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Productivity Improvement Through Machining Time Reduction and Machining Cost Reduction - Important Industrial Engineering Task.
Process improvement - What is machine time reduction? Man time reduction? Material usage reduction? Energy reduction? Information cost reduction?
The first president of ASME in his inaugural presidential address exhorted mechanical engineers to attention to the cost of reduction of machines and items produced through mechanical engineering design and production processes like cars.
The genius in F.W. Taylor resulted in proposing productivity improvement through machining time reduction (machine time reduction) and man time reduction as the core activity which will give cost reduction and income increase (to both employees and companies, this labor and capital).
Machining time reduction can be achieved by improving each of the elements that are used in machining. Taylor investigated each machine element - machine tools for power and rigidity, tool materials and tool geometry, work holding, use of coolant, cutting parameters (cutting speed, feed, depth of cut) and developed data and science for each element and increased productivity of machining. The framework laid by Taylor is followed even today and productivity improvement of machining is occurring.
TAYLOR (1906) - ELEMENTS AFFECTING CUTTING SPEED OF TOOLS IN THE ORDER OF THEIR RELATIVE IMPORTANCE
278 The cutting speed of a tool is directly dependent upon the following elements. The order in which the elements are given indicates their relative effect in modifying the cutting speed, and in order to compare them, we have written in each case figures which represent, broadly speaking, the ratio between the lower and higher limits of speed as affected by each element. These limits will be met with daily in machine shop practice.
279 (A) The quality of the metal which is to be cut; i.e., its hardness or other qualities which affect the cutting speed.
Proportion is as 1 in the case of semi-hardened steel or chilled iron to 100 in the case of very soft low carbon steel.
280 (B) The chemical composition of the steel from which the cutting tool is made, and the heat treatment of the tool.
Proportion is as 1 in tools made from tempered carbon steel to 7 in the best high speed tools.
281 (C) The thickness of the shaving; or, the thickness of the spiral strip or band of metal which is to be removed by the tool, measured while the metal retains its original density; not the thickness of the actual shaving, the metal of which has become partly disintegrated.
Proportion is as 1 with thickness of shaving 3/16 of an inch to 3.5 with thickness of shaving 1/64 of an inch.
282 (D) The shape or contour of the cutting edge of the tool, chiefly because of the effect which it has upon the thickness of the shaving.
Proportion is as 1 in a thread tool to 6 in a broad nosed cutting tool. ,
283 (E) Whether a copious stream of water or other cooling medium is used on the tool.
Proportion is as 1 for tool running dry to 1.41 for tool cooled by a copious stream of water.
284 (F) The depth of the cut; or, one-half of the amount by which the forging or casting is being reduced in diameter in turning.
Proportion is as 1 with 1/2 inch depth of cut to 1.36 with 1/8 inch depth of cut.
285 (G) The duration of the cut; i. c., the time which a tool must last under pressure of the shaving without being reground.
Proportion is as 1 when tool is to be ground every 1.5 hour to 1.207 when tool is to be ground every 20 minutes.
286 (H) The lip and clearance angles of the tool.
Proportion is as 1 with lip angle of 68 degrees to 1.023 with lip angle of 61 degrees.
287 (J) The elasticity of the work and of the tool on account of producing chatter.
Proportion is as 1 with tool chattering to 1.15 with tool running smoothly.
288 A brief recapitulation of these elements is as follows:
(A) quality of metal to be cut: 1 to 100;
(B) chemical composition of tool steel: 1 to 7;
(C) thickness of shaving: 1 to 3.5;
(D) shape or contour of cutting edge: 1 to 6;
(E) copious stream of water on the tool: 1 to 1.41;
(F) depth of cut: 1 with 1/2 inch depth to 1.36 with 1/8 inch depth of cut;
(G) duration of cut: 1 with 1.5 hour cut to 1.20 with 20-minute cut;
(H) lip and clearance angles: 1 with lip angle 68 degrees to 1.023 with lip angle of 61 degrees;
(J) elasticity of the work and of the tool: 1 with tool chattering to 1.15, with tool running smoothly.
Taylor's machining time reduction is given the name "Time Study." Time study became a principal technique of Industrial Engineering. But in the evolution of the discipline and profession, overtime, the s focus on study of man's work increased and time study became a subject or method that develops the standard time prescription for the method developed using method study. Method study also focused on manual work only primarily. A subject named "Work Study," a combination of method study and time study or work measurement became popular. Machine work based industrial engineering slow disappeared from industrial engineering. Professor Narayana Rao, brought the focus on machine back in industrial engineering by proposing "machine work study" as an important area in productivity improvement and industrial engineering. Machine based industrial engineering is part of Toyota Production System and was described by Shigeo Shingo in his book. Jidoka, a pillar of TPS, also is interpreted as machines that do not produce waste which indicates machine based productivity improvement. Machine work study involves evaluating each element of machine work with the current possible best practice, improving it appropriately and integrating all the elements to give the highest productivity, lower cost or lowest time. Element level improvement and integrating elements to get the best system improvement has to occur one after another in industrial engineering. Element level thinking and holistic thinking both have to take place in productivity improvement.
To do machine work study, industrial engineers required the basic knowledge and awareness of periodic developments in machine tool and cutting tool engineering and process planning. Productivity science discovers and codifies variables that have an effect on productivity. Industrial engineers have to combine productivity science with knowledge of machine tools and process planning to do productivity engineering.
Taylor's Contribution to Machining Time Reduction and Machining Science/Productivity Science
The first scientific studies of metal cutting were power requirements for various operations so that steam engines of appropriate size could be selected for tools. A number of researchers constructed crude dynamometers and conducted systematic experiments to measure cutting forces. The best known was E. Hartig, whose 1873 book was a standard reference on the subject for many years. Development of more advanced dynamometers occupied researchers after Hartig's book was published. In addition, several studies of the mechanism of chip formation were carried out, most notably by Time, Tresca, and Mallock. By carefully examining chips, these researchers recognized that chip formation was a shearing process.
In 1868 Robert Mushet, an English steel maker, developed an improved tool steel. It was a Tungsten alloy which proved to be self hardening. Mushet took extraordinary measures to prevent the theft of his recipe and the process he used is unknown to this day. The material was superior to carbon steel for cutting tools and was widely used in both Europe and America.
The great historical figure in the field of metal cutting, Frederick W. Taylor, was active at the end of the nineteenth century. Taylor became more famous as the founder of scientific management, and many books on scientific management do not mention his work in metal cutting. The metal cutting work, however, was crucial to the implementation of his productivity engineering and management theories. Books on machining still mention Taylor and his contribution to metal cutting theory.
As foreman of the machine shop, Taylor felt that shop productivity could be greatly increased if a quantitative understanding of the relation between speeds, feeds, tool geometries, and machining performance can be established and the right combination of cutting parameters are specified by managers and used by machinists. Taylor embarked on a series of methodical experiments to gather the data necessary to develop this understanding. The experiments continued over a number of years at Midvale and the nearby Bethlehem Steel Works, where he worked jointly with metallurgist Maunsel White. As a result of these experiments, Taylor was able to increase machine shop productivity at Midvale by hundred percent even though in certain individual jobs and machines, productivity increases was as much as a factor of five. One of Taylor's important practical contributions was his invention of high speed steel, a cutting tool material. The material permitted doubling of cutting speed, which in turn permitted doubling spindle speed for the same diameter of the work and thereby increase in feed which reduced machining time.
Taylor also established that the power required to feed the tool could equal the power required to drive the spindle, especially when worn tools were used. Machine tools of the day were underpowered in the feed direction, and he had to modify all the machines at the Midvale plant to eliminate this flaw. He also demonstrated the value of coolants in metal cutting and fitted his machines with recirculating fluid systems fed from a central pump. Finally, he developed a special slide rule for determining feeds and speeds for various materials.
Taylor summarized his research results in the landmark paper On the Art of Cutting Metals, which was published in the ASME Transactions in 1907. The results were based on 50,000 cutting tests conducted over a period of 26 years. Taylor's also indicated the importance of tool temperatures in tool life and developed the famous tool life equation. His writings clearly indicate that he was most interested in efficiency and economy in his experiments and writings.
Machine tools built after 1900 utilized Taylor's discoveries and inventions. They were designed to run at much higher speeds to take advantage of high speed steel tools. This required the use hardened steel gears, improved bearings and improved bearing lubrication systems. They were fitted with more powerful motors and feed drives and with recirculating coolant systems.
The automotive industry had become the largest market for machine tools by World War I and it has consequently had a great influence on machine tool design. Due to accuracy requirements grinding machines were particularly critical, and a number of specialized machines were developed for specific operations. Engine manufacture also required rapid production of flat surfaces, leading to the development of flat milling and broaching machines in place of shapers and planers. The development of the automobile also greatly improved gear design and manufacture, and machine tools were soon fitted with quick-change gearing systems. The automotive industry also encouraged the development of dedicated or single purpose tools. Early examples included crankshaft grinding machines and large gear cutting machines. It led to the development of transfer machine. An in-line transfer machine typically consists of roughly thirty highly specialized tools (or stations) connected by an automated materials handling system for moving parts between stations. The first was built at Henry Ford's Model T plant in Detroit. . Transfer machines required very large capital investments but the cost per piece was lower than for general purpose machines for the production volumes of hundreds of thousands required in auto industry.
In the 1930's a German company introduced sintered tungsten carbide cutting tools, first in brazed form and later as a detachable insert. This material is superior to high speed steel for general purpose machining and has become the industry standard.
A great deal of research in metal cutting has been conducted since 1900. A bibliography of work published prior to 1943 was compiled by Boston, Shaw and King. The shear plane theory of metal cutting was developed by Ernst and Merchant and provided a physical understanding of cutting processes which was at least qualitatively accurate for many conditions. Trigger and Chao and Loewen and Shaw developed accurate steady-state models for cutting temperatures. A number of researchers studied the dynamic stability of machine tools, which had become an issue as cutting speeds had increased. This resulted in the development of a fairly complete linear theory of machine tool vibrations. Research in all of these areas continues to this day, particularly numerical analysis work made possible by advances in computing. All these discoveries and their implementation in machine tools gives higher productivity in machining.
One of the most important innovations in machine tools was the introduction of numerical control. Today CNC machine tools are the most used ones.
New tool materials were invented. A variety of ceramics are currently used for cutting tools, especially for hardened or difficult-to-machine work materials. Ceramic and diamond tools have replaced carbides in a number of high volume applications, especially in the automotive industry. Carbides (often coated with ceramic layers) have remained the tool of choice for general purpose machining. There has been a proliferation of grades and coatings available for all materials, with each grade containing additives to increase chemical stability in a relatively narrow range of operating conditions. For many work materials cutting speeds are currently limited by spindle and material handling limitations rather than tool material considerations. Dozens of insert shapes with hundreds of integral chip breaking patterns are available now.
Chapter 13. Machining Economics and Optimization
in Metal Cutting Theory and Practice - Stephenson - Agapiou, 2nd Edition
Economic Considerations are important in designing the machining process of a component. Each operation done on a machine involved number of decisions. There is more than one approach for doing an operation and each approach will have as associated machining time, part quality and cost of machining. An effective and efficient methodology is to be employed to attain the specified quality of the operation with the least cost. The machining cost of an operation on a component is made of several components. They include machine cost, tool cost, tool change cost (includes set up), handling cost, coolant cost etc. Some of these costs vary significantly with the cutting speed is different directions. At a certain cutting speed we get the minimum cost and at certain other cutting speed we get the least machining time. There is a need to calculate these minimum point cutting speeds for each work material, tool material and machine tool combinations. F.W. Taylor developed slide rules for this purpose. Now those slide rules are not in place, but machining handbooks and machine tool/cutting tool manufacturers provide guidance. Process planners and industrial engineers need to do the required calculations depending on the trial production within their plans. Time Estimates Required Total Production Time for an Operation, TTO =
Tm + (Tm/Tl)Tlul + Tcs + Te + Tr + Tp + Ta + Td + Tx)
Where
TTO = Total Production Time for an Operation
Tm = Cutting time
Tl = Tool life
Tlul = Tool unloading and time
Tcs = Tool interchange time
Te = Magazine travelling time
Tr = Approach time
Tp = Table index time
Ta = Acceleration time
Td = Deceleration time
Tx = Tool rapid travel time
Time study used for machine work study has to determine these time times from formulas as well as time study observations for the existing way and proposed way to validate the time reduced by the operation analysis based on operation study and time data.
Constraints for Minimizing the Machining Time - Cost
Allowable maximum cutting force, cutting temperature, depth of cut, spindle speed, feed, machine power, vibration and chatter limits, and party quality requirement.
Industrial engineers must have knowledge of maximum permissible depth of cut, feed and cutting speed.
Industrial engineers have to monitor research and continuously update their understanding of limit to the constraints. Developments in engineering and industrial engineering keep increasing the quantity of limits in favor of more productivity.
Industrial Engineering leaders and managers have to plan productivity improvement or projects and studies for each financial year. Industrial engineers have to plan and achieve significant productivity gains year after year. These gains make possible increases in revenues and profits of the companies. As McKinsey authors write, if companies do not show growth, they will disappear. If IE departments cannot show productivity improvement they will diminish in importance and compensation. IE leaders have behave like entrepreneurs to identify productivity improvement opportunities at elemental level in operations. But, they have to be made Enterprise level contributions by horizontal deployment. It is important to remember that elements are common in many operations.
McKinsey authors write: Growing a business is a matter of do or die.
Top growth leaders are methodical in asking and answering three crucial questions:
Where is my growth going to come from?
How do I grow now and tomorrow?
How do I set up my growth engine?
Productivity or industrial engineering leaders have to ask similar questions
Where is productivity going to come from?
How do I increase productivity this year and next year?
PRODUCT INDUSTRIAL ENGINEERING (Value), - Customer Value Engineering, Cost Value Engineering, Design for Cost Efficient Manufacture and Assembly (DFMA).
FACILITIES INDUSTRIAL ENGINEERING (Lean), - Manufacturing Facilities, Inspection Facilities, Transportation - Material Handling Facilities, Warehousing - Storage Facilities, Data - Information Processing Facilities, Power Generation Facilities, Auxiliary Supplies Facilities
PROCESS INDUSTRIAL ENGINEERING (Minimizing Effort - Machine - Man).
Machine Effort Industrial Engineering - Human Effort Industrial Engineering
Modern Industrial Engineering - A Book of Online Readings.
Industrial Engineering of Products, Facilities, Processes, Machine Effort and Human Effort.
Version 1.0 - 27.12.2024
Readings Presented as Modules and Lessons of Modern Industrial Engineering.
You can download pdf version of this article.
Modern Industrial Engineering - A Book of Online Readings.
Cost Reduction of Products and Services at unit level through Productivity Improvement of all Resources used in Production Processes is the primary and core function of Industrial Engineering.
Others objectives and goals are included in later years.
Constraints like quality, machine health and human health are there right from the start of productivity improvement activity.
Industrial engineers (IE) are employed and productivity improvement and cost reduction are practiced in many companies using IE philosophy, principles, methods, techniques and tools. Apple Inc. - Industrial Engineering Activities and Jobs
It is important that industrial engineers have to recognize that scientific management was evaluated by Lilian Gilbreth, a psychologist from a human behavior perspective and a positive opinion was given. Industrial engineering, appeared as a part of the system of management and engineering developed to reduce cost of products made using engineering processes and methods.
After discussing the contribution of Taylor and Gilbreth in more detail, the contribution of many other industrial engineering researchers, professionals, consultants and authors are provided in a series of notes to introduce more industrial engineering concepts. These concepts and their applications will be discussed in more detail in various focus area modules of the course.
Unless special effort to know is made, engineers take 10 years to know engineering developments and implement them in their company processes - L.D. MILES. Prime Turning (TM) - New Turning Process with High Productivity RE-INVENTING TURNING, SANDVIK COROMANT TECHNICAL PAPER, 2018 https://nraoiekc.blogspot.com/2020/06/sandvik-coromant-cutting-tools.html
Toyota style Industrial Engineering - Waste Elimination - Ohno
"We have eliminated waste by examining available resources, rearranging machines, improving machining processes, installing autonomous systems, improving tools, analyzing transportation methods and optimizing the materials at hand for manufacturing. High production efficiency has also been maintained by preventing the recurrence of defective products, operational mistakes, and accidents, and by incorporating workers' ideas." Taiichi Ohno (P. 21)
Productivity Science - Taylor's Research on Machining Productivity Improvement Metal Cutting Theory - Productivity Focus Process Planning Principles Process Charting for Process Analysis Operation Analysis of Value Adding Transformation (Operation in Process Chart Terminology) Operation Analysis of Inspection Operation Analysis of Material Handling and Transport Operation Analysis of Temporary Delays Operation Analysis of Storage in Stores Operation Analysis of Information Generation and Communication
Various organization level issues like plant layout, JIT-lean thinking, and TPM will be covered in the module as part of operation analysis of various tasks in the processes.
Introduction to Process Industrial Engineering ______________
Engineering tasks are to be divided into elementary operations or elements, and the time to complete them has to be understood through various elements contributing to it. Through that understanding the time to do an element has to be reduced. These elements have to be classified into standard elements that are present in multiple tasks.
Time study has to be done at the start of the process improvement study. At intermediate points in the study. At the end of the study. Then after some training and practice in the new method, it has to be done to fix the output expected from the new process as standard.
Taylor's Time Study: Taylor wanted time study to generate standard data for specified elements of work of machines and men. This standard data can be at national or universal level, industry level or company level. Taylor and Gilbreth recommended study of the best person to understand the best way of doing a work element. They spent time to further improve the way of doing based on productivity science developed them on the work element. For them the output of time study has to be the best way of doing a work element and the minimum time in which it can be done. Taylor insisted from the beginning that the speed specified for operations has to be the speed that can be done comfortably, safely and healthily for the entire career span of the operators. What is that speed? Industrial engineering discipline later on developed a standard for that speed as 3 miles per hour. But is it scientifically validated? It may be necessary to provide solid logic and empirical foundation for this standard. Do people feel happy and comfortable to walk 24 miles per day in 8 hours? This standard has corresponding specification in various work elements. In which work element, people are happy and comfortable to do as per the standard? It is an important question to be answered IE discipline.
{Productivity Measurement within a new architecture for the U.S. National Accounts: Lessons for Asia http://www.apo-tokyo.org/files/mp_apo-keo_jorgenson_lec.pdf not available now.]
Waste measurement is highlighted by Taiichi Ohno and other Toyota industrial engineers. Material and information flow diagram is totally Toyota invention and it measures and highlights inventory. A setup time is the variable that controls inventory (lot size), it records setup times.
Taking the cue from TPS, industrial engineering discipline has to start measurement of waste as industrial engineering measurement area.
Ohno's Seven Wastes
Losses identified in TPM
16 Losses given by Yamashina in Manufacturing Cost Reduction Deployment
Value Stream Mapping to Identify Inventory Accumulations
372. PRINCIPLES AND APPLICATIONS OF OPERATIONS RESEARCH (from the perspective of an industrial engineer) (From Maynard's Industrial Engineering Handbook, 5th Edition, pp. 11.27-11.44) Jayant Rajgopal (From Rajgopal's website) http://www.pitt.edu/~jrclass/or/or-intro.html
Evaluation Improvement of Production Productivity Performance using Statistical Process Control, Overall Equipment Efficiency, and Autonomous Maintenance, Amir Azizi Procedia Manufacturing Volume 2, 2015, Pages 186-190 open access http://www.sciencedirect.com/science/article/pii/S2351978915000335
Test of hypothesis is to be used by industrial engineers to confirm or validate that their redesign or a process has resulted in the increase of productivity. This becomes useful when there is variation in the output from various workstations or persons. We can also visualize activities in different places. In such case we test the hypothesis that productivity has improved in the workstations where redesign is is implemented.
Applied Industrial Engineering - IE in Various Branches of Industrial Engineering
Industrial engineering is primarily an engineering discipline with productivity orientation. It major application is in incremental improvement of processes that give benefit within one year and hence it became closely allied with management in increasing profits, reducing costs and providing the company with the potential to reduce prices and increase profit. Hence Taiichi Ohno said industrial engineering is profit engineering. If a company is not using IE, it is losing an opportunity.
The application of industrial engineering is in processes of all engineering branches. Engineering activities like product design, production, maintenance of machines in factories, and service of consumer items are important engineering activities. In addition material handling and storage also involve engineering. Unfortunately, industrial engineering profession has not given enough attention to makes its presence in various engineering branches visible and systemic. Only limited attempts were done to create textbooks that discuss IE in specific engineering branches.
A Good Example of Applied IE - Improving Processes using New Technologies
Industry 4.0 Technology and Manual Assembly
By Amanda Aljinovic
March 15, 2023
Digital work instructions, cobots, radio frequency identification (RFID), augmented reality (AR) and other Industry 4.0 technologies can help. These technologies are designed to provide cognitive and physical support to people on the assembly line. How can engineers decide when such technologies are a worthwhile investment?
In a case study, industry 4.0 technologies application in a gear-box assembly line was studied.
Seven Industry 4.0 technologies were considered: RFID, digital work instructions, pick-to-light technology, AR, cobots, automated guided vehicles, and ergonomic manipulators.
Four quantitative criteria were used to rank the technologies: total investment cost, worker effort, workspace utilization and cycle time reduction.
RFID is one of the most important technologies for identifying and tracking assemblies in a production system. It provides precise information about the locations or states of goods in real-time and serves as a capstone for the establishment of the IoT within production.
Digital instructions are proven to reduce the assembly time and errors with complex assemblies.
Pick-to-light systems use LEDs on racks or shelves to show assemblers where to pick parts for an assembly and how many to retrieve. The lights guide assemblers through each step in the process. These systems are often connected with warehouse management systems.
AR also offers the possibility of significant improvement in cycle time, error rate, mental strain, worker focus.
Cobots are particularly desirable when people are confronted with heavy loads and repetitive, tedious activities. People can share the same workspace with the cobots, allowing managers to allocate tasks in a more flexible, efficient way.
AGVs can eliminate the need for people to transport parts and assemblies to and from the assembly line.
The ergonomic manipulator is an electronic device developed to improve ergonomics at the fifth assembly workstation. The device reduces the amount of physical effort needed to handle heavy components that must be mounted to the gearbox.
This article is a summary of a research paper co-authored by Aljinovic, Nikola Gjeldum, Ph.D., Boženko Bilic, Ph.D., and Marko Mladineo, Ph.D.
Shenzhen factory uses computer-controlled autonomous manufacturing in the dark, basically without assembly line workers in the production of electrical equipment components used in smartphones. It is equipped with an automated optimization system for Machine Learning and AI devices, an intelligent self-maintenance system, and an intelligent real-time monitoring system.
The factory’s production efficiency has been increased by 30% and the inventory cycle reduced by 15%.
The GSK plant has applied advanced technologies throughout its manufacturing operation, using advanced analytics and neural networks. This has improved line speeds at the site by 21%, cut downtime, increased yields, and delivered an OEE (overall Equipment effectiveness) improvement of 10%.
GSK has applied deep-learning image recognition to detect quality defects, and is using artificial intelligence to optimise machine throughput.
By implementing digital twin technologies, it has boosted capacity by 13%, while cycle time monitoring and the use of digital visualisation tools have cut cycle times by 9%.
Haier’s Hefei air conditioner factory applied advanced algorithms, digital twins, knowledge graphs and other cutting-edge technologies in the research and development (R&D), production and testing of household central AC systems, resulting in a 33% increase in energy efficiency, a 58% drop in the defect rate, a 49% increase in labour productivity and a 22% drop in unit manufacturing costs.
By deploying AI use cases across order forecasting, warehouse and production scheduling, product design, quality and assembly-testing domains, Foxconn Industrial Internet’s Taiwan factory has achieved a 73% increase in production efficiency, a 97% reduction in product defects, a 21% reduction in lead time and a 39% decrease in unit manufacturing costs.
486
Johnson & Johnson - Industrial Engineering - Productivity Improvement Activities - Industry 4.0 Lighthouse Plant
Johnson Xi’an replaced its manual facility with a Fourth Industrial Revolution-enabled new factory in 2019. This facility includes digital twins for technology transfer and material handling, intelligent automation of continued process verification (CPV) and batch execution processes.
This has shortened the product transfer time by 64% during site relocation and has enabled a 60% decrease in non-conformance, while improving productivity by 40%, operating costs by 24% and GHG emissions by 26%.
487
K-Water - Hwaseong - REPUBLIC OF KOREA - Industrial Engineering 4.0 - WEF - McKinsey Light House Plant
K-water launched a next-generation AI water treatment plant to reduce production costs, improve responsiveness and reduce human error. It is being scaled across 40+ other sites.
It has helped K-water to reduce its chemical usage by 19%, improve labour efficiency by 42% and reduce power consumption by 10%.
488
LONGi Solar - Jiaxing Plant - Industrial Engineering 4.0 - WEF - McKinsey Light House Plant
Jiaxing site implemented more than 30 Fourth Industrial Revolution use cases, using AI and advanced analytics to boost manufacturing operations.
The site achieved a 28% reduction in unit manufacturing costs, a 43% cut in yield loss and an 84% decrease in production lead time within one year, while also lowering energy consumption by 20%.
Mondelēz Beijing implemented 38 Fourth Industrial Revolution use cases, such as an AI-powered dough-making lights-off workshop and gas consumption optimization by machine learning. As a result, Mondelēz Beijing has achieved a 28% net revenue growth and 53% increase in labour productivity while reducing GHG emissions by 24% and food waste by 29%.
490
Novo Nordisk - Hillerød Plant - Industrial Engineering 4.0 - WEF - McKinsey Light House Plant
Novo Nordisk has invested in digitalization, automation and advanced analytics, building a robust Industrial Internet of Things operating system to be scaled across their manufacturing footprint, increasing equipment efficiency and productivity by 30%.
The site implemented Fourth Industrial Revolution use cases such as data flow integration, digital twin, machine learning across end-to-end value chain (from R&D to customers).
As a a result, the innovation lead time accelerated by 72%, shutdown days for trial were reduced by 21%, and order horizon from customers improved 14-fold.
The plant leverages 4IR capabilities such as data science, AI and machine learning across end-to-end value chain from R&D to retail customers. Altogether, it has been improving productivity and enabling faster reaction to market needs while growing production capability.
492
Quaker Houghton - Industrial Engineering 4.0 - Intelligent Die Casting
Over four years, the plant reduced its energy consumption by 59 per cent, improved waste optimisation by 64 per cent, decreased CO2 emissions by 61 per cent, and reduced water consumption by 57 per cent.
To improve energy efficiency and thereby reduce CO2 emissions, the Hyderabad team focused on the highest energy consumers in the plant: air compressors and chillers. An IoT-enabled device, Equaliser 4.0, was installed to regulate the compressors, thereby improving their efficiency. For the chillers, a data-driven energy management system with closed-loop control was fitted to constantly monitor and adjust energy consumption in real-time, optimising energy efficiency.
Unilever Sonepat implemented 30+ Fourth Industrial Revolution use cases in its E2E supply chain. Top use cases included boiler and spray dryer process twins, as well as customer data-informed no-touch production planning and inventory optimization.
This improved service by 18%, forecast accuracy by 53%, conversion cost by 40% and Scope 1 carbon footprint by 88%.