Nanonets 04月24日
Supplier order management: How a furniture retailer automated order confirmation processing
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这家欧洲家具零售商通过提供高度定制化的产品而成功。然而,订单量的增长导致了订单处理的难题,包括人工处理耗时、错误率高以及客户等待时间长。他们尝试过多种工具但效果不佳,最终通过Nanonets提供的AI解决方案实现了订单确认流程的自动化,包括自动提取订单信息、产品匹配、处理特殊情况等,从而提高了效率并降低了成本。

📧 **自动化文档接收**: 通过配置邮件转发规则和设置Dropbox文件夹监控,系统能够自动检测新的订单确认并进行处理,无需手动下载、分类。

🧮 **产品智能匹配**: 系统首先进行精确的描述匹配,如果需要,则回退到模糊匹配。同时,根据额外的标准(如内饰代码)过滤结果,并处理“一对多”的问题,即一个供应商项目对应多个Business Central行。

⚙️ **定制化规则**: 针对每个供应商的文档,系统采用定制化的处理方式。例如,对于供应商A,系统从产品描述中提取货号和变体代码,并检查“选项”字段中的特殊功能(如装饰钉);对于供应商B,系统处理混合的瑞典语-英语术语,并基于产品描述和数量进行匹配。

🚩 **异常处理机制**: 系统能够自动识别特殊订单,例如展厅订单,并通过跟踪多个确认来处理拆分订单。对于需要人工审核的异常情况,系统会进行标记,并通过简单的界面供团队进行审核。

Selling custom-made furniture in this age of mass production is not easy. But this mid-sized, Europe-focused furniture retailer was making it work. Their secret? Letting customers choose everything — from fabric choices to sofa leg styles, even down to the color of decorative nails.

However, as sales grew, the made-to-order created a major problem. Each order was unique — different fabrics, custom features, and special requirements. The team had to carefully handle each customization, create detailed specifications for suppliers, and ensure every special request was correctly manufactured. When suppliers sent back order confirmations, the real challenge began.

With an 8-week order cycle, processing delays meant customers waited without updates. Order backlogs grew, negative reviews increased, and 20-30% of all orders were experiencing some form of error or issue. They needed a way to process these documents accurately without hiring more staff.


The real cost of manual supplier order management

The supplier documents arrived as complex PDFs — some up to 16 pages long, in multiple languages, and with different technical notations. Their seven-person operations team spent 10-15 hours per week per person processing these documents. That's 70-105 hours weekly just matching codes and verifying details.

At an average hourly rate of 180 SEK for operations specialists in Sweden, the manual processing was costing them approximately 655,200-982,800 SEK (€59,600-€89,400) annually in direct labor costs.

On top of that, the manual process resulted in 20-40 order errors across 100-150 monthly orders. It meant either the customer had to be compensated or the incorrect item had to be sold off at a loss. The potential losses due to incorrect order could end up costing €12,000 monthly.

To sum up, the processing inefficiencies had major downstream effects:

Automation seemed like the obvious solution. However, their unique processes and high level of customization meant they had to find a system that could handle their specific needs. They needed something that could not only process complex PDFs accurately but also adapt to their careful, detailed verification process while working seamlessly with their existing systems.


Why traditional order confirmation processing failed the retailer

Let’s take a look at how this retailer’s order handling workflow looked like.

This complex process created a strange workflow where purchase orders were created after receiving packing lists rather than before placing orders with suppliers. This unusual approach was necessary because they collected website orders on specific days before sending them to suppliers. Additionally, not all items in a customer order would go to the same supplier, meaning parts of a single order might arrive at different times.

Additionally, three more factors made this process particularly difficult to automate:

Differences in product listings

Since customization was at the core of their business, they needed to track each component of a custom order separately. So a custom sofa won’t be recorded as a single item in Business Central but as separate line items — one for the sofa model, another for the fabric choice, and more for special features like bronze nails.

However, the supplier often lists all these details as a single item in their order confirmation. For example, if a supplier confirmed 'Valen three-seater in Blue fabric with bronze nails', the team would have to match this single entry to three separate lines in Business Central. This complex structure made processing order confirmations particularly challenging.

Language and notation differences

The furniture company's suppliers used different languages and technical notations in their documents. One supplier used English with German technical notations, while another mixed Swedish and English terms. This made matching with sales orders particularly challenging because Business Central needed clear, standardized product codes.

So, even something simple like steel nails could appear in multiple ways — as a technical code in one document, in plain English in another, or as a German notation in a third. The team had to manually interpret and translate these variations during data entry to ensure accurate matching. 

Special case handling

Some orders required specific handling rules. For instance, when an order confirmation was marked as 'Showroom' instead of having a customer reference, it needed different processing since it wasn't tied to a customer order. 

The team had to first spot these exceptional cases, then apply different verification rules — adding more steps to their manual processing. This meant constantly switching between different procedures depending on the type of order they were handling.

Split orders

Customers could order items that came from different suppliers. For example, a customer might order a sofa from one supplier and a footstool from another. 

So when order confirmations arrived, the team had to carefully match each to the right parts of the customer's order in Business Central. Since confirmations came separately from different suppliers, they needed to track which items were confirmed and which were still pending — all while ensuring they were updating the correct line items for each product.

They tried various tools, including Continia, but they couldn't effectively handle these complex documents while maintaining the accuracy their process demanded. They needed a flexible solution that could accurately extract and interpret rigid, lengthy PDFs while adapting to their specific workflow needs.

That's when they approached us at Nanonets.


How we automated the retailer’s order confirmation processing workflow

Looking at their complex order handling process, we knew automation needed to happen step by step. We started with order confirmations. We built a flexible workflow that could automate the process from receiving supplier documents to updating Business Central with delivery dates. The idea was to use this as a foundation for other potential workflows, like packing list processing.

Here's a quick overview of how the automated workflow worked:

Here’s how we went about solving different challenges in their document processing workflow:

1. Automated document intake:

We established reliable document intake channels by configuring email forwarding rules and setting up Dropbox folder monitoring. This eliminated the manual downloading, sorting, and classifying of order confirmations. The system automatically detects new confirmations and routes them for processing.

2. Product matching:

The biggest challenge was matching supplier product descriptions to multiple Business Central line items. 

So, we built a matching system that:

When a supplier lists "Valen three-seater in Blue fabric with bronze nails" as a single item, our system can now automatically identify and update the corresponding sofa model, fabric, and special feature lines in Business Central.

3. Supplier-specific rules

Each supplier's documents required custom handling:

4. Managing exceptions

To handle their special cases, we built specific detection and processing rules:

The interface lets the retailer review these exceptions efficiently. When they make corrections, the system learns from these changes — improving future extraction and matching accuracy.


The ROI of automated supplier order management

Within 3-4 months, the automated system delivered measurable results across four critical areas:

What moved the needle most for the retailer? Our system's ability to accurately process complex PDFs – it is something they didn't expect could be done effectively. Even 16-page documents with mixed languages and technical notations are now processed accurately.

They're also planning to extend the automated workflow to their Germany-region operations since the process would remain the same, more or less. The only difference would be the language – something that Nanonets would be able to handle seamlessly.

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