IntroductionManagement guru Peter Drucker is often quoted as saying that “You can’t manage what you can’t measure.” If you’re in manufacturing, you’re probably already obsessed with measurements and data—using it to track everything from capacity utilization, throughput, and overall equipment effectiveness to manufacturing tolerances, scrap and rework costs, and power consumption. What’s more, you probably have more data than you can use—or, more accurately, than you think you can or know how to use.

What if you could harness all that data to become more customer-centric, improve quality, increase operational efficiency, or optimize inventory? For example, what if you could predict which machine processes on your manufacturing line will slow or fail with superior accuracy, leading to double-digit savings in scrap, rework, and energy costs?

This is exactly what Jabil, the third largest manufacturing services provider in the world, was able to achieve by using advanced analytics to unlock new value within its existing operations. What’s more, Jabil did all this within the context of existing operations, with minimal disruption—using its existing equipment, systems, processes, and employee skill set.

Think about it for a moment: Jabil is one of the most sophisticated manufacturers in the world, with advanced quality systems and processes that have been in place for years. If a company that’s already this proficient in areas such as Lean Six-Sigma, continuous improvement, predictive maintenance, and statistical process control can unlock such substantial new value by using advanced analytics to look at existing data in innovative new ways, then chances are strong that you can too.

For forward-thinking manufacturers, innovating in such a way is nothing new; it’s merely the price of doing business. Manufacturers have always been leaders when it comes to adopting new technologies—not just to keep up with the demands of today, but also to create, sustain, or increase their competitive edge moving forward. It’s no different with advanced analytics in that, to remain competitive, you’ll need to get started soon. If you don’t, you may soon find yourself falling behind. Industry as a whole is already headed down this path, and, while the best-performing businesses are already seeing results from advanced analytics, they’re few and far between.

Manufacturing: hone your competitive edge with Microsoft advanced analytics

“Since deploying the Microsoft predictive analytics solutions we have seen at least an 80 percent accuracy rate in the prediction of machine processes that will slow down or fail, contributing to a scrap and rework savings of 17 percent.”

—Clint Belinsky, Vice President of Global Quality, Jabil

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Even if you’re already convinced of the value of advanced analytics, you probably still have questions. How do we get started? What are the essential elements of an enterprise-class solution, where can we get them, and how do we assemble them? And after we have a solution, how can we quickly begin using it to discover actionable insights, breathe new life into everyday operations, and drive tangible results?

The reality is that advanced analytics is complex; the data science behind it requires specialized skills, the amount of raw data can be immense, and the computing resources required to turn that raw data into actionable intelligence are substantial. Realizing this, in keeping with its long history of meeting the needs of manufacturers, Microsoft has already completed the “heavy lifting” needed to package these essential elements of an advanced analytics solution into a ready-to-deploy solution in the cloud—immediately available to start meeting your business needs for a modest monthly fee.

In the rest of this paper, we’ll explore key challenges faced by modern manufacturers, how technologies such as the cloud and machine learning can be brought to bear, and how Microsoft is meeting the needs of manufacturers through robust, easy-to-adopt, enterprise-ready solutions that are enabling customers such as Jabil to achieve great success.

The bottom line is that disruption and change are everywhere, time is short, resources are scarce, and the stakes are higher than ever. Fortunately, there’s a lot more you can do with your data—and Microsoft can help you adopt the same technology that Jabil used to transform your own operations and hone your competitive edge.

Framing the challenge: digital transformation in manufacturingThe manufacturing industry is in the midst of a digital transformation—from computer-aided simulation and analysis (aka “digital twins”) and additive manufacturing (digital printing) to shop floor telemetry and smart, connected products. There are many names for this next step in the evolution of manufacturing, including Industry 4.0, the Fourth Industrial Revolution, and the Smart Factory. Regardless of what you call it, the forces in play are the same: Windows of opportunity are shrinking, product lifecycles are decreasing, supply-chain complexity is increasing, and customers are demanding more and more personalized offerings. To complicate matters, the industry faces a skills gap that is threatening its ability to meet existing market demands, let alone what’s needed to survive, adapt, and prosper moving forward.

As a manufacturing executive, you’re likely well aware of these pressures. But what can you do to address them? Or, to be more specific, what assets and approaches can you bring to bear to address the core manufacturing

Research by International Data Corporation (IDC) shows that discrete manufacturers are expected to be among the top five industries in terms of investment in big data and business analytics solutions in 2016 and will continue to lead until 2020.

IDC Press Release, Double-Digit Growth Forecast for the Worldwide

Big Data and Business Analytics Market Through 2020 Led by Banking and Manufacturing

Investments, According to IDC. 3 October 2016.

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disciplines that all manufacturers must master to succeed and thrive—such as quality assurance, demand forecasting, operational efficiency, predictive maintenance, anomaly detection, and supply-chain and inventory optimization.

The answer, it turns out, is already in your possession: It’s your data. It’s everywhere, in every form, from your supply chain and QA systems to those used for order management and customer support. What’s more, new data sources—enabled by the proliferation of smart, connected products and the Internet of Things (IoT)—are coming online at an ever-increasing velocity. And the volume of all this data is growing exponentially, far outpacing the rate at which you can effectively harness it using the same approaches that have worked in the past.

Fortunately, most of your competition is in the same place, providing an opportunity for you to hone your competitive edge by looking at your data in new ways. But before we get into that more deeply, it’s worth taking a closer look at the key forces at play.

The time to adapt is shrinking—and will continue to do so

According to Kim Gittleson at BBC News, more and more companies are being “felled by economic turmoil or by unforgiving customers and tough rivals,” and the pace is only increasing. In the 1980s, the average lifespan of a company listed on the S&P 500 index was 25 years. Today, it’s 15 years. And in a decade, only 25 percent of current S&P 500 companies are projected to remain in the index. The cause of all this is the potential for disruption caused by technology. This is especially true for manufacturers, which is why they have traditionally led the pack when it comes to new technology adoption.

“Although there are exceptions to every rule, the most important factor for survival is an emphasis on innovation and reinvention.”Gittleson, Kim. “Can a Company Live

Forever?” BBC News. 19 January 2012. Article.

The time to adapt to disruptions is shrinking

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Source: BBC

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Increasing product complexity—and shorter product lifecycles

More and more, customers today expect to be able to get exactly what they want, regardless of whether it’s purchased off the shelf or built/configured to order. Manufacturers must deliver on this expectation, whether the product in question is an automobile, a personal computer, a phone, or a custom-fitted garment. Depending on the specifics, manufacturers may meet these needs through the use of digital manufacturing techniques that augment traditional industrial automation and CNC machines with 3-D printing (additive manufacturing) and robotics, and/or through custom configuration on the assembly line (think Dell and its custom configurator). Regardless of the specifics you use to meet customer demand, you’ll still need to do the best you can to shape and predict it—including attempting to forecast the optimal mix of sizes, colors, flavors, memory capacities, and other options and product characteristics that customers will desire.

Either way, for the manufacturer, this added complexity presents several challenges—from forecasting demand and supply-chain optimization to maximizing operational efficiency and inventory optimization. The ultimate goal: to meet customer demand for more and more options with minimal tradeoffs in cost, quality, and delivery—and do so while always keeping one eye on demand. Produce too much of a product and you may need to discount or scrap it. Produce too few and the customer will buy from someone else.

To complicate matters further, as technology continues to evolve at an exponential pace, product lifecycles are decreasing—as are the windows of opportunity for delivering new products. More and more of a company’s annual revenues are being derived from newer products versus old “cash cows”—meaning that the speed at which manufacturers can determine what the market will want next, develop it, and ramp-up production will continue to have an ever-increasing effect on their ability to compete.

Connected products—and customers

Every day, at a more and more rapid pace, companies are introducing new smart, connected products. In many cases, the product themselves aren’t new; they’re simply smarter—achieved by connecting them via the web to a source of additional value. For example, we’ve all seen the TV advertisements for the Peloton Cycle, a stationary exercise bike that connects to the web to put the rider in the midst of a virtual exercise class—replete with a trainer and instrumentation on the bike that, again via the web, lets the rider effectively “compete” with others in the class. Not only does this improve the user experience, but it provides Peloton with a recurring monthly subscription fee.

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Connected cars are a second example—capable of monitoring themselves and, if required, downloading software updates over the air while the vehicle sits in a parking lot or the owner’s garage. Connected refrigerators, a third example, let you manage your groceries, sync calendars across family members, and can even send you a notification when you leave the door open.

So what do all of these examples of smart, connected products have in common? In addition to delivering greater value, convenience, and an overall customer experience through connectivity via the web, they’re all capable of providing a wealth of new data that their manufacturers can use to better understand, serve, and ultimately delight their customers.

The question is, are you ready to do this? After all, gaining insight into how customers are using your products—and using this to focus on their desired outcomes—is at the heart of the digital transformation.

Skills shortage

Given that the expertise required for implementing advanced analytics in-house is beyond the existing skill sets of most manufacturers, how can they acquire these capabilities—especially considering that the industry is already suffering from a severe skills shortage? Seasoned employees are retiring or leaving for other jobs, and they’re taking a wealth of embedded knowledge with them—for example, the shop floor foreman who can predict when a system is likely to fail by a slight variation in the sound it makes.

At the same time, fewer qualified applicants are entering the manufacturing industry, in part due to a negative image among younger generations. Job applicants with adequate science, technology, engineering and mathematics (STEM) skills are getting harder to find as technical education programs in public high schools decline, and the requisite technical and computer skills are even rarer. Even with many manufacturers offering higher pay to attract new talent, many jobs are still being left unfilled.

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The Skills Gap in U.S. Manufacturing – 2015 and Beyond, a paper by Deloitte, sheds some more light on the issue:

“The skills gap problem comes into sharper focus when considering the increasingly technical nature of manufacturing work. Many manufacturers have redesigned and streamlined production lines while increasingly automating processes. While some job roles will require less technically skilled workers, ironically, these trends and innovations actually demand more skilled workers.”

Deloitte’s analysis goes on to state that:

“When asked which business areas will be affected most due to the talent shortage, more than three-fourths of manufacturing executives believe the greatest impact of the skills shortage will be in maintaining or increasing production levels (in line with customer demand) and implementing new technologies while achieving productivity targets.”

As you read on, keep in mind that many of the disruptions presented above are also faced by your peers, of which only a small percentage have successfully adapted. Advanced analytics can help you adapt too—and if you get started now, hone your competitive edge.

Understanding the opportunity: the answer lies in your dataThe answer to the challenges we’ve presented lies in your data—or more specifically, what you can do with it. By capturing more of the data within your enterprise, augmenting it with additional data from your supply chain and customers and other sources, pulling all it together (both historical and fresh), and examining it in new ways, you can extract the insights needed to improve all key aspects of your operations—including the ability to predict what’s going to happen. Put another way, you’ll be able to shift your attention from looking behind you to what’s coming next.

“Over the next decade, nearly three and a half million manufacturing jobs likely need to be filled. The skills gap is expected to result in 2 million of those jobs going unfilled.”

—The Skills Gap in U.S. Manufacturing – 2015 and Beyond.

Deloitte. 2015. White paper.

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Jabil’s story, covered in depth later in this paper, is a prime example. By collecting and analyzing more than 1 million data points from each circuit board assembly across a 32-step, four-hour manufacturing process, Jabil can now anticipate and avert more than half of all failures at the second step in the process, and the remaining 45 percent at step 6. In the past, such problems were typically detected at step 15.

With its newfound ability to identify errors much sooner in the manufacturing process, prior to adding expensive components, Jabil is seeing several quantifiable benefits—including the ability to predict which machine processes will slow down or fail with 80 percent accuracy, resulting in a 17 percent savings in scrap and rework costs, and an energy savings of 10 percent.

These results, which were much larger than expected, were achieved by harnessing and analyzing data that already existed in newer, more powerful, and more insightful ways—instead of continuing to look at things the same way that Jabil did in the past. That’s not to say that Jabil had to discard its existing quality systems and processes; rather, the company applied advanced analytics on top of them.

To follow in Jabil’s footsteps, you’ll need to essentially do three things:

• Extract your data from its various sources—potentially millions upon millions of data points each day—and pull it all together into a single, massive repository.

• Apply highly sophisticated data science and machine learning technologies to store and organize the data, analyze it, and predict future outcomes.

• Turn those new insights into your data into actionable intelligence—and deliver it to the right person (or system), at the right place, and at the right time.

Again, all this isn’t trivial. Unless you have a team of data scientists with substantial free time on their hands, and the budget for massive data storage and computing resources, it’s probably not a solution you’d want to build and maintain in-house.

Fortunately, thanks to the cloud and the work that Microsoft has already done, you won’t need the skills to implement advanced analytics on your own. What’s more, by adopting such a solution, you can alleviate part of the burden of your existing skills shortage by using advanced analytics to deliver actionable intelligence (versus raw data to be analyzed) to existing employees.

But more on how Microsoft can help you address all these challenges in a bit, after we ground the discussion by examining the recent technology developments that are putting advanced analytics within your reach in a manner that’s cost-effective, scalable, and helps ensure security—and how you can put them to use within the context of existing operations.

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Why now?The idea of applying analytics to the optimization of manufacturing and supply-chain operations isn’t new. The term Enterprise Manufacturing Intelligence (EMI) was coined by AMR Research in 2001, and adopted by Gartner when it acquired AMR. At a high level, EMI was used to describe software that could pull together manufacturing-related data from multiple sources for purposes of reporting, analysis, and more—with the goal of turning that large amount of raw information into knowledge that could be used to drive business results. The vision for EMI was to deliver these capabilities across a company’s global operations, not just a single assembly line or factory.

So why did EMI fail to catch on with all but the most disciplined? To start with, such solutions were custom-built and thus costly to develop, and nobody wanted to foot the bill. Individual factories didn’t want to share their data for fear of being compared to their peers, and possibly shut down. Key performance indicators (KPIs) weren’t standardized across locations, making it difficult to compare apples-to-apples, and there was no effective way to pull together data from multiple locations. On top of all this, extraction of each KPI had to be coded by hand. In summary, these early EMI systems were complicated, expensive, difficult to maintain, and provided only limited analytical capabilities. Put another way, to most manufacturers, they just weren’t worth it.

So what’s different today? At a high level, the viability of advanced analytics for modern manufacturers can be attributed primarily to two recent technology developments: advancements in data science and machine learning, and the cloud. While neither of these two areas is completely new, the availability of advanced analytics hosted in the cloud—with all the associated cost, skill set, and time-to-value benefits—is just becoming a reality, and is the answer to the question “Why now?”

Advancements in data science and machine learning

Data science focuses on scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. It draws from disciplines such as mathematics, statistics, information science, and computer science as a means to understand and analyze actual phenomena. Machine learning, a subfield of computer science from which data science draws heavily, is the ability of computers to learn without explicitly being programmed—for example, as required to predict future results based on historical relationships and trends within a set of data. From a software perspective, data science and machine learning are the capabilities that enable advanced analytics to predict what will happen in the future based on an examination of what happened in the past.

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Here’s a simple example, to help put all this science into perspective: Let’s assume you have failure data on a number of industrial motors, including the specific dates and times they failed. Let’s also assume that you have historical telemetry data from those motors before they failed, such as vibration, operating temperature, shop floor temperature, power draw, and so on. Using advanced analytics, you could ask the question “What changed in the historical telemetry data leading up to each failure?” — and then use the answer to monitor existing motors on your shop floor for similar behavior to predict when one will fail, rather than merely replacing each one on a set schedule (which may waste money) or waiting for one to fail and bring production to a halt.

It’s worth noting that the predictive power of machine learning in the cloud is already prevalent in many of the “disruptive” online services we use today. For example, popular streaming music services such as Spotify use machine learning to predict and present playlists of songs that you may like based on what you’ve listened to in the past, songs you have skipped, and so on. Similarly, leading online retailers use it to recommend products based on a combination of past purchases, products you have browsed, and searches done for products on their sites. Online ride-share companies, such as Uber, use machine learning to provide as accurate an ETA as possible.

Forrester Vice President and Principal Analyst Nigel Fenwick uses the term Digital Predators to describe such companies, labeling them the

“ones that successfully use emerging digital technologies to gain market share and/or displace traditional incumbent companies.” At the other end of Fenwick’s spectrum are the Digital Dinosaurs, which he describes as those who “struggle to leave behind their old business model” and are

“typically slow to change because they must defend large P&Ls, or they have a near monopoly position, or they simply don’t see the opportunity/threat.” [Source: Fenwick, Nigel. “The Top Emerging Technologies for Digital Predators.” Forrester. 16 March 2017. Blog.]

As a final example, it’s the power of machine learning in the cloud that has enabled researchers to decode DNA in a day—instead of the seven years that it used to take. A key takeaway here is that, in many cases, it’s far more efficient to let the machine predict the best way to solve the program.

The cloud and cloud computing

The cloud, or cloud computing, brings to bear the rest of what’s needed for advanced analytics: global connectivity and essentially limitless scalability in terms of data storage and computing power, with economies of scale that put leading-edge data science and machine learning within the reach of all manufacturers—all at an affordable cost. In addition, because cloud solutions can be prebuilt and ready to “plug into,” they offer opportunity to significantly accelerate time-to-value. Finally, with advanced analytics solutions in the cloud, workers can access them wherever they are, from virtually any device.

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Manufacturers and the Cloud: Digital Transformation Beyond the Shop Floor, an IDC Manufacturing Insight published in January 2016, provides additional insights into adoption of the cloud within the manufacturing industry:

• “Manufacturing is one of the most advanced industries in terms of adopting cloud… And 95% of manufacturers expect to launch new applications and infrastructure projects in the cloud this year.”

• “The combination of social, mobile, analytics, and cloud, which IDC refers to as the 3rd Platform, will represent 70% of all technology spending by manufacturers by 2020.”

• “Initiatives on the factory floor go by many names — smart manufacturing, future factory, Industry 4.0 — but share the objective of being able to significantly improve throughput, quality, and asset utilization across the factory network.”

• “Perhaps the greatest advantage of the 3rd Platform (cloud) is speed. As manufacturers shift to more individualized products for discerning customers, they must be able to move fast. If they are going to match the cadence of modern markets, they must be able to react quickly, and a hybrid-based deployment approach for prospecting, operational, and differentiation systems delivers both the scale and the speed.” (Note: A hybrid-based approach is one that combines on-premises systems with solutions hosted in the cloud.)

[Source: IDC Manufacturing Insights Analyst Connection, Manufacturers and the Cloud: Digital Transformation Beyond the Shop Floor. IDC. January 2016.]

Putting advanced analytics to useAt a high level, you probably already understand the value that data can bring to your organization. What you really want to know is how advanced analytics can be used to breathe new life into common, everyday operations to solve your business challenges more innovatively, efficiently, and productively. After all, while your data may be pervasive, the key is doing something meaningful with it. And with an advanced analytics solution in the cloud, the possibilities are practically endless; you’ll be able pull in data from virtually any source and use it to discover actionable insights that were previously beyond your grasp.

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Key manufacturing scenarios

Through its work with manufacturers, Microsoft has identified seven key “high-yield” scenarios where advanced analytics can help you drive tangible business results and hone your competitive edge:

• Quality assurance. You’ll be able to improve quality through root-cause analysis by correlating problems with their underlying operating conditions and team/product/machine attributes. This can include determining the factors that contribute to operational or product problems; predicting the scope of a problem and magnitude of its impact; and incorporating fixes or lessons learned into procurement, product design, and operational procedures. (Later in this paper, we’ll examine how Jabil optimized quality assurance using advanced analytics in the cloud.)

• Demand forecasting. By improving your ability to predict sales volumes and optimize your mix of product configurations with respect to various scenarios, you’ll be able to optimize staffing, asset utilization, and production planning. You’ll also gain insights needed to help you adjust price levels, as required to smooth demand and optimize profit; align procurement availability and supplier delivery timelines; minimize obsolete inventory; optimize customer service-level agreements; and incorporate all this into plant and equipment investment decisions.

• Optimize efficiency. With visibility into which factories, production lines, teams, and parts are outliers in terms of performance, you’ll have the insights needed to increase efficiency. Examples include identifying and sharing best practices; determining problematic parts or suppliers and chronic training or staffing issues; discovering process roadblocks and product quality issues; and reallocating work to the areas that are best equipped to support it.

Create a truly digital factory Drive continuous improvement with automated factory processes, intelligent devices, and analytics

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Securely connect factories to share information across regions and

departments, such as enabling experts to provide guidance across the business regardless of location.

Implement predictive maintenance practices to

eliminate accidental production issues, machine downtime, and

increase throughput.

Analyze plant data to gain production insights, respond to changes in demand, and provide cross-channel visibility into inventories to optimize the supply chain.

Drive quality assurance with aggregated supplier data, customer sentiment, and other product information to identify and correct quality issues.

Remotely monitor production flow in near-real time with

smart connected machines to get ahead of production issues.

Share best practices across sites to ensure quality, maximize efficiency, and improve workforce performance.

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• Predictive maintenance. You’ll be able to decrease downtime by predicting a part’s mean time to fail—and thus the probability of an issue during the next day, week, or month. You can use this information to automatically generate service tickets and initiate proactive actions, update asset tracking systems, and refine your spare parts purchasing and staffing plans.

• Anomaly detection. You’ll be able to accelerate response times by immediately identifying when a machine, system, or process is experiencing an exception condition. This can include automatically re-routing production activities to areas that are working normally, and/or triggering alerts or trouble tickets so that personnel can promptly investigate and resolve the issue.

• Inventory optimization. You’ll be able to lower costs and optimize stock levels by predicting the optimal inventory levels to hold in each location, as required to account for variations in demand, production velocity, materials costs, and delivery timelines. Elements of this can include identifying excessive scrap and waste producing conditions; maximizing sales by ensuring stock coverage to meet demand; minimization of working capital by optimizing work in progress; and incorporating projections into procurement and delivery plans to optimize purchase and logistics commitments. (Later in this paper, we’ll examine how a leading nationwide auto retailer optimized inventory using advanced analytics in the cloud.)

• Supply-chain optimization. You’ll be able to streamline procurement and logitics operations by identifying suppliers, parts, or channels that exhibit poor or exceptional performance. This can include reducing your number of vendors to simplify your supply chain; identifying areas of concentration and risk; determining acceptable substitute products or parts; and incorporating insights into pricing negotiations.

It’s worth noting that insights in these key areas can also be achieved through the analysis of traditional manufacturing data combined with other, nontraditional data sources. McKinsey cites an interesting example in Making data analytics work for you—instead of the other way around, an article published in October 2016:

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“One industrial company provides an instructive example. The core business used a state-of-the-art machine that could undertake multiple processes. It also cost millions of dollars per unit, and the company had bought hundreds of them—an investment of billions. The machines provided best-in-class performance data, and the company could, and did, measure how each unit functioned over time. It would not be a stretch to say that keeping the machines up and running was critical to the company’s success.

Even so, the machines required longer and more costly repairs than management had expected, and every hour of downtime affected the bottom line. Although a very capable analytics team embedded in operations sifted through the asset data meticulously, it could not find a credible cause for the breakdowns. Then, when the performance results were considered in conjunction with information provided by HR, the reason for the subpar output became clear: machines were missing their scheduled maintenance checks because the personnel responsible were absent at critical times. Payment incentives, not equipment specifications, were the real root cause. A simple fix solved the problem, but it became apparent only when different data sets were examined together.”

Opportunities beyond manufacturing

Although this paper focuses on the application of advanced analytics to core manufacturing operations, such a solution can also help discrete manufacturers achieve similar transformation in other areas of the business—including engaging with customers, empowering employees, and transforming products.

For example, in transforming products, data from in-house digital twins can be utilized to build and test models of which machine parameters contribute to failures and predict problems before they arise. Similarly, data from additional sources such as nonproduction information from lab tests for alternative designs can be added to the mix, as a means of avoiding the production of prototypes that are likely to fail.

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Putting it all togetherCortana Intelligence Suite: comprehensive, easy, and ideal for sophisticated operations use cases

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How Microsoft can help If you’re ready to adopt advanced analytics, or just want to learn more, Microsoft is ready to help. We’re enabling the possibilities and use cases covered in this paper possible through Cortana Intelligence Suite—a big data, advanced analytics, and artificial intelligence platform and solution portfolio hosted in the cloud. Cortana Intelligence leverages Microsoft’s $15 billion investment in cloud services and infrastructure, and it builds on Microsoft’s proven track record and extensive expertise in helping manufacturers meet their business needs.

Cortana Intelligence was built on years of Microsoft research and innovation in perceptual intelligence, speech recognition, natural user interaction, and predictive and advanced analytics. As an end-to-end cloud platform and solution portfolio, it provides an integrated, comprehensive set of analytics tools, services, and preconfigured solution templates—with new and improved features, components, and capabilities continually being introduced for immediate adoption via the cloud.

Cortana Intelligence Suite is also customizable—designed to work with the parameters and KPIs that you select. You can define terms for whatever scenarios, objectives, and constraints you want to focus on, such as optimizing inventory cost over time in a particular region or working with specific suppliers to track lead times, shipping times, storage space, and so on.

Everything you need to transform your data into actionable insights

Cortana Intelligence provides all the essential components and tools you’ll need to accelerate your digital transformation and start turning your mountains of data into actionable insights. For example, with Cortana Intelligence, you’ll be able to:

• Facilitate high-volume data ingestion and orchestration by using Azure Data Catalog and Azure Data Factory.

• Avoid the complexities of storing your data by taking advantage of SQL Data Warehouse, Azure Data Lake, and additional Big Data stores.

• Predict patterns and opportunities by implementing prescriptive recommendations based on powerful Azure Machine Learning algorithms.

• Create your models from a ready-to-use library of algorithms, and accelerate those efforts by taking advantage of preconfigured examples and solutions in the Cortana Intelligence Gallery.

• Take advantage of advanced services like Stream Analytics and HDInsight to improve your business processes and capture new prospects.

• Visualize outcomes with impactful Power BI dashboards—and engage with your data in natural, intuitive ways through Cortana and Cognitive APIs.

• Choose from industry-specific, preconfigured solution templates and partner offerings—or build a custom solution using your organization’s existing programming languages and frameworks.

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In short, Cortana Intelligence gives all you need to drive efficiency and value at every stage of the data and analytics continuum. You’ll be able to leverage the speed of the cloud to get started in minutes, and you’ll be able to seamlessly scale in seconds as your business needs and use cases grow—all while benefiting from market-leading security.

Other advantages of working with Microsoft

Beyond the capabilities provided by Cortana Intelligence itself, there are several other advantages to working with Microsoft. Odds are overwhelming that you’re already using Microsoft technology across the enterprise—from Windows and Office on your desktops to Windows Embedded in your shop-floor systems and our server products in your datacenter. In fact, most manufacturing systems containing data useful for advanced analytics are powered by Microsoft in some way, including many of the industrial automation solutions used by modern manufacturers.

With Microsoft, you’ll benefit from:

• A trusted, flexible, and open cloud platform. Today, the Microsoft cloud infrastructure supports more than 1 billion customers in more than 140 countries. With this unique experience and scale, Microsoft cloud services can achieve higher levels of security, privacy, and compliance than most customers can on their own. What’s more, Microsoft Azure is the only platform that supports a fully hybrid architecture, giving manufacturers complete flexibility and control of data and applications delivered between public and private clouds. (Support for a hybrid architecture also facilitates and eases a phased approach—for example, focusing on harnessing the data within your enterprise first, and later extending your advanced analytics capabilities to tie in your customers and/or suppliers.)

• Comprehensive, enterprise-ready solutions. Microsoft solutions span the full spectrum of business needs, including data access, high-performance computing, advanced analytics, visualization, and business process automation. Windows 10 offers unprecedented universal application capability across devices, including innovative devices like Surface, Surface Hub, and HoloLens.

• Industry-specific manufacturing solutions. Microsoft provides best practices, end-to-end solution design, and delivery services for common manufacturing industry scenarios including Remote Monitoring and Predictive Maintenance, Mobile Worker and Connected Field Service, Asset Management, and Connected Operations. Solutions for engineering include Mobility for Design Engineering and Big Compute for Visualization and Simulation. End-to-end platforms are also available for connected car and connected consumer devices, as well as customer experience management, especially in the automotive industry.

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• Extensive partner ecosystem. Microsoft has an extensive partner ecosystem—including widespread coverage across the manufacturing industry, cloud solutions, and advanced analytics. In fact, many existing industrial automation solutions across PLCs, SCADA, HMI, and DCS are based on Microsoft software, and the suppliers of that equipment are likely already Microsoft partners. And it’s broadly accepted that Microsoft stretches ecosystems. So if you need a trusted local partner to help you implement Cortana Intelligence Suite, odds are strong that you’ll be able to find one.

Solution templates for key manufacturing scenarios

To further accelerate time-to-value for manufacturers that adopt Cortana Intelligence, Microsoft is in the process of delivering Cortana Intelligence Solution Templates that map to the key manufacturing scenarios described earlier in this paper (under Putting Advanced Analytics to Use). You can find them in the Cortana Intelligence Gallery under Solutions, or by clicking here.

Customer case studies

As a manufacturer, you’re likely somewhat risk averse. At the same time, you realize that gaining and sustaining a competitive edge requires staying ahead of the competition—not waiting to see what they do and how it turns out before deciding to take action on your own. To help you balance these forces, next we’ll take a closer look at how existing Microsoft customers are putting Cortana Intelligence to use. Specifically, we’ll examine two customer scenarios in greater depth:

• How Jabil is using Cortana Intelligence to improve quality assurance.

• How a nationwide auto retailer is using Cortana Intelligence for Inventory Optimization.

Case study 1: quality assurance at JabilTraditionally, quality is assured on the assembly line, during production, with final quality tests performed after the product is assembled or produced. These efforts are guided by Six Sigma quality measurements for eliminating waste, with statistics-driven quality and process controls used to continually refine the process.

As processes and products grow more complex, manufacturers must find ways to ensure proactive quality control earlier in the process—potentially even before manufacturing begins. This new approach is built on predicting issues sooner in the process, as need to improve yield, reduce time to market, and minimize costs and downtime.

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By applying advanced analytics solutions such as Cortana intelligence Suite on top of existing approaches to quality assurance, manufacturers can improve quality by looking at new parameters such as telemetry from manufacturing equipment itself, including drift, tolerances, placement of components, and performance history—data that can’t be applied on the assembly line using traditional quality assurance processes. Through such an approach, manufacturers can predict that parts will fail even though they may pass traditional quality inspections.

Such were the challenges faced by Jabil, the third largest manufacturing services provider in the world, and one of the most sophisticated when it comes to disciplines such as Lean Six Sigma, continuous improvement, predictive maintenance, and statistical process control. A significant part of the company’s business involves the manufacture of printed circuit boards and other high-tech products, which means there are many factors and components that can affect quality. Some of these products are so complex that, even though they passed several visual tests during the manufacturing process, they would often fail to meet final quality standards due to tiny, undetectable faults that were introduced during the production process.

Working with Microsoft, Jabil used Cortana Intelligence Suite to implement an advanced analytics solution for circuit board manufacturing. Jabil also took advantage of:

• The Quality Assurance for Manufacturing Cortana Intelligence Solution Template, which combines existing test system and historical failure data with domain knowledge to predict production failures.

• Microsoft Azure services, which were used to collect and analyze millions of real-time data points from machines used throughout the 32-step, four-hour manufacturing process.

• Azure Logic Apps, which made it easy to connect to enterprise systems and manufacturing machines as data sources.

The solution is delivering significant new value by helping workers predict component defects before they happen. “Whether that means taking out a tool and cleaning it, to having to divert some raw material to a different location, or having to reject it,” says Clint Belinsky, vice president for Global Quality at Jabil. “Either way, you’ve avoided a problem, you’ve avoided downtime, you’ve avoided scrap.”

Upon embarking on the project, Jabil thought it would deliver a 1 to 2 percent improvement. But after implementing its advanced analytics solution, the results that Jabil experienced were far more substantial. These included an ability to predict which machine processes on the manufacturing line will slow or fail with 80 percent accuracy, resulting in a 17 percent savings in scrap and rework costs and a 10 percent savings in energy costs.

AnalyticsPolynomial and logistic regression algorithms for quality assurance

InsightMean time to fail over thresholdFailure before next maintenance window

Intelligent actionAuto-create service ticketAdd proactive replacement taskUpdate asset databaseUpdate procurement/supplier data

Building a quality assurance solution

Input DataOperating condition time seriesPart and operator detailsMaintenance history

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According to Gary Cantrell, senior VP and chief information officer at Jabil, one of the reasons that the company chose Cortana Intelligence Suite over other options was because of its support for fully hybrid architecture.

“With both what we want to do in the cloud and what we want to do on-premises, the Azure platform seemed to be the best fit for us, in trying to mix those two worlds together,” he says.

Other key benefits of Jabil’s decision to use Cortana Intelligence Suite included the following:

• Reach and scalability. As a cloud-based solution, the scalability and global reach provided by Cortana Intelligence Suite is expected to be a huge benefit, enabling Jabil to easily extend its solution to other sites for other uses by other production personnel.

• Ease of use. Jabil also found Cortana Intelligence Suite easy to use. After some initial instruction, the company’s own production people could build and run the necessary QA models. Considering the lack of data scientists who could assist with this task, this rated quite high on Jabil’s list of needs.

• Compatible with existing technologies. Connecting the company’s new solution in the cloud to shop floor systems went smoothly. This was achieved through a combination of the Azure IoT Suite and OPC Unified Architecture (OPC UA), an open standard for machine-to-machine communication.

If you’d like additional detail on Jabil’s use of Cortana Intelligence Suite, including more on why Jabil adopted it and how the company expects to use and benefit from it, you can find the full Microsoft case study on Jabil here.

Case study 2: inventory optimization at a nationwide automotive retailerIn discrete manufacturing, success depends largely on the ability to manage and optimize the end-to-end value chain—from suppliers to customers—in the face of rapidly changing markets. To achieve this, manufacturers must master several key disciplines, including demand forecasting, supply-chain optimization, just-in-time assembly, and inventory management. Afte all, at the end of the day, if the right products aren’t in the right place at the right time, you’re leaving something on the table.

One use case for inventory management is the automotive market, where dealers must optimize the mix of models and options that they order from automobile manufacturers. When it comes to profitability, getting this right is critical. If dealers order too many of a specific configuration, they may need to discount them to get them off the lot. And if they don’t have what the customer wants when he or she walks onto the lot, that customer may end up buying from someone else. To explore the value of advanced

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analytics in this scenario, Microsoft worked with a nationwide auto retailer to determine how Cortana Intelligence Suite could be applied to help them optimize the mix of dealer inventory—and thus profitability.

From the dealer ’s perspective, the business performance of each car depends on two key factors: the profit from the sale, and sales velocity—that is, the number of days that the car was on the lot. The key KPI for dealer inventory optimization, called profit-per-day, equals the profit from the sale divided by the number of days the car was in inventory. Overall inventory quality is defined as profit-per-day averaged over all cars in the inventory.

To enhance inventory quality and maximize profit-per-day, the auto retailer wanted automatic recommendations for the mix of cars that a dealer ’s inventory manager should order. This ordering is done between once a week and once a month, depending on each car manufacturer, after which it takes approximately two months for the car to be produced and delivered to the dealer. In determining what to order, the inventory manager must take into account not only the various styles or model lines for each car (such as four-door or coupe), but also the specific combination of options. Some options are mandatory, meaning that the dealer must choose one, such as color and engine type. Other options are optional, such as a cold weather package or navigation system. Each style typically has between five and 30 options, resulting in millions of potential unique configurations.

In framing the requirements for its advanced analytics solution proof-of-concept, the auto retailer decided to leave it up to individual inventory managers to decide on the number of each make and model to order. Furthermore, to limit project scope, the retailer decided to test the value of the insights that advanced analytics could bring to bear across four dealerships and four models from two manufacturers, focusing on one model per dealership. The models it chose included one truck, one economy car, and two luxury vehicles.

From an analytics perspective, the goal was to predict which specific configurations of each model would yield the optimal profit-per-day. This was complicated by the fact that dealers typically receive and sell less than one percent of all possible combinations, with the optimal configurations potentially falling outside that range. Fortunately, inventory exceeds sales most of the time, making true demand data easily available. In the end, the analytics solution used four data sets: historical sales, historical inventory levels, pending and historical orders, and a data set of the national retailer ’s stores.

From an algorithmic perspective, the analytics solution relies on a blend of two extreme strategies for determining which configurations to recommend:

Option 3(optional)

Option 2(mandatory)

Option 1(mandatory)

Navigationsystem

Blackleather seats

Style

Sedan with all-wheel drive

Model

Acme Zoom

Blueexterior

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• Recommending configurations to align future inventory and demand, the disadvantage of which is that some of these configurations have a lower historical profit-per-day.

• Recommending configurations that have a high historical profit-per-day, the disadvantage of which is that future inventory of highly profitable configurations is likely to exceed future demand, and is thus likely to decrease future profit-per-day.

This approach gives the inventory manager the flexibility to designate the exact mix of the above two strategies, which is controlled using an adjustable input called “profit-per-day percentage.”

To determine the accuracy of the automated recommendations output by the analytics solution, the project team used five years of training data and one year of test data to compare the orders that inventory managers had created using previous methods against the new, automated recommendations for the same time frame. The end result was an estimated 84 percent improvement in projected profit-per-day.

With the proof-of-concept complete, and its having satisfied the auto retailer and its inventory managers of the value of advanced analytics, the project team is now determining the exact parameters of a large-scale pilot.

ConclusionThe manufacturing industry is in the midst of a digital transformation. Disruption and change are everywhere, complexity is increasing, time is short, resources are scarce, and the stakes are higher than ever. Fortunately, you don’t need to conform to your old ways of looking at your world—or the limitations therein. With Cortana Intelligence, you’ll be able to turn your mountains of data into actionable intelligence, predict outcomes and opportunities, and engage with the metrics that define your business in newer, more natural and intuitive ways. What’s more, you’ll be able to get started sharpening your competitive edge in minutes, using the technical skills you already have, and seamlessly scale on demand as your business needs and use cases grow. And with Microsoft by your side, you’ll be in good hands.

For more information

• Cortana Intelligence Suite• Cortana Intelligence Gallery• Microsoft in Discrete Manufacturing• Azure IoT Suite

To get started with Cortana Intelligence, or to just learn more, contact Microsoft for a demonstration—or download one of the Cortana Intelligence Solution Templates and try it out for yourself.