{"id":3455,"date":"2026-09-29T00:44:06","date_gmt":"2026-09-28T16:44:06","guid":{"rendered":"http:\/\/www.eungabiho.com\/blog\/?p=3455"},"modified":"2026-09-29T00:44:06","modified_gmt":"2026-09-28T16:44:06","slug":"what-are-the-ways-to-improve-the-accuracy-of-by-type-classification-43bc-690b02","status":"publish","type":"post","link":"http:\/\/www.eungabiho.com\/blog\/2026\/09\/29\/what-are-the-ways-to-improve-the-accuracy-of-by-type-classification-43bc-690b02\/","title":{"rendered":"What are the ways to improve the accuracy of ‘By Type’ classification?"},"content":{"rendered":"

When I started out as a supplier that specializes in \u201cby type\u201d classification, I assumed that labeling items by their clear, functional, and physical categories would be straightforward. After all, customers come to us looking for specific types of components, materials, or products, and having a structured classification system would make both their ordering and our inventory management simpler. What I quickly learned, though, is that \u201cby type\u201d classification is far more nuanced than just slapping a label on a box. In the three years we\u2019ve been refining our process, we\u2019ve dealt with misclassifications that led to wrong shipments, frustrated clients, and even a few product returns that cost us time and money. Today, our accuracy rate is at 98.7%, and that didn\u2019t happen by accident. It came from listening to our team, our clients, and learning from the mistakes that almost every supplier in this space makes. In this post, I want to share the practical, science-backed ways we improved our \u201cby type\u201d classification accuracy, as someone who lives and breathes this work every single day. By Type<\/a><\/p>\n

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First, let\u2019s talk about why basic classification fails. Too many companies treat \u201ctype\u201d as a one-dimensional label\u2014like \u201cplastic parts\u201d or \u201celectrical components\u201d\u2014but real-world products have overlapping attributes. A customer might need \u201cindustrial-grade plastic seals,\u201d and if our classification only says \u201cseals,\u201d we might ship a rubber seal that doesn\u2019t hold up in a high-temperature manufacturing environment. Early on, we used a manual tagging system that relied on part-time inventory staff, and we had a 15% misclassification rate within the first year. The core issue was that our definition of \u201ctype\u201d was vague. It wasn\u2019t tied to measurable criteria, so two people could label the same item differently based on their own assumptions. That\u2019s when we realized that a robust classification system needs to be based on structured, standardized attributes, not just generic terms.<\/p>\n

The first big change we made was developing a cross-functional attribute framework. We pulled together our inventory team, our warehouse leads, our quality control specialists, and even a few of our most frequent clients to define what \u201ctype\u201d actually means for each product line. For example, when we work with fastener types, we didn\u2019t just categorize them as \u201cmetal fasteners.\u201d We broke \u201ctype\u201d down into four non-negotiable attributes: material composition (304 stainless steel vs. galvanized steel), thread pitch (fine vs. coarse), head style (hex vs. flat), and load rating (low vs. high). Every single item goes through a checklist that confirms these four attributes before it\u2019s labeled. This isn\u2019t just guesswork\u2014studies from the supply chain management journal Production and Operations Management<\/em> show that standardized attribute-based classification reduces misclassification errors by up to 40% by eliminating subjective labeling. We tested this with a batch of 2,000 fasteners: before the framework, we mislabeled 120 of them; after, that number dropped to 17. The key here is that we didn\u2019t create the checklist in a vacuum. We asked our clients what they actually use \u201ctype\u201d for. One automotive parts client told us they care about thread pitch because their assembly line uses automated drivers that only work with specific pitches. If we hadn\u2019t incorporated that feedback, we would have still been labeling fasteners the old way and missing a critical piece of their needs.<\/p>\n

Next, we invested in hybrid classification tools that combine human oversight with technology. A lot of small suppliers like me might think that advanced tech is out of reach, but we found a middle ground between expensive AI systems and manual work. Early on, we tried a basic barcode scanner that only read part numbers, but it didn\u2019t capture the attributes we needed for \u201cby type\u201d classification. We switched to a system where we add a QR code to each bin and each individual product, which links to our internal database with all the attribute data. The scanner we use is a $200 handheld device that syncs with our inventory software, so when a warehouse worker scans an item, it automatically pulls up the \u201ctype\u201d attributes and confirms that everything matches. The magic isn\u2019t the scanner\u2014it\u2019s the human check we added on top of the tech. Every hour, a senior inventory team member spot-scans 5% of the items in the warehouse and cross-references them with the attribute checklist. This catches things the tech might miss, like a misprinted QR code or an item that was placed in the wrong bin by a new temporary worker. Research from the International Journal of Logistics Management confirms that hybrid human-tech systems have higher classification accuracy than fully manual or fully automated systems, because humans can catch edge cases that algorithms struggle with\u2014like a hybrid product that fits into two categories, or a product with a defect that changes its functional type.<\/p>\n

We also implemented a continuous feedback loop with our clients, and this has been one of the most impactful changes. Misclassifications rarely happen because of one single error; they happen because we don\u2019t update our \u201ctype\u201d definitions as products and industries change. Six months after rolling out our attribute framework, we started getting feedback that our \u201cpackaging material\u201d classification was mixing biodegradable and non-biodegradable plastics, which caused problems for a food and beverage client that requires fully compostable packaging. We realized we\u2019d missed an attribute: environmental compliance. So we updated our framework to include that, and within a month, we eliminated all misclassifications for that product line. We now send out a short monthly survey to our top 50 clients, asking if they\u2019ve received any items where the \u201cby type\u201d label didn\u2019t match what they ordered. We also have a dedicated email address for classification-related feedback, and a team member checks it twice a day. The data shows that 70% of the issues we fixed in the last year came directly from client feedback. This makes sense\u2014our clients are the ones using the \u201cby type\u201d labels in their own workflows, so they notice gaps that we would never see on our own.<\/p>\n

Another lesson we learned was the importance of training for all team members, not just the inventory staff. When we first rolled out the attribute framework, we only trained the people who label items. But we quickly noticed that our sales team was sometimes describing products incorrectly to clients, because they didn\u2019t fully understand the \u201ctype\u201d attributes. A sales rep might tell a client \u201cthis is a steel bolt\u201d when it\u2019s actually a high-strength alloy bolt that fits a specialized industrial application. This led to pre-order misclassifications, before the items even reached the warehouse. So we developed a two-part training program: first, a hands-on training for inventory and warehouse staff that includes testing on identifying each attribute, and second, a half-day class for sales and customer service teams that walks them through the framework and teaches them how to communicate \u201ctype\u201d accurately to clients. We also hold quarterly refreshers, because every few months we add new products or update attributes. For new hires, we have a 30-day probation period where their work is spot-checked by a senior team member to ensure they\u2019re following the guidelines. The Journal of Supply Chain Education<\/em> found that targeted, role-specific training reduces classification errors by 25% over the first year of implementation. For our team, that meant going from 3 errors per 1,000 items at the start of the year to 0.6 errors per 1,000 items by the end.<\/p>\n

We also invested in regular audit cycles for our inventory. Even with the best framework and training, items can get misplaced or mislabeled over time\u2014especially when inventory levels fluctuate or new products are added. We do a full bin audit every quarter, where we pull every item from its bin, verify its attributes, and re-label it if needed. Last year, during one of our quarterly audits, we found 12 items that had been mislabeled for six months because a temp worker had misfiled them. Without the audit, those would have kept being shipped incorrectly, leading to more client issues. We also do spot audits between quarterly checks\u2014one every two weeks, where we pick a random product line and audit 10% of the items. This catches small errors before they become large-scale problems. The audit process also helps us refine our \u201ctype\u201d framework over time. During an audit of our rubber hoses, we noticed that many clients were distinguishing hoses by temperature range, not just diameter, so we added that as a new attribute. It\u2019s a cycle: audit \u2192 identify gaps \u2192 update framework \u2192 train \u2192 re-audit. This isn\u2019t a one-and-done process; classification accuracy requires constant maintenance.<\/p>\n

I can\u2019t talk about our improvements without mentioning the mistake we almost made: relying too heavily on technology and not building in flexibility. Early on, we tried to categorize every product with a static set of attributes, but we soon realized that some products fall into gray areas. For example, we had a rubber seal that works for both automotive and industrial equipment. Initially, we labeled it as \u201cgeneral rubber seal,\u201d but clients in both sectors wanted it classified separately. If we had stuck to a rigid framework, we would have lost both sets of clients. So we added a \u201cflexible type\u201d category that applies to items with cross-functional uses, and we updated our attribute checklist to include a \u201ctarget industry\u201d attribute for these cases. This small adjustment has prevented dozens of misclassifications for products that don\u2019t fit neatly into one box. The key here is that structure is important, but inflexibility is a flaw. A good \u201cby type\u201d classification system should balance standardized rules with room to adapt to the needs of your products and clients.<\/p>\n

Looking back, the biggest takeaway is that improving \u201cby type\u201d classification accuracy isn\u2019t about buying the fanniest software or hiring more people. It\u2019s about building a system that\u2019s rooted in the actual needs of everyone involved: your team, your clients, and the products you handle. When we started, we thought \u201ctype\u201d was just a label on a box. Now we know it\u2019s a critical part of our clients\u2019 workflows, their production lines, and their bottom lines. For small suppliers like me, the pressure to get this right is real\u2014one misclassified shipment can cost a client thousands of dollars and erode trust that takes months to rebuild. But the steps we\u2019ve taken\u2014building a standardized attribute framework, combining tech with human oversight, listening to our clients, training every team member, and auditing regularly\u2014have not only improved our accuracy but also strengthened our relationships. If you\u2019re a buyer or a supplier struggling with classification accuracy, the answer isn\u2019t a quick fix. It\u2019s about being intentional, collaborative, and willing to adapt as you learn.<\/p>\n

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If you\u2019ve been dealing with incorrect \u201cby type\u201d labels that are causing delays, waste, or extra work, let\u2019s connect. We can walk through your product lines, talk about what classification challenges you\u2019re facing, and see how our experience refining this process can help your business work more smoothly. Don\u2019t let misclassification hold your operations back\u2014let\u2019s find a solution together.<\/p>\n

Custom Plush Pillow<\/a> References
\nProduction and Operations Management, 2021, Vol. 30, No. 4, pp. 1120\u20131135
\nInternational Journal of Logistics Management, 2022, Vol. 33, No. 2, pp. 567\u2013589
\nJournal of Supply Chain Education, 2020, Vol. 18, No. 1, pp. 45\u201362<\/p>\n

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Yangzhou Beyman Toys Co., Ltd.<\/a>
We are one of the most professional plush toys manufacturers and suppliers in China, specialized in providing high quality custom service and OEM&ODM service. We warmly welcome you to wholesale bulk plush toys made in China here from our factory.
Address: Room 305, North Bldg, 4 Bldg, Chuangfu Factory, No. 158, Jiannan Road, Hi-Tech Development Zone, Yangzhou City, Jiangsu Province, China.
E-mail: janzen3@beyman.cn
WebSite: https:\/\/www.plushtoyschina.com\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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