The increasing marriage of IT and OT has made it possible for organizations to move many formerly plant-level decisions to the business-unit or enterprise level. It has also made the notion of the smart factory more of a reality than an abstract goal. While connectivity within the factory is not new, many manufacturers have long been stymied about what to do with the data they gather—in other words, how to turn information into insight, and insight into action.
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The shift toward the connected digital and physical technologies inherent in Industry 4. Multiple talent-related challenges—including an aging workforce, an increasingly competitive job market, and a dearth of younger workers interested in or trained for manufacturing roles—mean that many traditional manufacturers have found themselves struggling to find both skilled and unskilled labor to keep their operations running. The decision on how to embark on or expand a smart factory initiative should align with the specific needs of an organization. The reasons that companies embark or expand on the smart factory journey are often varied and cannot be easily generalized.
However, undertaking a smart factory journey generally addresses such broad categories as asset efficiency, quality, costs, safety, and sustainability. These categories, among others, may yield benefits that ultimately result in increased speed to market; improved ability to capture market share; and better profitability, product quality, and labor force stability. Regardless of the business drivers, the ability to demonstrate how the investment in a smart factory provides value is important to the adoption and incremental investment required to sustain the smart factory journey.
Every aspect of the smart factory generates reams of data that, through continuous analysis, reveal asset performance issues that can require some kind of corrective optimization. Indeed, such self-correction is what distinguishes the smart factory from traditional automation, which can yield greater overall asset efficiency, one of the most salient benefits of a smart factory. Asset efficiency should translate into lower asset downtime, optimized capacity, and reduced changeover time, among other potential benefits.
The self-optimization that is characteristic of the smart factory can predict and detect quality defect trends sooner and can help to identify discrete human, machine, or environmental causes of poor quality. This could lower scrap rates and lead times, and increase fill rates and yield. A more optimized quality process could lead to a better-quality product with fewer defects and recalls.
Optimized processes traditionally lead to more cost-efficient processes—those with more predictable inventory requirements, more effective hiring and staffing decisions, as well as reduced process and operations variability. A better-quality process could also mean an integrated view of the supply network with rapid, no-latency responses to sourcing needs—thus lowering costs further.
And because a better-quality process also may mean a better-quality product, it could also mean lowered warranty and maintenance costs. The smart factory can also impart real benefits around labor wellness and environmental sustainability.
The types of operational efficiencies that a smart factory can provide may result in a smaller environmental footprint than a conventional manufacturing process, with greater environmental sustainability overall. However, the role of the human worker in a smart factory environment may take on greater levels of judgment and on-the-spot discretion, which can lead to greater job satisfaction and a reduction in turnover. Manufacturers can implement the smart factory in many different ways—both inside and outside the four walls of the factory—and reconfigure it to adjust as existing priorities change or new ones emerge.
The specific impacts of the smart factory on manufacturing processes will likely be different for each organization. Deloitte has identified a set of advanced technologies that typically facilitate the flows of information and movement between the physical and digital worlds. Table 1 depicts a series of core smart factory production processes along with a series of sample opportunities for digitization enabled by various digital and physical technologies.
It is important to note that these opportunities are not mutually exclusive. Organizations can—and likely will—pursue multiple digitization opportunities within each production process. They may also phase capabilities in and out as needed, in keeping with the flexible and reconfigurable nature of the smart factory. It is important for manufacturers to understand how they intend to compete and align their digitization and smart factory investments accordingly. For example, some manufacturers could decide to compete via speed, quality, and cost, and may invest in smart factory capabilities to bring new products and product changes to market faster, increase quality, and reduce per-unit costs.
Just as there is no single smart factory configuration, there is likely no single path to successfully achieving a smart factory solution. Every smart factory could look different due to variations in line layouts, products, automation equipment, and other factors. However, at the same time, for all the potential differences across the facilities themselves, the components needed to enable a successful smart factory are largely universal, and each one is important: data, technology, process, people, and security.
Manufacturers can consider which to prioritize for investment based on their own specific objectives. Data are the lifeblood of the smart factory. Through the power of algorithmic analyses, data drive all processes, detect operational errors, provide user feedback, and, when gathered in enough scale and scope, can be used to predict operational and asset inefficiencies or fluctuations in sourcing and demand. How data are combined and processed, and the resulting actions, are what make them valuable. In order to move to higher levels of smart factory maturity, the data sets collected will likely expand over time to capture more and more processes.
For example, implementing a single use case might require the capture and analysis of a single data set. Implementing further use cases or scaling an operation to an industrial level will typically require expanding the capture and analysis of greater and different data sets and types structured vs.
Data might also represent a digital twin, a feature of an especially sophisticated smart factory configuration. At a high level, a digital twin provides a digital representation of the past and current behavior of an object or process. The digital twin requires cumulative, real-world data measurements across an array of dimensions, including production, environmental, and product performance.
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The powerful processing capabilities of the digital twin may uncover insights on product or system performance that could suggest design and process changes in the physical world. For a smart factory to function, assets—defined as plant equipment such as material handling systems, tooling, pumps, and valves—should be able to communicate with each other and with a central control system. These types of control systems can take the form of a manufacturing execution system or a digital supply network stack. The latter is an integrated, layered hub that functions as a single point of entry for data from across the smart factory and the broader digital supply network, aggregating and combining information to drive decisions.
This could require implementing the various digital and physical technologies inherent in Industry 4. One of the most valuable features of the smart factory—its ability to self-optimize, self-adapt, and autonomously run production processes—can fundamentally alter traditional processes and governance models. An autonomous system can make and execute many decisions without human intervention, shifting decision-making responsibilities from human to machine in many cases, or concentrating decisions in the hands of fewer individuals.
Additionally, the connectivity of the smart factory may extend beyond its four walls to include increased integration with suppliers, customers, and other factories. With a deeper, more holistic view across the factory and the broader production and supply network, manufacturers could face new and different questions. Organizations may want to consider—and perhaps redesign—their decision-making processes to account for these shifts. People are expected to still be key to operations. New, unfamiliar roles will likely emerge. Managing changes to people and processes will require an agile, adaptive change management plan.
The successful smart factory journey will require a motivated workforce that embraces the greater impact of their roles, innovative recruiting approaches, and an emphasis on cross-functional roles. By its nature, the smart factory is connected. Thus cybersecurity risk presents a greater concern in the smart factory than in the traditional manufacturing facility and should be addressed as part of the overall smart factory architecture. In a fully connected environment, cyberattacks can have a more widespread impact and may be more difficult to protect against, given the multitude of connection points.
Cybersecurity risk seems to only grow more pronounced as the smart factory scales and potentially moves beyond the four walls of the factory to include suppliers, customers, and other manufacturing facilities. Manufacturers should make cybersecurity a priority in their smart factory strategy from the outset. The challenge to begin may seem daunting.
The nearly limitless configurations of smart factory solutions provide a number of pathways to proceed on the journey that need to be defined, planned, and executed at a pace suitable to the organization and the challenge—or opportunity. As manufacturers consider how to build their smart factory, they can begin with the following steps:. Smart factory investments often start with a focus on specific opportunities. Once identified, digitization and insight generation fuel actions that can drive new value. Building and scaling the smart factory, however, can be as agile and flexible as the concept itself.
Manufacturers can get started down the path to a true smart factory at any level of their network—value creation can begin with and scale from a single asset, and use an agile approach to iterate and grow. In fact, it can be more effective to start small, test out concepts in a manageable environment, and then scale once lessons have been learned. Customizing the approach to each scenario and situation can help ensure the resulting smart factory meets the needs of the manufacturer. The smart factory journey requires more than just a set of connected assets.
Manufacturers would need a way to store, manage, make sense of, and act upon the data gathered. The industrial application of the Internet of Things IoT and data analytics will lead to fact-based decision-making that will, in turn, be executed as a matter of routine by automatons. Even with this digital monitoring of physical processes through sensor-based technologies, people will still drive the future of manufacturing.
People, freed from routine by technology, will find ways to direct their new-found productivity into tasks that can only be executed with human imagination and intelligence — like the creation of useful new products that must be manufactured to solve the problems of our times. The views expressed in this article are those of the author alone and not the World Economic Forum. I accept.
Global Agenda Fourth Industrial Revolution Advanced Manufacturing and Production Advanced Manufacturing This is how a smart factory actually works Connected factories track the location of labour, materials, machines, and moveable assets in real time. How do we build a sustainableworld? Submit a video. Most Popular.
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