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PAC Strapping Products’ plans for expanding the business were being held back by manual approaches to managing inventory, billing, pricing, orders, and other processes. These manual processes led to errors, delayed customer payments, and days to weeks of extra work for employees. As the management team began mapping out a strategy for expanding the business across both traditional customers and new markets, they realized that they would need to automate their operations to keep pace. An evaluation of enterprise resource planning (ERP) systems led the company to select the DELMIAWorks manufacturing ERP system.

SEA-LECT Plastics Corporation, a leading supplier of injection molding manufacturing, design, product development and tooling services, was facing the challenge of automating all facets of injection-molding production. This included estimating, quoting, sales, order processing, planning, scheduling, tooling, design for manufacturing, production, inventory, procurement, and delivery. The company aimed to increase efficiency and improve quality, as well as to better understand actual costs per job to maintain profit margins while improving the company’s competitive position. The company initially relied on Sea-Dog’s custom-developed enterprise resource planning (ERP) system and Excel spreadsheets to manage its injection-molding operations. However, the manufacturing services provider needed to find innovative solutions to streamline and improve all functions related to injection molding production to support growth.

Bastian Solutions, a leading materials handling company owned by Toyota Advanced Logistics, was faced with the challenge of meeting the high expectations of today's shoppers for on-demand fulfillment. Retailers require innovative materials handling systems that can navigate tight warehouse and shelving spaces to fill orders quickly. They also need picking technologies that can handle a wide variety of product shapes, sizes, and densities without having to invest additional capital in multiple custom grippers for the picking arms. To address these complex set of problems, Bastian Solutions set out to engineer an improved solution. This effort resulted in the Bastian Solutions Shuttle System, an advanced, customizable electrical robot arm-and-shuttle combo that autonomously picks and moves a range of differently sized and weighted products. However, creating a new disruptive materials handler like the Bastian Solutions Shuttle System required Bastian Solutions to use a disruptive method—additive manufacturing. But without on-site 3D printing capabilities, Bastian Solutions sought out a partner that could help make their vision a reality.

Husqvarna Group, a leading producer of outdoor power products, was looking to evolve with shifting technology and customer demands. They were interested in exploring how 3D printing and additive manufacturing could drive impact for their business. The challenge was to identify parts that could be made more efficiently and sustainably through additive manufacturing, driving a reduced carbon footprint, improved customer experience, and lower costs. The parts needed to be produced at production scale and quality, and they needed to pass rigorous testing and certification for production and sale.

Cobalt Robotics is a company that produces security robots designed to augment human security efforts rather than replace them. The robots are designed to be approachable and warm, making people feel comfortable interacting with them. This necessitated special attention to the cosmetic components of the robots, which are the external parts that humans will see and interact with. Cobalt needed these cosmetic parts to be made exactly to their design specifications. Prior to working with Fast Radius, Cobalt struggled to find a cost-effective supplier that could deliver on the exact specifications of the product.

Coapt, a Chicago-based company that produces myoelectric pattern recognition systems for upper limb prostheses, was looking to produce the second generation of their product, COMPLETE CONTROL. This required not only retooling the system’s software, but redesigning the hardware components as well. They needed a manufacturing partner that could move quickly to meet their product release timeline. They also wanted to test and make many parts differently in the next wave of COMPLETE CONTROL, including the geometries, textures, and aesthetics of the parts. With so many variables to consider, it didn’t make sense for Coapt’s engineering team to prototype using legacy processes like injection molding.

Rawlings, a company with over 150 years of experience in the baseball industry, aimed to revolutionize the baseball glove by developing the REV1X. The glove leverages the Carbon Digital Light Synthesis (DLS) process to improve gameplay and speed up reaction times. The traditional foam or wool parts in the glove’s thumb and pinky were replaced with latticed pieces, made from FPU 50, that are lighter and thinner. The REV1X lattices are tuned with variable stiffness that better conforms to the player’s hand, leading to better control of the ball. Additionally, the REV1X is ready for gameplay immediately, and it’s more durable and long-lasting than traditional gloves. Once Rawlings perfected the design for the REV1X, they needed to mass produce a glove that would meet their exacting standards for performance and durability on the field.

The executive aviation company had been providing award-winning services for the past 50 years and had a requirement for a quality lighting fixture by a company that stands behind their product. The challenge was to find the best product for the application to achieve the recommended light levels, with a special request. In this particular hangar, the lights hung down below the water suppression unit located above the airplanes. Due to a past situation when the fire sprinklers were accidentally activated, the customer had to replace their entire lighting system after it had been damaged. They requested the replacement lights to be wet location rated. The last challenges to overcome were to seek final approval from the executive team – CFO and Head of Maintenance – coupled with working the lighting upgrade into the budget.

Absci, a drug and target discovery company, was facing several challenges in its operations. The success of their work heavily relied on coordination across teams. However, they were managing sample handoffs in spreadsheets, which not only had the risk of error but also lacked sophisticated collaboration features, making it difficult to share and reference data. Additionally, the performance of their AI models depended on the quality of the training data. They occasionally experienced data-related deviations such as duplicate or incomplete datasets and risked copy/paste errors. Lastly, they lacked a universally accessible tool for data connectivity, making it difficult for stakeholders to drive organizational and scientific decisions.

Serotiny, a therapeutic discovery company, was facing challenges in managing their research data and processes. They were using a collection of Microsoft products to keep track of protocols, experiments, samples, and results. However, these tools were not designed for biotech R&D, making it difficult to organize and find past samples or results. The lack of easy-to-use templates meant that teams often had to write new protocols from scratch, taking precious time away from research. Additionally, their legacy molecular biology tools were clunky and did not integrate with their other systems, leading to tedious, slow, and error-prone copy/paste actions.

Joywell, a company on a mission to replace sugar with nutritious sweet proteins, faced several challenges in its quest. The company's data lacked context, making it difficult and time-consuming to make business decisions. The data was scattered across multiple people, notebooks, and machines, resulting in weeks of extra work and preventing teams from answering important business questions quickly. The teams struggled to aggregate large sets of data as data generated by different instruments would come out in different formats. This required scientists to perform a manual, tedious last step of standardizing 10-20 different file formats or risk leaving questions unanswered. To meet its ambitious goals, Joywell needed to ensure its processes are robust and repeatable not just within its lab but also at partner labs.

Lead Pharma’s new entity discovery and development capability has grown significantly during recent years. In response, assay throughputs and analysis requirements have increased within the company’s research functions and based on their experience within larger organizations, scientists at Lead Pharma were concerned about the limitations of traditional paper laboratory notebooks. Paper-based cross-referencing of scientific information heavily relied on team knowledge and was consequently vulnerable to staff turnover. Industry processes also required laboratory journals to be checked and countersigned by a scientific colleague or supervisor; a procedure dependent on the timely availability of appropriate staff members. Paper notebooks were used in combination with conventional Microsoft software packages (e.g. Excel, Word) and digital databases, resulting in a labor intensive, mixed-media approach.

Redx Pharma, a UK-based pharmaceutical company, is undergoing rapid expansion and developing new drug candidates across eleven different therapeutic classes using its innovative Redox Switch™ platform. This growth creates a dynamic research environment that requires an informatics platform capable of holding increasing amounts of complex and diverse data. The system needs to be user-friendly and intuitive to facilitate quick and efficient adoption by users. It also needs to prevent input errors and comply with directives from regulatory authorities.

Antabio, a biopharmaceutical company, was initially a small start-up with a single program in the hit to lead phase. As the company expanded, the scientists were making more molecules and introducing different assays. They were using a simple chemistry enabled database to associate structures and data. However, they quickly became frustrated with the time taken to analyze data, risks inherent in manual data manipulations, inability to capture or link the data to the raw data and parameters of the assays, lack of ability to follow any variations in assay protocols, and lack of project oversight.

Fundación MEDINA, a non-profit research organization based in Granada, Spain, specializes in natural products microbiology, chemistry, and high throughput screening. They are developing drug discovery programs in infectious diseases, oncology, and neurodegeneration rare diseases. The organization is also establishing contract research collaborations and partnerships with pharma and biotech companies to discover novel therapeutics. However, they needed a system to manage their experimentations in terms of time, efficiency, and drug discovery processes.

LifeArc, a UK medical research charity, is dedicated to transforming early-stage science into medical breakthroughs. The organization has a diverse range of scientific activities, from small molecules to engineered antibodies, which generates a variety of data types. This data is stored in operational databases and needs to be accessible for visualization and analytics. LifeArc also collaborates with a number of external organizations, adding to the complexity of the data sources. The challenge was to create an informatics system that could handle this complexity and streamline workflows, while also facilitating the process of ideation, which had historically been separated from informatics.

Debiopharm, a pharmaceutical company, was facing challenges in managing its Active Pharmaceutical Ingredient (API) stock and shipments. The company synthesizes compounds at internal labs and at numerous Contract Research Organizations (CROs). Similarly, analysis is done both internally and at specialty CROs around the world. With so many stock movements, there was a risk of error. The company was looking for a solution that could provide real-time, accurate account of shipped samples and remaining stock, streamline the request and fulfillment process, and offer real-time tracking of stock sent to Debiopharm sites and between CROs.

BASF's Agricultural Solutions division was facing challenges with handling increasingly complex and comprehensive data in R&D for new crop protection products. The division had a strong mandate for continuous innovation and digitalization and sought to optimize workflows involved in the design cycle. The digitalization team invested time mapping and analyzing their R&D digital workflow to understand who generates and who uses the data. They established the requirements for a system to support both data capture and its subsequent application to decision making.

CalciCo Therapeutics, a biotechnology company based in Oxford, United Kingdom, focuses on the development of novel CRAC channel inhibitors. Their research efforts are devoted to developing selective molecules to treat a range of inflammatory and autoimmune conditions where there is clear evidence linking CRAC to human disease. The company has developed proprietary ‘know-how’ comprising novel systems for selective CRAC channel screening and molecule characterisation. However, they faced a challenge in storing, analysing and sharing their distributed research data. They needed a system that could manage their chemistry and biological data, and also facilitate collaboration with multiple third-party organisations.

The challenge in this case study is the need for efficient and effective processing, management, and analysis of omic and phenotypic data in translational research activities. These activities are crucial for characterizing and profiling patients using omic technologies to understand their response to new therapies, stratify patients for trials, or search for new disease biomarkers. The existing in-house software solutions such as tranSMART, while useful, require enhancement and expansion to fully optimize the process of translational research. The data quality and curation is critical to making the right scientific conclusions, and the process of loading omics and clinical sample annotations (metadata) into tranSMART can be time consuming and expensive.

Novozymes, a world leader in biological solutions, uses bacterial and fungal hosts to manufacture bulk enzymes for a variety of industries. Host cell proteins (HCPs) are a major source of contamination that can adversely affect product stability and performance. The company needed to automate and streamline each stage of their LC-MS approach to keep pace with the large number of samples that their lab is required to analyze. Implementing robotic sample preparation, a shortened HPLC separation step, and high-speed MS acquisition increased sample throughput but generated large amounts of complex data requiring time-consuming analysis and review. The company also needed a fast and efficient way to ingest results into their corporate data lake and provide fast and efficient ways to share relevant information to key stakeholders and decision makers.

Biopharmaceutical companies are facing the challenge of managing and utilizing their R&D data, which is often siloed within different functions of the company. This siloed data structure hinders the effective federation of distributed data, which is crucial for increasing clinical trial success rates. The industry is recognizing the value of sharing and reusing data for multiple analyses, which requires breaking down these data silos. Furthermore, the data generated in biopharmaceutical R&D is complex and originates from various sources, making it crucial to implement the FAIR (Findable, Accessible, Interoperable, and Reusable) principles for scientific data management and stewardship. However, making data FAIR is not an easy task and requires the right infrastructure that can handle the data volume and privacy of patient-level information.

AstraZeneca, a global biopharmaceutical company, was facing challenges in automating data processing and increasing the quality of results. The company was using Acoustic Droplet Ejection (ADE) technology for high-throughput screening (HTS), which introduced samples into a mass spectrometer. However, the increase in throughput brought by Acoustic Mist Ionization Mass Spectrometry (AMI-MS) technology resulted in large amounts of data that needed efficient processing. The main challenges included eliminating laborious and potentially error-inducing manual interventions, guaranteeing data quality and traceability, adapting data processing to different targets and screening strategies, obtaining insightful visualization to speed assay development, and integrating MS data into screening data analysis platforms.

Immatics is a clinical-stage biopharmaceutical company that focuses on the development of novel therapies to treat cancer patients with solid tumors. The company employs two innovative proprietary technologies, XPRESIDENT® and XCEPTOR®, to develop T cell receptor-based immunotherapies. The TCR-related R&D workflow at Immatics is very sophisticated, involving hundreds of individual steps and iterations to generate new molecules and cell lines, engineer and optimize the therapeutic modalities, and constantly test and monitor them to see if they are fit-for-purpose. Each step generates critically important data that needs to be captured, processed, analyzed, and made available to various R&D teams. The complex data stream includes all molecule, sequence, and sample details, assay, and analytics values, as well as decisions such as the CDR combinations to be used to engineer distinctive TCR bispecific molecules. This data must be integrated, stored, related, and interpreted in real time to support the day-to-day activities of the various Immatics’ R&D teams.

MorphoSys, a late-stage biopharmaceutical company, was facing challenges due to its rapid growth. The increase in molecule and sample throughput led to a significant amount of R&D data, which was difficult to capture, process, and interpret. The scale and throughput of their operations necessitated the use of an enterprise workflow platform to manage and streamline the growing number of their discovery programs. The goal of the new system was to make MorphoSys’ sophisticated R&D processes more efficient and to facilitate handovers between various R&D teams and functions, including screening, molecular biology, engineering, expression, purification, and analytics. The new system also needed to be able to handle the increasing amount of data and samples produced by external partners, such as CROs, which had to be integrated into MorphoSys’ R&D process.

Bayer Pharmaceuticals obtained an exclusive license from Ionis Pharmaceuticals to develop new oligonucleotide-based treatments for a wide range of medical conditions. To support release testing and the imminent clinical trials of an advanced drug candidate, the Bayer Analytical Development Team for Biologics was tasked with implementing a validated drug substance and drug product release ID test that required establishing new analytical methods, standard operating procedures, and organizational responsibilities. The tight go-live timeline and anticipated costs of any delay led Bayer to work together with our long-term partner Genedata to develop a confirmatory identification (ID) workflow of oligonucleotide species by mass spectrometry so that an approved quality control (QC) could be implemented and validated by Bayer’s Quality Assurance (QA) team. The main challenges were implementing an oligonucleotide analytics workflow, satisfying GMP requirements, and meeting a wide range of new analytical demands.

Bayer Pharmaceuticals was facing several challenges in their lab. They were developing new methods to characterize novel classes of biopharmaceuticals, which was compounded by the novel nature of the analytes, for which no established MS-based protocols were available. Interpreting results and iteratively optimizing experimental and analytical methods for such molecules required a significant amount of area expertise in MS-based characterization of biotherapeutics. They also needed to deliver productive output in a timely manner to meet the needs of their internal customers involved in time-critical projects and maximize Bayer’s return on investment in new infrastructure. The novel nature of their analytes and protocols and the limitations of the available software made interpretation of results far from straightforward. When troubleshooting a complex process issue, it was extremely helpful to have not only domain expertise, but also to have a comprehensive and unbiased overview of all data at all stages of processing. Any solution that they implemented had to be scalable and able to handle high sample throughputs. Using the available software, data processing and the production of targeted reports were laborious and time-consuming processes that required frequent manual interventions.

Pfizer, a global biopharma giant, faced the challenge of aligning workflows and centralizing data for more than 200 scientists located at 6 sites around the world. The company's diverse and growing biopharma R&D operations required a central system to share data and align large-molecule R&D processes. With R&D teams spread across the globe, vast amounts of data were being collected in silos, making it difficult for teams to share information. This impeded collaboration and led to process inefficiencies. After an aborted attempt with a develop-as-you-go approach, Pfizer decided to evaluate the market in search of a commercial enterprise data management system for their large-molecule R&D groups. The desired system would need to act as a backbone platform and central repository for Pfizer’s biological R&D workflows and include tools able to capture specific instrumental data, workflows, analyses, and each group’s unique contribution to the overall biological drug discovery process at Pfizer.

The Microbial Process Development Group at Merck KGaA was tasked with developing and producing recombinant proteins expressed in Escherichia coli and Pichia pastoris. They used mass spectrometry (MS) as a routine tool for supporting process development in recombinant protein production. However, routine use of Multi-attribute method (MAM) in this environment meant overcoming various scientific, technological, and methodological challenges. These challenges included managing large amounts of data, producing unbiased audited results, and meeting process validation requirements. The overall production process for recombinant proteins involves multiple processing steps that are driven by defined process parameters. The characteristics of the resulting protein product must be such that the final drug substance is both safe and efficient. Studying the desired protein characteristics, then defining, monitoring, and managing critical quality attributes (CQAs) is key to success.

Zeleros, a startup developing an innovative hyperloop system to revolutionize ground travel, was facing challenges with its local computing capabilities. The company's local computing capabilities lacked the flexibility to accommodate the changing requirements for simulations and modeling. The on-premises computing capacity was fully occupied by a single simulation, causing significant impediments to productivity. The team was also losing a significant amount of time benchmarking to find the appropriate HPC instance for each simulation.