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 & Analytics Transforms The Enterprise From Data Driven to Information Driven

**This article was originally published on AI Business magazine**Sinequa-AI-for-Business

The quest for actionable insights and answers from within vast troves of data is neverending within the modern enterprise. There’s good reason for that – it is the end goal of all information work – but the process is anything but optimized. Global analytics firm Forrester revealed as much in a 2017 report, which found that more than 54% of global information workers are interrupted from their work a few times or more per month by time wasted trying to gain access to information, insights, and answers.

It’s a problem that goes far beyond the limitations of conventional enterprise search technology – it’s a Sisyphean challenge, thanks to the sheer volume of data being created every single second.

“As organizations in data-intensive industries strive to create value, enhance customer experiences, and differentiate themselves from their competition, they are placing demands on their knowledge workers in unprecedented ways,” explains Laurent Fanichet, VP of Marketing for Sinequa. “Frequently, the data and knowledge they are looking for is isolated, segmented, and fractured. It’s difficult to surface the right information at the right time to see the patterns in the data.”

Fanichet has a clear grasp on the key problem Sinequa, an independent software vendor specialising in cognitive search & analytics, is trying to address. In its recent report, Forrester Wave: Cognitive Search and Knowledge Discovery Solutions, (Q2 2017), Forrester defines cognitive search 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’ – and, in the same report, go on to highlight Sinequa for the applications of their NLP technology in enterprise search.

The kind of cognitive search and analytics platform Sinequa offers, Fanichet explains, refers to an information system that is capable of automatically extracting relevant insights from diverse enterprise datasets for users within a specific work context. “Cognitive search brings the power of AI to enterprise search,” he says. “It helps organizations in data-intensive industries to become information driven.”

A recent IBM Watson report highlights the applications of cognitive search in the aerospace sector. One company uses these enhanced search capabilities “to improve supply chain visibility and reduce cycle time, saving millions of dollars on critical parts deliveries.” Furthermore, the system enables aircraft technicians to search through “reams” of maintenance records and technical documentation. “Now, if a worker needs to know what’s causing high hydraulic oil temperatures, the [cognitive solution] identifies historical cases with similar circumstances, finding patterns that point to the root cause of the overheating.” The report goes on to note that the solution in question saves the airline manufacturer up to $36 million per year.

Cognitive search and analytics likewise has its applications in the health and pharma sector. AI Business recently spoke to Karenann Terrell, GlaxoSmithKline’s first ever Chief Data and Analytics Officer, and former CIO of Walmart. She explained that a big component of what it takes to develop medicine can benefit from next-generation computing and machine learning. “Approximately 1/3 of the total cost of developing a medicine (>$2.5bn) is spent during the time it takes from identifying your target (the process in the body that you want to affect) to testing your molecule in humans for the first time,” she explained. “This process can take around five years. [GSK’s] goal with artificial intelligence is to reduce this time to just one year in future.”

“These are just a few of the many business areas where surfacing the information from within their data can drive better decisions,” Fanichet argues. He explains that cognitive search and analytics also have a range of powerful potential applications within customer service, enabling organizations to:

  • Provide personalized and highly relevant communication to their customers
  • Nurture customer relationships and prevent customer churn
  • Improve productivity, reduce operating expenses, and gain operational efficiencies
  • Minimize customer service representative turnover and knowledge loss

The Challenges Ahead for Cognitive Search

The potential use cases speak for themselves, but that doesn’t mean there aren’t challenges ahead for enterprises looking to incorporate cognitive search technology into their work. While working with clients, Fanichet explains, Sinequa helps them to understand that there are a set of common machine learning challenges along the path ahead. Expertise is often the first hurdle, but he maintains that there are many different types of AI implementation challenge. “Assuming that enterprises are able to resolve a dearth of expertise, there are still other challenges – most of which are specific to the type of AI being pursued.”

Take supervised machine learning, where the system learns to recognize patterns by observing ‘correct’ patterns provided by humans. “The greatest challenge is around providing sufficiently labelled training datasets from which the system can learn,” Fanichet explains. This is something Matt Buskell highlights in his ‘10 keys to AI implementation‘, recommending that following the initial loading of data and knowledge base, the system needs to go through a phase of refinement once the software has launched. “During this phase, things like gain and variance for Machine Learning, or intent training for NLP and maybe model refinement to cognitive reasoning need to be improved. During this phase, it is essential to carefully release the software and measure how well it’s performing over a 6-12 week period, at the least.”

Fanichet likewise highlights the obstacles unique to unsupervised machine learning, in which the system identifies existing patterns and a human determines their usefulness. “The greatest challenge is balancing the system’s need for sufficient data with the proper human guidance and interpretation needed to train the system,” Fanichet argues. This is as much an issue of skills and process culture as it is technical expertise, and is reflected in a recent Genpact survey of over 300 senior executives, which argues “AI cannot be implemented piecemeal. It must be part of the organisation’s overall business plan, along with aligned resources, structures, and processes.” Collaboration is therefore key.

Finally, there’s a need to formulate clear goals and outcomes, Fanichet says. “When pursuing reinforcement learning, where the system makes many attempts and learns from the outcome to take better actions, the greatest challenge is providing the system with a defined goal and sufficient practice in a dynamic environment so that the system can effectively learn from trial and error.”

With Sinequa, researchers, designers and engineers have immediate access to all the information needed to work productively.

With Sinequa, researchers, designers and engineers have immediate access to all the information needed to work productively.

Sinequa Brings the Power of AI to Enterprise Search

Fanichet believes Sinequa offer a range of unique intelligent capabilities within the analytics space:

  • Robust Indexing Engine: “If cognitive search was all about matching a keyword, a single index would suffice. The best results are obtained when multiple indexes are combined, each providing a different perspective or emphasis, providing a comprehensive overview of the information available. This provides the best possible understanding of the meaning it carries.”
  • Enterprise Grade: “Sinequa was designed from the start to support the complexities and multiple security layers of today’s enterprises. It was also designed to be immersed in diverse enterprise environments and can operate within the context of a specific industry and the language of the specific organization.”
  • Topically Aware: “Connecting information along topical lines across all repositories surfaces the collective expertise of the organization and makes it transparent. This is especially valuable in large organizations that are geographically distributed. By connecting people with expertise, the overall responsiveness of the organization increases.”
  • Natural Language Processing: “Sinequa’s world-class NLP offers automated language detection; lexical and syntactical analysis; and automatic extraction of dozens of entity types, including concepts and named entities like people, places, companies, etc. It also supports text mining agenda that is integrated into the indexing engine. This enables the extraction of virtually any function, relationship, or complex concept from the content.”
  • Machine Learning: “Sinequa leverages ML to enhance and improve search results and relevancy. This is done during ingestion but also constantly in the background as humans interact with Sinequa. It has become an essential part of the platform since it can handle complexity beyond what’s possible with rules.”
  • Well Designed User Experience: “Sinequa’s front-end serves as an intelligent agent that employees can consult for institutional knowledge that can be readily applied to the task or situation at hand. The experience is well designed in the sense that it is aesthetically pleasing, it is understandable in that it makes use of the user’s intuition, it is unobtrusive, and perhaps most importantly, it is contextual to the user’s goals.”
  • Ubiquitous Connectivity: “Sinequa’s product comes with over 160 ready-to-use connectors, all of which were developed in-house, thus ensuring consistency, quality control, and high performance.”

 

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5 Ways Information-Driven Companies Optimize Sales

This article was originally published on RT Insights.

Teamwork and corporate profitStreamlining sales and customer data eliminates the burden learning and mastering multiple applications — increasing agility and reducing operating expenses.

In an increasingly sophisticated economy where customers are inundated with options, sales forecasting is challenging, and achieving revenue goals is even harder.

Sales teams are constantly trying to identify lucrative target markets, close more deals and shorten sales cycles. Companies must become information-driven by equipping their sales team to be nimble, efficient and knowledgeable to focus on sales opportunities with the highest chance of success. Whether it’s lead generation, ecommerce or direct sales, sales teams need the power of relevant and timely information more than ever.

Access to information isn’t enough to optimize sales

With information in the typical global enterprise scattered across a growing digital landscape, including CRM, ERP and myriad internal and external repositories and applications, harnessing it can be a tremendous challenge. Mere access to this information is pointless if it is not timely and relevant. Successful information-driven organizations have learned how to address this issue, fueling sales productivity and increasing revenues as a result.

Every sales leader, regardless industry, faces these challenges:

  • Increase average deal size and drive top line revenue.
  • Shorten sales cycles and increase close rates.
  • Increase the number of net new customers.
  • Capture as much business as possible from existing customers.
  • Train new reps to become effective in their new roles as quickly as possible.

While high-performing corporations expect their sales teams to accomplish the following:

  • Maximize contract value and increase revenues.
  • Make informed strategic decisions.
  • Anticipate and respond faster to customer needs.
  • Create a thriving business based on thorough understanding of key clients.
  • Know what markets to target and who the players are within an organization.
  • Fuel higher operational efficiencies.

5 ways information gives you a competitive advantage

With these challenges and expectations in mind, here are five examples of how information-driven sales teams are leveraging modern data analytics technologies to improve their effectiveness and creating distinctive competitive differentiation for their organizations:

  1. Seamlessly aggregating and integrating all the company’s diverse data repositories toward delivering relevant, real-time information to sales teams around the world.
  2. Providing a comprehensive view of every customer interaction within their organization from a single access point, even if the basic data is stored in separate systems and databases. This helps maximize contract value by providing sales professionals with the visibility to better understand the customer’s overall needs in order to customize offers and services.
  3. Delivering unified information at both the contact and company level to enable information-driven sales teams to prioritize where they spend their time and energy to develop better relationships with their prospects. This includes the business drivers of senior leadership, the latest public financial information, changes in key management, buying behaviors relevant to cross-selling other products and more.
  4. Contextualizing information by product or by territory. Based on a sales group or individual profile, the information is automatically filtered by product and/or territory assignment.
  5. Enabling easy collaboration and knowledge-sharing uniformly across disparate silos of information. This promotes knowledge transfer among sales reps, helps surface important content, simplifies training and reduces the learning curve as new hires get up to speed quickly.

Optimize sales data for real cost and time savings

Eliminating the need to navigate multiple systems and databases to find information simplifies the sales process and creates a highly productive and efficient environment where sales professionals thrive. This translates to real cost and time savings.

Take technology vendors, for example, a group that Forrester Research found spends close to 20 percent of their selling, general and administrative (SG&A) costs — more than $135,000 per quota-carrying salesperson — on support-related activities.

By streamlining sales and customer data, information-driven sales organizations eliminate the burden and time consumption of learning, retention and mastery of multiple applications, thereby increasing agility and reducing operating expenses. This creates a critical competitive differentiator as it frees up sales teams to elevate their performance toward maximizing contract values, making informed strategic decisions and responding faster to client needs.

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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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IDC Highlights Sinequa Strengths and Leadership in Cognitive Applications Vendor Spotlight

IDC-Vendor-Spotlight-Cognitive-Applications-Sinequa-2017 (2)A new IDC report is recognizing Sinequa for our Cognitive Search & Analytics platform around critical technologies, including machine learning and advanced natural language processing. This Vendor Spotlight looks at how Sinequa leverages artificial intelligence and cognitive computing-based analytics to meet the needs of companies that are looking to address complex problems with easy-to-use, powerful solutions featuring simplified interfaces.

According to the report’s author David Schubmehl, Research Director for IDC’s Cognitive/Artificial Intelligent Systems and Content Analytics research, “The capabilities being offered by cognitive knowledge discovery systems, such as Sinequa, provide many opportunities for enterprises to innovate and advance their organization using approaches that were either not possible or not easily implemented several years ago. Within many enterprises, these opportunities are limited only by the imagination and creativity of those seeking to improve their business and information handling processes.”

The report states that Sinequa’s software provides organizations with real-time, relevant results from unstructured and structured internal data, and that the we are developing our Cognitive Search & Analytics platform on an extensive foundation of unstructured information access technologies that include advanced natural language processing capabilities in 21 different languages.

Schubmehl adds: “While Sinequa has offered a flexible information collection, access, and analysis architecture for many years, it has now built capabilities around cognitive technologies, such as machine learning, advanced natural language processing, improved relevance, and better decision support while offering strong user and data interaction capabilities.”

The advancement of natural language processing and increased maturity of machine learning are creating substantial demand for cognitive search and analytics solutions. At the same time, the growth of unstructured data and pressure to improve worker productivity makes it even more critical to find the right information at the right time. This report highlights the fact that Sinequa’s platform meets this demand and by combining our solution with human ingenuity, we can produce the best possible search and analytics results.

The full report is available here: https://www.sinequa.com/idc-vendor-spotlight-2017/

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