3 Ways Manufacturers Can Leverage Insight Engines

This article was originally published on Manufacturing.net.

As distributed manufacturing gains adoption by some of the world’s largest companies, products are finding their way into the hands of customers faster than ever before. But in the process, companies are becoming increasingly disconnected and less efficient in new areas.  Unlike in traditional manufacturing, where materials are assembled in remote, centralized factories and shipped to customers, distributed manufacturing takes place at multiple, decentralized plants where products are assembled closer to the customer.

Global manufacturing

The process of leveraging a network of geographically dispersed manufacturing facilities connected over the Internet helps eliminate many of the inefficiencies of traditional manufacturing. However, distributed manufacturing creates new challenges in the form of separate entities, languages and processes.

Teams once gathered under a single roof are now spread across multiple sites, working on projects and programs unique to their local area. These distributed teams are inadvertently scattering knowledge and data across continents and systems, creating information silos that leave workers disconnected.

Content and data have been scattered across a myriad of applications and repositories without any means for end users to surface accurate information with any confidence. In some cases, access to critical institutional knowledge, insights and innovation is restricted to a small few. As a result, disparate teams are spending countless hours making and solving the same problem over and over again, lowering productivity and delaying projects.

A digital solutions manager at a global manufacturing firm once told me, “We had thousands and thousands and thousands of places where our documents were shared, managed by check-in, check-out. In fact, we once had thousands of applications with no search capabilities.”

But once information is accessible from a single place, there are no longer countless teams scavenging for data and insights. With insight engines, manufacturers are bringing the best people together for new projects. Critical parts are easier to locate instead of rebuilding or re-designing them. Even proposals and RFP responses are being developed faster with information found and shared from other RFPs.

These improvements are the result of insight engines that combine cognitive analytics, artificial intelligence and NLP (natural language processing) to helps computers understand, interpret and manipulate human languages, dialects and patterns.

This technology helps manufacturers quickly respond to changing conditions and identify products, parts and components across multiple data sources despite their distance from one another. Here are three ways manufacturers can leverage insight engines to rediscover their synergies and avoid mistakes.

Building Teams Through a Patchwork of Data

Many companies struggle to create the best teams for new projects because expertise is hidden in large organizations with multiple divisions and product groups. Sometimes this task is made more difficult by employees who fail to enter their background and experience in their HR profiles or update them with new skills or certifications.

AI-powered insight engines leverage NLP to surface information from large volumes of data stored in hundreds of millions of files, and thousands of repositories and databases to uncover work histories, experience on different projects, expertise, training and education in order to locate experts. Projects employees have worked on, languages they write in, and locations where they’ve traveled are all easily queried and analyzed to identify experts within the company.

Avoiding Duplication of New Parts

In a distributed environment, it’s difficult to find parts across systems and continents. Sometimes it’s easier to make new ones. That’s because parts are generally indexed by labels, which can be difficult to identify correctly. As a result, the exact same part is recreated, sometimes up to five times, each version with its own label.

This is a complete waste of time and money. And when a part is recreated, there’s a greater chance of a new flaw being introduced, which can be catastrophic in industries like transportation or medical devices.

With insight engines, parts data can be contextualized for engineers to identify duplicate parts and eliminate duplications. For any company with a broad base of engineers, the cost savings from this approach can be enormous.

Increasing the efficiency of proposal development

Companies with multiple departments and divisions often fail to share technical information for sales proposals and RFPs. Consequently, proposals for an opportunity in one part of the world with many of the same requirements as others in different markets or regions must be developed from scratch.

And, if questions arise during the proposal process, it is time-consuming and difficult to get answers from engineers. Acquiring new customers in a complex environment with a highly engineered, configurable project is time-consuming by its nature. The inaccessibility of data makes it worse.

Insight engines surface information in context across emails, databases, and records. With this information in hand, companies can respond faster to sales opportunities and present a common face for the company overall.

In all three of these scenarios, information and insights are securely surfaced from content and data in every application and every repository from every location across globally distributed companies, which enables teams to cultivate, improve quality and take advantage of new opportunities.

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Sinequa 2018 Roundup… 2019 Here We Come!

2018Sometimes it helps to look at an entire year to gauge just how far you’ve come in a relatively short period of time. Sinequa experienced some very positive developments in 2018 that are worth highlighting. Our software platform evolved on several fronts to help us accelerate our mission to power the information-driven economy. In parallel, our customers demonstrated what the platform can do, even when stretched in creative and unexpected ways.

On the Technology Front

The Sinequa platform evolved with some very useful and powerful new capabilities in 2018.

Content-related Capabilities

Many of the new capabilities improved on the platform’s ability to integrate with even more enterprise applications and content formats, including:

  • New connectors to support the goal of ubiquitous connectivity across the enterprise. Among these were connectors to Atlassian products to incorporate information from software development projects, including source code files. Also addressed were new versions of popular repositories like SiteCore (a leading web content management platform according to Gartner), along with the likes of Azure storage, AODocs, Beezy, and Teamcenter.
  • New converters to index more formats like OCR on PDF and Images, AutoCAD and Windchill files, Visio, Improvements on PowerPoint, and a dedicated converter for source code files
  • Tighter integration with SalesForce.com
  • In a year full of major data privacy breaches being reported, the Sinequa platform continued to strengthen support of additional levels of encryption like in-flight encryption between all components in a distributed deployment and encryption at indexing time to secure the document cache, which contains elements like HTML preview and thumbnails to better serve customers operating in highly secure environments

Further Automation for the Interpretation of Meaning

The platform’s ability to interpret the meaning of content also evolved in 2018.

  • Query Intent: It is now possible to configure rules to be applied on queries to change the behavior of the underlying search process. This new query intent capability analyzes the query to detect certain words and entities and triggers actions based on the specified rules and classifications. New default entities were also introduced in the platform in 2018 that can be leveraged by the query intent capability and for enrichment during indexing.
  • Enhanced Linguistics: There were some language-specific improvements added to the platform to help automate the interpretation of meaning. These included things like enhanced linguistic processing for compound words in French, improved lexical disambiguation in English, enhanced detection of ordinal numbers for Danish & Swedish.

Improvements in Machine Learning

The year 2018 brought several significant improvements in the Sinequa platform’s ability to leverage machine learning, including:

  • The platform evolved to embed Online Machine Learning, applying machine learning models based on Spark or TensorFlow directly in the indexing pipeline. This represents the first of many new components that can serve machine learning models in real time. Deep learning is also used during indexing to detect new entities or concepts. These are immediately fed into machine learning algorithms, for example in the classification of incoming documents.
  • Packaged with the platform is a new unsupervised Deep Learning application for text analysis that detects the key words, key phrases, and key sentences of a document.
  • The platform now supports the Spark 2.3 implementation.
  • Packaged integration with 3rd party spark distribution providers – e.g. AWS EMR, HortonWorks.
  • Battle testing of supervised classification algorithms – i.e. Sinequa reached a threshold training set size over 10M documents
  • First machine learning customers are now in production
  • Packaging of hierarchical classification
  • Ongoing transition to Software 2.0 paradigm where software is effectively “trained” rather than manually programmed with the packaging of the lifecycle of the model and the model feedback from the search based applications into the Sinequa platform.

Presentation Enhancements for End Users and Admins

Sinequa invested significantly during 2018 to evolve the way the platform presents insights to end users as well as status information and optional settings to administrators. Here are a couple of the most significant developments:

  • A very exciting development from 2018 involved a complete overhaul of the user interface framework to a responsive design based on Angular 7. This will not only ensure optimal flexibility and performance for end users on all kinds of devices, but will open up Sinequa application development to a much wider audience.
  • On the Admin front, components have been reshaped to offer administrators of the platform more functionality and a better user experience for their work behind the scenes.

On the Customer Front

There were a few compelling themes driven by our customer base in 2018, each of which was rewarding in its own way.

Customer satisfaction and retention is a predominant theme for Sinequa. We are extremely pleased by the sheer number of existing satisfied customers that came back to us in 2018 with additional use cases to accelerate their information-driven journey. For instance, business drivers related to governance, risk and compliance with the advent of GDPR and related regulatory demands spurred a lot of activity this past year.

We also had a significant number of customers who experienced that “light bulb moment”, which often occurs when they realize their existing return on Sinequa investment could be significantly amplified by extending the use of the platform with information-driven applications in other parts of the business – e.g. areas like customer service, R&D, supply chain, and other knowledge-intensive arenas.

We even had a few long-time customers take a pause to re-evaluate their vendor choice and, without exception, decided to double-down on their commitment to Sinequa for years to come.

Of course, the disappearance of the Google Search Appliance brought some new customers into the fold, most of them fiercely determined to go beyond their previous use of a dying application and truly become information-driven.

Possibly the single most exciting development for Sinequa in 2018 was the surge in machine learning projects, which contributed significant business value back to the respective organizations, especially in the Financial Services industry. As the underlying technology matures, we see a steady trend for machine learning projects going from research to production stages. Some of the projects from 2018 focused on applying machine learning models to automate the curation of enterprise content and improve relevance. For example, one customer demonstrated how trained machine learning models could be used to make the enrichment of their enterprise corpus more efficient. It turns out that by proactively identifying what content qualifies as “scientific”, both time and money can be saved by preventing non-scientific content from even being considered for scientific enrichment during ingestion. Another customer took a completely different tack, using machine learning to automatically reproduce confidentiality policies to classify large volumes of banking documents with measurably higher quality at a fraction of the cost they would have spent to do it manually or even with a more traditional rules-based approach.

Now it’s on to 2019!

As we turn the corner into 2019, we are grateful for both the accomplishments of our R&D team and for all of our partners and customers, especially those who provide the challenges, creativity and critical feedback necessary for Sinequa to continue providing the leading platform for information-driven applications and solutions.

We wish you all the best and look forward to serving all of you in 2019 and beyond.

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How organizations can evolve from data-driven to information-driven

This article was originally published on Information Management.

Over the last several years, data analytics has become a driving force for organizations wanting to make informed decisions about their businesses and their customers.
With further advancements in open source analytic tools, faster storage and database performance and the advent of sensors and IoT, IDC predicts the big data analytics market is on track to become a $200 billion industry by the end of this decade.
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Streamline Global Manufacturing with the Information Driven Supply Chain

This article was originally published in Manufacturing Business Technology.

A new kind of manufacturing company is emerging that leverages big data and analytics for a unified view of the supply chain. This new approach provides supply chain insights that enable these organizations to respond quickly and decisively to changing conditions despite geographically dispersed suppliers and customers. And yet at the same time, they can also pursue long-term opportunities by identifying products, parts and components across all the data sources where supply and demand spans states, countries and continents.

No matter the supply chain model, customers expect quality service, on-time delivery and the right product every time, which can be challenging if an organization manages erratic supply and demand on a global basis.

For most organizations, products consist of numerous parts that move through the enterprise and its network of suppliers, creating a need for parts logistics. Every part number within the organization takes on a life of its own and every department must have access to all the information surrounding it.

As organizations build new products, and service existing ones, they need cohesive and comprehensive visibility for a unified view of the entire supply chain.  This approach helps organizations optimize their supply chain and increase responsiveness by focusing on achieving greater visibility into products and parts inventory. Organizations that focus on these objectives can tighten the gaps in their supply chain and enhance their overall operations.

Supply Chain Unification

A unified view of the supply chain connects the enterprise and suppliers seamlessly to various applications and databases—such as enterprise resource planning, a data warehouse and customer relationship management systems.

This connected environment helps organizations keep abreast of the manufacturing process and supply chain management, and share relevant information across design, engineering, procurement, quality control and more. From understanding customer needs to building requirements, product prototyping and selling products, everything is streamlined and simplified across disparate systems.

By adopting a unified view of the supply chain, organizations can see what parts are in stock, which suppliers they re-order from and if those suppliers have available inventory. This gives engineers visibility into the specifications of components, the mean times between failures
for components, discontinuation plans and recent negative reports. It also promotes accurate shipping expectations and on-time delivery, while connecting all departments and partners in the supply chain into one efficient manufacturing shop.

Finding the right part information when and where needed

An information-driven supply chain makes it easier for workers to search and locate specific parts for production. Workers can create alerts to be notified when relevant information surfaces. Empowered and informed workers can then concentrate on manufacturing products on schedule.

A unified view of the supply chain helps engineers know who has previously worked with each part and learn from their experiences. If a component is found faulty during production, engineers could spend days trying to find who completed the original design. A unified view of the supply chain helps pinpoint the most knowledgeable workers and provides immediate access to information about the component and its design specifications. By empowering engineers, organizations are better able to meet customer demands.

This approach also empowers sales with information about specific parts to understand when to sell a specific version, and to know who to talk to if they need more information. Customers then get a confident, knowledgeable sales associate to help them make the right decision.

Knowing how and where to get parts in a hurry

Organizations must be able to respond immediately to customers who need replacement parts and immediate service. If a part is not available, they must know expected shipment dates, transit times and who can supply it. This is increasingly challenging with globally distributed suppliers and a dispersed customer base.

A unified view of the supply chain can resolve this issue by giving customer service representatives visibility into all parts across the enterprise, regardless of location, repository or format in which the information is stored. It can also extend access to information from supplier sites and applications.

To assist customers with support requests, customer service representatives need to be aware of past problems and how to identify and resolve them. With a unified view of the supply chain, they immediately know the parts associated with a problem and how it can be fixed.

In the final analysis, managing the supply chain is about information access. Although many applications are necessary to manage information at different stages of the supply chain, a unified view provides cohesive visibility across all applications that manage information about products, suppliers and customers. It is a critical part of streamlining and optimizing the use of an organization’s supply chain.

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Sinequa Helps Box Customers To Be Information-Driven

noiseMany customers that use Box for cloud content management are typically large, geographically distributed organizations. The four scenarios below describe common ways that Sinequa helps these customers leverage their enterprise information to become information-driven.

Increase the Signal, Decrease the Noise
Customers who have migrated even a portion of their enterprise content to Box have made a significant step.  Workers in their organization can no doubt share and collaborate more easily than ever before; they no doubt have reduced email overhead; and they are probably working the way they want to given all of the friendly integrations with Box, including Outlook, Office365, Google Docs and the like.   However, being in the cloud does not automatically mean the valuable “signals” in your data rise above the “noise”.  Messy data migrated to the cloud is still messy data.  Sinequa helps workers quickly narrow in on the information and insights necessary to do their job effectively and with confidence.  By analyzing the content and enriching it using natural language processing and machine learning algorithms, Box users can quickly find the information and insights they need to be effective and responsive.

Connect Data

connect-data

Many Box customers run their business with other enterprise applications and information repositories, all of which contain data and content related to the information
stored in Box.  Sinequa brings advanced analytics and cognitive techniques to “connect” the data and bring context across all of the various enterprise sources, whether they be in the cloud or on premise.  By connecting the data, knowledge workers can better navigate and see how the data and connect fit together along topical lines, regardless of how many repositories make up the enterprise information landscape.

Identify Knowledge & Expertise

Screen Shot 2017-10-13 at 2.40.37 PMAs previously mentioned, many Box customers are large (or even very large) geographically distributed organizations with expertise in a wide variety of subject matter areas.  In these organizations, specific experts are difficult to identify given the size and distributed nature of the organization.  This is a modern problem that requires a modern solution.  As users store content and collaborate within Box, Sinequa’s advanced cognitive capabilities analyze that content to determine not only the areas of expertise across the organization but who the specific experts are and surfaces that information to end users.  This connects people across geographic and departmental boundaries, accelerating innovation and elevating the performance of the overall organization.

Leverage 360º Views

Screen Shot 2017-10-13 at 2.42.23 PM

Think of all the “entities” that are critical to Box customers running their business.  These business entities include customers, either specific individuals (B2C) or accounts (B2B), products, parts, drugs, diseases, financial securities, regulations, etc.  Having all of the enterprise data virtually connected by Sinequa makes it possibly to provide a unified “360º View” of these various entities to bring all of the right information to the right person at the right time.
As you can see, leveraging Sinequa to contextualize the information within Box and other enterprise repositories not only boosts productivity and keeps knowledge workers in the flow but has repeatedly proven to enhance customer service, improve regulatory compliance and increase revenue within different areas of the business.  Achieving these benefits positively impacts the bottom line and serves as validation that an organization has become truly information-driven.
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