aPriori

GE Aviation, a leading provider in the Aerospace and Defense industry, aimed to advance their product cost management technologies. The company wanted to build a business culture where everyone was focused on product cost and had the tools to do so. However, they faced challenges around cost data. The data was available but was hidden among disparate and discrete systems, making it difficult for users to access and utilize the right information. Additionally, the company struggled with departmental and organizational silos, which led to connected handoffs and interdependencies that cost valuable time and money. The company needed a solution that would consolidate and streamline cost data, break down silos, and provide the right cost estimation tools for different phases of the product life cycle.

Eaton, a premier power management company, was facing challenges in improving time to market and profitability in the highly competitive electric vehicle (EV) market. The traditional manufacturing environment, characterized by siloed departments each with its own database and processes, was hindering timely, accurate, and effective collaboration. This was particularly problematic given the current prevalence of supply chain shortages. The company needed a solution that would allow it to quickly understand the impact of design on the supply chain, identify potential manufacturability issues early in the design process, and innovate faster.

Jabil, a manufacturing services company with 238,000 employees and a revenue of $34.5 billion, faced a significant challenge in streamlining target costing and improving communication between its design and manufacturing teams. The lack of a clear method of communication between these two teams often led to inefficiencies in the product development process. Each team was responsible for target costing measures of the products they were designing and building, but without a shared platform for communication, meeting or beating company target cost goals was a daunting task. The challenge was to find a solution that would not only streamline target costing but also foster seamless collaboration between the design and manufacturing teams.

Caterpillar, a leading manufacturer of construction and mining equipment, diesel and natural gas engines, industrial gas turbines, and diesel-electric locomotives, was seeking to be more proactive with their cost and design decisions. The company was dealing with large, complex, interconnected, and interdependent product and part designs, which required a more proactive strategy for their product cost and design decisions. Caterpillar was also struggling with manual costing, which was time-consuming and less efficient. The company was trying to eliminate the need for cost reduction, which required additional resources and thus increased costs. Furthermore, Caterpillar was negotiating the cost of a part with a supplier for over a year without success.

Carrier, a manufacturer of products across HVAC, refrigeration, and fire/security segments, faced a significant challenge in modeling manufacturing costs for complex parts requiring multiple manufacturing processes. The company's cost engineering group was tasked with optimizing product cost and value through improved supplier negotiation and cost-effective design. However, traditional tools like Excel spreadsheets or traditional cost estimation software were not well-suited to generating accurate manufacturing cost models for this high-tolerance, multi-faceted production process. Precisely modeling manufacturing costs for this assembly without the capability to analyze an actual 3D model was simply not possible. Additionally, Carrier was also aiming to meet their emissions reduction goals, which required a detailed understanding of the carbon emissions associated with their manufacturing processes.

HARMAN, a high-tech electronics company with 30,000 employees and $8.8 billion in revenue, was facing a challenge in managing supplier costs. The company was in need of a technology that could efficiently integrate with design and sourcing workflows, work directly with 3D CAD models, and handle the varied manufacturing processes required by their diverse and often complex product offerings. The main issue was the lack of transparency in the pricing offered by third-party suppliers. HARMAN needed a solution that could provide detailed insight into product cost structure and quickly analyze a design to correctly allocate costs using unique, supplier-specific cost drivers. This was crucial for building a true partnership with suppliers and avoiding contentious negotiations.

Rafael, a pioneer in defense, cyber, and security solutions for over 70 years, had set challenging design to cost targets as part of their company roadmap. The goal was to manage cost from the design stage all the way up to manufacturing, including initiatives related to procurement, project management, system engineering, and requirements management. However, achieving these targets required the right tools and technology. Rafael's engineers, who are typically expected to have vast amounts of expertise and knowledge, needed a technology that they could rely on to work together efficiently, seamlessly, and collaboratively to meet these design to cost initiatives.

Signify, a global leader in the lighting industry, was seeking to enhance its procurement practices to ensure the acquisition of parts with the best cost, quality, and availability. The company aimed to expand its capabilities for generating should cost analyses for its products purchased from external vendors by 8 times. A 'should cost' refers to a projection of a given component’s cost if efficient manufacturing and distribution practices are followed. This understanding can help negotiate lower prices, identify opportunities for cost reduction, and foster more collaborative supplier relationships. However, manual should cost estimation is a time-consuming task. Signify needed an automated approach to optimize costs for their diverse portfolio of products. The company had previously partnered with aPriori in 2017 to generate select should cost models for mechanical parts, which proved successful.