiple circuit boards with firmware and hardware working together. This requires interoperability between systems and increases the probability that something may go wrong. It was difficult for engineers to identify issues early on which often led to late-stage scrap and rework. H2 PowerTech wanted to remove setbacks associated with product information being hidden in various silos and ensure communication and accuracy across all their locations.

Due to the complexity of their IoT devices, Haltian must ensure seamless interoperability between the devices’ sensors, software, and electrical components. Previously, Haltian relied on multiple disconnected systems to store and manage their product data. Bills of materials (BOMs) were maintained in Excel spreadsheets, making it difficult for teams to keep track of the latest product designs and ensure data integrity. As the company continued to expand globally and extend their product portfolio, it became nearly impossible to effectively manage product information using this manual, siloed approach. In addition, Haltian’s transition to a remote work model because of the COVID-19 pandemic created communication gaps and other inefficiencies as teams scattered to different locations. Ultimately, the company needed a single, centralized solution that would bring more structure to their product data and keep their dispersed teams on the same page.

As a high-growth satellite communications company, ALL.SPACE attracted the interest of many customers and investors with its breakthrough technology. To present itself in the best light and stand out from the competition, ALL.SPACE needed a systematic approach to showcase how their product was progressing and how it addressed evolving market needs. In addition, ALL.SPACE relied on manual, disconnected systems to maintain CAD/CAM design files and other documents associated with their product record. Given the complexity of the product design, ALL.SPACE required a system that could better manage and control high volumes of product data throughout the entire product lifecycle.

As a global company with operations in the U.K. and U.S., Filtronic struggled with an internally developed product development tool. Tracking, maintaining, and releasing products was slow and inefficient. Filtronic had design, manufacturing, and quality silos that made it difficult for teams to see the interconnectedness of products, procedures, and documents. Managing compliance, audit actions, and corrective actions required cumbersome manual processes and constant human intervention. In Filtronic’s fast-paced development environment, there was zero tolerance for engineering design roadblocks, part issues, or regulatory compliance delays. In addition to solving these challenges, Filtronic wanted to focus on what they do best by eliminating their reliance on internal IT and engineering resources that were required to keep their homegrown tool afloat.

Instagrid recognized the need for a professional-grade power supply that would enhance efficiency across the mobile workforce while reducing its environmental impact. To fill this gap and bring their innovative portable battery technology to fruition, the company searched for a solution that could expand their product development capabilities and accelerate time to market (TTM). Previously, instagrid relied on Excel spreadsheets as well as a cloud-based MCAD/ECAD system to manage their product designs and bills of materials (BOMs). As the designs progressed to later stages and more assemblies were added, it became difficult for engineering teams and external suppliers to identify the latest revision and stay on the same page. Additionally, these disconnected point software solutions did not provide instagrid a way to ensure revision control and manage engineering changes.

To stay ahead in the highly regulated and competitive life sciences market, SomaLogic needed to fast-track innovation and demonstrate compliance with various industry regulations and standards. Previously, SomaLogic relied on a content management tool to maintain their product and quality records. Because that system primarily focused on document management, it did not provide SomaLogic visibility and traceability across the full product record. This made it harder for SomaLogic to drive compliance and meet their new product development and introduction (NPDI) goals.

In September 2017, the Arlington City Council opted to replace a low-volume fixed route bus, the Metro Arlington Xpress (“MAX”), with Via’s comprehensive microtransit solution. Before MAX, Arlington was the largest U.S. city without public transportation. Arlington’s story was a familiar one: The city government recognized a need for alternatives to driving alone in personal vehicles, yet the residents voted down measures supporting transit investment time and again, rejecting three transit ballot measures since 1979.

In January 2020, the Sacramento Regional Transit District (SacRT) partnered with Via to re-launch and expand their SmaRT Ride Microtransit Service, an on-demand transit service for Sacramento residents. The Sacramento Transportation Authority (STA) awarded SacRT a $12 million grant to help expand microtransit to communities throughout the region. SmaRT Ride was previously powered by Transloc across two zones. With nine zones, SmaRT Ride is now the largest microtransit system in the United States.

Remove the need for personal vehicles for transportation around a large industrial complex and increase traffic safety. Improve mobility for employees and visitors at BASF’s Ludwigshafen Site while reducing traffic.

At Baldor, management believes in product quality just as much as production quantity. To ensure a high level of quality, Baldor decided to institute a policy dedicated to customer satisfaction. The policy would involve continuous quality and reliability improvements, with each employee playing a specific role. With the new quality initiatives, Baldor’s objectives were to: Improve gauging and measurement accuracy, Decrease production cycle times, Lower the cost of scrap, Make inspections faster and more accurate. To achieve these goals, Baldor needed to monitor quality data around the clock and use data analysis to make process adjustments when necessary. The solution also needed to be intuitive for the production and quality teams to quickly adopt as part of their job responsibilities. With the new quality policy, Baldor instituted the methodology of Statistical Process Control (SPC) into the corporate culture with quality personnel, engineers, and managers all using data to improve processes.

Ben & Jerry’s maintains the strictest standards of product quality to ensure its customers get the full flavor experience each time they enjoy a pint or cone of Vermont’s finest ice cream. This attention to detail can be seen from “cow to cone,” as the company says, meaning that each step of its supply chain – from suppliers and distributors to manufacturing operations – must comply with the company’s three-part mission statement, which emphasizes product quality, economic reward, and a commitment to the community. Focusing on its manufacturing operations, Ben & Jerry’s maintains quality procedures for key performance indicators (KPIs) that ensure consistent product quality for every pint produced. To track quantitative data, the ice cream manufacturer had previously been using a paper-based system, which was proving to be cumbersome for operators and data administrators alike. Operators would take individual readings and calculate an average of those readings to plot on a paper chart. Quality assurance personnel would then perform manual calculations to compute trends and create reports. This system was not only slow and inflexible, but also costly in terms of man hours required for calculation and analysis. Ben & Jerry’s needed a fast and reliable way to collect and analyze the vital quality data of its super-premium ice cream products.

Cochlear was looking for a quality-control solution that builds quality into production processes rather than testing failures out. Cochlear needed a system to increase efficiency with real-time data analysis to help them move past the paper-based reporting system they had previously relied on. Cochlear’s primary objectives were to decrease build variability, reduce reliance on manual systems and paper-based records, and proactively manage machine performance.

Along with creating cost-effective operations, Cooper has sought to streamline its supply chain with low-cost, high-quality raw materials that include natural rubber, synthetic rubbers, carbon black, reinforcing fabrics, and steel. Cooper’s continuous improvement activities are leading the company to develop innovative quality improvements. To establish more efficient production processes, they first had to understand and benchmark their baseline capabilities. One goal was to make better use of production data and, from an operations standpoint, use the data to help guide the decision-making process. They needed a quality solution that could satisfy their scalability needs while offering insight into potential quality improvements. Logistically, Cooper needed an enterprise-wide standard for reports in a system that would initially be implemented in North America, with the ability to go worldwide. As a global entity, Cooper’s implementation would take place in phases, so they needed a flexible solution with options for training, consulting, and support. One of Cooper’s objectives was to employ a quality expert at each location to oversee the implementation and ensure the stability of corporate standards.

As part of continuous improvement efforts, Coty determined that its filling process was generating a higher level of waste than expected. This was due in part to some lines overfilling containers to ensure aesthetic fills were met, which led to higher expenses on supplies. Considering the price of some of the fill liquids, this was a significant opportunity to reduce overfill and save money. However, the company did not have enough historical data on these lines, which meant that process engineers and quality professionals did not have sufficient information to truly understand the entire problem and develop a viable solution. In 2010, the manufacturing team at Coty’s Sanford, North Carolina, manufacturing facility turned to Statistical Process Control (SPC) analysis to better understand scrap at the point of manufacture on its filling lines. As part of an SPC Fill Height Project, Coty wanted to determine ways to reduce liquid scrap and better understand its process capability.

As the company continued to grow and acquire more product brands, General Cable saw the need for consistent production data analytics across its global manufacturing base. Although the company continued to produce high-quality products, it wanted to implement a system that improved the ability to control process variation from plant to plant. With a standard approach to data collection and analysis, the company could ensure high quality, control raw material use, and prevent operational inefficiencies associated with rework, giveaway, production delays, or customer complaints.

Always customer-focused, GSI is committed to providing the highest quality products and services. To ensure product quality and compliance, GSI relied on a homegrown Statistical Process Control (SPC) program created in Microsoft Access. The company realized that its system lacked the robust data collection and analysis capabilities needed to meet client specifications, support a fact-based decision model, and fuel continuous improvement. Rapid growth of GSI’s Functional Printing division increased the need for a new SPC solution. Functional Printing yields active products and components such as medical electrodes, sensors, antennas, and circuits. GSI had to verify that components were fully functional after printing and also demonstrate that processes complied with specific requirements. GSI’s disparate systems (e.g., CMM scales, multi-meters, Vision inspection, and document control systems) made data aggregation difficult, and delivery of Manufacturing Intelligence to the organization and its customers nearly impossible.

Leggett & Platt needed to decrease the amount of time spent on measuring items in production. Additionally, they needed to eliminate lapses in reporting and transform their data collection process into a completely paperless system. Their objective was to standardize on a single analytics solution to become more efficient, improving quality during production and preventing defects.

With a quality policy incorporating continuous improvement and the 4.5 Sigma Way, Lin Engineering has long established itself as a manufacturer of high-quality products. As it expanded on its 4.5 Sigma initiatives, Lin Engineering realized that it would need a Statistical Process Control (SPC) system that would allow for real-time quality monitoring and process control. The company’s overseas operations created an additional challenge; Lin Engineering needed to ensure that products produced in its facility in China were without defects. If a product failed inspection after it was received in the U.S., it was costly to ship it back to China. Plus, Lin Engineering wanted quality analytics to verify that each product meets a stringent quality standard to strengthen the company’s leading market position and drive new revenue.

As a Medical Device manufacturing company, medi is committed to delivering quality garments that consumers feel safe and confident wearing. At its Whitsett, North Carolina, facility, medi was using its own proprietary software for data collection and quality monitoring in its manufacturing processes. However, error codes in the software produced a large amount of paperwork because operators could not easily make changes to incorrectly entered information. Making corrections often took as long as an hour and a half, and duplicate data entry created variations in reporting. Also, in order to maintain compliance with medical safety regulations on technology used in manufacturing, medi needed to validate its software to demonstrate its reliability and effectiveness. The homegrown system was outdated, and medi was uncertain whether it could satisfy the strict validation requirements. Rather than continue with its homegrown software, medi sought a new solution that could drive quality improvements and increase efficiency while facilitating the validation process.

As part of its ongoing commitment to deliver safe, wholesome and trustworthy products, Michael Foods maintains a strong focus on the wants and needs of its customers, introducing innovative, value-added food technology and customer solutions across the enterprise. In its manufacturing facilities, the quality assurance teams were practicing Statistical Process Control (SPC) to improve the operational efficiency of processes and machines. However, data collection was largely performed manually; the analysis software was developed for quality assurance rather than statistical analysis and didn’t offer the necessary capabilities to truly understand the value of the collected data. The Michael Foods quality assurance team wanted to improve its overall statistical approach to quality and reduce the amount of product that was being lost through overpacking.

Nestlé Waters had been using a cumbersome, paper-based system to collect and analyze data. When issues arose that required immediate attention, the company’s quality engineers had to disrupt operators on the production line to retrieve the necessary data. Nestlé Waters’ goal was to implement a system that would enable them to easily monitor, review, and trend real-time quality data. In addition, they wanted to standardize on one solution—across all their facilities—to complement their existing IT infrastructure.

As SanDisk’s product offerings have evolved, so has the need for a Statistical Process Control (SPC) platform that ensures consistent quality throughout every level of production. SanDisk required an SPC system that was flexible enough to analyze real-time process data over the full product range, and powerful enough to predict potential quality issues in order to prevent scrap and rework. SanDisk’s factory Manufacturing Execution System (MES), Camstar Manufacturing, controls manufacturing business processes, but is not designed to perform granular real-time process data analysis at the shop floor level. To complete that loop, SanDisk chose InfinityQS® ProFicient™ SPC software to integrate real-time process data with Camstar Manufacturing and SanDisk’s factory Quality Management System (QMS), Camstar Quality.

Trek needed a way to quickly and reliably monitor weights in the OCLV carbon molding area. There was no effective system in place to do so, and Trek was looking to automate this function. Though weight data was already being collected in the carbon molding area, they needed to implement a paperless system to help the operators respond in real time to out-of-control signals. Improving Trek’s use of SPC in the OCLV molding area was another way of ensuring their 'Best in Class' status as a bicycle manufacturer. In the aluminum machining area, measurements were being recorded with a pencil and paper, and there was no electronic repository for storing these records. Calculating control limits and periodically checking the process capability were time-consuming and tedious.

EMD Millipore had been relying on post-production testing to control quality in pleated cartridge assembly. Under the previous system, the cartridges were bonded and then placed on carts. The carts would then be moved to a testing room where post-production quality inspection would occur. If they passed the inspection, the cartridges would be placed back on the carts and moved to a drying area where they would dry before they could be packaged. This approach was costing EMD Millipore a great deal of time. They were looking for a way to perform the quality tests during production so the cartridges wouldn’t need to be removed from the line. In-process testing would prevent quality issues from occurring, and would reduce the number of defects found during post-production. An ideal system would demonstrate that the bonded parameters were in statistical control and specification in a highly automated production environment—so the post-production testing and the associated handling could be eliminated.

In the semiconductor industry, any variation is undesirable. TEL NEXX’s chip-manufacturing customers require strict variation control. To meet customers’ needs and expectations, TEL NEXX strives to learn and continually improve its processes. Historically, TEL NEXX relied almost entirely on manual data collection and quality control, and operators had to enter data into spreadsheets for report generation. These systems were time-consuming and required rechecking to avoid errors. To run reports in the consumables area, for example, Hart had to locate specific spreadsheets and analyze them for answers. When customers submitted inquiries, response times could be slow. With management looking to modernize and improve data collection and response, a move to InfinityQS® ProFicient™ offered many benefits and opportunities. But the transition had to occur in a way that supported operator adoption and didn’t disrupt production.

This strong customer-centric focus and drive toward futureproof technologies has produced a culture of quality at Umicore. As Quality Engineer Mark Gaumond explains: • Customers want zero defects and often require capability studies. • Processes must be stable and meet specifications that vary from customer to customer. • Umicore must meet strict national and international standards, including ISO 9001 and 14001; European Union (EU) Directive 2002/95/ED, Restriction of Hazardous Substances (RoHS); EU regulation Registration, Evaluation, Authorization and Restriction of Chemicals (REACH); and Conflict of Materials. For Umicore, quality isn’t only an external issue: It’s internal as well. Mr. Gaumond notes, “We are careful to track internal complaints, customer complaints, and internal issues so that we have a historical record to inform us about trends and opportunities for improvement and prevention.” But quality never stands still. To stay ahead of the needs of future-looking customers, Umicore needed to make quality and process control even more efficient. To do that, the company turned to InfinityQS® ProFicient™.

Mid South Baking Company, Mississippi Division, faced significant challenges in their manufacturing processes. The primary issue was the inefficiency of their data tracking methods, which relied on clipboards, pencils, paper, and Excel. This outdated system made it difficult to respond to issues quickly, leading to delays and inefficiencies. The company needed a solution to streamline their processes, improve response times, and reduce product giveaway. Additionally, they had to comply with the Food Safety Modernization Act, which required a focus on preventing contamination rather than responding to it. The company also faced the challenge of conducting multiple audits annually, which required quick and efficient data retrieval.

Ocean Spray's Markham, WA Craisins® dried cranberries manufacturing facility began using InfinityQS in 2006 to display and analyze its manufacturing data visually. The quality team wanted to move away from paper-based records and access real-time data for immediate process improvements. Before implementing SPC tools, operators were trained not to tweak in-spec processes, which led to inefficiencies and higher costs. The challenge was to shift the mindset to drive towards target using SPC, ensuring consistency in quality and consumer experience across multiple plants.

Out-of-spec elevator wall panels were causing job site construction delays that ranged from 2 to 7 days—angering customers, costing time and money, and jeopardizing the goodwill of installation crews. The company needed a solution to quickly identify where panel processes were flawed and take immediate corrective action. Additionally, the company faced challenges in reducing elevator panel production time and improving data collection and analysis processes.