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· 2025年12月11日 8m read

Supply Chain Orchestration for dummies

Supply Chain refers to a set of processes and activities performed by the company's business areas and its suppliers and partners (stakeholders), from the acquisition of raw materials, through production, to delivery to the end consumer. It can be better managed using SCM solutions with the orchestration of the InterSystems IRIS:

 

Planning process

All Supply Chain processes demand strong planning, always supported by data and analyses necessary to plan and execute activities in line with quality, accuracy, agility, and economy in the acquisition of raw materials, production, storage, delivery (logistics), and, if necessary, return (reverse logistics) of products to the end consumer. The InterSystems IRIS can support Supply Chain planning with its interoperability, database and analytics features:

 

The InterSystems IRIS has some adapters available to capture data:
 

Adapter

Edge

Use case

SQL Inbound Adapter

SQL Edge

Collects data about orders, inventory, and occupied capacity from SQL databases in the systems of stores, e-commerce platforms, warehouses, and factories.

File and FTP Inbound Adapter

File/FTP Edge

Collects data on orders from stores and distributors submitted via files.

REST, HTTP and SOAP Inbound Adapter

HTTP Edge

Collects data about orders, inventory, and occupied capacity from SOAP and REST services in the systems of stores, e-commerce platforms, warehouses, and factories.

MQTT Adapter

IoT Edge

Collect sensor data about production capacity

Kafka and MQ adapters

Messaging Edge

Collects data about orders, inventory, and occupied capacity from Kafka and MQ topics in the systems of stores, e-commerce platforms, warehouses, and factories.

After capturing data, it is necessary to process the data using business processes, business rules, transformations and store the results into operational and analytics repositories:

 

In the context of Supply Chain, the following examples of information are generated for planning:

Data for Planning

Term

What does it help with planning?

Number of orders per product and per location

Next 1 to 3 months

Quantity of raw materials to be used

Factory production schedule

Inventory schedule

Delivery schedule

Current and short-term inventory

Next 1 to 4 weeks

Factory production schedule

Inventory schedule

Delivery schedule

Production capacity

Next 1 to 3 months

Quantity of raw materials to be used

Factory production schedule

Inventory schedule

Delivery schedule

Financial planning

 

Sourcing Process

The sourcing process is fundamental for acquiring all the raw materials and other services and products necessary for the manufacturer's production from suppliers. This process requires integrating suppliers with the manufacturer to collect data and execute automated actions between systems to establish flows for price quotations, orders, purchases, deliveries, and continuous supplier evaluations, prioritizing economy, quality, and speed.

 

InterSystems IRIS operates at each stage according to the following table:

Sourcing step

IRIS component

Actions

Price quotation

REST, Kafka, HTTP Adapters 

Business Process

IRIS database

IRIS BI

Integrated ML

Get prices from suppliers

Select best prices

Store prices

show price quotation dashboards

Predict prices

Select suppliers

Business Process

Analyze prices and demands and select right suppliers

Order

Business Process

Send orders to SCM and ERPs

 

Manufacturing process

The manufacturing process consists of activities necessary to transform raw materials into goods.
The main activities involve starting with production planning and then separating the raw materials, assembling the production line, preparing and maintaining the machinery, carrying out production, performing quality control, and packaging for delivery. These activities have a high degree of automation. In this way, InterSystems IRIS becomes fundamental:

 

The following table details where the IRIS components operate to assist in the automation and improvement of the manufacturing process:

 

Manufacturing step

IRIS component

Actions

Raw material allocation

REST, Kafka, HTTP Adapters 

Business Process

IRIS database

IRIS BI

Integrated ML

Automated orders to SCM/ERP

Orchestrate Production/SCM/ERP 

Store orders, traces, inventory

Store, analyze and predict material allocation and quality analysis

Monitoring and preventive and predictive maintenance of production line machines.

REST, MQTT Adapters 

Business Process

IRIS database

IRIS BI

Integrated ML

Receiving data from machine sensors to control production and schedule maintenance.

Automate parts requisition, maintenance requests, and production reporting to the ERP and SCM systems.

Generate analytical reports and dashboards on productivity, material quality, machine monitoring, and maintenance.

Labor allocation

Business Process

IRIS database

IRIS BI

Integrated ML

Send labor allocation requisitions to ERP and HR systems

Analyze and Predict labor workforce requirements

Produce the goods

REST, MQTT Adapters 

Business Process

IRIS database

IRIS BI

Integrated ML

Orchestrate data, requests and responses between manufacturing systems/ERP/SCM

Produce production reports, analyzes and predictions 

Delivery and Return Processes

The delivery and return processes are challenging and complex, involving the formation of a network of stores, distributors, distribution centers, and air, land, and sea carriers at a national and international level to ensure the manufactured product arrives at the right time and place at the lowest possible cost. This requires integrating data and processes from all parties involved to synchronize production and delivery capacity, and short-, medium-, and long-term demand:

 

 


The following table details where the IRIS components operate to assist in the automation and improvement of the delivery and return processes:

 

Delivery/Return step

IRIS component

Actions

Match orders and deliveries

REST, Kafka, HTTP Adapters 

Business Process

Sends order confirmation messages to the relevant internal and external systems

Define the delivery route according to the desired destination, product, and shipping cost

Business Process

IRIS database

IRIS BI

Integrated ML

It runs mathematical and AI models to refine air, sea, and land routes to all intermediate and final destinations of products and records the routes for all systems involved

Maintain and communicate delivery traceability to all parties involved

REST, Kafka, HTTP Adapters 

Business Process

IRIS BI

It updates every step in the delivery route and maintains traceability in the systems of all those involved, in addition to generating dashboards and monitoring reports

Address and record deviations and failures in deliveries

REST, Kafka, HTTP Adapters 

Business Process

IRIS BI

It detects route deviations and delivery failures and records them in the systems of all parties involved, allowing for autonomous (AI) or manual (BPL) decisions on how to correct the delivery

Address and record successful deliveries

REST, Kafka, HTTP Adapters 

Business Process

IRIS BI

It updates all systems involved in successful delivery and generates statistics and analyses that enable continuous process improvement

Manage tax and transportation documentation.

REST, Kafka, HTTP Adapters 

Business Process

It works in conjunction with the ERP system to issue and synchronize data and documents necessary for the transportation and fiscal and legal registration of products being delivered

Registers customer's return request

REST, Kafka, HTTP Adapters 

Business Process

It retrieves the return data, registers the return event in the systems of all parties involved, and correlates it with the delivery to maintain traceability

Identifies delivery opportunities for another customer.

REST, Kafka, HTTP Adapters 

Business Process

It reviews orders to fulfill returned products that do not have quality issues and synchronizes with the systems of all parties involved to register delivery to another customer

Generates reverse logistics for returned items in case of non-delivery to another customer.

REST, Kafka, HTTP Adapters 

Business Process

It generates the product return route, either to the factory (defect) or to store or distribution center stock. To do this, it updates the systems of all involved parties, in addition to generating analyses and follow-up reports.

Compilation of IRIS components used in Supply Chain and how to learn to use them

Below is a table detailing each component of IRIS used in Supply Chain projects, with links to resources on how to use them:

 

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記事
· 2025年12月11日 2m read

Reviews on Open Exchange - #60

If one of your packages on OEX receives a review, you get notified by OEX only of YOUR own package.   
The rating reflects the experience of the reviewer with the status found at the time of review.   
It is kind of a snapshot and might have changed meanwhile.   
Reviews by other members of the community are marked by * in the last column.

I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix.    
Some were accepted and merged, and some were just ignored.     
So if you made a major change and expect a changed review, just let me know.

# Package Review Stars IPM Docker *
1 iris-image-reducer my very personal 7* 7.0   y  
2 OPNEx-ECP Deployment Reloaded and polished 6.5   y  
3 interface-explorer modern interface 5.5 y y  
4 FHIR SQL Builder with Vector Search excellent demo 5.2   y  
5 DBsizeWatch useful summary 5.0   y *
6 IRISConfigurationDiagrams impressive graphics 5.0 y y  
7 IrisOASTestGen interesting 4.9 y y  
8 iris-fastjsonschema interesting exercise 4.7   y  
9 workshop-openehr a good starting point 4.7   y  
10 IrisTest-Fmtserializer another view of UnitTests 4.6   y  
11 IRISSystemCheck incomplete 4.3 y y  
12 RAGBookRecommender hackaton is with you 3.9   y  
13 Testify room for improvement 3.6 y y  
14 iris-global-statistics-chart no result 3.5 y y  
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記事
· 2025年12月11日 2m read

轻松创建问卷——借助IRIS、FHIR SQL构建器与向量搜索

在当今的医疗数据领域,FHIR 已成为结构化临床数据交换的标准。然而,虽然 FHIR 擅长互操作性,但其 JSON 格式却给分析带来了挑战——包括FHIR QuestionnaireResponse数据

本项目演示了如何将 FHIR QuestionnaireResponse 数据从嵌套 JSON 转换为关系 SQL 表和向量嵌入。通过集成 InterSystemsIRIS FHIR SQL 生成器向量搜索,我们揭开了患者回答背后的语义。

构建的三个步骤

1.设计和收集问卷

首先使用 美国国家医学图书馆(NLM)表格生成器。该工具有助于设计符合 FHIR 标准的结构化临床表格在本项目中,收集了 100 份合成患者回复,并将其保存为 FHIR QuestionnaireResponse JSON 文件,准备导入 FHIR 服务器。

2.通过 SQL 转换和查询问卷数据

将 FHIR QuestionnaireResponse 资源加载到服务器后,使用InterSystems IRIS FHIR SQL 生成器自动创建关系 SQL 表。这将使嵌套的 JSON 结构扁平化,从而能够使用标准 SQL 轻松分析问卷数据——所有配置只需点击几下即可完成。

  • 有关 FHIR SQL 生成器的完整配置,请参阅README。

  • 从 QuestionnaireResponse 数据生成的 SQL 表可用于查询和分析

3.为语义理解添加向量搜索

最后,集成IRIS 向量搜索,在结构化问卷数据的基础上添加语义智能。它允许用户根据含义和上下文而不是准确的单词来搜索问卷回复并与之交互,从而将数据转化为更直观、更智能的工具。

举例说明:

  • 搜索"糖尿病用药",可检索到提及二甲双胍、格列奈胰岛素或阿卡波糖等药物的回答(即使没有准确的短语)。
  • 当用户询问"哪些患者有心脏病家族史并服用降胆固醇药物?"系统会语义链接相关数据,将心脏病与普萘洛尔、螺内酯或阿托伐他汀等药物联系起来,并生成一份高危患者的简明摘要。

要了解完整的工作流程和代码实现情况,请访问Open Exchange

主要收获

通过三个关键步骤——使用 NLM Form Builder 设计问卷、将问卷转化为 SQL 表以及使用向量搜索增强问卷——该工作流将 FHIR QuestionnaireResponse 数据转化为临床理解和决策支持的强大工具。

参考文献

美国国家医学图书馆(NLM)表格生成器 2.

InterSystems IRIS For Health FHIR SQL Builder 3.

InterSystems IRIS 向量搜索 4.

https://openexchange.intersystems.com/package/iris-fhirsqlbuilder

https://www.youtube.com/watch?v=ewxyh2XNLv0

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記事
· 2025年12月10日 19m read
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記事
· 2025年12月10日 2m read

在开发者社区撰写文章时使用生成式人工智能的指导原则

我们在开发者社区的目标是培养由开发者创建并为开发者服务的高质量、值得信赖的原创技术内容。虽然 ChatGPT 等人工智能工具在写作过程中会有所帮助,但我们希望所有发布的内容都能反映真实的专业知识和个人理解。如果您有任何疑问或希望与人工智能讨论编码问题,请考虑使用我们的Developer Community AI Chat

可接受的人工智能使用

我们认识到人工智能工具可以帮助编辑和提高清晰度。您可以在以下场景中应用人工智能:

  • 修正语法、拼写和标点符号。
  • 改善句子结构和可读性。
  • 更清晰地重新表述您自己的内容。
  • 将其作为写作助手,而不是技术观点或解释的来源。

例如 自己撰写一篇 关于如何使用 %JSON.Adaptor 类的文章 ,并使用 ChatGPT 改善句子的流畅性和清晰度。

不允许的行为

为保护我们平台的完整性,不允许使用以下由人工智能生成的内容:

  • 使用 ChatGPT、Copilot、Gemini 或其他大型语言模型撰写完整文章(或大部分内容)。
  • 将人工智能生成的解释、教程或代码说明作为您自己的见解发布。
  • 提交主要由人工智能生成的内容,仅进行少量编辑或审核。

例如提示 ChatGPT "撰写一篇强调 FHIR 标准优点的长文",然后将其作为自己的作品发布。

透明度

为了保持高质量的内容并维护社区的信任度,您应努力使生成式人工智能的使用透明化。具体来说:

  • 如果人工智能对写作有实质性贡献(即使是经过编辑的),您应在文章末尾的注释中披露这一点。
  • 您无需披露人工智能在编辑方面的少量使用(如语法修正、清晰度编辑)。

例如:本文使用 ChatGPT 进行了语法和可读性方面的编辑。

开发者编程竞赛

生成式人工智能在用于加速编程和开发时可以成为一个强大的工具,但就像人类编码员一样,它也会犯错。在开发应用程序时,可以使用编码助手(如 Cursor、Windsurf)和其他生成式人工智能产品,并根据上述指导原则提交给开发者编程竞赛。

此外,参赛者有责任确保其应用程序符合所有竞赛标准,并能代表自己的专长。利用人工智能生成技术制作的参赛作品与完全由人类编写的代码一样,须遵守相同的质量标准。

滥用的后果

我们保留拒绝或删除不符合这些原则的文章或取消参赛资格的权利。屡次违反可能会导致出版权限受到限制。

为何这很重要

人工智能可以成为您的得力助手,但它无法取代您的观点。技术文章的价值在于您的思维过程、经验和理解。这正是我们希望在社区中强调和支持的。

让您的声音成为主导。负责任地使用工具。

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