OpenText™ Capture (Intelligent Capture), which includes machine learning (ML) and artificial intelligence (AI) technologies, provides omnichannel capture of digitized paper documents and native digital documents to extract content and route information efficiently and securely to the right users and systems in the organization.

June 2025: What’s new in OpenText Capture CE 25.2

Complex backgrounds and mobile images

• Expand the actionable data available and improve accuracy with enhanced processing of mobile-captured images and inbound documents that have complex and colorful backgrounds.
• Reduce manual validation and empower continuous machine learning with the additional extracted data.

For more information about OpenText CE 25.2, check out the release notes on OpenText MySupport.

December 2024: What’s new in OpenText Capture CE 24.4

Increase admin efficiency with large language models (LLM)-created profiles 

• Benefit from LLMs without your data being used to train the models, whether you upload a blank or completed sample document  

• Save time and reduce the need for experts by eliminating the need for administrators to manually create the profiles used by continuous machine learning (CML). 

• Automatically identify and label key data fields using Google Vertex AI to configure CML.  
   
  Note: This capability requires a Google Vertex subscription.

Expand extracted data with use-case specific LLM  

• Automatically identify, extract, and process critical information from structured or semi-structured documents using advanced language models like Robustly Optimized BERT Approach (RoBERTa). 

• Extract specific fields and values as defined for targeted use cases, to increase accuracy from documents such as invoices.  

Modernize with new human-in-the-loop web UI 

• Boost productivity of remote and hybrid employees with responsive web-based UI for configuration and validation. 

• Keep your brand in front of users with no-code configuration to customize the header with your 
  company name and logo.  

• Embed capture in line-of-business web apps. 

Advanced Cloud OCR with Microsoft^® Azure Read OCR 

• Expand the document types and fields that can be processed by Advanced Recognition. 

• Extract actionable data from unconstrained handprint, cursive, and low-quality mobile images with new built-in integration with Microsoft^® Azure Read OCR.  

For more information about OpenText Capture CE 24.4, check out the release notes on OpenText My Support. 

January 2024: What’s new in OpenText Intelligent Capture CE 24.1 

Ingest image formats from mobile devices 

• Process mobile images faster with automated conversion of HEIF, HEIC, and 64-bit PNG images into TIFF images 
• Automatically extract data from HEIF, HEIC, and 64-bit PNG images, often received in emails and as documents 

For more information about OpenText Intelligent Capture CE 24.1, check out the release notes on OpenText My Support. 

October 2023: What's new in OpenText Intelligent Capture CE 23.4

Extract accurate checkbox data automatically with continuous machine learning

• Accelerate business processes by leveraging AI for data accuracy from structured forms

Expanded Integration with Microsoft

• Ingest email content from M365 Outlook by directly connecting inbox folders using Microsoft Graph protocol.

Manage secure connections

• Set up a secure SFTP connection with the new option to use SSH keys for export.

For more information about OpenText CE 23.4, check out the release notes on OpenText My Support.

July 2023: What’s new in OpenText Intelligent Capture CE 23.3

Updated audit logs and admin reports

• Support compliance with new exportable detailed audit logs and admin reports that help verify the “who” and “what happened” related to captured information and extracted data.

Arabic support: capture and extraction

• To expand the documents that can be classified and extracted, Arabic is supported, including the creation of text-searchable PDF files and document classification/data extraction.

For more information about OpenText CE 23.3, check out the release notes on OpenText My Support.

April 2023: What’s new in OpenText Intelligent Capture CE 23.2 

Smart document classification using continuous ML and NLP 

• Increase document separation and classification accuracy with two new continuous machine learning models for document separation and NLP-based document classification. 

Postal address recognition using continuous ML 

• Process documents faster with new continuous machine learning for postal addresses that automatically selects the correct address when multiple addresses are in a document. 

View fuzzy search alternatives in the human-in-the-loop UI 

• Reduce the need to consult other applications when matching against a database that includes multiple entries that are nearly identical. The new Enable Alternatives setting for the SnapMatch capability will show not only the best match, but also the next best matches for user consideration.  

For more information about OpenText Intelligent Capture CE 23.1, check out the release notes on OpenText My Support. 

February 2023: What’s new in OpenText Intelligent Capture CE 23.1

Natural language processing for unstructured content   

• Built-in integration with Magellan Text Mining automatically extracts named entities, such as people, organizations and locations, and creates content summaries with less time spent on data entry and IT customization. 

Expanded Integration with Extended ECM and Documentum 

• New workspaces can be created in OpenText™ Extended ECM when documents are exported from Intelligent Capture.  
• OpenText™ Documentum™ D2 native workflows can be set up directly through the Intelligent Capture REST-based exporter  

For more information about OpenText Intelligent Capture CE 23.1, check out the release notes on OpenText My Support. 

October 2022: What’s new in OpenText Intelligent Capture CE 22.4  

Enhancements to Advanced Cloud OCR integration 

Organizations that use the product’s Advanced Cloud OCR capability can improve OCR accuracy and regional data privacy compliance by specifying the content’s language and the required region for data residency.  

Information extraction for intelligent automation 

Business users will save time with the new capability that uses machine learning to automatically extract postal addresses. This update supports many EU and North American country formats.  

Integration to Documentum with REST Services 

Organizations with the OpenText™ Documentum™ D2 UI or Documentum xCP can securely export captured content from Intelligent Capture to Documentum through a new REST-based exporter. Integration with REST services is key for modern data centers and cloud implementations of Documentum.    

For more information about OpenText Intelligent Capture CE 22.4, check out the release notes on OpenText My Support. 

November: 2021: What's new in OpenText Intelligent Capture CE 21.4

OpenText Intelligent Capture (formerly Captiva) Cloud Edition (CE) 21.4 highlights OpenText's commitment to build more machine learning, native cloud and advanced automation capabilities into a market-leading capture platform. As capture is the starting point of ECM and ERP solutions, being able to automatically ingest incoming documents and data provides organizations with an immediate and demonstrable return on their investment. The CE 21.4 release includes three powerful new capabilities:  

Information Extraction Engine (IEE) machine learning 

OpenText Intelligent Capture now includes OpenText™ Information Extraction Engine (IEE), a proven third-generation machine learning engine, which drastically reduces set-up time. It has the ability to recognize and learn new incoming document types and auto-classify and extract data from these documents (as well as variations of those documents,) significantly reducing the need for manual set-up and sorting.  

Although Intelligent Capture has utilized machine learning, such as Production Auto-learning (PAL) for nearly a decade, the addition of IEE has many partners and end users delighted. They anticipate major cost savings by being able to automatically recognize and learn new document types without the need to manually identify new documents and variations via scripting or configuration.  Improved recognition results begin immediately and IEE never stops learning and improving! 

Containerization for the REST subsystem   

In addition, Docker container support for Real-Time/ REST subsystem and Web Client has been added to simplify software deployments, scaling and upgrades.  Intelligent Capture 21.4 also includes Advanced Cloud OCR (separately licensed) that provides excellent results on handwritten and cursive text, documents with challenging backgrounds and documents captured on cell phones.   

New integrations for Advanced Cloud OCR and OpenText™ Magellan 

OpenText™ Magellan™ Text Mining sentiment analysis (also separately licensed) is now seamlessly integrated into IEE and uses Natural Language Processing (NLP) and can provide document-level insights: positive, negative, or neutral.

June 2020: What’s new in OpenText Intelligent Capture Cloud Edition (CE) 20.2

OpenText™ Intelligent Capture (formerly Captiva) Cloud Edition (CE) 20.2 continues to deliver innovation in automating manual processes, enhanced user interfaces (UIs) for increased user productivity and integration directly with OpenText™ Extended ECM.

The Intelligent Capture Web Client has been significantly enhanced in CE 20.2 to include powerful new automation capabilities and an updated, ergonomic and intuitive UI for remote users to accomplish more with less manual sorting and fewer keystrokes.

These significant automation and UI updates are made specifically for the Web Client, enabling field and home workers to process more documents with fewer manual steps. These enhancements include Single-Click Entry, Auto-table complete and On-Image Navigation.

July 2019: What’s new in OpenText Intelligent Capture

July 2019: Introducing OpenText Intelligent Capture

The post What’s new in OpenText Capture appeared first on OpenText Blogs.

In an era defined by digital transformation, organizations generate and store more data than ever before. From sensitive customer records to proprietary business strategies, data is at the heart of nearly every operation. But with this explosion of digital information comes increased risk—both from external threats and internal mismanagement. That’s where the intersection of Digital Forensics and Incident Response (DFIR) and information management becomes crucial.

While often viewed as separate disciplines, DFIR and information management share a common goal: protecting and making sense of data. When used together, they create a powerful synergy that enhances not just cybersecurity posture, but also business continuity, regulatory compliance, and operational efficiency.

The core roles of DFIR and information management

Information management is the systematic organization, storage, governance, and retrieval of data throughout its lifecycle. It ensures that information is accurate, accessible, and protected in accordance with policies and regulations.

Digital Forensics and Incident Response (DFIR), on the other hand, focuses on identifying, investigating, and responding to cyber incidents. It involves collecting digital evidence, analyzing activity, and mitigating damage after a breach or malicious event occurs.

DFIR may seem like a reactive, technical function, while information management appears proactive and operational. But in practice, they overlap in meaningful ways especially when data integrity, visibility, and governance are at stake.

DFIR as a data intelligence tool

One of the often-overlooked benefits of DFIR is the deep visibility it provides into an organization’s digital environment. During an investigation, DFIR tools comb through vast amounts of structured and unstructured data—emails, logs, cloud storage, endpoint activity—to reconstruct what happened and why.

This investigative process, though born out of necessity, often reveals gaps in data governance, inconsistencies in retention policies, or unauthorized data access. In other words, DFIR shines a light on the quality of your information management. It answers questions like:

Where is sensitive data stored, and who has accessed it?

Are users following data retention and deletion policies?

Has information been altered, moved, or exfiltrated without authorization?

In this way, DFIR solutions don’t just detect threats, they expose inefficiencies and risks in how data is handled.

Strengthening information governance with DFIR insights

When integrated with a broader information management strategy, DFIR can help organizations:

1. Identify high-risk data repositories

DFIR investigations often uncover shadow IT, forgotten file shares, or poorly secured data repositories. These insights help information managers prioritize remediation and improve access controls.

2. Improve data classification

Understanding what data attackers targeted during an incident can guide better classification efforts. If attackers consistently go after a specific type of document or database, that information is likely more sensitive than previously assessed.

3. Support regulatory compliance

Many regulations—such as GDPR, HIPAA, and CCPA—require both strong data management and breach response capabilities. DFIR tools provide the forensic evidence needed to demonstrate compliance in the aftermath of an incident, while also informing better data governance practices to prevent future violations.

4. Reduce data sprawl

DFIR solutions often find stale, duplicated, or orphaned data that poses security risks. Working with information management teams, organizations can use these findings to streamline data storage, reduce surface area for attack, and align with retention policies.

DFIR and the information lifecycle

Information management follows a lifecycle: creation, use, storage, archiving, and disposal. DFIR intersects every phase of that lifecycle:

Creation & use: DFIR tools detect policy violations or misuse of sensitive information.

Storage: DFIR investigations may highlight insecure or non-compliant storage practices.

Archiving & disposal: Evidence of improper deletion or retention uncovered during forensic review can guide better enforcement of retention schedules.

By integrating DFIR into the information lifecycle, organizations ensure that data is not just well-managed but also defensible and resilient.

Conclusion

DFIR is no longer just a cybersecurity emergency response function. It’s a critical partner to information management—providing insights that enhance governance, reduce risk, and strengthen compliance. Together, DFIR and information management form a powerful alliance that ensures data is both protected and purposeful.

OpenText plays a central and strategic role in information management, helping organizations capture, govern, access, and secure information across its entire lifecycle—from creation to disposition. As one of the world’s leading providers of Enterprise Information Management (EIM) solutions, OpenText enables businesses to harness the power of their data, ensure compliance, improve productivity, and mitigate risks.

OpenText’s Digital Forensics and Incident Response (DFIR) solutions effectively investigation cyber attacks, aligning seamlessly with OpenText’s long-standing expertise in Information Management by bridging two essential but often siloed areas: defending information and managing information. Together, these capabilities create a unified strategy for securing, governing, and extracting value from data—before, during, and after a cyber incident.

In a world where information is an organization’s most valuable asset—and its biggest liability—the ability to manage and defend that information is more important than ever.

Learn more about OpenText Digital Forensics and Incident Response solutions.

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OpenText™ Knowledge Discovery (IDOL) provides a data analytics platform for enterprises who need to extract maximum value from all their text, audio, video, and image data, from any repository in any file format. Providing data extraction, data enrichment, precise search and knowledge discovery, Knowledge Discovery helps organizations discover valuable information they did not know they had, whilst also identifying compliance risk associated with document contents such as personally identifiable information (PII) using entity grammars. 

With an unparalleled history in artificial intelligence and machine learning, Knowledge Discovery provides a unique set of optimized models to fit any application, accelerating time to value. Connecting and gathering information from diverse locations with over 160 connector types, including Public and Private Cloud and On-Premise, Salesforce, Microsoft® 365, Google Workspace and OpenText Content Solutions and can access and retrieve information and knowledge from over 2000 file types.  

May 2025: What’s new in OpenText Knowledge Discovery CE 25.2

This OpenText Knowledge Discovery (IDOL) CE 25.2 release includes various functional and performance improvements, new connectors, file format support, and many other additions. 

New connectors

• Improved OpenText Information Archive connector – An updated bidirectional connector for the Information Archive repository allows it to archive data as well as search and understand previously archived data.

• New Guidewire connector – A searching connector enables the ingestion of contract and policy content into the OpenText Knowledge Discovery index.

Named Entity Recognition - Landmarks exposed to pre-process

• Defined improvements in Named Entity Recognition – Added elements for Turkish Address, more city names in NZ Addressand the NZ Social Welfare number. 

• Defined improvements in PCI - Added extra delimiters for card details, in names and medical details, as well as expanded landmarks for Telephone context matching. 

• PII Japanese names accuracy improved - Increased accuracy of Named Entity Recognition detecting Japanese names, and improved ability to correctly identify Japanese names within a document.  

Opentext File Content Extraction updates and other improvements

• File formats - New Design Web Format (DWF) filter support, metadata now available from password-protected iWork files and additional formats detected. 

• Performance improvements – A reduction in the iWork reader disk footprint as well as a reduction of the footprint of the common third-party libraries. 

• Easier access to HTML Export from Filter – Improved ability to select XMP metadata elements and subfile extraction arguments added to C++ API 

• OEM platform – OEM Easier fault diagnosis with library-wide logging, improved security of inter-process mechanisms and better HTML Export fidelity for DWG Docs.  

January 2025: What’s new in OpenText Knowledge Discovery CE 25.1  

Organizations are looking to access all their Enterprise Data; but in a world where 90% of existing corporate knowledge is in Unstructured formats, over 50% of organizations are not tapping into this aspect of their knowledge with any form of discovery. Customers can use OpenText Knowledge Discovery with new interfaces, including using Natural language questioning to reach their information securely across all users. 

This OpenText Knowledge Discovery (IDOL) CE 25.1 release includes various functional and performance improvements, new connectors, file format support, and many other additions. 

New APIs added 

PII named entity resolution REST API – created a published REST API to access OpenTex Named Entity Recognition for PII functionality. Simplifies the adoption of Knowledge Discovery Al technology by internal and external OEMs 

Rich-Media analysis REST API – created a published REST API to access all Rich Media analytics functionality. Simplifies the adoption of Knowledge Discovery Al technology by internal and external OEMs 

Ingest and connectors 

New OpenText DAM connector

• A highly functional connector to the OpenText DAM product. This allows OpenText Knowledge Discovery to ingest and enrich content from the DAM repository and keep the data synchronized between the two systems to provide DAM content filtering using the Search Abstractor API 

Improved security in OpenText Content Management (Extended ECM) Connector  

• Increased alignment of document security policies between OpenText Content Management and OpenText Knowledge Discovery

• OpenText Content Management has numerous document security models above and beyond document folder based, where possible these have now been mapped to OpenText Knowledge Discovery's understanding of access rights

OpenText Named Entity Recognition - Landmarks exposed to pre-process 

Defined grammar landmarks are now visible at the pre-process stage of OpenText Named Entity Recognition

• Pre-processing can significantly help in reducing latency and workload, landmarks can be used to help make selection decisions at this early stage. DLP OEM vendors will benefit from this.  

PII Thailand names accuracy improved

• Increased accuracy of OpenText Named Entity Recognition detecting Thai names - the new grammar has an improved ability to correctly identify Thai names within a document  

Deployment and other improvements 

Automatic scaling OpenText Knowledge Discovery platform for Kubernetes

• Published Helm charts to deploy OpenText Knowledge Discovery with the ability to automatically scale resource - deployment size flexibility along with simplified expansion as and when required 

Vault integration with OpenText Knowledge Discovery components

• OpenText Knowledge Discovery microservices can securely store passwords and keys in the Vault repository (or equivalent)  
• Customers will benefit from cloud deployment best practice of secure central storage of passwords and keys etc. 

December 2024: What’s new in OpenText Knowledge Discovery CE 24.4 

Organizations are looking to access all their enterprise data; but in a world where 90% of existing corporate knowledge is in unstructured formats, over 50% of organizations are not tapping into this aspect of their knowledge with any form of discovery. Customers can use Knowledge Discovery with new interfaces, including using natural language questioning to reach their information securely across all users. 

This OpenText Knowledge Discovery (IDOL) 24.4 release includes various functional and performance improvements, new connectors, file format support, and many other additions. 

New API added 

Search Abstractor Rest API – created to support conversational question and answering with better context for AI generated responses. Conversation server, working with FAQ answers through Answer bank, Fact bank and now LLM’s remembers the context of questions within the conversations service in Knowledge Discovery. Adding to the great governance provided by curated answers. 

Filtering support in the Search Abstractor adds parametric fields for pre-filtering of documents. The search results can provide microscopic view of either single or multiple documents through criteria for fine grain filtering. The searching can now utilise prior knowledge to allow focus on RAG document retrieval, by using parametric fields to build a subset of entities for RAG. 

Working across text documents and images simultaneously in the search abstractor. Searching can now take criteria of both text and images as search criteria, and the results will also include similar images making Multi Modal search available. 

Ingest & connectors 

• Salesforce – Updating the connector to stay up to date with the changes in Salesforce. 

• Additional Pre filtering for focused selection added to OpenText Core Content Management & OpenText Content Management.

• IBM FileNet P8 Each document in FileNet can have multiple binary files which we now interrogate. 

Text analytics 

• Analytics: LLM based image matching - Image based transformers can be used to generate vectors for the Knowledge Discovery (IDOL) Index. Customers can now search for similar images based on not just the textual description but also the actual image content.   

Media Server 

• The Media Server as a NiFi Processor is now available with full functionality. This allow NiFi workflows and the ability to scale and manage the Media Server app though Nifi clustering. 

• Demo application with abilities to process and analyze images and other functionalities is now available in the Media Server for ease of use. 

File content extraction

• Filtering of Source Code comments 

• Support added for Metadata output for .mht files & QuickTime (.mov) files. 

• Extended format detection, with support for 93 additional file formats. 

• In HTML Export the pdf2sr reader to extract images from pages in a PDF file, you can now configure the size of the images to produce. 

Deployment & other improvements 

• HELM charts for Kubernetes provide a comprehensive set of options to assist complex deployments. With the 24.4 release we bring further enhancements to our HELM charts. 

• Named Entity Recognition SDK: You can load multiple grammars and then dynamically turn off what it is not needed for a particular document, allowing the flexibility of swapping grammars with less degradation of performance in comparison to the loading unloading cycle of grammars. The new thing is a reducing of the overall impact of this process.  

July 2024: What’s new in OpenText Knowledge Discovery (IDOL) CE 24.3

OpenText Knowledge Discovery CE 24.3 is a significant release for the third quarter of CY24. There is an increasing need for companies to reach a greater variety of data, which adds to the complexity of their search types. We allow all customers to achieve a suitable response in their enterprise-wide search across all levels of an organization.

Now part of Content Services, the combination of best of breed products will help organizations who are looking to access all their corporate knowledge through enterprise search. Bringing additional abilities to derive deeper insights, reach actionable conclusions quicker, and gaining a new level of investigative analytics, across content, teams, and projects. Document level security is also enhanced, with extra features to report and export securely and track data delivery end to end.

The Knowledge Discovery CE 24.3 release includes various functional and performance improvements, new connectors, file format support, and many other additions.

The main improvements in version CE 24.3 are listed below:

Ingest 

• Microsoft Teams, Zoom, Cisco Webex and Google Chat connectors – New connectors allow customers to collect collaborative video meetings for retrospective analysis and/or content retrieval as part of aviator search.

Text Analytics 

• Search abstractor – Customers will have better context provided by the RAG to pass on to the GenAI, therefore experiencing a higher likelihood of a correct answer being produced.

• Specific document retrieval – Customer can ask the system to find and return a specific document that matches their unique specification.

Rich Media Analytics 

• OCR improvement – Improved OCR for better support of scrolling text. Customers will experience a higher accuracy of extracted text from video imagery. 

• Speaker ID improvement – Customers will experience a higher accuracy of Speaker ID from video or audio files. 

KeyView 

• Added Audio and Video to the Export SDK.

• Added 30 new formats to File detection. 

• Metadata API Improved to limit duplicated data.

• New .NET API.

• Added support for Python 3.12.

Solutions 

• Knowledge Discovery Discover – Discover provides investigative analytics and advanced UI for searching and analyzing relations between objects, with project and team collaboration along with full oversight on the analytics process.

• Knowledge Discovery for Microsoft Exchange – Knowledge Discovery can be added as a BOT in MS Exchange allowing customers to directly access the functionality via simple natural language prompts emailed to a BOT email address in MS Exchange. 

Deployment & Licensing changes 

All components are now published to the Public Docker Hub repository to allow easier installation, maintenance and upgrades.

• Eduction Python EDK – Customers who prefer to program using the popular Python language can now use it to control our Eduction engine, simplifying their integration experience. 

• Eduction for Windows ARM – OEM users of Eduction EDK can now deploy on Windows ARM systems. 

• Additional license feature
  • From CE 23.2, we are introducing an additional license feature – a “version.key” 
  • The file is available in the SLD portal for customers with an active support contract and is required for all Knowledge Discovery installations running CE 23.2.
  • New key will be issued with every future Knowledge Discovery release. 

April 2024: What’s new in OpenText Knowledge Discovery (IDOL) CE 24.2

Organizations are looking for conversational access to enterprise data; in a world where 90% of existing corporate knowledge is in unstructured data, over 50% of organizations are not tapping into this aspect of their knowledge with any form of discovery. Customers can use the new interfaces with Q&A using natural language questioning to deliver their information with data securely to all users.

This OpenText IDOL 24.2 release includes various functional and performance improvements, new connectors, file format support, and many other additions.

New solution added: Teams client

• Customers can access functionality using simple natural language prompts directly via the Teams interface.

Ingest & connectors

• Drupal Connector – Updated to support the latest API changes and allow data extraction from old and new Drupal versions.  

• Google Workspace Connectors – Content extraction is now accessible from the major Google Workspace apps including Mail, Calendar and Chat using dedicated connectors.

• Web Connector 2FA – Updated to allow 2Factor Authentication. 

• Additional new connectors – Stack Exchange; Moodle and OpenText eDocs.

• IDOL Media Server – Can now run within NiFi, allowing processing of media streams & sources.

Text analytics

• Analytics: Search abstraction – IDOL can now automatically decide which index type to use to achieve the best response. The functionality abstracts the complexity of search types, democratizing Enterprise search, and allowing all users to achieve a suitable response, across all levels of an organization. 

• Multi-document summarization – Generative summary compiled from answers sourced from multi documents. The functionality provides richer answer to any question asked as it can be the combination of multiple parts sourced from different documents. 

• Dynamic clustering of vector results to identify grouping – Customers can use the grouping of results to select a variation in documents returned rather than returning very similar documents with no added information. 

• Analytics: LLM based image matching – Image based transformers can be used to generate vectors for the IDOL Index. Customers can now search for similar images based on not just the textual description but also the actual image content.   

Media Server

• The Media Server as a NiFi processor is now available with full functionality. This allow for NiFi workflows and the ability to scale and manage the Media Server app though Kubernetes. 

• Demo application with abilities to process and analyze images and other functionalities is now available in the Media Server for ease of use. 

KeyView

• Tabular data detection for One Note & pipe separated text.  

• OCR is now available on MacOS through the KeyView SDKs.

• Header & Footer are now configurable through Python APIs.

• Significant Performance improvements through Shared Memory Streaming.

• Additional Formats, Security & Compatibility improvements.

Deployment & other improvements

• HELM charts for Kubernetes provide a comprehensive set of options to assist complex deployments. CE 24.2 brings further enhancements to our HELM charts. 

• Eduction SDK Post-processing access to match context.

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Let’s start with the elephant in the room: predictive analytics has been overhyped and underutilized in IoT. Most organizations claim to be “data-driven,” yet struggle to operationalize even basic foresight. 

The problem isn’t a lack of models. It’s that predictive intelligence is being treated like a dashboard feature instead of a business enabler. 

When predictive analytics is siloed from your operational workflows, it becomes retrospective—ironic, isn’t it? To truly unlock its value, you must embed predictive intelligence in the orchestration layer, where decisions are made, not just visualized. 

Let’s explore 7 uncommon ways high-maturity organizations are using predictive analytics to fundamentally transform their IoT operations—not just report on them. 

Why it’s time to add a new brain to your IoT stack 

The next evolution of predictive analytics isn’t just mathematical — it’s conversational. OpenText Aviator IoT is now integrating a Large Language Model (LLM) into its orchestration layer, adding natural language understanding to your operational intelligence. 

This means operators, engineers, and supply chain leaders can ask complex questions in plain English — like “What’s the predicted failure risk for our pumps next week?” or “Show me anomalies in energy usage across our top 5 facilities” — and get real-time, contextual responses from the system. No queries to write, no dashboards to configure. 

By embedding LLM capabilities into Aviator IoT, OpenText is laying the groundwork for more intuitive human-AI interaction. Rather than simply visualizing predictive outcomes, users may soon be able to engage with them conversationally—potentially transforming how insights are accessed and applied across frontline operations. This paves the way for AI to become more explainable, usable, and operationally relevant at scale reducing the reliance on complex query languages.  

And in high-stakes industries like energy, manufacturing, and transportation, that means faster decisions, fewer errors, and smarter orchestration — without deep technical training. 

What are predictive analytics?  

Predictive analytics aren’t crystal balls. They provide clarity. Predictive analytics leverage historical data, statistical techniques, machine learning, and real-time inputs to anticipate what is expected to happen next. In today’s hyperconnected industrial ecosystems, where thousands of signals stream in from products, sensors, and machines, predictive analytics distills chaos into foresight. 

At its core, predictive analytics transform fragmented IoT data into forecastable outcomes—whether identifying a failing conveyor belt before it halts production or forecasting a demand spike for a critical pharma SKU. To operationalize predictive intelligence effectively, organizations must go beyond models and vendors. They must adopt platforms that connect insight with orchestration, enabling frontline execution at machine speed. That’s exactly where OpenText Aviator IoT delivers. 

7 Ways to leverage predictive intelligence for your IoT strategy 

The promise of predictive analytics in IoT is clear: fewer breakdowns, optimized operations, and smarter decisions. But while many organizations collect data and run models, few are turning those insights into real-world ROI.  

The difference? Execution. The following seven principles reveal what it takes to embed predictive intelligence into your operations, and unlock its full strategic value. 

1. Predictive maintenance isn’t about maintenance—It’s about asset strategy 

Yes, predictive analytics can help prevent downtime, but that’s just the starting point. The real unlock is using predictive insights to rethink your entire asset lifecycle strategy, and reshape how and when you deploy assets. 

Instead of reacting to failure, organizations are leveraging IoT-enabled models to simulate wear, stress, and fatigue long before equipment is deployed. This forward-looking approach allows teams to determine not just when to maintain, but where, how, and even if an asset should be deployed at all. 

By proactively identifying underperforming components or high-risk usage scenarios, enterprises can optimize asset placement, extend equipment life, and prioritize capital investment where it delivers the most value. The result isn’t just fewer breakdowns—it’s smarter capital planning, reduced total cost of ownership, and greater confidence in long-term infrastructure decisions. 

Key stat: Manufacturers using predictive analytics report up to 30% savings in maintenance costs (McKinsey). 

2. Anomaly detection should trigger autonomous action 

Most anomaly detection stops at alerts. But alerts without orchestration are just noise. The leaders are taking it further by tying anomalies directly into automated workflows. That means triggering repairs, routing alerts to service bots, or initiating self-healing routines.  

This approach transforms detection into resolution, reducing response times and minimizing downtime without requiring manual intervention. By embedding autonomous actions into everyday operations, organizations can move from reactive problem-solving to proactive system performance, so that issues are handled before users even notice. 

Think about it this way: If your system knows it’s breaking, why can’t it start fixing itself? 

3. Demand forecasting is obsolete without edge intelligence 

Using yesterday’s demand to plan tomorrow’s operations doesn’t cut it anymore. Forecasting needs to happen at the edge, where demand is shaped in real time by weather, market signals, or human behavior. Static models sitting in the cloud can’t keep up with dynamic environments.  

Edge intelligence allows organizations to process and act on data locally, without the latency of round trips to centralized systems. This means faster adjustments to inventory, pricing, and resource allocation based on what is happening right now. Not what happened last week. It’s how modern businesses stay agile in constantly shifting conditions. 

Use case: Smart retailers adjust supply chain inventory based on in-store footfall and ambient temperature, streamed via IoT. 

4. Asset optimization is the new sustainability strategy 

Everyone’s talking net zero. Few are connecting it to predictive analytics. Yet IoT can predict energy surges, idle time, and asset strain, allowing you to reduce emissions while optimizing performance. ESG meets ROI.  

Predictive insights help identify inefficiencies that traditional monitoring might miss, enabling smarter scheduling, right-sizing of energy loads, and timely asset adjustments. The result is a more sustainable operation that not only meets regulatory expectations but also drives measurable cost savings and long-term resilience. 

OpenText Insight: Unified intelligence and digital twins can model energy outcomes and adapt asset schedules in real time. 

5. Operational efficiency must be preemptive 

Don’t wait for a KPI to drop. Use predictive models to simulate bottlenecks before they cascade. Logistics, production, and facility management teams are now using digital twins powered by live IoT data to test "what-if" scenarios daily—not quarterly.  

This shift enables proactive adjustments to scheduling, routing, and resource allocation, reducing the likelihood of costly delays or disruptions. By modeling stress points before they occur, teams can make faster, smarter decisions that keep operations running smoothly. It means no surprises, and no scrambling. 

Why it matters: Operational resilience isn’t a nice-to-have. It’s now a board-level mandate. 

6. Predictive personalization is the frontline of retention 

You’re not just predicting churn. You’re predicting what makes people stay. Use interaction data from connected systems to forecast when to upsell, when to support, and when to innovate. If your product isn’t adapting to user behavior in real time, someone else’s will. 

Predictive personalization turns passive usage data into proactive customer engagement, creating moments of value before users even ask for them. It’s how connected brands move from one-time purchases to lasting loyalty, anticipating needs and delivering relevance at every touchpoint. 

Modern example: Smart appliances offering feature updates based on user behavior patterns. 

7. Energy forecasting is infrastructure resilience 

Power grids, manufacturing floors, and HVAC systems now face extreme volatility from climate disruptions and unpredictable consumption patterns. Predictive energy analytics allows organizations to simulate peak loads, optimize distribution, and preempt outages.  

It enables better planning for energy-intensive operations, supports sustainability targets, and reduces the risk of equipment failure due to overload. By anticipating demand and dynamically adjusting supply, businesses can maintain uptime, control costs, and build resilience into every layer of their infrastructure. 

Urgency: According to the IMF, technological fragmentation alone could shave 5% off GDP.  

Predictive analytics is not just a feature, it’s a foundation

Predictive analytics isn’t just a capability; it’s a strategic mindset shift. When integrated natively into your IoT orchestration layer, it transforms your entire operation into a self-correcting, insight-driven system. 

And that’s exactly what OpenText Aviator IoT delivers. Aviator IoT enables predictive maintenance to reduce downtime and optimize asset utilization 

️It’s not just about predicting failure. It’s about embedding foresight into every process. From asset orchestration and supply chain traceability to compliance and customer connection, Aviator IoT brings predictive insight to the edge, in real time. 

Ready to stop reporting and start orchestrating? 
Explore OpenText Aviator IoT 
Dive into our Track & Trace Solutions 

The post 7 Ways to operationalize predictive intelligence in IoT  appeared first on OpenText Blogs.

Choosing the right Internet of Things (IoT) platform provider is a critical step in any successful IoT implementation. As enterprises scale up their digital transformation initiatives, the complexity of managing diverse devices, ensuring security, and extracting actionable insights grows rapidly. The right IoT platform doesn’t just connect your devices—it connects your business to real-time intelligence, actionable insights, and future-ready innovation.  

But not all providers are created equal. Here are 7 expert tips to guide your evaluation process. Use these 7 enterprise-grade tips to evaluate and select a platform built for performance, security, and future innovation 

7 Must-know tips for IoT platform success 

1. Assess their track record and experience 

Start by evaluating the provider’s experience with similar use cases and industries. A strong track record shows the provider understands real-world implementation challenges and can offer proven methodologies, best practices, and frameworks. Ask for case studies, references, and measurable results. Ensure they can support both your current needs and future growth—scalability is key as your IoT network expands. 

2. Prioritize identity-driven security 

Security should be non-negotiable. The best IoT platforms take an identity-centric approach, authenticating and authorizing every device, user, and system interaction. This is critical in reducing attack surfaces and ensuring compliance with regulations like GDPR and HIPAA. Look for features like dynamic security contexts, autonomous authentication, and role-based access controls to protect both your devices and data from evolving threats. 

3. Evaluate integration flexibility 

An effective IoT platform must integrate seamlessly with your existing systems—ERP, CRM, WMS, cloud applications, and legacy technologies. Look for platforms that offer “any-to-any” communication protocol support (e.g., MQTT, FTP, APIs), making it easy to exchange data across disparate systems. This enables holistic insights and drives more value from your IoT data. 

4. Look for managed services and support options 

Managing an IoT ecosystem is complex, and not every organization has the in-house expertise to handle it. Check if the provider offers managed services, including 24/7 monitoring, maintenance, and technical support. This can significantly reduce your internal burden and ensure the platform performs optimally—especially important for mission-critical or regulated industries. 

5. Confirm end-to-end data protection and compliance 

Data generated by IoT devices often includes sensitive operational or personal information. Your platform should offer comprehensive data encryption—both in transit and at rest—as well as robust compliance management. Identity management, audit trails, and geographic data privacy controls are must-haves to meet evolving regulatory demands across regions. 

6. Demand extensibility and ecosystem compatibility 

Your IoT journey doesn’t end with device connectivity. You’ll want a platform that supports analytics engines, digital twins, reporting services, and other advanced capabilities. Make sure the provider supports an open architecture and has a portfolio of complementary solutions (e.g., AI, machine learning, or edge computing) that you can plug into as your needs evolve. 

7. Test their vision for innovation and longevity 

IoT is a long-term investment. Choose a provider that’s continuously innovating—one that can evolve with the pace of technology. Ask how often their platform is updated, what features are on their roadmap, and how they plan to incorporate advancements like AI, 5G, or blockchain. A provider with a strong vision for the future will help ensure your IoT strategy remains competitive for years to come. 

Don’t choose just a platform: Choose a partner 

Selecting an IoT platform provider goes beyond comparing feature lists—it’s about choosing a trusted partner who understands your goals, mitigates your risks, and grows with your business. From secure device onboarding to advanced analytics and ecosystem integration, the right provider will empower your organization to unlock the full potential of IoT. 

If you're just getting started or looking to scale an existing IoT implementation, use these tips as a foundation to guide your evaluation and selection process. The success of your digital transformation depends on it. 

Track every product. Protect your brand. Explore end-to-end supply chain traceability from OpenText.

The post How to build an enterprise-ready IoT platform: 7 essential tips  appeared first on OpenText Blogs.

As businesses across industries embrace the Internet of Things (IoT) to drive real-time insights, predictive maintenance, and operational efficiencies, the complexity of managing connected devices, data, and security continues to grow. That’s why an identity-driven IoT orchestration platform has become essential—not just for enabling IoT, but for scaling it securely and successfully. 

6 Key benefits of using an identity-first IoT orchestration platform 

Let's unpack six compelling reasons your organization needs an identity-driven IoT orchestration platform.  

1. Secure your IoT ecosystem from the ground up 

IoT ecosystems are prime targets for cyber threats due to the massive number of connected endpoints. On average, every week 54% of organizations suffer from attempted cyber-attacks that target IoT devices.  

An identity-driven orchestration platform ensures that every device, user, and system is authenticated and authorized before being granted access.  

This framework helps enforce zero-trust principles, reduces vulnerabilities, and provides auditability to meet data protection regulations. With the rise of edge computing, identity-first security also enables adaptive responses to emerging threats in real time, ensuring robust and scalable defense. 

2. Orchestrate diverse devices and protocols seamlessly 

IoT environments often include a mix of devices using different standards and protocols. An identity-driven IoT orchestration platform enables seamless orchestration by acting as a unified layer that abstracts these complexities.

It standardizes device communication and ensures consistent roles, permissions, and interactions. This allows for smooth onboarding, integration, and real-time coordination of devices, reducing operational overhead and enhancing responsiveness, especially in dynamic settings like logistics or smart manufacturing. 

3. Enable scalable and flexible integration 

Integrating IoT with enterprise systems can be difficult without the right foundation. An identity-centric IoT orchestration platform enables secure, any-to-any communication across legacy systems, cloud services, and modern applications.

It ensures that IoT data flows are governed, traceable, and secure. The platform's modular architecture allows businesses to expand capabilities quickly and efficiently while maintaining control, making IoT a strategic enabler of digital transformation rather than a standalone initiative. 

4. Ensure data integrity and compliance 

In an IoT environment, massive volumes of data are constantly generated, transmitted, and analyzed. An identity-driven orchestration platform ensures that each data transaction is tied to a verified identity, maintaining integrity and traceability.

This level of accountability supports compliance with regulations like GDPR and HIPAA, while also preventing data misuse. By enforcing secure data flows and encryption protocols, an IoT orchestration platform provides confidence that sensitive information is protected at every stage. 

5. Automate decisions with trustworthy data 

Automation is only as effective as the data that powers it. Identity-driven orchestration ensures data is collected from verified sources and validated before entering analytics pipelines or triggering automated workflows.

This improves the accuracy of predictive maintenance, real-time alerts, and supply chain decisions. With trusted data, organizations can confidently implement AI-driven strategies that enhance efficiency and responsiveness across operations. 

6. Manage the lifecycle of every ‘thing’ 

Every connected device has a lifecycle that must be managed—from onboarding and configuration to updates and retirement. An identity-centric IoT platform assigns and tracks unique identities for each device, enabling precise control throughout its lifecycle. This streamlines provisioning, enhances monitoring, and simplifies the process of revoking access when a device is decommissioned. It ensures your IoT environment remains secure, efficient, and scalable as it evolves. 

Get started with an identity-driven IoT orchestration platform 

By adopting an identity-driven IoT orchestration platform, your organization lays a secure, scalable foundation for innovation. Whether you’re managing a global fleet or optimizing a smart factory, putting identity at the center of your IoT strategy ensures you stay agile, compliant, and in control. 

Track every product. Protect your brand. Explore end-to-end supply chain traceability from OpenText.

The post 6 Reasons why businesses need an identity-driven IoT orchestration platform  appeared first on OpenText Blogs.

In today’s fast-moving, interconnected world, businesses are turning to digital supply chains to stay competitive, resilient, and customer-focused.  

By leveraging advanced technologies like AI, IoT, and blockchain, digital supply chains offer a smarter, faster, and more efficient way to manage operations—from procurement to delivery.  

In this blog, we explore eight powerful benefits of supply chain digitization and how it can transform your business for long-term success. 

What is the digital supply chain? 

Today’s most successful companies have achieved advanced levels of digital sophistication and maturity within their supply chains. Research from IDC shows a strong correlation between digital maturity and superior business performance, including higher revenue growth and profitability. 

Unlike traditional supply chains—which are often linear, fragmented, and reliant on outdated systems—digital supply chains operate in real time, adapt dynamically to changing conditions, and foster stronger collaboration. They are built around ecosystems of interconnected partners and suppliers, linking internal systems with external data sources to enable seamless information sharing, responsiveness, and end-to-end visibility. 

At the heart of a digital supply chain is a digital backbone—a foundational infrastructure that allows all transactions to occur digitally. This enables efficient, transparent collaboration across stakeholders, including suppliers, customers, logistics providers, and financial institutions. The digital backbone serves as a platform on which companies can deploy advanced technologies to pursue strategic goals and seize new opportunities. 

The real power of the digital supply chain lies in its ability to collect accurate, comprehensive, and timely data—and transform that data into actionable insights. These insights lead to better decisions, improved operational efficiency, reduced costs, and more resilient supply networks. 

Supply chain digitization vs. supply chain digitalization 

While often used interchangeably, supply chain digitization and supply chain digitalization represent two distinct stages in the evolution of modern supply chains. 

• Supply chain digitization is the process of converting physical documents, such as paper-based records, into digital formats. This foundational step improves data accessibility and enables more efficient handling of information. 

• Once data is digitized, companies can move to supply chain digitalization, which involves using digital tools and technologies to automate workflows, enhance visibility, and improve process efficiency. Digitalization is about transforming operations—not just making them digital, but also smarter, faster, and more integrated. 

Together, these steps lay the groundwork for building a fully connected and intelligent digital supply chain. 

Key benefits of the digital supply chain 

Digitalization of the supply chain enables better use of resources, optimized production, stronger supplier relationships, increased visibility, and a healthier bottom line. Let's dive into the key benefits of the digital supply chain.  

1. Reduced cost and improved revenue 

By virtually eliminating manual tasks, the digital supply chain dramatically reduces human error and input time while freeing staff for higher value activities. According to one study, the average annual cost for organizations to manually enter data into ERP and back-end systems alone was more than $1 million. 

At a strategic level, improving the speed, quality and accuracy of tasks, such as demand forecasting, inventory management and order fulfilment, directly drives revenue and profitability. 

2. Increased supply chain visibility 

In a recent survey (Reuters Events, The state of European Supply Chains 2024) addressing supply chain visibility gaps was prioritized as a top area for investment, reported by 68 percent of the respondents.  

The problem is the vast number of disconnected legacy systems used to address each stage in the traditional supply chain process. This includes a lack of integration between information technology (IT) and operational technology (OT) systems. 

Digitizing the supply chain creates opportunities for breaking down these barriers and connecting disparate systems both internally and externally. As a result, data can pass quickly and securely across the entire supply chain, enabling near real-time visibility. Supply chain visibility makes it possible for staff to instantly see the current status of any activity, enabling them to make informed decisions. 

3. Improved decision-making  

For most businesses, decisions need to be made quickly, and agility is vital. Basing decisions on historical reports and spreadsheets is far from optimal. Research has shown that more than two thirds of supply chain managers still use Microsoft® Excel® as an inventory management tool. In the world of big data, this isn’t ideal.  

A digitized supply chain allows organizations to gather and analyze massive amounts of data with far less effort and in far less time. The ability to gain insight from real-time data generated anywhere in the supply chain offers significant benefits in every aspect of the business, from product development to sales and marketing to customer experience. 

4. Building supply chain resilience  

The COVID-19 pandemic revealed serious and systematic weaknesses in supply chains. Global supply chains have become extended and complex to take advantage of low-cost sourcing, lean inventories, and Just-in-Time manufacturing practices.  

When supply chains became disrupted, organizations found it difficult to maintain logistics routes or switch to alterative suppliers. IDC found that diversifying their sourcing strategies, along with improving supply chain visibility, were the most important focus areas for organizations to mitigate the impacts of disruption. 

Digital supply chains allow for deeper connections and improved collaboration between trading partners, as well as an ecosystem of suppliers, which makes alternative sourcing arrangements faster and more effective.  

Overall, digitalization helps build supply chain resilience and more sustainable supply chains. This enables increased mobility and accountability while driving proactive responses to emerging problems.  

5. Supply chain automation  

Automation improves overall supply chain performance by eliminating friction and choke points between functions. In addition, it creates new business opportunities and a better customer experience through enabling innovative self-service options.  

However, most organizations have yet to realize the full benefits of supply chain automation. As the use of AI expands and more complex processes are targeted for automation, the effectiveness of these efforts increasingly depends on robust process transformation, high-quality data, and strong digitization and digitalization capabilities to deliver desired outcomes. 

6. Driving collaboration and innovation  

The digital supply chain enables multi-enterprise collaboration by breaking down data silos within an organization and between external partners. It allows seamless and secure integration between the systems of the organization and its suppliers, customers, logistics, and financial institutions.  

Through digital enablement, data becomes actionable; workflows are streamlined; and critical information such as order milestones, inventory statuses, and payments can be shared securely and instantly.  

Organizations and their partners can quickly establish shared responsibilities and accountability and track them in real time. As organizations look towards their partners to improve their competitiveness in the market and drive increased collaboration in product design and development, a digital supply chain is the foundation of success. 

7. Enhancing sustainability  

With consumers increasingly favoring companies and products they perceive as sustainable, brands are seeking external partners with good sustainability records and may require their existing partners to address identified gaps. The digital supply chain has a major role to play in helping businesses reduce their impact on the environment through efficient management of stock and avoidance of waste.  

Digitalization enables improved inventory and materials management, ensures accurate tracking of goods in transit, and helps avoid materials shortages, all of which leads to more efficient use of resources. It also improves visibility into the supply chain, which helps organizations to better identify, and address risks related to sustainability and ethical business practices. 

8. Enabling technologies 

Organizations can increase revenue, save expenses, reduce risk, and boost customer experience by developing digital supply chain capabilities to improve operations and make smarter decisions faster. 

 There are several complementary digital technologies that enable this, including: 

• Multi-Enterprise Collaboration Networks 
  These cloud-based platforms connect all supply chain partners on a single network, enabling seamless collaboration, real-time data sharing, and greater agility across the ecosystem. 

• Internet of Things (IoT) 
  IoT devices provide real-time visibility into assets and shipments, linking the physical and digital worlds to improve tracking, operations, and decision-making. 

• AI and Machine Learning 
  AI/ML analyze large volumes of supply chain data to enhance forecasting, optimize operations, and automate decision-making for improved efficiency and cost savings. 

• Digital Twins 
  Digital twins create virtual models of physical products or processes using real-time data, allowing predictive simulations and better insights into operations and supply chain scenarios. 

• Supply Chain Command Centers 
  Supply chain command centers centralize and analyze supply chain data to enable proactive monitoring, issue resolution, and faster responses to disruptions and changing conditions. 

• Blockchain 
  Blockchain secures and tracks supply chain transactions in an immutable ledger, enhancing transparency, reducing fraud, and eliminating intermediaries. 

• Identity and Access Management (IAM) 
  IAM systems control and secure access to supply chain platforms and data, ensuring only authorized users and devices can interact with sensitive systems in real time. 

Digital supply chain transformation: How to get started 

Today, digitally transforming supply chains is crucial for most organizations. It is an increasingly important step to ensure long-term business success, drive efficiency, deliver sustainability, and, most importantly, create better customer experiences.  

However, this is no simple undertaking. It is unlikely that any business can implement a “rip and replace” strategy when digitizing their supply chain. Instead, an incremental, planned approach designed to produce tangible benefits is the best way to get started. 

 To take the first step toward a smarter, more connected supply chain, organizations need the right partner and platform. OpenText Digital Supply Chain Collaboration provides tools to digitize operations, enhance visibility, and drive seamless, secure collaboration across your entire ecosystem. Learn how OpenText can help you accelerate transformation and unlock the full value of your supply chain.  

The post 8 Benefits of leveraging the digital supply chain  appeared first on OpenText Blogs.

Supply chain collaboration is more critical than ever as global networks grow in complexity and customer expectations continue to rise. However, achieving seamless collaboration across suppliers, partners, and internal teams is no easy feat.  

In this blog, we explore the key benefits of effective supply chain collaboration and share four practical strategies to help you overcome common obstacles and build a more connected, agile supply chain. 

The benefits of supply chain collaboration 

At its core, supply chain collaboration is the process of connecting and aligning all stakeholders—suppliers, partners, logistics providers, and internal departments—to work together more effectively. When done well, it creates a transparent, responsive network that delivers better outcomes for everyone involved. 

The benefits are substantial: improved visibility and communication, faster time to market, lower operational costs, greater agility in responding to disruptions, and a better customer experience. With increasing global uncertainty, supply chain collaboration has evolved from a competitive advantage to a business necessity. 

While many challenges stem from technical limitations like fragmented systems and manual processes, others are organizational in nature—such as data inconsistency, lack of transparency, or misaligned objectives between partners.  

Read on to learn four technology-driven strategies that address both technical limitations as well as organizational barriers to collaboration. The right supply chain collaboration software, combined with professional expertise tailored to your business needs, plays a pivotal role in enabling and optimizing these improvements. in enabling and optimizing these improvements. 

4 solutions for common supply chain collaboration challenges 

Modern supply chains face a range of collaboration challenges, from fragmented processes to security risks. These four solutions help businesses overcome common pain points and build stronger, more efficient partnerships.  

1. Consolidate supplier onboarding into a unified workflow 

Many organizations struggle with complex, resource-intensive supplier onboarding processes that span multiple systems and departments. To address this, businesses can simplify onboarding by centralizing it on a single platform that integrates with their ERP system.  

By streamlining workflows and enabling self-service capabilities, companies can automate data collection, accelerate approvals, and ensure consistent supplier interactions. The result is faster onboarding, lower administrative costs, easier community management, and a more seamless, professional experience for trading partners. 

2. Eliminate manual processes with system-to-system integration 

Manual processes continue to bog down supply chains—especially in critical functions like order-to-cash and procure-to-pay. These tasks often involve re-keying data between disconnected systems, which not only slows things down, but also introduces errors and inefficiencies.  

By replacing manual steps with automated data exchanges using EDI, APIs, and other integration technologies, companies can create seamless, system-to-system connectivity across their internal operations and external trading partners. This shift doesn’t just save time. It improves data quality, reduces costly errors, and gives teams real-time visibility into transactions, helping them respond faster and work smarter. 

3. Strengthen access and security through centralized identity management 

When users need multiple logins to access different systems, it creates more than just frustration—it introduces serious security risks and administrative complexity. A centralized identity and access management (IAM) solution can solve this by enabling federated access control across your entire supply chain ecosystem.  

With a single, secure framework for authenticating both internal and external users, IAM simplifies access while strengthening your security posture. This approach not only improves compliance and reduces IT overhead but also supports customer self-service and smoother collaboration. Ultimately, this helps your teams work more securely and efficiently. 

4. Leverage AI to enhance collaboration and predictive insights  

Supply chains thrive on speed and agility, but delays, inefficiencies, and slow responses remain all too common. That’s where AI comes in. By automating key collaboration tasks—like supplier onboarding, performance tracking, and risk detection—AI removes friction from the process and frees teams to focus on higher-value work.  

Tools like intelligent assistants and predictive analytics help teams anticipate issues before they escalate and make faster, data-driven decisions. Beyond improving responsiveness, AI also lightens the load on customer support and IT, unlocking scalable gains in efficiency across the entire ecosystem. 

Streamline your supply chain collaboration 

Building an efficient, secure, and connected supply chain requires more than just good intentions—it requires the right tools and strategies. By consolidating onboarding, integrating systems, securing access, and leveraging AI, organizations can overcome the most common collaboration challenges and unlock significant operational value. 

OpenText Digital Supply Chain Collaboration solutions offer the capabilities needed to make this transformation possible. From cloud-based integration to identity management and AI-powered automation, OpenText helps businesses modernize their supply chain operations and foster stronger, more collaborative relationships with partners.  

With the right supply chain collaboration software and decades of professional B2B integration experience, OpenText enables faster partner onboarding, stronger compliance, and improved operational resilience.

Learn more about OpenText Digital Supply Chain Collaboration. 

The post 4 Strategies to overcome supply chain collaboration challenges  appeared first on OpenText Blogs.

Supply chains report that 3% to 20% is lost to chargebacks each year (Source: Gartner Top 5 Practices to Reduce Retailer Chargebacks and Lower Costs). And in today’s fast-paced, data-driven supply chains, chargebacks are a costly and often avoidable problem.  

These penalties, typically issued when a trading partner fails to meet specific requirements, can erode profit margins, damage relationships, and disrupt operations.  

Fortunately, digital supply chain collaboration offers ways to reduce and even prevent retailer chargebacks. By improving visibility, communication, and data accuracy, businesses can align more closely and avoid the pitfalls that lead to chargebacks. 

What is supply chain chargeback management?  

A supply chain chargeback is a financial penalty that a retailer or distributor imposes on a supplier for failing to meet agreed-upon requirements, such as late shipments, incorrect labeling, or incomplete orders. Supply chain chargeback management aims to reduce these penalties by identifying root causes, improving compliance, and streamlining operations across the supply chain. 

5 tactics to prevent supply chain chargebacks  

Supply chain management software streamlines fulfillment by improving order accuracy, automating workflows, and providing real-time visibility, helping businesses avoid chargebacks due to delays or errors. 

Here are five tangible ways to tackle chargebacks using digital tools and smarter collaboration: 

1. Identify and group chargeback root causes 

The first step in solving any problem is understanding it. Supply chain chargebacks often stem from recurring issues like late shipments, incorrect labeling, or mismatched purchase orders. However, without a clear view of the data, these issues can seem random or isolated. 

What to do: 

• Use digital dashboards to track chargeback trends over time. 

• Group chargebacks by root cause categories—such as shipping errors, documentation issues, or compliance failures. 

• Visualize this data to spot patterns and prioritize the most frequent or costly issues.  

By categorizing chargebacks, you can move from reactive firefighting to proactive problem-solving. 

2. Assign owner for compliance improvements 

Once you know what’s going wrong, the next step is to make sure someone is responsible for fixing it. Too often, chargeback management falls through the cracks because no one owns the process. 

What to do: 

• Assign clear ownership of chargeback categories to specific teams or individuals—such as logistics, warehouse operations, or customer service. 

• Set KPIs and accountability metrics for reducing chargebacks in each area. 

• Use workflow tools to track progress and ensure follow-through. 

When everyone knows their role in compliance, it becomes easier to make consistent improvements. 

3. Boost collaboration with suppliers, retailers 

Chargebacks are often a symptom of poor communication between trading partners. Misaligned expectations, unclear requirements, or last-minute changes can all lead to costly mistakes. 

What to do: 

• Use cloud-based platforms to share real-time data with suppliers and retailers. 

• Set up automated alerts for order changes, shipment delays, or compliance risks. 

• Hold regular collaboration meetings to review performance and align with expectations. 

Digital collaboration tools help ensure that everyone is working from the same playbook—reducing misunderstandings and improving execution. 

4. Use data tools to improve performance 

Modern supply chains generate massive amounts of data. The key is turning that data into actionable insights that help prevent supply chain chargebacks before they happen. 

What to do: 

• Implement predictive analytics to flag potential issues—like shipments at risk of delay or orders with missing documentation. 

• Use machine learning models to identify which suppliers or SKUs are most likely to trigger chargebacks. 

• Integrate data from multiple systems (ERP, WMS, TMS) to get a 360-degree view of supply chain performance. 

With the right tools, you can shift from reacting to chargebacks to preventing them altogether. 

5. Strengthen quality checks and dispute handling 

Even with the best systems in place, mistakes can still happen. That’s why it’s important to have strong quality control and a clear process for disputing chargebacks when they’re issued in error. 

What to do: 

• Introduce automated quality checks at key points in the supply chain—such as before shipping or receiving. 

• Use digital documentation (photos, timestamps, scan logs) to verify compliance and support dispute claims. 

• Create a centralized chargeback portal where teams can track, investigate, and respond to chargebacks efficiently. 

A strong dispute process not only helps recover lost revenue but also builds trust with trading partners. 

Prevent chargebacks: Collaboration is the cure for chargebacks 

Supply chain chargebacks are often a symptom of deeper issues, such as miscommunication, poor data quality, or lack of visibility within a supply chain. OpenText Business Network Cloud helps businesses reduce chargebacks by providing end-to-end visibility and automation across trading partner interactions. Our Trading Grid and Command Center solutions enable businesses to identify and group chargeback root causes through advanced analytics and real-time monitoring of transaction data.  

It supports assigning ownership for compliance improvements, such as e-invoicing, by integrating workflows and role-based dashboards that track and trace accountability throughout your entire supply chain.  

OpenText enhances collaboration with suppliers and retailers via a centralized, cloud-based environment that facilitates seamless data exchange and communication. No matter your industry or geographic location, the path to fewer chargebacks and better performance starts with a smarter, more collaborative connected community. 

The post How to prevent retailer chargebacks in your supply chain: 5 proven tactics  appeared first on OpenText Blogs.

Every June 27th, we celebrate Micro, Small, and Medium Enterprises (MSMEs). These businesses are the backbone of our economy, driving innovation, creating jobs, and fostering vibrant communities. This year, MSME Day 2025 carries a powerful theme: 
“Enhancing the Role of MSMEs as Drivers of Sustainable Growth and Innovation.” 

 At OpenText, we recognize the unique challenges faced by MSMEs. Often, MSMEs lack the resources of larger corporations, yet they need enterprise level integration to compete in the global marketplace. That’s why we are proud to support MSMEs with easy EDI integration solutions that help them grow, compete, and innovate while building long-term sustainability.  

What is MSME Day? 

Recognized by the United Nations since 2017, MSME Day raises awareness about the critical role MSMEs play in building strong, inclusive, and sustainable economies. These businesses: 

• Represent 90 percent of all companies worldwide 

• Provide over 70 percent of global employment 

• Contribute approximately 50 percent of global GDP 

This year’s theme highlights the importance of MSMEs in leading innovation and driving responsible growth. 

OpenText is aligned with this mission by offering integration and automation solutions that help MSMEs operate more efficiently, reduce waste, and adapt to changing global standards for sustainable business. 

Why supporting MSMEs matters more than ever  

MSMEs often face significant hurdles, including: 

• Limited access to financing and technology 

• Disruption from global economic shifts or supply chain issues 

• Difficulty scaling digital operations 

Yet these businesses are uniquely positioned to lead innovation at the local level and shape resilient, sustainable economies. 

OpenText bridges the gap by delivering digital infrastructure that enables MSMEs to overcome resource limitations. Our EDI solutions are designed to simplify supply chain integration and support long-term business continuity regardless of size or budget. 

How OpenText supports MSMEs with flexible, scalable digital solutions  

With decades of experience in EDI integration, OpenText offers solutions that go far beyond basic document exchange. Our technology is built for the evolving needs of MSMEs, supporting a wide range of communication protocols, data standards, and deployment models. We help businesses stay compliant with global mandates, adapt quickly to changing partner requirements, and harness the power of AI and analytics to unlock new opportunities. 

Key benefits of OpenText EDI for MSMEs: 

• Quick and scalable deployment 
  Get started quickly with expert onboarding and personalized support. 

• Custom supply chain integration 
  Integrate with internal ERP systems and external trading partners for operational efficiency. 

• Real-time transaction visibility 
  Gain full oversight of your data to make more informed, timely decisions. 

• Flexible support models 
  Choose between self-service tools or managed services based on your business needs and budget. 

• Growth-ready technology 
  Scale confidently with a solution built to support long-term sustainability and innovation. 

By reducing manual effort and enabling smarter, data-driven logistics, OpenText solutions also help MSMEs lower their environmental footprint, further supporting the theme of sustainable growth. 

Join the celebration and embrace growth  

MSME Day is a time to celebrate the incredible achievements of small businesses. But it’s also a call to action. Learn more about OpenText and discover how to overcome EDI integration hurdles to unlock new levels of efficiency and growth.  

Happy MSME Day!  

The post MSME Day 2025 – Celebrating the engine of our economy  appeared first on OpenText Blogs.