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New Data-centric Innovations for Your High-Tech Manufacturing Company

April 7, 2021 TIBCO Spotfire

Reading Time: 2 minutes

For high-tech manufacturers, new data solutions are industry necessities. Digital transformation remains the number one priority for manufacturers to stay viable and competitive during unprecedented circumstances like disrupted supply chains and socially-distanced factories.

By 2021, Microsoft reports that 20 percent of G2000 manufacturers are predicted to depend on technologies like IoT, blockchain, and machine learning to automate large-scale processes. Here are some things your competition is already doing:

Monitoring factory equipment remotely
Detecting anomalies in complex processes
Preventing equipment breakdowns
Reacting to supply chain disruptions in real-time

Don’t get left behind by new data innovations. Read on to learn about how the high tech manufacturing industry is responding to change and what you can do to stay ahead.

Launch Your Digital Factory

Some of the most transformative innovations in high tech manufacturing come from businesses that apply integration, data, and analytics technology to all facets of their operations. Manufacturers can transform business outcomes by using data-driven insights and strategies to reshape their value chain.

Break free from legacy attitudes and embrace new business models to transform your enterprise. By introducing a Digital Twin and feeding it with data from various data sources, you can have a holistic view of the entire factory and simulate new business scenarios before actually starting the production of new products. Use digital twins to:

Accelerate the adoption of new channels and ecosystems
Generate new revenue streams
Reinvent existing business models

Focus on Customer Experiences

High Tech Manufacturers who place customers at the center of their strategy and deliver their business model in sync with customer expectations build trust, deliver greater value, and develop sustainable loyalty.

Create a data-centric approach to customer satisfaction by connecting manufacturers with customers for more shared information. Use advanced analytics to:

Measure and track customer engagement (such as product recalls, on-time deliveries, and defect rate)
Optimize end-to-end customer journeys and improve supply chain resiliency
Place customers at the center of business strategies, processes, and systems

Unleash the Power of Your Data

Data-driven insights and decision-making allow businesses to optimize every opportunity. Better insights lead to streamlined decision-making, improved return on capital investment, and to more optimized expenses.

Improve overall performance to become more efficient and profitable. Use predictive maintenance and operational 360 views to:

Increase your overall equipment effectiveness (OEE)
Proactively respond to events through real-time insights

Don’t Delay Operations Any Longer

Hemlock Semiconductor has already reduced its manufacturing costs and improved output quality and yield with its digital transformation. What are you waiting for?

Hemlock Semiconductor has already reduced its manufacturing costs and improved output quality and yield with its digital transformation. What are you waiting for? Click To Tweet

Read this e-book to start your journey to become a data-centric enterprise; it details success drivers, potential challenges, and competitive solutions for high tech manufacturers so you can optimize your business and increase your market growth.

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Teradata Joins Open Manufacturing Platform

March 2, 2021 BI News and Info

Teradata will work with leading manufacturers in the Open Manufacturing Platform community to develop solutions for industrial IoT and Industry 4.0

Group aims to enable faster and more cost-effective innovation and production in manufacturing and automotive through cloud-based data analytics

Teradata (NYSE: TDC), a leading multi-cloud data warehouse platform provider, joined the Open Manufacturing Platform (OMP) – a global alliance initiated by the BMW Group and Microsoft to drive industrial IoT developments and build future Industry 4.0 solutions in the manufacturing and automotive sector. As a member of the OMP, Teradata will help manufacturers accelerate innovation and achieve production efficiencies across the entire value chain by unlocking the potential of their data.

Common Challenges for Industry 4.0

While working to build their “smart factory,” in which all plants, products and processes are digitalized and interconnected, manufacturers worldwide share similar advantages and challenges: They benefit from a rapid adoption of new technologies that allow for greater operational efficiencies, factory output, customer loyalty and net profits, but too many manufacturers still struggle with the real-world implementation of IoT concepts. One such challenge is ensuring that data from legacy, as well as modern systems, is interoperable and usable across a multi-cloud environment so that holistic decisions and predictions can be made. Proprietary systems, which so often become data silos, make reliable visibility throughout the supply chain daunting, and thus hinder future business models. In addition, thousands of analytical models are needed to evaluate the petabytes of data produced by machines and robots every hour in order to generate actionable insights.

Building the Future of Manufacturing with Data Analytics

To solve these pressing challenges, Teradata will bring its extensive experience, expertise, and solutions in cloud-based data analytics to the OMP community, with a specific focus on the “Manufacturing Reference Architecture” and “Semantic Data Structuring” working groups. In open collaboration and knowledge sharing with leading manufacturers, Teradata will work to develop a blueprint for an industrial IoT architecture, and to simplify the deployment and management of the entire cloud and edge manufacturing environment. Teradata will also help develop a semantic data structuring layer so that manufacturers can easily share, reuse, and evaluate heterogeneous data.

Both are critical projects in terms of the objective of the working groups: to define and to use a common open data model, to influence existing and future standards, and to build open solutions for industrial IoT. By being completely independent of cloud providers or other vendors, Teradata’s technology substantially aligns with the core principle of the OMP to bring forward platform-agnostic solutions that enable a smarter future in manufacturing.

“Teradata has been helping the world’s leading manufacturing and automotive companies boost their production through data for more than 40 years,” says Peter Stadler, Vice President, Central Europe at Teradata. “As a member of OMP, we are pleased to accompany them on their way to Industry 4.0 and to contribute to new cutting-edge solutions with the help of data analytics and thus also to smart manufacturing of the future.”

Founded in 2019 under the umbrella of the Joint Development Foundation, a part of the Linux Foundation, the OMP helps manufacturing companies accelerate innovation at scale through open cross-industry collaboration, knowledge and data sharing as well as access to new technologies. Current OMP steering committee members are Anheuser-Busch InBev, BMW Group, Bosch Group, Microsoft, and ZF Friedrichshafen AG.

Teradata also contributes to other industry alliances, such as the Open Subsurface Data Universe (OSDU) Forum, focused on developing a standard data platform that brings together exploration, development, and wells data in the oil and gas industry.

More information about the Open Manufacturing Platform: https://open-manufacturing.org/

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Siemens and IBM extend alliance to IoT for manufacturing

February 28, 2021 Big Data

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IBM and Siemens this week expanded an existing alliance to include deployments of MindSphere, a managed internet of things (IoT) service provided by Siemens, on an instance of the Red Hat OpenShift platform.

The goal is to make it simpler to build edge computing applications that process and analyze data as it is being created, Siemens senior VP Raymond Kok said.

As part of that effort, Siemens was looking for an application development platform based on Kubernetes that would enable the conglomerate to build and deploy applications based on a modern microservices-based platform. “We were looking for an industrial-grade Kubernetes platform,” Kok said.

Manufacturers currently use MindSphere to collect and analyze real-time sensor data from products, plants, systems, and machines. Siemens now wants to feed that data into analytics applications that would run locally versus in the cloud. That’s critical because it eliminates latency that would otherwise be created when data is transferred over a wide area network (WAN).

IBM claims a single manufacturing site can in one month generate more than 2,200 terabytes of data from 100 to 200 sensors, most of which is never analyzed. Of course, not all data is worth analyzing, but companies aim to maximize manufacturing output, particularly during uncertain economic times.

It’s becoming more feasible to collect and analyze data on the factory floor in near real time because of the rise of 5G wireless networks capable of providing the necessary network bandwidth, Kok noted. While IoT devices collect data in real time, the analytics applications deployed by IT teams have historically resided in a datacenter. With the rise of 5G networks, it’s now easier to push IT applications onto the factory floor, Kok added.

Some of the funding for this initiative comes from an IBM Cloud Engagement Fund that is part of a $ 1 billion investment to advance hybrid cloud computing. In fact, one of the reasons IBM spent $ 34 billion to acquire Red Hat is to drive adoption of edge computing applications in private clouds that will ultimately need to be integrated with applications residing on multiple clouds. The definition of hybrid cloud computing now encompasses a wide variety of edge computing platforms, in addition to local datacenters that all need to be integrated with backend cloud services.

In the case of manufacturing environments, Siemens sees an opportunity to expand the scope of the digital services it provides to include both IT and operational technology (OT) applications running on the factory floor. In fact, the traditional pyramid relationship between OT and IT applications is starting to collapse as these platforms become more integrated, Kok said. But the IT and OT cultures within many organizations are vastly different, and bridging that divide requires a significant amount of technical expertise and political capital.

Longer-term, IBM expects the data being collected at the factory floor to also be used to train AI models, said Pierre-Henri Gabriel, Industry 4.0 executive for IBM’s Global Industrial Sector. “We have AI in the line of sight,” Gabriel said.

It’s clear both Siemens and IBM are betting manufactures would rather rely on them to automate manufacturing processes than attempt it on their own.

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Top 5 IIoT Implementations in Manufacturing

November 6, 2020 BI News and Info

The widespread implementation of the Internet of Things in industry, along with concurrent advances in machine learning and cloud storage, is thought to represent a fourth Industrial Revolution.

Manufacturers who can integrate digital, analog, physical, and human components into their production systems will generate unparalleled efficiency and value, while manufacturers who lag behind on implementation will struggle to compete.

Here’s what you should understand about the industrial Internet of Things and how it’s already being leveraged to create smarter, safer, and more productive plants.

What is the Industrial IoT?

The industrial Internet of Things, often abbreviated as industrial IoT or simply IIoT, refers to the network of machinery, instruments, and other physical devices that have been embedded with digital sensors for the purpose of monitoring, collecting, and sharing data over private internet connections.

The industrial IoT effectively takes the same wireless technology that drives the consumer Internet of Things (think fitness trackers, home security systems, and smart thermostats) and applies it towards industrial purposes, including core manufacturing operations.

Some aspects of the industrial IoT may not seem particularly new or novel. Manufacturers have, after all, collected and analyzed machinery data for decades.

What’s changed, though, is the emergence of small, low-cost sensors along with expansive, high-bandwidth wireless networks. By combining connected sensors with machine learning software that can analyze the data they’re collecting in real-time, it becomes possible to quickly and autonomously address inefficiencies while optimizing and synchronizing any number of processes.

Top 5 Industrial IoT Implementations in Manufacturing

Industrial IoT technology is already transforming manufacturing operations across the globe through several common implementations. Let’s look at some examples.

1. Asset Monitoring

It’s 3 a.m. Do you know if your next big shipment from Shanghai is on time? If your equipment in Canton has been serviced recently? If power outages across Central Europe will impact your operations in Hungary?

IoT-enabled sensors are helping manufacturers, especially remote manufacturers, track production processes in real-time—across locations—and keep key personnel updated on status changes. This means production problems at remote sites can be solved from a centralized location based on the exact same data that site operators are seeing.

Industrial IoT provides not only increased connectivity with specific devices and facilities, but more comprehensive intelligence about entire production systems. This data can be combined with other sources of information, including weather conditions and historical enterprise figures, to help augment logistics, sustain inventory, and avoid quality control problems.

For example, if you have shipments coming from multiple factories in China that are running late, and you know this in advance, you can draw up new shipping plans to save you money.

2. Predictive Maintenance

Across the globe, countless millions of dollars are spent each year on machine operational and maintenance costs.

Factories have historically either adopted a reactive (meaning run-to-failure) or preventive (meaning periodic examinations) model for keeping their equipment up-and-running. No matter the approach, the objective is always the same: avoid downtimes and expensive pauses on production.

Today, though, IoT sensors are driving a transition to a predictive maintenance model. That’s because the data these sensors are continuously collecting can be fed into machine learning models, which will compare new numbers with older data to actually predict when failure is likely to occur. What’s more, combining IoT with cloud computing lets you leverage information from multiple machines, making your predictions even more reliable.

This predictive approach translates to fewer instances of mechanical error, increased machine lifetimes, and huge cost savings. In fact, a recent McKinsey study notes that “predictive maintenance typically reduces machine downtime by 30-50% and increases machine life by 20-40%”.

3. Optimizing Legacy Machines

The high-tech benefits described above might seem unattainable for older manufacturing plants. After all, legacy equipment represents the backboard of manufacturing in the United States, as most facilities continue to depend on decades-old production equipment. Replacing these older machines can cost anywhere from hundreds of thousands to millions of dollars.

Thankfully, industrial IoT upgrades are not prohibitively expensive.

Smaller IoT-enabled sensors that detect factors like vibration or temperature can be attached to legacy machinery to provide connected feedback and data at a fraction of the cost of replacement. And even simple sensors like these can provide great benefit by identifying normal operating parameters and then sending out warnings when data indicates the machine is beginning to malfunction.

This is a key consideration for older pieces of equipment, which typically have higher maintenance costs and greater risk of failure. Upgrading existing machines with sensors can reduce such costs while saving time and labor.

4. Operational Intelligence

Operational intelligence is something of an umbrella term that reflects asset monitoring, predictive maintenance, and other industrial IoT benefits.

But it’s more than that, too.

Operational intelligence can be thought of as a real-time analysis of operational visibility—i.e., the monitoring of your system’s operations, performance, and readiness. Industrial IoT facilitates end-to-end operational visibility, including data from remote assets and systems. By collecting and analyzing operational visibility data and comparing it with historical information, organizations can generate actionable insights.

What’s more, the sort of data-driven, prescriptive advice generated by OI not only informs decision-making, it also reduces the time necessary to operationalize those decisions by automating key actions (especially time-sensitive ones).

The benefits to this approach are clear. Imagine, for instance, how operational intelligence could help you not only detect potential machine failures at overseas facilities before they occur, but also automatically order replacement parts and take other steps necessary to reduce disruptions via prescriptive analytics.

Operational intelligence for manufacturing is the most common industrial IoT application according to PTC, and it’s easy to understand why.

5. Safer Workplaces

Maintaining a safe work environment is critical for numerous reasons, not least of all protecting workers and avoiding production interruptions. Industrial IoT can help improve safety by providing facility managers with real-time understanding of worker activity, safety shutdown causes, machinery compliance, and other relevant trends.

The potential safety applications are quite numerous. For example, wearable smart devices can track workers’ biometrics (including body temperatures) and alert supervisors when there are health concerns. And the increased visibility provided by sensors can provide insights into safety-system performance and even help identify the root causes of shutdowns. Safety-system diagnostics can also be used to identify leading indicators and address machine issues before they lead to machine accidents.

In short, connected systems present greater opportunities to monitor performance, assess risk, and proactively improve safety.

Why it matters

Manufacturing has been transformed over the past decade by the convergence of several related technological advances. When working together, these complementary technologies can help you create automated plants that produce large volumes at maximum cost efficiency, or even smart plants that produce highly customized products.

But coordination is the key, as harnessing the most value from Industrial IoT-enabled sensors, cameras, and other devices requires sophisticated machine learning software to analyze the data.

As a leading data science platform, RapidMiner provides a blueprint to make your operation successful. We help manufacturers across the globe deliver business impact with machine learning and AI.

If you’d like to see how we can help your business, sign up for a free, no-obligation AI Assessment, and see what kind of impact machine learning can have on your organization.

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5 Principles for Sustainable Innovation with Cloud Manufacturing

October 17, 2020 TIBCO Spotfire

Reading Time: 3 minutes

Though the rise of Industry 4.0 in the last few years has encouraged manufacturers to digitally transform, the latest global events have made it more of a priority than ever before. It’s become clear that manufacturers need to change the way they implement operations, use data, engage with customers, and create an overall more sustainable and resilient ecosystem. And all this can be done with a cloud-based strategy.

The truth is that many manufactures are not adequately prepared for disruptive events. Disruptions are caused by a number of different reasons:

Advancements in technology can cause strong disruptions in the way of doing business.
Disruptive innovative competitors can affect your business; therefore, having the right insights can avoid investing in the wrong ideas or products.
Customers’ tastes and behaviors continuously influence the company’s strategies forcing them to adapt production to be able to quickly react.
Economic volatility leading to changes in customer behaviors. Think of the automotive industry during this global pandemic; if you’re locked at home you don’t need access to a car.

The good news is that digital transformation is still top of mind for most manufacturers in many areas. Trends that have been found include:

40% of manufactures will use IoT sensor data to diagnose issues and reduce downtime, which requires predictive maintenance¹
50% of new IT infrastructure will be deployed outside of data centers¹
35% growth in digital twins to create a virtual factory²
20% of G2000 manufactures will employ IoT, ML, and AI to automate decision making³

The pandemic pushed us towards the future of manufacturing by almost five years. Before many companies had already started or had plans to start a digital transformation. Suddenly, a new unforeseen critical factor was added: staying in business.

Since the pace of digital transformation won’t slow down, the question is if today’s manufacturing industry is agile and innovative enough. And more importantly, is it resilient enough to survive the next disruptive event and outpace the competition?

The future of manufacturing leverages the concepts of cloud manufacturing to create a sustainable ecosystem to navigate through the next major event by successfully implementing these five core principles or 5Cs:

Connected
Collaborative
Cloud-centric
Customer focused
Continuous improvement

In order to achieve the 5Cs, you need to implement a cloud-based strategy that utilizes the following:

Integration to support SaaS providers and an API-first strategy.
Low-code approach to speed up the time to market, create newer services, and innovate faster.
Power back to the people to empower all levels of users and create more synergy with processes and technology.
Hybrid architectures to decide what data to migrate and when.
Scaling and deployments to support out-of-the-box scaling to increase service availability and speed up the release cycle of new services.
Processing at the edge to allow more edge IoT, 5G, and serverless computing patterns.

Cloud also enables modern event-driven responsive architecture. With a responsive architecture, you can process critical factory events and support your operations with immediate actions. You gain the necessary agility for the so-called “new normal”.

Here are a few of our customers who have implemented cloud manufacturing:

Campari is one of the largest spirits beverage producers in the world. Since 1995, Campari has made 27 acquisitions so it needed to integrate with all different kinds of systems. The beverage producer had the pressure to minimize the time to market. Only by moving to the cloud was Campari able to gain the necessary business agility to sell the newly acquired products as fast as possible on the market and to sustain future acquisitions.

Cosentino is a family-owned business founded 40 years ago in Spain and active in mining hills for granite and selling unfinished slabs to stonemasons. It completely transformed its business when it went digital to become a leader in high-end kitchen and baths finishing. But with the expansion, Cosentino had to face challenges to scale operations and keep running many different business processes. By integrating their global IT systems in the cloud and adopting a cloud-native architecture, a customer is now able to see in real-time if a product is available in two to three days compared to three to six months.

The future of manufacturing leverages the concepts of cloud manufacturing to create a sustainable ecosystem to navigate through the next major event. Click To Tweet

Manufacturing isn’t going away any time soon. But, if manufacturers want to remain competitive in this new environment, they have to look towards digital transformation, specifically with cloud. This will give manufacturers the necessary sustainability, agility, and resiliency to be prepared for the next disruptive event.

Learn more about how TIBCO enables manufacturers of the future and how these capabilities can help your business.

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Henry Ford, Industry 4.0, And A Recovery For Manufacturing

April 24, 2020 BI News and Info

There is a quote attributed to Henry Ford that I think holds the key to the future success of manufacturing. It may even change our thinking during the current crisis and provide the basis for successful recovery, digital transformation, and the adoption of Industry 4.0. And perhaps it can unlock significant value from an organization’s legacy investments.

Now, these are bold and potentially career-ending statements. So, before I tell you the quote I am referring to, I need to provide the context and the rationale behind such a claim.

To understand where we are today, we first need to look back to see where we came from.

Historic trends in manufacturing before Industry 4.0

Over the past 30 to 40 years, global manufacturers have followed four major trends in parallel that have been aimed at improving their manufacturing performance:

1. Strategy

In its simplest form, manufacturing strategy is essentially what to make, how to make it, and where to make it. Strategy is influenced by a huge range of internal and external factors. Pre-2020, the typical examples I used to illustrate strategic influences were sustainability, the circular economy, Brexit, changing consumer behavior, disruptive business models, trade wars, emerging markets, workforce skills, labor, energy, and material costs.

Strategic factors drive mergers, acquisitions and divestments, new market entry, new product development, and capital investment in manufacturing facilities.

The objective is to gain a competitive advantage from manufacturing operations in whatever form that takes for the organization in question, such as increased market share, cost reduction, product quality, shorter time to market, and product innovation.

A look at any global manufacturer’s history will provide an example of this trend. Clearly, the current crisis will have the biggest impact on manufacturing strategy we have ever witnessed.

2. Operational excellence

The second major trend has been the introduction of operational excellence programs. Most major manufacturers have their own internally branded methodology, but the core principles can be traced back to the fundamentals of Taylor, Ford, Shingo, Toyota Production System, LEAN, Six Sigma, and Theory of Constraints.

Examples of these tools and techniques can be seen in action during a walk around most production facilities.

3. Information technology

The third major trend aimed at improving manufacturing performance has been the investments made in information technology. Here, I am referring to the solutions that typically fall into the domain of the CIO and the IT department: ERP, CRM, and SCM applications, IT infrastructure, networks, and platform standards, to name just a few. These enterprise-level IT systems have a huge impact on manufacturing performance because they are the solutions that run the supply chain outside of the “make” activities. They capture demand, plan production, procure raw material, manage work in progress (WIP), store finished product, and transport it.

4. Operational technology

The fourth major trend has been the investments made in operational technology. Operational technologies are the solutions that perform the “make” activities inside the four walls of the factory, and this is normally the domain of the engineering department. OT includes instrumentation, measurement and control, automation, PLCs, HMI, SCADA, DCS, historians, APC, batch control, MES, LIMS, and OEM equipment. These investments are centered around capacity increases, quality improvements, process optimization, labor savings, and waste reduction.

The results: falling short of expectations

Most organizations can see clear improvements in manufacturing performance that are directly attributable to each of the four investment areas. Manufacturing has made huge strides during this time period and is clearly far more efficient and effective than it was.

However, the overwhelming feedback is that none of the four investment areas have fully delivered on their potential. The manufacturers didn’t quite achieve the level of manufacturing performance improvement they expected, and their current KPIs show that there is room for improvement.

Why is this the case? In a word: silos. Not just internal departmental silos in the manufacturer’s own organization but silos in the external ecosystem of suppliers.

Internal silos

Let’s begin by looking at the internal dynamics inside a typical manufacturer. The strategic manufacturing goals and objectives are cascaded down from CXO level to plant directors and manufacturing teams. The operational excellence program provides the principles, tools, and methodologies for manufacturing to utilize in the pursuit of performance improvement. And the business looks internally to IT and engineering functions for solutions that will provide both the information and processes needed to support manufacturing’s objectives.

As mentioned previously, IT provides many of the core Level 4 solutions in the classic ISA95 model. On a typical manufacturing site, it is IT solutions that provide the production planning, warehousing, logistics, and financial reporting systems. The choice of technology, vendor, and implementation partner and the budget normally rest with IT.

Meanwhile, OT is the domain of engineering: process, chemical, mechanical, electrical, and automation engineers, to name just a few. These are the people who design and build new production facilities, whether it is a car production line, soft drinks, chemicals, or pharmaceuticals. The choice of technology vendor, implementation partner, and budget normally rest with engineering.

In my 30 years of experience, IT and engineering have not collaborated particularly well. In many cases, they appear to be in competition, as they champion different solutions to solve the same business problem. At times, there also appears to be a lack of understanding of their respective domains. This is understandable, as the roles within each domain have their own full-time education and career paths. The example I often use to illustrate this point is their respective interpretations of the term “‘real-time.” Real-time in the world of IT is a transaction. Something has moved from A to B and has a different value (e.g., WIP). In the world of OT, real-time is usually millisecond scans on a process to ensure it is in control.

The net result is that these internal frictions and challenges often leave manufacturing operations without the solutions they need to hit their goals, and the overall results tend to plateau below expectations.

External silos

Let’s look at the dynamics of the external ecosystem of suppliers.

The dominant players advising on strategy in the C-Suite are the “pure” consulting firms, such as McKinsey, BCG, Bain, AT Kearney, and Bearing Point. In addition, there are the “Big Four” advisory arms and the consulting arms of the large system integration companies.

In the traditional IT ecosystem, you will find the major enterprise software application vendors, their integration partners, and a wide range of hardware and platform providers. But in the OT ecosystem, we find a very different set of hardware and software vendors because the implementation partners differ and OT is also where we find the OEMs. Yet, the operational excellence ecosystem is largely populated by consultancies that provide professional services that specialize in teaching and implementing operational excellence techniques and programs.

In many respects, the external ecosystem of suppliers is a mirror image of the silos found inside manufacturing.

My experience leads me to believe that there are very few manufacturers that have truly broken down both the internal and external silos and fully optimized their investments in these four areas. The point to emphasize here, however, is that what we are still discussing is Industry 3.0. In recent years, the picture has become more complicated by the advent of a fifth global trend, Industry 4.0.

The emergence of Industry 4.0

The past decade has seen the emergence of a range of disruptive technologies such as AR/VR, AI/ML, IoT, 3D printing, and several others. When grouped together and applied in a manufacturing context, we have the basis of Industry 4.0.

The Industry 4.0 market, however, is emerging and immature. The Industry 4.0 “white space” is being entered by most of the established strategic consultancies as well as the traditional IT and OT ecosystem players. This area has seen the emergence of the hyperscalers together with a large number of startups. Acquisitions, mergers, and failures are a regular occurrence, and this will continue to be the case until the market consolidates.

This is an exciting area to be involved in, and I genuinely believe that the potential value Industry 4.0 offers manufacturers is enormous. My prediction, however, is that on our current path, investments in Industry 4.0 will also fall short of expectations. They will be investments that again under-deliver against both their potential and promise.

Why? Silos still persist.

The traditional pre-Industry 4.0 internal and external silos are still largely intact. The players are still grouped into their traditional domains and following their established business models. We now have the added complication of a fifth silo in Industry 4.0, which is certainly blurring the lines but also making the challenge more difficult for manufacturers.

An audit of a typical manufacturing facility will illustrate the large number of players who currently have an influence on manufacturing performance. However, solving this problem is challenging but not impossible.

The solution

Most articles currently being published recognize that a major rethink of global manufacturing supply chains is required in what is being termed “the new normal.”

It is my contention however that radical change and a new way of working should not just apply to manufacturers. It should go much further and apply to the entire ecosystem of external suppliers. We must break down the competency silos and the competitive “arms-length” way of working.

Success will be delivered through collaboration and coopetition.

The facts are that each external supplier has significant value to add to bring to the equation. Clearly there are some areas of overlap but, once the competitive process is completed, there is no reason not to cooperate, as that is in everyone’s best interests.

And while this crisis has been a monumental shock to us all, it has highlighted the need for huge change in manufacturing. It seems illogical to expect manufacturing to successfully execute a paradigm shift while the external ecosystem of suppliers remains unchanged.

So what is the quote from Henry Ford that encapsulates the message I am trying to convey to both the manufacturers and the supplier ecosystem alike?

“If you always do what you’ve always done, you’ll always get what you always got.”

In this time of uncertainty, we are opening access to technologies that can help employees, companies, communities, and governments continue to move forward.

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The Intelligent Industrial Manufacturing Enterprise: Five Ways Forward

March 11, 2020 SAP

Gone are the days when the industrial manufacturing value proposition was relatively simple: industrial manufacturers made products and delivered them.

Now, the industrial manufacturer’s job does not end with delivery. Driven by ever-more-demanding customers and supported by the widespread uptake of the Internet of Things (IoT) and the emerging power of machine learning and artificial intelligence, industrial manufacturers are developing new capabilities to track huge volumes of data generated by thousands of devices and are adjusting their service depending on the circumstances.

The goal is to be more responsive, always-on, and highly adaptable. Industrial manufacturers seek to collaborate more fully with their customers from discovery through design, service, and beyond. Wherever possible, they aim to deliver the kind of experiences and outcomes that customers reward with loyalty and ongoing business.

But how do companies move forward? Successful ones are focusing on the following five strategic priorities:

Be customer-centric

Industrial manufacturers are looking for ways to maintain customer-for-life relationships based on a 360-degree understanding of their customers. The starting point is a holistic view of their customers’ business processes – ending with the knowledge of how those customers use the products in their daily operations.

To get there, industrial manufacturers are moving toward omnichannel models for managing customers’ interactions across channels (Web, direct sales, IoT, and more). The goal is seamless interactions with customers, the ability to quickly see all products bought, and real-time visibility into how products are performing. This will help companies position the customer’s point of view at the center of every decision.

Serve the segment of one

Increasingly, industrial manufacturers will be able to deliver completely customized products, services, and solutions that precisely fit the needs of individual customers based on sophisticated platform, configuration, and mass-customization strategies.

Industrial manufacturers are moving toward this goal by rationalizing existing product options using machine learning to understand what really sells and what doesn’t. In addition, organizations are pulling in customer experience data to better understand requirements – and then using this data to inform requirements in product configurators that let customers define their own products on the fly.

Embrace digital smart products and solutions

Industrial manufacturers are shifting to products with more digital functionality, allowing even more flexible configuration of products. Thus, software-based features are on the rise with connectivity to enable remote access and monitoring.

Industrial manufacturers are extending original (physical) products with digital services that augment and extend product functionality. Combining insights into the end-user experience and the relevance and value of digital capabilities, manufacturers will extend the lifecycle of the product and increase lifetime revenue. With a direct feedback loop from the product back to the manufacturer, product enhancements and future developments will be based on the actual usage and experience of the product, from first interaction to product retirement.

Implement the digital supply chain and smart factory

Supply chains and manufacturing networks are becoming modular and flexible to allow the seamless execution of different manufacturing strategies. Industrial manufacturers, accordingly, are using Industry 4.0 philosophies and new digital technologies to implement “shop-floor-to-top-floor” connectivity for real-time visibility.

Subsequent steps will increase machine-to-machine connectivity and collaboration, allowing autonomous decisions based on sensor data and machine learning algorithms. Industrial manufacturers will combine feedback from connected stakeholders (customers, workers, suppliers) and associated processes to further improve overall manufacturing and supply chain performance. Intelligently connecting manufacturing, logistics, and supply chains enables companies to quickly address short-term demand impulses, supply fluctuations, and changes to customer orders, allowing a truly modular production process. This production flexibility enables industrial manufacturers to produce higher-quality individualized goods at lower costs.

Develop service-based business models

As revenues are increasingly linked to services that are based on and built around smart products, more industrial manufacturers will offer products as a service based on the value delivered to the end customer.

Remote condition monitoring of assets is critical to success with such models – enabling manufacturers to identify and provide additional value-added services. Based on the data collected, organizations can get better insight into how products are used. This enables them to offer pay-for-outcome models where the risk and long-term value of each customer is clearly understood.

The future is bright

Ultimately, the winners in the industrial manufacturing industry will be those companies that successfully transform themselves into fully customer-centric companies. Ahead for the industry is unprecedented change at unprecedented speed – but our industry is positioned to be a driver of progress. Together, we can lead the way.

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Want to find out more about the latest trends and innovations in today’s global industrial manufacturing business? Register t oday to join our flagship 60-minute virtual industry event:

SAP Industries Forum 2020: Industrial Manufacturing
Experience the Intelligent Enterprise
Thursday, March 26, 2020, 8 a.m. PDT, 11 a.m. EDT, 4 p.m. CET
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Predictive Analytics in Manufacturing: A Winning Edge

February 1, 2020 Sisense

The modern manufacturing world is a delicate dance, filled with interconnected pieces that all need to work perfectly in order to produce the goods that keep the world running. In Moving Parts, we explore the unique data and analytics challenges manufacturing companies face every day.

Building an accurate predictive analytics model isn’t easy. It requires a skilled data team, advanced tools, and enormous amounts of clean data from the right combination of inputs. It’s a difficult process, but an effective predictive analytics engine is an enormous asset for any organization.

Manufacturing Data banner 770X250 Predictive Analytics in Manufacturing: A Winning Edge

Big challenges, big rewards

Manufacturing companies are in a unique position regarding data: they create and capture tons of it every day. The process of producing goods is an enormous opportunity for data optimization. Raw materials need to be ordered, received, constructed, packaged, and shipped out for sale in the most efficient manner possible. Because the steps are repeated so many times through the process, a small edge created via predictive analytics in manufacturing will be magnified at every repetition to produce significant benefit.

Because of the cyclical nature of the manufacturing process, data-driven companies are building superior processes to create bigger and bigger advantages. Here are a few examples of companies using manufacturing analytics to win the future:

Predicting return rate

Skullcandy’s dive into predictive analytics started with the challenge of understanding return rates on new products. The logic was that if the team could predict certain features or aspects of a product that would lead to a return, they could optimize those policies around returning products. They used BigSquid to blend and analyze historical data related to returns and added their learnings to features and products on their roadmap. From there, the team could ask new questions of that dataset to understand the way customers were interacting with their products and ultimately build a better warranty policy for products before they were even released. This data was also useful for product managers, giving them a clear picture of what was making customers adopt Skullcandy’s products (or not).

Once the return rate questions were answered, the team focused their efforts on uncovering insights around reviews and warranty claims to generate insights about positive and negative drivers. Data like this is ideal for making decisions for product roadmaps. All those customer insights can be used in a number of creative ways to better focus resources and improve products.

Improve forecasts and maximize revenue

Gentex made the most of their budget to optimize their incoming revenue. Just six months after implementing predictive analytics, their ecommerce sales increased by 50%!

Gentex deployed Sisense to comb through millions of records after switching over from an outdated ERP system. They needed a platform that could churn through all that data quickly and deliver quick intelligence about both current and future revenues. Initially, Gentex created dashboards for their sales and operations teams that collected information about sales, quotes, and orders across the company. Those dashboards answered immediate questions about the current state of the business.

To answer more forward-looking questions, Gentex creates a sales forecast for an entire year using just a few months of data. They use predictive models to forecast revenues based on spending. They even incorporate trend data to improve accuracy over time. Currently, Gentex builds visualizations of year-to-date revenue data to forecast up to 15 months into the future.

Operating off those accurate forecasts, Gentex made the most of their budget to optimize their incoming revenue. Just six months after implementing predictive analytics, their ecommerce sales increased by 50%!

Improve inventory management with demand forecasting

Making a product that consumers want to buy is only useful if a company can find a way to get that product in front of the consumers who demand it. Several of today’s most cutting-edge manufacturers are blending historical customer data and external factors to predict demand for goods so they can increase production when demand will be high and decrease production when demand will be low. These companies aren’t just building for the future, they’re building the future.

The need to accurately forecast demand is crucial to these manufacturers. Assessing demand in real-time is ineffective since companies need to make decisions about demand far enough in advance to complete an entire production cycle and get that product in front of customers. With a solid predictive analytics model in place, manufacturers can create exactly the right amount of products (and the right variety of those products) to satisfy future customers.

These forecasts optimize sales revenue, but it also avoids unnecessary costs associated with producing, shipping, and stocking items that won’t sell. Accurate predictions are a win-win for any manufacturer.

Build your manufacturing business with analytics

Predictive analytics in manufacturing have gone from being science fiction to being a make-or-break addition to any company’s technology stack. Using a platform like Sisense for manufacturing analytics, combining internal and external information into a series of accurate forecasts is incredibly invaluable to any manufacturer. Improving any step of the manufacturing process is an advantage over the competition, but improving every step is a data-driven way to become an industry leader faster.

Predictive Analytics in Manufacturing: A Winning Edge

Adam Bonefeste is a veteran content marketing manager. When he isn’t writing copy, he’s probably reading books, running through San Francisco or getting lost in YouTube holes about math/logic problems.

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3 Manufacturing Inefficiency Problems Solved with Microsoft Dynamics 365

December 10, 2019 Microsoft Dynamics CRM

There are IT inefficiency risks that are consistent among the manufacturing industry. Businesses that leverage CRM software see their sales increase by 29%, sales productivity increase by 34%, and sales forecast accuracy increase by 42%. Let’s identify the problems, and then talk about a simple approach to significant process improvement using Microsoft Dynamics 365 CRM.

Manufacturing, more than some other industries, greatly benefits from a full-service CRM solution such as Microsoft Dynamics 365 Customer Engagement. This shouldn’t be surprising, yet it is a requirement that some manufacturers have ignored.

From not having a 360-degree view of customers to poor demand forecasting, and limited visibility into your sales pipeline and inefficient sales processes, these challenges can be met with a robust, integrated CRM solution.

Which of These Inefficiency Problems Does Your Manufacturing Business Face?

Problem 1: Excessive Internal Communications About Order and Invoice Information

Obtaining order and invoice information can be a nightmare for some sales teams in manufacturing. Sales reps will call or email someone in their department to obtain order and invoice information. In most cases, the required information is in an ERP or accounting system to which the sales team doesn’t have access. Phone calls are made, and emails are sent back and forth to get the information to the sales team, which would relay to the sales rep.

Problem 2: Double Entry of Sales Order Information

Your sales reps are responsible for getting orders and closing deals. Once an order is received, it’s not uncommon to see the sales rep hand off the order information to someone else within the sales department. This could be done by email, Word template, or hand-written paper document delivered to the back-office staff for manual input. When such information is manually being passed across people and departments, there is great risk of sales order information being lost, entered multiple times, entered erroneously, or siloed into systems or outdated spreadsheets.

Problem 3: Sales People Need to See Their Open Order Status

For your sales rep, any open order represents pending revenue. If an order remains open, the sales rep (and the customer) needs to know why. Sales representatives want to be able to see open order information in real time, either to manage customer expectations or to assist in resolving an order that hasn’t been fully processed or shipped. Since sales reps generally earn their commission on invoiced and delivered products, income from these open orders will not show up in their paychecks.

Solving Inefficiency Problems Through System Integration

One of the simplest ways to resolve these common inefficiency problems is through System Integration, the process of connecting your business systems so they can work together seamlessly. For sales people who don’t have access to your company’s ERP system, System Integration offers a host of benefits:

System Integration pushes data from the ERP to the CRM system and vice versa.
Sales people can initiate orders from the Dynamics 365 CRM system; they can populate products (which are also integrated with the ERP’s product catalog) as well as any other information needed about the order.
When the order is ready, it is pushed to the ERP system where it is processed.
As the order travels through its normal process, the order status managed in the ERP is passed back to the CRM system in real time for much needed visibility for the sales team.

The Advantages of Microsoft Dynamics 365

It is reported that for companies that use a CRM system, the average return on their technology investment is $ 8.71 for each dollar spent. Given how critical a CRM system is for manufacturers, Microsoft Dynamics 365 offers a bevy of benefits to sales teams. The out-of-the-box solution comes with a robust quote-to-order system so that sales reps can convert a quote to an order with a simple click. Another key advantage is that order and invoice data tables and fields already reside in the system and are ready to be received from the ERP via the integration.

Microsoft Dynamics 365 CRM when fully integrated with your ERP provides improved ability to sort and filter through order and invoice information. Sales people can manage a “My Open Orders” list where orders fall off automatically as they are invoiced. Invoice information is also available for sales people for real-time visibility and for summing invoices on a daily, weekly, monthly, and annual basis.

Achieve Efficiency Gains

In today’s customer-centric era, the importance of an integrated CRM solution has never been greater. A quality CRM solution not only allows manufacturers to facilitate basic sales tasks, but it also helps them overcome the many challenges they face. Microsoft Dynamics 365 Customer Engagement is a full service CRM solution that can add value to your distribution business by creating a leaner organization that will manage customer demand and meet it quickly and efficiently. If you’re looking to achieve efficiency gains, invest in Microsoft Dynamics 365 today!

Learn more about how to leverage Dynamics CRM and help improve the business processes of your manufacturing company.

As Senior CRM Consultant of Synoptek, Julie brings a solid business background in addition to 9 years of hands-on experience in D365 CRM. It’s with this broad understanding of business processes that aids in serving companies well and knowing how and why the D365 CRM provides positive change. As a Microsoft Dynamics Certified and ClickDimensions Certified professional, Julie provides leadership that helps the deployment team stay focused on quality and project priorities.

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Add Muscle And Flexibility To Your Manufacturing Operations With 3D Printing

November 3, 2019 BI News and Info

Part 3 of a 3-part “Manufacturing Fitness” series

Concluding our series on manufacturing fitness, we now turn our attention to 3D printing – the process of physically manufacturing a product directly from a CAD model. Simply upload the CAD file to a 3D printer, feed it with some sort of base material, and the printer builds the object layer by layer.

This layering approach is what gives the technique its other name: additive manufacturing. The base material that gets added or layered can be almost anything imaginable. Typically, it’s plastic dust – but it can also be metal or concrete. In some futuristic scenarios, it can even be human tissue. So, if you’re looking to add muscle to your manufacturing operations – lean, flexible muscle, that is – then additive manufacturing or 3D printing may be the answer.

While 3D printing is certainly on the rise, perhaps the most significant factor holding back wider adoption is its slow speed of production. This issue, however, may soon be solved. According to UPS, one of the most prominent players in the 3D printing space, the average speed of production for 3D printing is expected to increase by 88% by 2023. Already, 3D printing represents a multibillion-dollar market that is projected to hit $ 21 billion by 2020.

Use cases today

Most manufacturers want to know what 3D printing is good for today. In terms of products that can be produced, the list is quite long: eyewear, dental molds, prosthetics, jewelry, smartphone cases, musical instruments, architectural models, car parts, medical devices – the list goes on.

But why would 3D printing replace traditional manufacturing approaches for any of these products? It all depends on the manufacturing context. Here are some ways in which 3D printing is being used today:

Prototyping: 3D printing is quite flexible for single-use scenarios. Organizations with design and engineering skills can quickly draw up CAD models for new product ideas and generate physical versions of products with a 3D printer. Traditional manufacturing approaches – such as machining, casting, and forging – can be cost-prohibitive for quick prototypes. 3D printing helps drive a more iterative approach to innovation – one that makes it easier and more cost-effective to get physical products into the hands of stakeholders, decision-makers, and focus groups who can then determine whether or not to move forward with further development.

Replacement parts: Keeping all possible spare parts in inventory is costly, and depending on third-party providers to deliver a needed part can mean long wait times. But if you have a 3D printer and the needed CAD drawings available – or if you can find them quickly – you can use 3D printing to produce the part on-site. For organizations running large vehicle fleets, warehouses, or manufacturing facilities, 3D printing offers a way forward that can help minimize downtime due to equipment failure or maintenance.

Profitable individualization (configurable products): 3D printing represents an intriguing solution for highly configurable products where keeping all possible combinations of parts in stock can be a logistics headache. At a time when organizations seek ways to profitably address the expectations of customers for individualized products that fit their unique needs, companies can use 3D printing to meet demand by simply printing out the parts that customers require.

Sustainability: 3D printing localizes production – which cuts down on shipping costs and the carbon footprint associated with it. When products are produced using plastic dust or powder, the products are also 100% recyclable – so whatever is produced can be returned to the dust from which it is formed, making the base material available once again for use in new products. For organizations serious about their sustainability efforts, 3D printing can help put the circular economy into action.

Networks for 3D printing

As the 3D printing market matures, the cost of printers is coming down. Today, lower-end hobbyist machines for in-home use weigh in at just around $ 300 while professional-grade machines can cost more than $ 10,000.

But does it make sense to own your own printer? Increasingly, the idea of 3D printing as a service is taking hold – with 3D printing farms emerging to serve the needs of the market. UPS, in fact, offers 3D printing services from its many retail outlets across the U.S. Organizations or individuals can simply send CAD drawings to UPS, place an order, and pick up the final product when it’s ready – or have it delivered by UPS as needed.

But how do you get the CAD drawings if you don’t have the ability to generate them in-house? Here is where the idea of a manufacturing network for 3D printing comes into play. Manufacturing networks that connect organizations to suppliers and manufacturing service providers have been around for some time. Increasingly, these networks are offering pre-made CAD drawings for, say, spare parts – or services for custom drawing as needed.

Let’s say you’re a refrigerator manufacturer. In a 3D printing world, instead of keeping all of the parts for every model you produce in stock, you can provide the schematics for each part to manufacturing networks where suppliers can offer the service of 3D printing the part on demand. Or you can maintain your own printer farm and serve your customers yourself.

The point is that as 3D printing takes off, manufacturing networks will play an increasingly important role in facilitating the market – enabling more organizations to take advantage of the benefits that this new mode of manufacturing has to offer. So when it comes to maintaining your manufacturing fitness in a highly competitive environment, think of 3D printing as a way to add some of the muscle and flexibility you need to succeed.

For more information, download the latest IDC report on digital manufacturing.

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What is the Industrial IoT?

Top 5 Industrial IoT Implementations in Manufacturing

1. Asset Monitoring

2. Predictive Maintenance

3. Optimizing Legacy Machines

4. Operational Intelligence

5. Safer Workplaces

Why it matters

Historic trends in manufacturing before Industry 4.0

The results: falling short of expectations

The emergence of Industry 4.0

The solution

Be customer-centric

Serve the segment of one

Embrace digital smart products and solutions

Implement the digital supply chain and smart factory

Develop service-based business models

The future is bright

Join our virtual event to learn more

Big challenges, big rewards

Predicting return rate

Improve forecasts and maximize revenue

Improve inventory management with demand forecasting

Build your manufacturing business with analytics

Use cases today

Networks for 3D printing

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