Five Ways Discrete Manufacturers Reframe Existing Engineering Data to Reduce Costs and Accelerate Decisions
Originally posted in Manufacturing Engineering & Technology magazine
Engineering teams in discrete manufacturing routinely search for parts, drawings and specifications that already exist somewhere in the organization. When that data is locked in legacy systems, stored in disconnected folders or tied to individual institutional knowledge, the cost shows up in reordered parts, repeated design work, slower quotes and procurement decisions made without full information. The gap is access and structure, not volume
And yet, access alone does not capture the value. In my work helping manufacturers adopt and scale a data platform, the pattern I see most often is this: Structuring the data is the easy half. The harder half is changing how teams actually work—whether engineers trust the system enough to search it first, whether procurement builds it into how they buy, whether leadership treats the rollout as a change program rather than a software install. The manufacturers that get real returns are the ones that manage change deliberately.
Here are five areas where structured engineering data produces measurable operational change:
1. Reduce Search Time and Connect Drawings to the Full Record
Engineers at Amerequip, a manufacturer with more than 100 years of accumulated engineering data, previously navigated disconnected systems to locate drawings, assemblies and cross-referenced part numbers. After structuring that data through CADDi Drawer, a single search returns drawings alongside associated assemblies, specifications and cost data. For engineering leaders managing large catalogs across long product histories, that consolidation changes how quickly teams can act on new requests for quote (RFQs).
Sumitomo Drive Technologies applied CADDi’s AI data platform to 60 years of engineering records. Search time for parts and drawings decreased by up to 90%. That figure is consistent with what I observe across customer onboarding: The time engineers spend on retrieval, locating the right drawing in the right version from the right system, is substantial and largely invisible until it is measured.
For engineering and quality teams that spend significant portions of their day locating reference documents, that reduction compounds across every project, every RFQ and every corrective action review. The engineers do not workless. They work on problems that require their judgment rather than on retrieval tasks that require only their persistence.
2. Enable Procurement to Consolidate Suppliers
Discrete manufacturers accumulate excess part numbers over years of design cycles, acquisitions and supplier changes. When engineers cannot locate an existing drawing quickly, they create a new one. In the deployments I work with, this is one of the first conditions manufacturers identify once their drawing archives become searchable: part numbers they did not know they already had.
Structuring drawing archives so that similar parts are identifiable before a purchase order is placed allows engineering and procurement teams to consolidate part numbers at the source. Fewer active part numbers reduce supplier relationships, lower inventory carrying costs and simplify incoming inspection requirements downstream. When drawings are organized and cross-referenced, procurement teams identify parts with similar geometries or specifications that are currently purchased from different suppliers.
Consolidating those orders reduces administrative cost, creates volume leverage with fewer suppliers and simplifies incoming inspection. Tadano Ltd. deployed CADDi’s AI data platform across operations in Europe and the Americas to achieve global parts integration and procurement optimization following merger and acquisition activity that integrated multiple group entities. The platform gave procurement teams visibility into what existed across the combined organization before placing new orders. For engineering leaders managing post-acquisition integration, that structure determines how quickly the combined organization can operate as one.
3. Preserve Institutional Knowledge to Boost Onboarding, Upskilling and Enablement
When senior engineers retire or change roles, accumulated design knowledge does not transfer automatically. The institutional memory held by a 30-year veteran—which drawings solved which problems, which suppliers delivered which tolerances, which assemblies failed under which conditions—exists in that person’s experience, not in any system the next engineer can access on day one.
This is one of the most consistent conditions our Customer Success team encounters across deployments. Organizations that have operated for decades carry enormous engineering knowledge that is functionally inaccessible to anyone who was not present when the decisions were made. Amerequip’s deployment of CADDi Drawer addresses this directly. The single-search system connects drawings, assemblies and specifications built over more than a century of production, allowing new engineers to query what was previously designed and see how similar problems were solved.
In the case of Nichirin Tennessee, Sales Manager Takehiro Ishimoto shared, “Our goal wasn’t just to implement new software—it was to eliminate barriers and operate as one unified team. CADDi is a tool, but the real success comes from our team’s commitment to make knowledge accessible and use it to optimize our workflow.”
Both cases demonstrate that knowledge does not transfer through conversation. It becomes actionable through structured, searchable records.
4. Use Retrievability as a VA/VE Foundation
Value Analysis and Value Engineering (VA/VE) initiatives require engineers to compare current designs against alternatives. An engineer by training, I know that without a searchable record of past designs, that comparison is manual and incomplete.
“Engineering knowledge is the foundation of effective VA/VE work,” CADDi CEO Yushiro Kato told Design World in April 2026. When drawings are structured and searchable, engineers retrieve prior iterations of a component, identify where material or complexity was added without corresponding performance gain and surface candidates for redesign. The data required for VA/VE work already exists in most organizations that have operated for decades. The barrier is retrievability, not volume.
5. Build Data Reliability for AI-Driven Decisions
AI tools in manufacturing require structured, accessible data to return useful output. Organizations that deploy AI applications without first structuring their underlying engineering data get inconsistent results. The AI tool surfaces what the data contains.
The engineering information already in a manufacturer’s possession—drawings, specifications, procurement records and quality history—represents the primary input for any meaningful AI application in a design or production environment. CADDi structures that data so the AI tool presents it to engineers and procurement leaders in a form they can act on. The engineer reads the output, applies context and makes the decision. The system provides the reach that no individual engineer has across decades of records.
The common thread across each of these applications is that the underlying data already exists. Manufacturers in discrete production have accumulated decades of engineering knowledge in drawings, specifications and procurement records. The operational question is whether that knowledge is accessible to the people who need it, at the moment they need it. The manufacturers that move first on structuring their existing engineering data establish an operational advantage that compounds. The ones that wait continue to pay for knowledge they already own.
About the Author
Yongli Deng is the Vice President of Customer Success at CADDi, an international technology company offering an AI-powered data intelligence platform for design, engineering and manufacturing. Deng partners with discrete manufacturers to drive successful adoption of CADDi’s platform, leading change management and value realization from deployment through scaled use. Before CADDi, Deng was a Principal at a global management consultancy, advising manufacturers on growth strategy and large-scale transformation. She holds a master’s degree in electrical engineering from Fudan University and an MBA from Northwestern Kellogg School of Management.
CADDi is an international technology company that develops AI-powered data intelligence platforms for manufacturing, engineering and design. CADDi brings AI technology expertise to help manufacturers preserve decades of engineering and design knowledge. To learn more, visit us.caddi.com/company.
Sources
¹ CADDi. "Amerequip Transforms 100 Years of Engineering Data into Actionable Intelligence with CADDi AI Data Platform." CADDi News, 2026. us.caddi.com/resources/news/amerequip-transforms-100-years-of-engineering-data
² CADDi. "Sumitomo Drive Technologies and CADDi Transform Six Decades of Engineering Data into an AI-powered Intelligence Hub, Cutting Search Time by up to 90%." CADDi News, 2026. us.caddi.com/resources/news/sumitomo-drive-technologies-caddi-transform-six-decades-engineering-data
³ CADDi. "Manufacturing Leaders CADDi Inc. and Tadano Ltd. Advance Global Technology Utilization to Accelerate M&A-Driven Group Synergies." CADDi News, 2026. us.caddi.com/resources/news/manufacturing-leaders-caddi-tadano-advance-global-technology
⁴ CADDi. "Democratizing knowledge to empower independence with Nichirin Tennessee." CADDi Case Study, 2026. https://us.caddi.com/case-studies/nichirin
⁵ Design World. "Is Engineering Knowledge the Key to Driving More Effective VA/VE Initiatives in American Manufacturing?" Q&A with CADDi CEO Yushiro Kato, April 2026. designworld.com/caddi-ceo-yushiro-kato-qa-april-2026