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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Cognitive Search Brings the Power of AI to Enterprise Search

Forrester, one of the leading analyst firms, defines Cognitive Search in a recent report¹ as: The new generation of enterprise search that employs AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources. Here is a shorter version, easy to memorize: Cognitive Search = Search + NLP + AI/ML
Of course, “search” in this equation is not the old keyword search but high-performance search integrating different kinds of analytics. Natural Language Processing (NLP) is not just statistical treatment of languages but comprises deep linguistic and semantic analysis. And AI is not just “sprinkled” on an old search framework but part of an integrated, scalable, end-to-end architecture.

AI Needs Data, Lots of Data
For AI and ML algorithms to work well, they need to be fed with as much data you can get at. A cognitive search platform must access the vast majority of data sources of an enterprise: internal and external data of all types, data on premises and in the cloud. Hence the system must be highly scalable.

Continuous Enrichment
Cognitive Search uses NLP and machine learning to accumulate knowledge about structured and unstructured data and about user preferences and behavior. That is how users get ever more relevant information in their work context. To accumulate knowledge, a cognitive search platform needs a repository for this knowledge. We call that a “Logical Data Warehouse” (LDW).

The Strength of Combination
To produce the best possible results, the different analytical methods must be combined, not just executed in isolation of each other. For example, machine learning algorithms deliver much better results much faster if they work on textual data for which linguistic and semantic analyses have already extracted concepts and relationships between concepts.

Whitepaper-kmworld-07-2017Get your copy of the full paper here and learn more about current use cases of cognitive search and AI at large information-driven companies.

(1) Forrester Wave: Cognitive Search & Knowledge Discovery Solutions, Q2 2017
Read the full report on https://www.sinequa.com/forrester-wave-2017/

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Sinequa’s Big Splash at Bio IT World 2017

PHARMA CONNECTION
Sinequa has taken part for the 4th consecutive year in Bio IT World Conference & Expo on May 23-25 in Boston. We’ve been delighted to meet with our Biopharma and Life Science customers and partners at the show and share innovative use cases of our solution for the Pharma industry via live demos.
Bio IT Demo

“OPEN” LIVE DEMOS

Bio ITBio IT World conference is always for us a great venue to showcase our platform and present how leading biopharma organizations leverage our Cognitive Search & Analytics platform. This year, the attendees were very interested to see how Sinequa combines advanced Search, NLP and Machine Learning capabilities to extract relevant insight from vast structured and unstructured data silos.

 ALEXION’S CONTENT ANALYSIS PROJECT: MINING CONTENT FOR ACTIONABLE INSIGHT WITH SINEQUA

Alexion-Martin-Leach-Bio-IT-2017-SinequaIn our joint talk, our customer Alexion shared a testimonial on the implementation of Sinequa for their content analysis project. The presentation highlighted the technology and approaches they used with advanced data visualizations that help explain information sources. ICYMI – please feel free to get your copy here.

UNLIMITED THEATER PRESENTATIONS

Once again, we were very pleased to see the strong interest of many biopharma professionals toward Sinequa insight platform. Our team gave more than a hundred presentations and live demos in the Sinequa Theater Area where they explained a large panel of use cases including R&D Enterprise Search, Clinical Trial Data Discovery & Exploration, Key Opinion Leaders & Subject Matter Experts… .) BioIT17-Demo-TheaterWe hope you enjoyed the conference as much as we did and you could understand how our Cognitive Search & Analytics platform enable leading pharmaceutical organizations drive innovation, accelerate research and shorten drug Time-to-Market. We are already getting excited for next year’s edition! See you all in spring 2018!

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Cognitive M&A – Leveraging Cognitive Search & Analytics for Successful Mergers and Acquisitions

Mergers and acquisitions provide one avenue for organizations to grow via synergistic gains, strategic positioning and diversification. Even with an abundance of M&A activity, mergers tend to fail at the business process and information integration levels. The success of a merger can be greatly enhanced when business processes are integrated and information is seamlessly unified by gathering it from both organizations, analyzing it, establishing clusters of semantically similar information, and finding common patterns. Cognitive search & analytics platforms provide the necessary capabilities to accomplish all of this, thereby helping facilitate merger and acquisition initiatives and significantly increasing the odds of success.

ANATOMY OF A SUCCESSFUL MERGER

Let’s envision the details of how this impacts the relevant stakeholders. At the outset, a cognitive search & analytics platform provides the organization with unified access to information from both organizations and beyond. Users can leverage out of the box machine learning algorithms to explore and navigate this information. For example, the Clustering algorithm groups documents into topically-related clusters by analyzing the content and metadata. This is very useful for topical navigation and helps stakeholders identify similar documents based on named entities within the content. Automated classification is another useful technique for unifying information and improving navigability. In certain circumstances such as when classification rules do not exist but a properly classified sample set of content does, a Classification by Example algorithm can automatically create a model from the sample set, which can subsequently be applied across the combined set of content from both organizations to further enhance findability for stakeholders.

Sinequa CollaborationMachine learning algorithms can also help match experts with other experts as well as relevant documents across the consolidating organizations. This is done dynamically by analyzing what people write and collaborate around to compute user profiles, which are subsequently analyzed to compute “peer groups” that connect stakeholders with similar interests and expertise across the consolidating enterprise. With these peer groups established, experts can be more effectively presented with relevant content using a collaborative filtering technique that compares preferred content across the peer group and surfaces valuable content to members of the peer group who have not previously been exposed to it. As you can see, a cognitive search & analytics solution facilitates smart information sharing across the consolidating enterprise. Usually a lack of sophisticated security controls impedes greater openness between consolidating entities. A search-based application, however, respects existing security profiles—making it easier to merge infrastructures securely.

A cognitive search & analytics solution also helps to identify areas of risk and to solve outstanding issues before financial consequences occur. For example, risks could include content containing Personally Identifiable Information (PII) or content with no security associated. This is done by employing text-mining agents (TMAs), which provide out-of-the-box rules-based capabilities to extract elements from unstructured text. TMAs can be configured to incorporate terms and phrases specific to any part of the business. A cognitive search & analytics solution enables a quick, seamless and successful consolidation of organizations. Typically, in a large enterprise this is done as a series of search-based applications (SBAs) that each pull from a Logical Data Warehouse (LDW), which is essentially central cache of unified information.  In the next sections, we will look at specific areas of the business that typically benefit the most from this approach.

SALES AND MARKETING 

Once consolidation is underway, the organization must move quickly to combine sales and marketing activities, sales methodologies, pipelines and channels to drive revenue in the field and promote up-selling and cross-selling into new and existing market segments. The organization wants to minimize any potential lapse in the sales cycle for the newly merged company.

A cognitive search & analytics solution immediately equips sales teams with a single global access point to relevant, real-time and insightful information on products and customers—sales and customer notes, sales processes, product information and sales training are all immediately accessible. As previously mentioned, this is typically done using a dedicated search-based application (SBA).

An SBA for Sales and Marketing could provide unified access to the separate CRM systems and allow for the addition of shared content. As a result, no sales note, customer quirk or prospect is lost during consolidation. An SBA also offers the ability to push alerts and notifications out to users.  As sales representatives learn about new products,

The SBA provides unified access to new marketing materials, sales process documentation, research documents and news articles to assist in training and ensures that they are effectively representing the newly expanded product line. Often when a merger occurs, customers know more about the products from the other company than the sales reps. With a cognitive search & analytics solution in place, an empowered and unified sales team can competently sell the acquired products and services.

FINANCE, ACCOUNTING, AND HUMAN RESOURCES

Finance, accounting and human resources are other key departments that need to be unified. A dedicated SBA provides a complete and consolidated access to information from the disparate ERP systems.

A cognitive search & analytics solution provides administrators and content curators with the visibility across the enterprise necessary to manage all documentation from both parties related to the merger effectively and securely.

Multiple IT systems and finance systems always increase the complexity of a merger.  Organizations acquiring a large IT infrastructure need to identify the systems acquired and the value of the data in these systems.

A cognitive search & analytics solution enables the required visibility and helps to expose any redundant or unused systems that might be eliminated. For example, a cognitive search & analytics solution can be used to monitor and analyze usage of underlying repositories and applications to show what sources are being used less frequently. Some organizations have even been able to standardize and eliminate the need for multiple search applications. In other cases, a cognitive search & analytics solution works as a stop gap providing access to legacy systems that should be migrated, thereby reducing the cost of forcing an immediate migration.

Organizations can leverage Sinequa’s scalable platform as the cognitive search & analytics solution to connect information across the consolidating enterprise by leveraging previously mentioned machine learning algorithms like Clustering, Classification by Example, etc. as well as more traditional rules-based enrichment techniques. This helps the organization deliver unified access to stakeholders, ensure accuracy, reduce risk and gain insight into the complex systems across all affected departments. Enterprise Application Integration is usually a multi-year project. While cognitive search & analytics solutions do not replace such an integration, they provide comprehensive visibility in a very short time.

RESEARCH AND DEVELOPMENT

The cost of R&D increases when multiple teams unknowingly work on solutions to the same problems or fail to recognize and utilize the work done in past research. Cognitive search & analytics solutions positively impact R&D by accurately combining research data from the consolidating organizations, giving users real-time access and reducing the duplication of content, efforts, and sometimes entire projects. They do this by connecting experts working in the same subject area.

Sometimes a key driver in a merger or acquisition is gaining access to intellectual property, which often includes the expertise of the other organization’s knowledge workers. Sinequa’s Find the Expert capability gives employees from each organization the ability to discover the most knowledgeable people on a variety of topics, to view their profiles and to find associated information. This accelerates R&D discovery by enabling users to navigate information in clusters—leveraging the machine learning algorithm—and by content refinements.

These tools enable users to find past research and hidden relationships – including relationships with external experts with whom both companies have collaborated in the past – that would have otherwise been missed, thereby increasing speed-to-market.

The organization is also able to gain greater market share by leveraging and optimizing the information acquired instead of simply discarding projects in progress. One of the key drivers in a merger is the ability to retain and share as much knowledge as possible. Sinequa’s collaborative features spawn greater innovation in the newly expanded R&D department in the form of capabilities such as tagging, bookmarking and automatic feedback loops. For example, a scientist can comment on an old publication and explain how it relates to new research, which subsequently increases go-to-market speed by enabling broader collaboration.

HOW IT WORKS

Sinequa has developed an innovative and simple-to-use Cognitive Search and Analytics platform that offers Unified Information Access (i.e. information from any source, in any format, whether structured or unstructured, internal or external, through a single platform and user interface) to respond to even the most difficult information access challenges of large companies and organizations. The solution is composed of three main components:

  • A powerful (natural language) analytics platform that gives structure to the most unstructured content. The analysis and indexing of data solve typical enterprise challenges involving multiple sources, unstructured and structured content, multiple languages, high data volume and high update frequencies.
  • Simple and intuitive user interface that brings simplicity to complex search and analytics. Sinequa’s out-of-the-box user interface leverages the familiarity of common search tools enhanced with faceted search tools (i.e. filters) and analytics, giving the user an immediate picture of the information available from all enterprise sources. The UI is easily customizable to reflect specific appearance and navigation goals as well as corporate standards/branding, and is extensible, allowing third-party or custom UIs to be easily integrated via a provided search API (Application Programming Interface).
  • A powerful, open and scalable technical architecture (GRID). Sinequa’s highly-scalable architecture was designed from the ground up to support multiple search needs starting on a single server cluster, providing a cost-effective solution that allows companies to respond to multiple business needs now and in the future with minimal hardware investment (whether the application is deployed on premise or in a private cloud). The architecture is distributed and modular to support virtually any data source addition and growth, often without any additional infrastructure investment.

Leveraging these combined components from within the Sinequa platform has proven to accelerate the integration of business processes and to amplify the expertise of the whole by seamlessly and securely unifying disparate information from each organization involved in a merger or acquisition.

CONCLUSION

A cognitive search & analytics platform facilitates information transparency and communication across the entire enterprise, minimizing disruptions while integrating teams and departments during mergers and acquisitions. Applying this technology as a solution effectively operationalizes the information access, speed and control needed to ensure long-term success in the newly formed organization.

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Machine Learning Becomes Legit, but Not Mainstream in 2017

ML-Sinequa-Predictions-2017

There has been a lot of hype around machine learning lately. Over the past decades, we’ve heard about various concepts around machine intelligence that in most cases didn’t get anywhere. But more and more frequently, organizations are learning how to bring together all the ingredients needed to leverage machine learning, and there is a simple reason for that: according to Moore’s law, the performance of microprocessors has increased since 1980 be a factor of more than 16 million! A program that ran on a 1980 computer for more than half a year today delivers its results in one second!

That is why I think Machine Learning will be the story for 2017. We’ll see it move from a mystical, over-hyped holy grail, to more real-world, successful applications. Those who dismiss it as hocus-pocus will finally understand it’s real; those who distrust it will come to see its potential; and companies that apply ML to appropriate use cases will achieve real business benefit without the high cost of entry that was common in years past. In 2017 it will be clear that it has a credible place in the business toolkit.

The four necessary enablers for machine learning – huge parallel processing resources, cheap storage, large and appropriate data sets, and accessible machine learning algorithms – are all now mainstream. Most large organizations have readily-available access to all these components (appropriate data sets are potentially the only open question, as they are always business- and use-case-specific), which makes machine learning a real possibility to reduce risk, increase customer satisfaction and loyalty, create new business models, identify patterns, and optimize complex systems.

One area where machine learning is growing rapidly and already showing success is for cognitive search and analytics applications. It won’t take over core algorithms anytime soon, but ML is already supplementing and enhancing search results based on user actions and smart analysis of content.

I don’t foresee machine learning achieving “mainstream” status in 2017, but it will within the next few years because the technology is advancing exponentially, quickly enabling its use in broader contexts.

For more on my complete prediction on machine learning, check out this article in Virtual Strategy Magazine.

 

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