The Intelligent Enterprise for Paper and Packaging Part 3: Using Technology to Build Smart Factories
Part 2 of the Intelligent Enterprise for Paper and Packaging series we talked about how digital technology such as Internet of Things is helping
paper and packaging companies improve their production to offer smaller lot sizes and individualization. By 2025, a substantial part of paper and packaging companies' value, reputation, and differentiation will come from services. These services will be delivered around highly customized products, enriched by digital information. To get there, paper and packaging companies need to focus on five strategic priorities:
Offering small lot sizes and individualization
Running smart factories and digital networks
Supporting value-added services and new business models
Producing for purpose
Part 3 goes into detail on strategic priority 3: Running smart factories and digital networks.
Digital technologies on the shop floor and in the supply chain is not new. What is new is the way production and logistics are intelligently connected to the rest of the business and are able to deal with external impulses like short-term demand and supply fluctuations or changes in the configuration of a customer order.
Supply chains and manufacturing networks must be completely modular and flexible to react to short-term changes. At the same time, they must execute seamlessly for quick order completion, and they must respond directly to demand signals and changing customer orders. Higher customer expectations will require increased automation throughout all processes, and not only on the shop floor. This will include the use of new technologies such as bots, drones (for analyzing wood stock or forest conditions), augmented reality, and machine learning to increase efficiency and be able to promise and deliver orders on time as expected.
In 2025 the supply chains and manufacturing networks of paper and packaging companies will allow the seamless execution of producing and shipping the right product at the right time in the most profitable way, because businesses will have transformed into a responsive network. Companies will be able to act on volatile customer demand and heightened expectations of responsiveness. Innovative technologies such as the digital twin or machine learning can help meet these higher expectations by providing vital business information across the network, improving real-time analysis, and enabling better collaboration across departments and trading partners. Repetitive tasks will be automated, and processes will be managed by exception only.
Paper and packaging companies start toward this goal by enabling seamless data exchange within a factory and with external business partners. Subsequent steps make use of smart sensors to improve insights into physical reality and apply autonomous processes in production. Finally, they will connect every business to a digital network and run fully automated processes beyond their own company borders. As an example, a machine can autonomously order the parts it requires to run. Even complex scenarios - such as answering the classic questions in maintenance of "repair, replace, or retire?" - can be calculated on the fly for the most profitable, least risky solution.
Manufacturing and operations are becoming more connected and autonomous. Siloed, incomplete, and outdated information on assets, products, and customers means that processes cannot be optimized. Companies need a virtual, real-time representation of their business - a digital twin - not only of assets but of any element within the company. This allows all partners to collaborate in real time and provides remote monitoring of internal assets as well as the entire supply chain across company boundaries. This will result in optimization of material throughput reduced downtime, and better outcomes for customers - at a lower cost.
In addition to machine learning and digital twin concepts, many companies are testing the use of other new technologies such as bots and drones. For example, companies that own their own forests such as SAPPI or Asia Pulp and Paper may use these technologies to analyze wood stocks or forest conditions via satellite images. Or they might use drones to do monthly inventory of chip piles by estimating the size. Others track their fleet and can analyse root causes for trucks breaking down unexpectedly and too often, for example when certain drivers use the truck in certain environments. Analysts can come up with simple solutions that improve efficiency and safety such as using an additional gear or perhaps offering additional training for certain terrains or weather conditions.
Example scenario: Collaboration the old way
Disconnected departments and limited access to the business network prohibit responsive business. Plans are not consistently created and shared, so information cannot flow quickly. R&D, sourcing, sales, manufacturing, and planning are not aligned, wasting time and money. Reliance on a few external partners and manual communication means visibility is limited, collaboration is difficult, delays are inevitable, and the risk of error is high.
A new world using technology
One plan can be shared with all critical resources and partners to achieve visibility, agility, and responsiveness leading to reduced asset service and maintenance cost and reduced asset master-data creation and maintenance effort. In addition, companies gain:
Collaborative product design with customers
Insight into future demand for manufacturing and procurement, optimizing inventor
Alignment of sales, manufacturing, and delivery, improving customer satisfaction through in-time order
Linear supply chains transforming into digital supply networks through simultaneous collaboration of all relevant stakeholder
For more information about companies can use Industry 4.0 to organize their production listen to this podcast.
In our next issue, we will talk about another strategic priority of paper and packaging companies - Producing for Purpose.
Alfred Becker, SAP SE, Global Lead for Paper and Packaging
Is it time to perform maintenance on that critical piece of equipment? You know, the one that if it suddenly fails would shut you down, and possibly create a safety concern?
One approach is to use your calendar. When was the last time maintenance was performed on this unit? An Asset Management System (the subject of my June article) can help you with that. The system can tell you when any asset you have is due for service.
The problem with that approach is that the unit may have been idle that whole time, and not require maintenance. Your technicians likely are stretched thin and it is important to have them effectively utilized, so having them service equipment that doesn't need it is costly in several ways. On the other extreme, it may have been in use more than you expect and is overdue. It can fail before you expect it to.
A better approach is for your control system to calculate runtimes for your equipment. When the service exceeds a certain number of runtime hours, service should be scheduled. This is a simple form of Predictive Maintenance. Predictive Maintenance is the use of real-time process data to predict the health of an asset.
The problem with this approach is that a failure can occur before the predicted service life.
The third approach is a more comprehensive use of data for Predictive Maintenance. You have process data that can indicate the health of your asset. Predictive Maintenance uses that data and compares it to patterns to identify whether there is a problem.
In this third approach, there are two techniques being used in industry:
High speed sampling of vibration can yield a failure signature. As the equipment is running and in production use, the system can be continuously sampling and comparing the spectrum to known failure patterns. An advantage of this approach is that when there is a match it can tell you exactly what is wrong and what to fix. The disadvantage is that if there is a problem that vibration does not identify, you won't know about it.
There is instrumentation other than vibration that pertain to the asset. Amperage, inlet and outlet pressure, flow, winding temperatures, and other measurements can be combined into a Machine Learning model. This model can identify what normal patterns are, and therefore notify when the asset is behaving abnormally. This is a more comprehensive use of data to catch failures that vibration alone might not be able to predict. A disadvantage is that when there is a prediction of abnormal behavior, you may not be able to tell the technician what to fix. Also, the caveats of Machine Learning that I covered in my prior article still apply.
You are dependent on your assets to operate. They have finite service lifespans. Your maintenance staff is also finite, and in many cases have more work on their plate than time in the day. The data in your process can help keep your assets healthy and efficiently deploy your maintenance staff. Predictive Maintenance, properly applied, can help you stay running efficiently and safely.
Pat Dixon is Southwest Region Engineering Manager for Global Process Automation (GPA), a controls system integration firm.
8 Reasons Why Your IIoT Project Is Stuck in Pilot Purgatory
Most of the latest consulting and industry reports on IoT technology and pure digital innovations (from Capgemini, Cisco, McKinsey, and the like) recognize that deploying and scaling IoT and digital opportunities in B2B and industrial markets is happening more slowly than expected. It is not leading to the explosive growth that it has for B2C organizations.
A 2018 McKinsey survey found that 84% of companies working in IoT are stuck in pilot mode, 28% of them for over two years. McKinsey refer to this phenomenon as languishing in "pilot purgatory." The CEO of a manufacturing company who participated in a recent Industry 4.0 Monetization event in Milan reacted to this comment thus: "It is not purgatory, it is hell!"
All manufacturing organizations want to grow as fast as Uber, Google, and Amazon. The reality in the industrial world is different. The slow scaling process is frustrating organizations and forcing research institutions in 2018 to cut the IoT potential forecast by half,
according to IoT World Today.
This situation raises the following questions: What is so different in the industrial and manufacturing sector that makes it difficult for digital IoT solutions to be scaled quickly? What leads most IoT projects that manufacturers are trying to scale to be stuck in pilot mode?
The aim of the cooperation is to offer platform services for their industrial Internet of Things (IIoT) solutions. The two companies are joining forces to digitalize business with smart applications and efficiently share development resources and competencies.
The joint platform provides both companies the opportunity to use new applications across different industries in the other's worldwide core markets and speed up the development process of customer-relevant applications. For example, SMS digital can introduce its Smart Alarm more efficiently in Voith's core markets and the process industries. Meanwhile, SMS benefits from Voith's OnCumulus apps designed for production efficiency enhancement and asset management.
A comprehensive view of the IoT architecture layers
If your company is like many organizations, it's actively engaged in or considering launching one or more IoT initiatives. It has a goal, a strategy and a desired outcome -- whether to drive revenue, cut costs or optimize business processes. And it may have already selected technologies and suppliers.
But does it have an IoT architecture? And is that architecture specific to this project or a customized version of a more general IoT framework?
Seven things you need to know about IIoT in manufacturing - and its powerful influence
Global spending on IIoT platforms for manufacturing is predicted to grow from $1.67 billion in 2018 to $12.44bn in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years
IIoT platforms are beginning to replace MES and related applications, including production maintenance, quality, and inventory management, which are a mix of information technology (IT) and operations technology (OT) technologies
Connected IoT technologies are enabling a new era of smart, connected products that often expand on the long-proven platforms of everyday products. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020
These and many other fascinating insights are from
IoT Analytics' study,
IIoT Platforms For Manufacturing 2019 - 2024 (155 pp., PDF, client access reqd). IoT Analytics is a leading provider of market insights for the Internet of Things (IoT), M2M, and Industry 4.0. They specialise in providing insights on IoT markets and companies, focused market reports on specific IoT segments and Go-to-Market services for emerging IoT companies. The study's methodology includes interviews with twenty of the leading IoT platform providers, executive-level IoT experts, and IIoT end users. For additional details on the methodology, please see pages 136 and 137 of the report. IoT Analytics defines the Industrial loT (lloT) as heavy industries including manufacturing, energy, oil and gas, and agriculture in which industrial assets are connected to the internet.
IMAGINE having the ability to know when something is about to break down - be it your mobile device, your car or a machine in your manufacturing facility. The pre-emptive knowledge would certainly come in handy, because even if you cannot avoid the breakdown, you can at least prepare for it. Either way, there is something that can be done about the impending failure.
In the industrial world, this foresight is just one of many benefits reaped by companies that embrace Industry 4.0. Commonly referred to as the fourth industrial revolution, it is the current trend of manufacturing technologies that enable businesses to operate seamlessly, smartly and efficiently in the digital world.