Manufacturing automation: quotes, chemical surveys, molds

Portal orders and drawings, quote entry, chemical surveys, and molds held at suppliers: work on each customer's own screens, automated with AI and human review.

  • Runs on your own PCs and servers
  • Use Azure OpenAI, Amazon Bedrock, Google Vertex AI, or an in-house model
  • Its engine is Descarty's open-source Dagu (GPL-3.0)

Common problems

  1. Staff fetch orders, drawings, and survey requests from each customer's portal where EDI doesn't reach

  2. Chemical content surveys arrive in each customer's own format and channel, and answers are built part by part

  3. Japan's amended Subcontract Act puts negotiation requests and molds held at suppliers under scrutiny, with records to keep

Use cases in this industry

  • Manufacturing
  • Wholesale & consumer goods
  • Sales
  • Operations & orders

Enter an Excel list into a web system

For each row of an Excel list of orders, AI enters it into a web system and writes the receipt number back into the row. Only failed rows are redone.

  • Manufacturing
  • Wholesale & consumer goods
  • Accounting & finance
  • Operations & orders

Enter records into a desktop accounting app

For each row of an Excel list, AI enters the record in a desktop app. A person approves before it is saved, and the slip number goes back into the row.

  • Manufacturing
  • Operations & orders

Collect orders and due dates from supplier portals

Every morning, collect new orders and delivery date changes from several customers' supplier portals and, after review, record them in the sales system.

  • Manufacturing
  • Operations & orders

Check stock and lead times for a parts list

For each part on an Excel bill of materials, stock, price, and lead time from distributor sites go into its row, and hard-to-source parts are flagged.

  • Manufacturing
  • Operations & orders

Watch customer portals for drawing revisions

Each morning, check the customer portal's drawing list, download drawings whose revision went up, and tell the engineer, recording that they checked.

  • Manufacturing
  • Legal & compliance
  • Operations & orders

Answer customers' chemical content surveys

Read part numbers and due dates from chemSHERPA survey requests in customer portals, build answer files from your data, and submit them after QA approves.

  • Manufacturing
  • Sales

Enter customer RFQs into your quoting system

AI reads part details from emailed RFQs and drawings, and they go into your quoting system with past quotes and costs. Estimators set the price.

  • Manufacturing
  • Operations & orders
  • Legal & compliance

Review molds held at suppliers

Find the last order for each mold on your ledger, list long-idle molds, and have AI draft notices once buyers decide to return, scrap, or pay for storage.

  • Wholesale & consumer goods
  • Manufacturing
  • Sales
  • Operations & orders

Read Excel purchase orders from email and enter them

AI reads Excel purchase orders in each customer's own layout, checks them against the product master, and enters them. A person checks orders that do not match.

  • Wholesale & consumer goods
  • Manufacturing
  • Sales
  • Planning & marketing

Turn supplier price increases into customer requests

Read suppliers' price-increase emails and list the items, dates, and margin impact. AI drafts each customer's revision request for the sales manager to approve.

Manual work stays in dealings with customers

Japan’s manufacturing workforce fell from 10.46 million in 2024 to 10.33 million in 2025. Among small and medium-sized manufacturers, the staffing diffusion index stood at minus 17.9 in 2025, meaning more firms report too few staff than too many. In a survey shown in METI’s briefing on the 2026 White Paper on Manufacturing Industries, 70.9% of manufacturers named labor shortages as a condition affecting their business, second only to rising raw material and resource prices (fiscal 2025).

Inside the company, orders, production, and stock can be managed in a production management system, and drawings and bills of materials in PLM or drawing management systems. Industries also have shared systems for dealing with customers. The auto industry has JNX, a common network with 2,286 member companies (April 2026). The electronics industry has JEITA’s EDI standard, ECALGA.

Even so, only 16.4% of manufacturers exchange data with other companies in their supply chain (2025 survey), and METI says data exchange between companies has made no progress and has barely changed in two years. Where EDI doesn’t reach, business runs through each customer’s web EDI or portal, email, and fax. JEITA, the electronics industry association, publishes guidelines for web EDI so that it does not cause undue inconvenience or poor usability for the companies that use it. In a 2024 survey of manufacturers’ order staff by AI inside, an AI OCR vendor, 73.3% of those who still exchange paper documents said some of their work involves receiving documents by fax, and 60.4% of those enter faxed documents into their order system by hand.

Work Screens and systems used What stays manual
Orders and delivery date changes Customers’ web EDI and portals, production management system Signing in to the portals of customers outside EDI and copying orders and date changes
Drawing revisions Customers’ portals, internal PLM or drawing management Noticing revised drawings posted on a customer’s portal, pulling them in, and telling the engineer
Quotes RFQs by email or portal, quoting system, production management system Picking materials and quantities off drawings, and looking up past quotes and costs in separate screens before entry
Chemical content surveys Customers’ procurement portals, the chemSHERPA data creation tool Collecting requests, building an answer file for each part, and submitting it the way each customer asks
Quality complaints Customers’ quality portals (8D reports and similar) Entering cause and corrective action reports stage by stage in each customer’s format
Price negotiations and molds Email, Excel ledgers, purchasing system Tracking suppliers’ requests to negotiate, and orders for molds held at suppliers, against the ledger

Chemical content surveys differ by customer in format and channel

Electronics and automotive parts makers receive surveys from customers, part by part, on the chemical substances their products contain. Many customers use chemSHERPA, the industry’s common format, but it is rarely the whole story. In a 2021 survey of chemSHERPA users by the industry consortium JAMP, 96.90% of the 3,062 upstream and midstream respondents who receive surveys were asked to use chemSHERPA, 74.95% to provide non-use certificates, and 55.52% to fill in customers’ own forms. JAMP concluded that customers’ own forms and non-use certificates were increasing.

How requests arrive differs by customer, too.

  • Hamamatsu Photonics sends requests by email as a rule
  • Fujitsu sends requests through its ProcureMART procurement portal. Suppliers load the requester information from Fujitsu’s request data, build a chemSHERPA file for each item, and attach the compliance guarantee as a pair of Excel and PDF files
  • Canon sends its own additional forms alongside chemSHERPA, to survey what chemSHERPA cannot cover

Each regulatory change means surveying again. The chemSHERPA substance list is revised twice a year. The CMP Consortium says that each time regulated substances are added or changed, companies must re-survey and pass information to trading partners by email and other means, and that this workload has become a problem.

To reduce it, the CMP Consortium launched CMP on October 1, 2026, a platform that passes chemical content information to designated partners along trading relationships, and aims for several thousand participating companies in 2028. CMP and chemSHERPA will run side by side, complementing each other, for the time being. Trades that move to CMP connect through data. Requests through the portals and email of customers that have not moved still mean manual work.

Quotes mean reading drawings and finding past records before entry

RFQs arrive by email or through customer portals, with drawings and specifications attached. Estimators pick out the material, dimensions, quantity, and surface treatment from the drawing, look up past quotes and actual costs for similar parts, and then enter it all in the quoting system.

In a Kanagawa Prefecture DX demonstration project, the foundry Koiwai reported that quoting and casting design had become work tied to individual veteran employees and had not been passed on to younger staff. Customers were asking for quote replies in three days instead of a week. Finding similar shapes among 10,000 or more sets of 3D data took inexperienced staff a full day. The company trialed a system that uses AI to find similar shapes.

Dedicated systems like this are one way to search for similar shapes and estimate costs. Either way, the work around them remains: reading the request, finding past records, and entering the quote. Estimators set the price.

The amended Subcontract Act calls for records on price talks and mold storage

An amendment passed in May 2025 renamed Japan’s Subcontract Act, now known as the Toriteki Act, and it took effect on January 1, 2026. Manufacturers are on the receiving side with their customers, and on the ordering side when they outsource machining or mold making. Whether the act applies depends on thresholds for capital and number of employees.

The amendment bans setting prices unilaterally without negotiating (Article 5(2)(iv)). The guide by the Japan Fair Trade Commission (JFTC) and the SME Agency says that ignoring a request to negotiate, or repeatedly putting off negotiation, also counts as refusing to negotiate. A supplier presenting a quote calculated at a raised unit price counts as a request to negotiate. Such requests may arrive as a revised quote attached to an email.

Records are required too. The ordering party must create records of each transaction (known as Article 7 records) and keep them for two years. When it changes a price, it records the increase or decrease and the reason. The promotion standards included in the guide say that both parties are each to keep records of their price negotiations.

Mold storage is under scrutiny as well. The guide treats making a supplier store molds without paying storage costs, while not ordering the parts for a long time, as an unjust request for economic benefits. Its examples include having a supplier store molds free of charge when no products made with them have been ordered for a year or more. Where tracking each mold’s use at all times is too burdensome, it also allows checking orders once a year and paying that year’s storage costs together. Of the 16 recommendations the JFTC issued in fiscal 2026 (through October 9, 2026), 10 included having suppliers store molds or similar tools without bearing the storage costs.

What automation reduces is missed negotiation requests and overlooked idle molds, and the work of pulling records together. Staff negotiate and decide prices and what happens to molds.

What to check before automating

  • Whether data exchange is enough: where a customer or trade runs on EDI, JNX, or CMP, use that
  • Each customer portal’s terms of use: check whether each one allows automated operation (Check the terms before you automate a website)
  • Which AI models may see drawings: drawings and specifications from customers fall under confidentiality agreements. Decide first whether they may go to outside AI models, or only to in-house or local models
  • Documents the company stands behind: QA staff check chemical content guarantees and non-use certificates before they go out
  • How each portal signs in: IDs and passwords, client certificates, and one-time passwords differ by customer portal
  • Where people decide: staff set quote prices, conclude price negotiations, and decide whether to take back or scrap molds

What Kitewell does

Descarty’s Kitewell is integration and automation middleware that connects systems through APIs, files, databases, and screens.

  • Operates each customer’s portal: AI reads the screen to collect orders, download drawings, and answer surveys. Operations that worked are remembered and repeated the same way on later runs
  • Keeps going when screens change: when a remembered operation no longer fits the screen, AI reads the screen again, does the step, and remembers the new operation. If it still cannot proceed, the run stops, and AI looks into the cause and suggests the fix
  • Operates desktop apps too: quoting systems, production management systems, the chemSHERPA data creation tool, and other apps without an API are operated by reading the screen. Desktop steps run one at a time on a signed-in, unlocked screen
  • Reads drawings and requests: steps can have AI read PDFs and images. You choose the AI model for each step, including in-house and local models
  • Handles sign-in hurdles: for sites that ask for a client certificate, it presents the certificate registered on the computer for that site. When a one-time password is required, the run pauses until staff enter it. Passwords are referenced by name and typed in without showing the value to the AI
  • People check where it matters: approval can be set on any step, such as before answering a survey or sending a quote. Requests reach Slack, Microsoft Teams, and email, and sending one back restarts the run from the step you choose
  • Everything on record: each run keeps its logs, screenshots, and approval decisions
  • Runs from email and Excel: it checks email at set intervals and saves attachments. Register an Excel list, such as a mold ledger, as a batch sheet, and it runs row by row and writes each result back to its row. It can also convert Excel files to Shift_JIS CSV for older production management systems’ import screens

The computers that run workflows can be VMs or dedicated PCs in your own cloud, on your premises, or in a closed network. With in-house or local AI models, nothing is sent outside, drawings included. AI models that can read and operate a screen are limited, though, and small local models usually cannot operate one, so the PoC settles which model to use.

For connecting hand-offs between in-house production, sales, and core systems, see on-premises integration.

You can start from one piece of work, such as receiving a customer’s chemical content survey through gathering each part’s data and preparing and submitting the answer. A demo shows how it runs, a free connection test confirms Kitewell reaches your systems, and a one-month PoC measures the result against success criteria agreed up front, before you license it for production. See From evaluation to production for details.

Sources

Frequently asked questions

May a tool operate customers' portals and web EDI?
It depends on each portal's terms of use and your agreements with the customer. The PoC checks the terms and automates only what they allow. Where a customer exchanges data through EDI or CMP, use that instead.
May AI read the drawings our customers give us?
It depends on your confidentiality agreements with customers. In Kitewell, you choose the AI model for each step that reads documents, including in-house and local models. The computers that run workflows can sit on your premises or in a closed network, so with in-house models nothing is sent outside. How well a model reads drawings varies, though, so the PoC settles which model to use.
Can automation take care of the amended Subcontract Act for us?
No. Staff respond to negotiation requests, set prices, and decide whether to take back or scrap molds. What automation reduces is missed requests and overlooked idle molds, and the work of pulling records together.

Use cases for any industry

  • All industries
  • Accounting & finance

Read invoices into the accounting system

AI reads incoming invoices, checks them against purchase orders, and records them in the accounting system. Invoices whose amounts differ wait for approval.

  • All industries
  • Accounting & finance

Create invoices and send them by email

Invoices are filled into your template from an Excel billing list and emailed once a person approves. AI first points out amounts far from last month's.

  • All industries
  • Sales

Research a list of prospects

For an Excel list of hundreds of companies, each one's business, locations, and size are collected from its website into its row, with a priority for sales.

  • All industries
  • HR & payroll

Find missing clock-ins

Collect yesterday's clock-ins from the attendance system and, once HR approves, ask employees with missing entries to fix them in Teams.

  • All industries
  • Accounting & finance

Take in emailed invoices and draft the entries

Find unread email with an invoice attached, have AI read the amount, and record a draft ledger entry. Uncertain entries wait for accounting to confirm.

  • All industries
  • IT

Review SaaS accounts

A SaaS account review matches each service's users to current staff. Collect each console's users, match them, and disable leavers only after approval.

  • All industries
  • Accounting & finance

Partner registration that starts on approval

A kintone approval starts the workflow by webhook. It looks up the tax registration number, AI matches it to the request, and a match enters the sales system.

  • All industries
  • Planning & marketing

Collect KPIs from SaaS tools into a weekly report

Collect figures from advertising, sales, support, and other SaaS tools, and produce a weekly report with week-on-week comparisons and likely causes of change.

  • All industries
  • Accounting & finance

Check supplier invoice registration numbers

Check your supplier master's registration numbers monthly through the NTA's Web-API, and list suppliers that were revoked, lapsed, or differ in name.

Prove Kitewell on your own systems

Try it free for 14 days on your own PC. To see it working first, ask for a 30-minute demo. There is also a one-month onboarding (¥500,000 before tax), where we build your first job together.

What the onboarding checks

  • Which method connects each target system: API, file, database, or screen
  • Whether Kitewell can work a system with no API through its screens, as a person would
  • Whether AI relearns a step and processing continues when a screen changes
  • Whether errors are handled as agreed: stop, notify, or retry
  • Whether it runs in your environment, with the AI model you choose
  • Whether processing time and AI usage cost fit daily operation

How the onboarding runs

  1. Week 1Requirements and success criteria
  2. Weeks 2 to 3Build
  3. Week 4Verify on production screens
  4. Final dayReport

One-month onboarding

Length
One month
Cost
¥500,000 (tax excluded). Your staff build, with the developer beside them
If you go on to a license
The onboarding fee is credited to the first year's contract
Without the onboarding
At Team scale, start on Team monthly (¥14,800 per computer a month, tax excluded), and cancel in any month

You can try it first on your own PC, in the free 14-day trial.

See how the onboarding runs and its terms