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

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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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4 Ways Real-Time Data Improves Customer Service

This article was originally published on RT Insights.

Instant access and 360-degree views of all customer and product data is mandatory to enable customer service representatives to operate more efficiently.

Customer Service

Customer service centers serve as organizational information hubs, resonating with the voices of the customers. They are strategic to an enterprise, as they are often the most recent and most frequent point of contact that the customer has with an organization.

Properly used, customer service centers can satisfy customers and improve retention. They can also drive revenue by cross-selling and upselling. To do this, they must manage the volume of interactions efficiently and control average handle time (AHT). Increasingly, they must achieve this with tighter budgets. Instant access and 360-degree views of all customer and product data is mandatory to enable customer service representatives (CSRs) to operate more efficiently.

With people and information spread across various locations, this task can seem daunting. The right mix of technology can enable customer service centers to overcome these challenges and run at peak performance. Below are four tips for CSRs to manage high volume of interactions:

Improving visibility into real-time customer data

CSRs need visibility into customer data across all contact and interaction points within the enterprise — regardless of location, repository and format. By aggregating all data and providing a single, secure access point to relevant and real-time customer and product information, a unified view of information can be formed to help CSRs respond to customers’ concerns and issues quickly and accurately.

Relieved of the burden of navigating multiple applications to find a single piece of relevant information, CSRs can immediately concentrate on the callers’ concerns and quickly resolve their issues — increasing first call resolution and reducing average handle time to minimize the volume of customer interactions. Automatically providing a unified view of customer information effectively enables the customer service center to improve productivity and reduce operating expenses.

Automating access to relevant information

High attrition has always been a major concern for customer service center managers. Rehiring and retraining costs directly impact the bottom line. More importantly, high turnover rates burden CSRs, affect productivity and hamper the customer service center’s ability to provide quality service.

Automating access to relevant information can help customer service centers lower attrition by minimizing the excessive pressure and stress of the customer service center environment, which is cited as a major reason for attrition.

Leveraging automated analytics on top of customer and product information, customer service center managers can quickly spotlight new products for training and push information out to their CSRs. Simplifying the way CSRs access customer and product information and providing ways for CSRs to easily collaborate and share knowledge reduces CSR stress and consequently turnover. When CSRs have the information needed to answer customer questions and resolve issues confidently, they are much better able to interact comfortably and build close and lasting customer relationships.

Accelerating time to proficiency

CSRs never know what inquiry or problem they will face on the other side of an inbound call. As such, they must be well-versed on the products, services and policies of their organization. Successfully training CSRs is vital to the success of the customer service center. The cost of attrition per CSR is high, with new employees taking up to three months to complete initial training in many industries.

This can be exacerbated as many customer service centers have myriad applications and repositories, such as CRMs, ERPs and external databases, that CSRs must learn to navigate to prepare for and complete a call. The ability to seamlessly connect to these applications and provide a unified view to information greatly reduces training time and cost.

Sharing CSR knowledge

Collaboration capabilities that promote knowledge sharing and retention — even if employees leave — enable the remaining CSRs to maximize and enrich each customer interaction. Enterprise data is continually growing; as a result, CSRs have even more information to learn and retain. In addition, customer service centers are often scattered across far-reaching locations without sufficient support for their distributed organization. A scalable, distributed platform for information access solves this problem and allows data to grow without compromising access or speed for CSRs. They can then concentrate on listening to customer concerns and ensuring complete satisfaction, enhancing the entire customer experience.

Companies that employ the right mix of technology in their customer service centers empower their CSRs to go beyond solving customer issues to being customer champions — listening and responding fittingly to their needs.  By actively listening, CSRs can turn complaints into revenue. By having relevant information consistently and securely available, organizations can react quickly to customer demands, innovate business processes, profile new target markets and formulate ideas for new product features.

Consolidating silos and promoting the quick and easy transfer of information and insight captured in the customer service center across the entire enterprise allows executives to make informed decisions that positively impact the direction of the company.

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Six Ways R&D is Leveraging Machine Learning to Stay Ahead of the Game

This article was first published in R&D magazine.

As organizations strive to create value, enhance customer experiences and differentiate themselves from their competition, they place tremendous demands on their R&D departments. From accelerating the delivery of innovative products to improving compliance to understanding consumer demands and improving responsiveness to gain and keep customer trust, R&D has a lot on its plate.

Global competition, narrow margins, higher product development costs and tenuous holds on exclusivity, drive organizations to push innovation, seek cost cutting strategies and go to market as quickly as possible. Consumer demands change frequently while regulatory and compliance standards become even more stringent. Organizations must keep up, and the pressure on R&D never stops. R&D is the epicenter of an organization, whether within a large aircraft manufacturer or a leading automobile company looking to develop cutting edge products and services or a pharmaceutical company accelerating time-to-market for new drugs.

rANDd magazine

R&D thrives on information: customer information, expert information, product information, scientific information, market information, regulatory information and competitive information. To be at the forefront of innovation, R&D departments need complete visibility into both new and historical information across the entire enterprise as well as access to research from external public and premium information services. This is no easy feat in today’s world where we are inundated with data — more data, more opportunities and more challenges. As a result, many companies depend on machine learning solutions to harness insightful, high-quality information and fuel innovation within their product and solution portfolios.

Here are six examples of how R&D departments are leveraging machine learning to improve their effectiveness and create competitive differentiation for their organizations:

  1. Machine learning algorithms objectively connect researchers and developers based on the work they do, which at a minimum results in greater efficiency and optimally streamlines the path from academic innovation to product development.
  2. Machine learning techniques are the only effective means currently available to adapt security countermeasures based on historical hacking techniques to deal with sophisticated cybersecurity threats aimed at stealing trade secrets and intellectual property.
  3. Machine learning algorithms are revolutionizing product and service quality by determining which factors impact quality enterprise-wide and to what extent. For example, machine learning can yield much greater manufacturing intelligence by predicting how quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework.
  4. Cognitive search and analytics solutions powered by machine learning amplify the expertise of R&D departments by surfacing insights from data across the enterprise, regardless of location and format. From a single, secure access point, these solutions enable R&D professionals to unlock relevant and timely product research from internal and external sources that helps make informed decisions.
  5. Healthcare prediction and prevention are being revitalized and reinforced by machine learning. The pace of machine learning-powered prediction and prevention research is now faster than that of research that does not utilize the technology. From patient wellness scores to risk scores, machine learning is transforming the healthcare landscape.
  6. Open source software libraries like Google’s TensorFlow are enabling researchers to leverage machine learning for everything from language translation to early cancer detection to preventing blindness in diabetics.

Machine learning can leverage and build on relevant customer and market information to give R&D organizations insight and the ability to react quickly to demands. Teams are utilizing this technology to eliminate data silos and deliver increasingly relevant information from data to users in their business context, such that they can make better decisions, drive innovation, reduce risk and be more efficient. This in turn enables forward-thinking R&D departments that thrive on continuous product improvements and introductions to amplify the collective expertise of the organization.

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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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