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This free service is available to anyone who has published and whose publication is in Scopus. Researcher Academy Author Services Try out personalized alert features. It addresses the Read more. Giselle Rampersad Carolin Plewa Si Zhang Delin Yang Digitalizing the maritime industry: A case study of technology acquisition and enabling advanced manufacturing technology Oda Ellingsen Knut Einar Aasland. Martin Carree Boris Lokshin Agile project management and stage-gate model—A hybrid framework for technology-based companies Edivandro C.
Conforto Daniel C. Lorenzo Ardito Antonio Messeni Petruzzelli Qinxuan Gu Wan Jiang Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars - Open access Kanwaldeep Kaur Giselle Rampersad. Patrick van der Heiden Christine Pohl Kanwaldeep Kaur Giselle Rampersad. Oda Ellingsen Knut Einar Aasland. Most Cited Articles The most cited articles published since , extracted from Scopus. In Henry Laurence Gantt developed the Gantt chart which outlines actions the organization along with their relationships.
This chart opens later form familiar to us today by Wallace Clark. With the development of assembly lines , the factory of Henry Ford accounted for a significant leap forward in the field. Ford reduced the assembly time of a car more than hours to 1. In addition, he was a pioneer of the economy of the capitalist welfare "welfare capitalism" and the flag of providing financial incentives for employees to increase productivity.
Comprehensive quality management system Total quality management or TQM developed in the forties was gaining momentum after World War II and was part of the recovery of Japan after the war.
The American Institute of Industrial Engineering was formed in The early work by F. Taylor and the Gilbreths was documented in papers presented to the American Society of Mechanical Engineers as interest grew from merely improving machine performance to the performance of the overall manufacturing process; most notably starting with the presentation by Henry R. Towne - of his paper The Engineer as An Economist In to , with the development of decision support systems in supply such as the Material requirements planning MRP , you can emphasize the timing issue inventory, production, compounding, transportation, etc.
Israeli scientist Dr. In the seventies, with the penetration of Japanese management theories such as Kaizen and Kanban , Japan realized very high levels of quality and productivity. These theories improved issues of quality, delivery time, and flexibility. Companies in the west realized the great impact of Kaizen and started implementing their own Continuous improvement programs. In the nineties, following the global industry globalization process, the emphasis was on supply chain management and customer-oriented business process design. Theory of constraints developed by an Israeli scientist Eliyahu M.
Goldratt is also a significant milestone in the field. Engineering is traditionally decompositional. To understand the whole, it is first broken into its parts. One then masters the parts and puts them back together, becoming the master of the whole. Industrial and systems engineering's ISE approach is the opposite; any one part cannot be understood without the context of the whole. Changes in one part affect the whole, and the role of a part is a projection into the whole. In traditional engineering, people understand the parts first, then they can understand the whole.
In ISE, they understand the whole first, and then they can understand the role of each part. Also, Industrial engineering considers the human factor and its relation to the technical aspect of the situation and the all of the other factors that influence the entire situation,  while other engineering disciplines focus on the design of inanimate objects.
In addition to manufacturing, Industrial Engineers work and consult in every industry, including hospitals, communications, e-commerce, entertainment, government, finance, food, pharmaceuticals, semiconductors, sports, insurance, sales, accounting, banking, travel, and transportation. One of the main focuses of an Industrial Engineer is to improve the working environments of people — not to change the worker, but to change the workplace. Industrial Engineering is different in that it is based on discrete variable math, whereas all other engineering is based on continuous variable math.
We emphasize the use of linear algebra and difference equations, as opposed to the use of differential equations which are so prevalent in other engineering disciplines. This emphasis becomes evident in optimization of production systems in which we are sequencing orders, scheduling batches, determining the number of materials handling units, arranging factory layouts, finding sequences of motions, etc.
As, Industrial Engineers, we deal almost exclusively with systems of discrete components. While originally applied to manufacturing , the use of "industrial" in "industrial engineering" can be somewhat misleading, since it has grown to encompass any methodical or quantitative approach to optimizing how a process, system, or organization operates. In fact, the "Industrial" in Industrial engineering means the "industry" in its broadest sense. Industrial engineering has many sub-disciplines, the most common of which are listed below.
Although there are industrial engineers who focus exclusively on one of these sub-disciplines, many deal with a combination of them such as Supply Chain and Logistics, and Facilities and Energy Management. Industrial engineers study the interaction of human beings with machines, materials, information, procedures and environments in such developments and in designing a technological system. In the United States, the undergraduate degree earned is the bachelor of science B.
The typical curriculum includes a broad math and science foundation spanning chemistry , physics , mechanics i. Integration of data from operations and business systems, as well as from suppliers and customers, enables a holistic view of upstream and downstream supply chain processes, driving greater overall supply network efficiency.
An optimized smart factory allows operations to be executed with minimal manual intervention and high reliability. The automated workflows, synchronization of assets, improved tracking and scheduling, and optimized energy consumption inherent in the smart factory can increase yield, uptime, and quality, as well as reduce costs and waste.
In the smart factory, the data captured are transparent : Real-time data visualizations can transform data captured from processes and fielded or still-in-production products and convert them into actionable insights, either for humans or autonomous decision making. A transparent network can enable greater visibility across the facility and ensure that the organization can make more accurate decisions by providing tools such as role-based views, real-time alerts and notifications, and real-time tracking and monitoring.
In a proactive system, employees and systems can anticipate and act before issues or challenges arise, rather than simply reacting to them after they occur. This feature can include identifying anomalies, restocking and replenishing inventory, identifying and predictively addressing quality issues, 9 and monitoring safety and maintenance concerns. The ability of the smart factory to predict future outcomes based on historical and real-time data can improve uptime, yield, and quality, and prevent safety issues. Within the smart factory, manufacturers can enact processes such as the digital twin, enabling them to digitize an operation and move beyond automation and integration into predictive capabilities.
Agile flexibility allows the smart factory to adapt to schedule and product changes with minimal intervention. Advanced smart factories can also self-configure the equipment and material flows depending on the product being built and schedule changes, and then see the impact of those changes in real time. Additionally, agility can increase factory uptime and yield by minimizing changeovers due to scheduling or product changes and enable flexible scheduling.
These features afford manufacturers greater visibility across their assets and systems, and allow them to navigate some of the challenges faced by more traditional factory structures, ultimately leading to improved productivity and greater responsiveness to fluctuations in supplier and customer conditions. Traditional factories and supply chains can face challenges in keeping up with ever-shifting fashions. Located close to the point of customer demand, the new smart factories can better adapt to new trends and allow shoes to reach customers faster—an estimation of less than a week, compared with two to three months with traditional factories.
Both smart factories will leverage multiple digital and physical technologies, including a digital twin, digital design, additive manufacturing machines, and autonomous robots. The company plans to use lessons learned from the two initial smart factories as it scales to more facilities in other regions, such as Asia. While automation and controls have existed for decades, the fully smart factory has only recently gained traction as a viable pursuit for manufacturers.
Five overarching trends seem to be accelerating the drive toward smart factories:. Until recently, the realization of the smart factory remained elusive due to limitations in digital technology capabilities, as well as prohibitive computing, storage, and bandwidth costs.
Such obstacles, however, have diminished dramatically in recent years, making it possible to do more with less cost across a broader network.
As manufacturing has grown increasingly global, production has fragmented, with stages of production spread among multiple facilities and suppliers across multiple geographies. The rise of smart digital technologies has ushered in the threat of entirely new competitors who can leverage digitization and lower costs of entry to gain a foothold in new markets or industries in which they previously had no presence, sidestepping the legacy of aging assets and dependence on manual labor encumbering their more established competitors.
Factory automation decisions typically occur at the business unit or plant level, often resulting in a patchwork of disparate technologies and capability levels across the manufacturing network. As connected enterprises increasingly push beyond the four walls of the factory to the network beyond, they are beginning to have greater visibility into these disparities. 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.
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.http://ikmf-china.com/best-smartphone-monitoring-software-honor-10.php
Advanced Manufacturing Technologies. Skoltech Master of Science
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.