Thursday, March 15, 2018

Productivity-Improvement Technolgy - Monitor for Industrial Engineers

American Machinist  -        The


March 2018

Robotic Process Automation

Unlock the next wave of business productivity and sophistication in the fastest way possible with the most advanced Enterprise RPA Platform. Designed, tested and proven to perform elegantly in the toughest enterprise challenges.

UI Path

UI Path started the  journey in 2005 on a mission to create technology that would eventually enable new ways of working through what was then the emerging field of Robotic Process Automation (RPA). Today, RPA technology has matured into the legitimate and established way to run business processes. Behind our revolutionary approach at the time was our product’s unique computer vision technique. Think of the human visual system, which can detect and interpret information from visible light to build a representation of the surrounding environment.  We came up with a solution to capture and recreate automatically some of the actions on the screen that are normally driven by human perception. This is how we brought much-needed disruption to the market, enabling highly reliable automation in difficult scenarios like Citrix. It took us almost a decade to develop this technology. We put forth our greatest engineering efforts to accomplish what has now become the most widely adopted RPA Platform, capable of executing reliably to deliver millions of hours of productivity to enterprises.

Today, UiPath is a new standard in Enterprise RPA, leading with a cutting-edge Platform whose performance has been validated through phenomenal customer growth – last year we grew our enterprise customer base from less than 100 to more than 700 – through what is now the largest community of skilled RPA users, and through official recognition from global analyst firms.

The AI world is maturing. We will accelerate our product roadmap – the computer vision we have built into the product is now powered by machine learning – and extend our collaboration with solutions from the AI space that will tie in perfectly with RPA’s agnosticism to create powerful new use cases for our customers’ digital businesses.

We will also invest heavily in the expansion of UiPath’s global operations, with a focus on robust product support, customer success teams and sales efforts across all time zones and geographies, to benefit our customers as well as our partners–counting more than 200 today and including strong alliances such as Cognizant, Deloitte, KPMG, PWC, and TCS.

Optimize or Automate? How RPA is redefining Rapid Cycle Improvement
By   Gregory North  -   May 8, 2017

An In-Depth Look at Robotic Industrial Automation and Its Impact
January 6, 2016

Robots increase manufacturing productivity
April 13, 2015       
Combining the cognitive abilities of human operators and robots will increase the productivity of automated manufacturing processes.
Andrew Wilson, Editor


Feb 2014
Hex-drive tools enable 360-degree deburring, cleaning


New robotic standards to improve safety and productivity
Revised RIA R15.06 standard addresses control advancements and the need for risk assessments

US scientists have created a type of printed electronics they claim is up to 10 times more efficient than existing technology.

8 Ways to Improve Productivity With a Weld View Camera
High dynamic range imaging may hold the key to unlocking the secrets of process improvement in automated welding

Increasing machine tool productivity with CNC Technology - 2011


Boosting Catalyst Productivity - Chemical engineering

Chemical Process Simplification: Improving Productivity and Sustainability - Wiley Book - 2010 - Girish K. Malhotra

Improving Productivity with submerged arc welding

Innovative Milling Tool path programs for 10 times increase in metal removal rates

Robotic Sheet Metal Bending - December 2010 Canadian Industrial Machinery

8 technologies for improving Boring Mill Productivity

Advances in Press technology for productivity improvement
Bending similation software

CIM Canadian magazine for Metal Working and Fabrication Technology has a number of articles on Productivity-improvement Technology There is a search link to search for productivity

Updated 2018 - 16 March 2018
5 February 2014

Wednesday, February 28, 2018

February - Industrial Engineering Knowledge Revision Plan

February 1st Week

The Nature of Organizing - Review Notes
Departmentation in Organizations - Review Notes

Line-Staff Authority and Decentralization - Review Notes
Effective Organizing and Organizational Culture - Review Notes

Summary - Principles - Organizing
Human Resource Management and Selection

Performance Appraisal and Career Strategy
Manager and Organization Development

Summary - Principles - Staffing
Resourcing; A Function of Management

Feb 2nd week

Human Factors and Motivation
Leadership - Koontz and O'Donnell - Review Notes

Supervision - Introduction - Public Administration Point of View
Committes and Group Decision Making - Review Notes

Communication - Koontz and O'Donnell - Review Notes
Summary of Principles - Directing - Leading

The System and Process of Controlling - Review Notes
Control Techniques and Information Technology

Productivity Control
Overall Control and Preventive Control - Review Notes

Feb 3rd Week

Summary - Principles of Controlling
Global and Comparative Management

Organizing - Global Management Issues - Review Notes
Staffing - Global Management Issues

Leading - Global Management Challenges
Controlling - Global Management Challenges - Review Notes

Management and Entrepreneurship: Science, Theory and Practice
Managerial Skills

Principles of Management - List
Principles of Management - Subject Update Articles Recent Years

February 4th Week

Marketing Management Revision Articles

The Marketing Concept Kotler
Marketing Strategy - Marketing Process - Kotler's Description

Scanning of Environment for Marketing Ideas and Decisions
Marketing Strategy - Differentiating and Positioning the Market Offering

Management of Marketing Department and Function
Marketing Research and Market Demand Forecasting

Consumer Behavior
Analysis of Consumer Markets

Organizational Buying Processes and Buying Behavior
Market Segmentation and Selection of Target Segments

One Year Industrial Engineering Knowledge Revision Plan

January - February - March - April - May - June

July - August - September - October - November - December

Updated 3 February 2018
16 February 2016


Tuesday, February 27, 2018

Productivity Engineering - Smart/Intelligent/Autonomous/IoT Tractors

16 February 2018

Autonomous Solutions, Inc. (ASI)

Autonomous Solutions, Inc. (ASI) has been named a finalist for the 2018 Edison Awards for its work in the development of the Autonomous Tractor Concept with CNH Industrial and its brands Case IH and New Holland Agriculture.

The Autonomous Tractor Concept is the first fully functioning large scale autonomous tractor. It is capable of autonomous seeding, planting, and tillage for broad acre and row crop farming. The vehicles are also capable of obstacle detection which will enhance safety in the agriculture industry.

Design Award for Magnum,  Case IH Autonomous Tractor

Autonomous Tractor Corp

Some of the key players in the autonomous tractor market include Aurotron Pty Ltd, John Deere US, Case IH, Kubota Tractor Corporation, New Holland, AGCO Corporation, Yanmar, Kinze Manufacturing, Autonomous Tractor Corporation and Fendt Corporation.

Not a Tractor

Jun 30, 2017

New approach to an Autonomous Farm Power Equipment

A Canadian engineer and inventor rethought the idea of a farm power unit to create a new way to maximize labor-free farm work.

June 1, 2017
John Deere Rolls Out Smarter S700 Combines & Front-End Equipment
New harvesting solutions includes 4 combine models and new headers

John Deere introduces its smarter S700 Combines for model year 2018 production

Developments in autonomous tractors

19 Jul 2017

1. Technology underpinning autonomous tractors is relatively advanced
2. The technology is in the early stages of commercialisation.
3. Tractor manufacturers i.e. John Deere and CNH have successfully tested concept vehicles.

30 August 2016

CNH Industrial brands - Magnum concept autonomous tractor

                                 Driverless Tractor along with Case IH Early Riser 2150 Planter

CNH Industrial brands reveal concept autonomous tractor development: driverless technology to boost precision and productivity

Based on the existing Case IH Magnum and New Holland T8 high-horsepower conventional tractors, and using GPS in conjunction with the most accurate satellite correction signals for ultra-precise guidance and immediate recording and transmission of field data, the CNH Industrial autonomous tractor concept has been designed to allow completely remote deployment, monitoring and control of the machines.

CNH Industrial’s autonomous technology to completely remove the operator from the cab – in the case of the cabless concept Case IH Magnum.

For more details see the press release

Smart Tractor



Nonlinear modeling and Analyzing of Tractor-Semitrailer Driving Stability Based on Simulink
Chuan-jin Ou et al.
Page 104
International Symposium for Intelligent Transportation and Smart City (ITASC) 2017 Proceedings: Branch of ISADS (The International Symposium on Autonomous Decentralized Systems)
Xiaoqing Zeng, Xiongyao Xie, Jian Sun, Limin Ma, Yinong Chen
Springer, 06-Apr-2017 - Technology & Engineering - 301 pages
This book presents research advances in intelligent transportation and smart cities in detail, mainly focusing on green traffic and urban utility tunnels, presented at the 3rd International Symposium for Intelligent Transportation and Smart City (ITASC) held at Tongji University, Shanghai, on May 19–20, 2017. It discusses a number of hot topics, such as the 2BMW system (Bus, Bike, Metro and Walking), transportation safety and environmental protection, urban utility design and application, as well as the application of BIM (Building Information Modeling) in city design. By connecting the theory and applications of intelligent transportation in smart cities, it enhances traffic efficiency and quality. The book gathers numerous selected papers and lectures, including contributions from respected scholars and the latest engineering advances, to provide guidance to researchers in the field of transportation and urban planning at universities and in related industries.

Navigation of Autonomous Tractor: Positioning and Sensors 

by Tofael Ahamed (Author)
188 pages
Publisher: LAP LAMBERT Academic Publishing (September 30, 2011)

Sensor Architecture and Task Classification for Agricultural Vehicles and Environments

Francisco Rovira-Más
Departamento de Ingeniería Rural y Agroalimentaria, Universidad Politécnica de Valencia, Camino de Vera s/n,  46022 Valencia, Spain;
Received: 20 October 2010; in revised form: 26 November 2010 / Accepted: 1 December 2010 / Published: 8 December 2010

The complexity inherent to intelligent vehicles is rooted in the selection and coordination of the optimum sensors, the computer reasoning techniques to process the acquired data, and the resulting control strategies for automatic actuators. The article proposes a sensor architecture especially adapted to cope with them. The strategy proposed groups  sensors into four specific subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception. The designed architecture responds to vital vehicle tasks classified within three layers devoted to safety, operative information, and automatic actuation.

The solutions brought by the new technologies, i.e., precision farming and
agricultural robotics, seem to better match the revolution sought in farming. The incorporation of the
technologies of precision farming and agricultural robotics, into agricultural production not only benefits productivity and environmental conditions, but it also improves the working conditions of farm managers, laborers, and vehicle operators.

The farm machinery automation started as early as 1924, when Willrodt  designed a steering attachment capable of following furrows to guide a machine automatically across the field. Until the appearance of
electronics and computers, the sensing devices used to automate operations were purely mechanical. In fact, the majority of sensors used in agricultural vehicles have been related to autonomous navigation. For this purpose, the devices used both in North America  and in Europe  have been mechanical feelers, computer vision cameras, global positioning systems, geomagnetic direction sensors, laser scanners, and ultrasonic rangefinders. However, there are many other sensors of frequent use in precision agriculture such as yield monitoring estimators, soil properties probes, moisture content analyzers, and many others being developed at present. The usage of sensors in agricultural vehicles has evolved through time.

In a study of patents devoted to in-field automatic navigation, Rovira-Más found that beacons, pseudolite localization devices, and optical sensors excluding cameras were popular during the period
1985–2000, but inertial measurement units, GPS-based applications, and imaging devices became
predominant in the 2001–2008 period. The particular case of GPS can be justified by the cancellation
of selective availability in May 2000, which permitted the use of more accurate positioning data for
civilian applications.

Blackmore et al. provide a list of behaviors for an autonomous tractor, where simple processes as watching and waiting mingle with complex tasks such as route planning and navigation.

Typical agricultural vehicles weigh between 2 and 20 tons, incorporate diesel engines with a rated
power between 20 kW and 500 kW, and can reach retail prices over $300,000.

The sensor architecture proposed to meet the requirements of agricultural environments, vehicles,
and tasks is articulated around four structural subsystems: local perception, global localization, actuation and control, and data processing. The fourth subsystem, data processing, comprises the set of computers, processing units, DSPs (digital signal processors), and embedded controllers hosting decision making algorithms, receiving sensor data, and sending actuation commands according to a given software architecture. The other three subsystems incorporate a multiplicity of sensors that have been grouped and explained  in the subsections 4.1 to 4.4.

The complete automation of an agricultural vehicle involves many more functions than automatic steering. Navigation, for example, may require gear shifting, brake activation, throttle control, or differential locking. All these actions, when executed automatically, need to track the position of levers and pedals with potentiometers and encoders. An intelligent implement, for instance, needs to sense its position (up for road  traveling and headlands; down for farming) as well as the drag force incurred by the pulling vehicle (axle load cells).


A more convenient alternative to map ranges
is offered by lidar (light detection and ranging) heads, optical devices based on the principle of
time-of-flight whose beams of coherent light—usually laser—provide a way to estimate ranges with
high resolution. The main disadvantage of lidars is the need to spin the beam in order to cover the
widest possible area in front of the vehicle, typically between 180m and 270m, which requires a
mechanism permanently in rotation. The speed of this circular movement limits the real-time
capabilities of the sensor.

4.1. Sensors for Local Perception and Vicinity Monitoring
4.2. Sensors for Global Localization
4.3. Sensors for Vehicle Attitude and Motion Control
4.4. Non-visual Sensors for Monitoring Production Parameters

4.5. Onboard Integration of the Complete Sensor Network

A second battery, independent from the vehicle’s own battery, is always very helpful to preserve the desired autonomy of the diesel engine.

For many sensors, there is an advantageous, or even unique, location in the vehicle.

Taken as a whole, the actuation plan for the vehicle can follow the biology-based reactive approach of the subsumption architecture developed by Rodney Brooks , or on the contrary it may include a cognitive engine inside the Actuation Layer.

Mechatronics and Intelligent Systems for Off-road Vehicles

Francisco Rovira Más, Qin Zhang, Alan C. Hansen
Springer Science & Business Media, 30-Nov-2010 - Technology & Engineering - 277 pages

Rapid developments in electronics over the past two decades have induced a move from purely mechanical vehicles to mechatronics design. Recent advances in computing, sensors, and information technology are pushing mobile equipment design to incorporate higher levels of automation under the concept of intelligent vehicles. Mechatronics and Intelligent Systems for Off-road Vehicles introduces this new concept, and provides an overview of the recent applications and future approaches within this field. Real examples are provided of vehicles designed to move in off-road environments, including agriculture, forestry, and construction machines. These examples describe and illustrate features such as automatic steering, safeguarding, and precision agriculture capabilities.

Mechatronics and Intelligent Systems for Off-road Vehicles will be of great interest to professional engineers and researchers in vehicle automation, robotics, and the application of artificial intelligence to mobile equipment.

Control of Autonomous Tractor
Master's Thesis at Ørsted•DTU, Automation
March 31st, 2006
Authors:Asbjørn Mejnertsen, Anders Reske-Nielsen

Company and Specific Product Based Developments

Mahindra Showcases its First Ever Driverless Tractor in India
September 19, 2017

Developed at Mahindra Research Valley in Chennai, Driverless Tractor technology set to take farm mechanization to new heights



Driverless Tractor set to make farming more productive & profitable, reduce health hazard for farmers and change the future of food production
This technology is designed to enable tractors to perform a variety of farming applications & operate varied implements
The tractor equipped with this technology can be programmed to carry out specific tasks & can also be operated remotely to perform in the field
To be available commercially from early 2018, in a phased manner

Mahindra & Mahindra Ltd., displayed its first ever Driverless Tractor. Developed at the Mahindra Research Valley, the Group’s hub of innovation and technology located in Chennai.

The driverless tractor is all set to redefine the mechanization process for the global farmer.

This innovation will change the future of farming by increasing productivity, leading to increased food production to feed the growing needs of the world. This innovative mechanization for the global farming community, in line with Mahindra's Farming 3.0 proposition.

Rajesh Jejurikar, President, Farm Equipment Sector, Mahindra & Mahindra Ltd. said, “Today the need for farm mechanisation is higher than ever before, due to labour shortage and the need to improve productivity and farm produce yield. Coupled with our ‘DiGiSENSE’ technology that we launched last year, the driverless tractor offers a distinct advantage to the Indian farmer by bringing an unprecedented level of intelligence to the tractor”.

This technology will be deployed across Mahindra tractor platforms in due course of time. It will also be deployed across international markets such as USA and Japan.,  Mahindra plans to offer the driverless tractor technology across its range of tractors from 20 HP to 100 HP over a period of time.

Unique Features of the Driverless Tractor

The pioneering driverless tractor is equipped with state-of-the-art technology and boasts of several unique features:

Auto steer – GPS based technology that enables a tractor to travel along a straight line.

Auto-headland turn – Enables the tractor to orient itself along adjacent rows for continuous operation without any steering input from the farmer.

Auto-implement lift – Feature in the tractor that automatically lifts the work tool from the ground at the end of a row and lowers the tool after the tractor has oriented itself for operation at the next row.

Skip passing - This technology feature enables the tractor to steer to the next row for continuous operation without any intervention of the driver.

Safety Features

In addition, the driverless tractor is also equipped with some unique safety features as below:

Geofence lock - Prevents tractor from going outside the boundaries of the farm

Control via Tablet User Interface – Enables the farmer to program various inputs needed to farm efficiently. Also offers controls to prevent the tractor veering off from its intended path or desired operation. He can also control the tractor remotely via a tablet.

Remote Engine Start Stop - Ability to stop the engine and hence, bring the tractor to a complete STOP if needed in cases of emergency

With the deployment of this technology on Mahindra tractors, the farmers can work their fields for long hours without exposing themselves to harsh weather or difficult operating conditions. They can also protect themselves from potential health hazards resulting from operations like insecticide spraying which now can be done without human intervention. It will also ensure better quality and consistency in farming operations, leading to higher productivity and farm produce yields.



New Holland Fiat (India) launched the GPS and the GPRS technologies on its tractors under
the name of "Sky Watch" in 2012.  This technology will enable farmers to monitor and trace their tractors'' health and performance for better control and
maintenance, easy operations, and improved productivity. Tractor owners  can know the hourly
usage, performance parameters, and the maintenance when the tractor is rented out.

Automating Agriculture

Internet Of Things Based Innovative
Agriculture Automation Using AGRIBOT
SSRG International Journal of Electronics and Communication Engineering - (ICRTECITA-2017) - Special Issue - March 2017

Agricultural Automation: Fundamentals and Practices
Qin Zhang, Francis J. Pierce
CRC Press, 19-Apr-2016 - Science - 411 pages

Agricultural automation is the core technology for computer-aided agricultural production management and implementation. An integration of equipment, infotronics, and precision farming technologies, it creates viable solutions for challenges facing the food, fiber, feed, and fuel needs of the human race now and into the future. Agricultural Automation: Fundamentals and Practices provides a comprehensive introduction of automation technologies for agriculture.

From basics to applications, topics in this volume include:

Agricultural vehicle robots and infotronic systems
Precision agriculture, with its focus on efficiency and efficacy of agricultural inputs and the spatial and temporal management of agricultural systems
Specific agricultural production systems, including those related to field crops, cotton, orchards and vineyards, and animal housing and production
Automation relative to specific inputs in agricultural production systems, such as nutrition management and automation, automation of pesticide application systems, and automated irrigation management with soil and canopy sensing
Liability issues with regard to surrounding awareness and worksite management
Postharvest automation—perhaps the most advanced component of agricultural production in terms of automation and an important factor in global agriculture
Agricultural mechanization, one of the top ranked engineering accomplishments in the past century, has created revolutionary change in crop production technology and made it possible to harvest sufficient products to meet the population’s continuously growing needs. Continued progress is essential to the future of agriculture. This book provides an up-to-date overview of the current state of automated agriculture and important insight into its upcoming challenges.

Agricultural Mechanization and Automation - Volume I
Front Cover
Paul McNulty, Patrick M. Grace
EOLSS Publications, 28-Jul-2009 - Technology & Engineering - 518 pages
0 Reviews

Agricultural Mechanization and Automation is a component of Encyclopedia of Food and Agricultural Sciences, Engineering and Technology Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias.

The mechanization of farming practices throughout the world has revolutionized food production, enabling it to maintain pace with population growth except in some less-developed countries, most notably in Africa. Agricultural mechanization has involved the partial or full replacement of human energy and animal-powered equipment (e.g. plows, seeders and harvesters) by engine-driven equipment. The theme on Agricultural Mechanization and Automation cover six main topics:  Technology and Power in Agriculture; Farm Machinery; Facilities and Equipment for Livestock Management; Environmental Monitoring; Recovery and Use of Wastes and by-Products; Slaughtering and Processing of Livestock, which are then expanded into multiple subtopics, each as a chapter.  These two volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs.


J. De Baerdemaeker, H. Ramon, and J. Anthonis
K.U. Leuven, Leuven, Belgium
H. Speckmann , and A. Munack
Federal Agricultural Research Centre(FAL), Braunschweig, Germany

Updated 2018 - 10 March 2018, 27 February

Sunday, February 25, 2018

Industry 4.0 - IIoT - Productivity Engineering

The technologies of  Industry 4.0 offer engineering opportunities to increase productivity of engineering products, production and maintenance system. They also offer engineering opportunities to increase productivity in business processes. Information technology is widely used in various service industries and thus offers the opportunity for industrial engineering to improve productivity in services sector.

Industry 4.0 - IIoT - Productivity Pathways

Dimensions, Characteristics, Parameters or Variables of Industry 4.0 (IIoT) that  Increase Productivity.

The attributes of Industry 4.0 that provide the scope for increasing productivity of products and engineering processes of design, production, operation, and maintenance. Productivity engineers (industrial engineers) have to creatively redesign by employing the productivity pathways.

*Low cost monitoring of products, equipment and persons

*Low cost control through software embedded in the product or the cloud

*Low cost optimization.

Smart, connected products can apply algorithms and analytics to in-use or historical data to dramatically improve output, utilization, and efficiency. In wind turbines, for instance, a local microcontroller can adjust each blade on every revolution to capture maximum wind energy.

How Smart, Connected Products Are Transforming Competition
Michael E. PorterJames E. Heppelmann

*Enabling  measurements in demanding conditions with low maintenance cost and without hindering production.

VTT has developed a solution for measuring temperature, inclination, humidity, strain and other characteristics from machines and inside structures (e.g. walls) at distances up to 10 m, without batteries in the sensor. The principle is based on powering the sensor with radio frequency energy waves and using the return transmission to read out the resulting data (see Link ‘Zero Power Sensor’). The IPR protected technology allows for several individually identifiable sensors to be operated in the same space.

*Energy-efficient communications

A platform has been developed for energy-efficient communications between mobile devices that identifies the most appropriate combination of technologies for different exchanges

*Maintenance when needed

The Internet of Things will help individual companies to limit the waste.  Products which are connected to the web can communicate how they're being used or their current status. This data will be used to schedule maintenance when it's really needed, instead of the current  set of relatively inefficient rules.

*Predictive analytics based on data collected on products will be used to reduce failures and improve product design. This will boost the efficiency of products and contribute to productivity in user organizations.

*The IoT will change how manufacturers and service companies interact with customers. Reliance on massive call centers and customer service departments will be greatly reduced and  IoT enabled products will be directly connected to a service assessing their condition and taking relevant action.

*Smart lighting

*Smart heating

*Smart cities

* Machine level information system
Each machine can be aggregated into a single information system that accelerates learning across the machine portfolio.

Source: Industrial Internet: Pusing the boundaries of Minds and Machines, Peter C Evans and Marco Annunziata, GE, 2012

Industry-Wise Opportunities

Food Industry

Revolutionizing the Food Supply Chain with IoT

Company Implementation Examples

To Increase Productivity, UPS Monitors Drivers' Every Move

Proposals for Systems

Sensors 2017, 17(11), 2588; doi:10.3390/s17112588
An Intelligent Cooperative Visual Sensor Network for Urban Mobility

Sheet metal fabricators: Evaluating the odds of success with Industry 4.0
Published: 22 December 2017 - Sarah Mead

IoT Technology Components and Issues

IoT Operating Systems

LiteOS C,
Mantis OS

Survey of Open Source Operating System for the IoT Devices
-Jalpa Patel
International Journal for Scientific Research & Development (IJSRD)
Volume-4, Issue-8, August 2016, pp. 113-114

Updated  2018 - 27 February
12 December 2017

Wednesday, February 21, 2018

High Productivity Through Smart Factories - Industry 4.0 - Bulletin Board

Productivity  - Productivity Science - Productivity Engineering - Productivity Management

Productivity Science

Productivity Engineering

Productivity Management


Future of Driverless Vehicles

Tidfore and FLSmidth cooperate in bulk material handling equipment intelligentizing

Innovations for the Digital Production of the Future - Volkswagen


Siemens CEO on Industry 4.0

Cube Automation,212,0,0,html/Smart-Factoree-Overview

We Deliver Results in Productivity


Industry 4.0 and Smart Factory Logistics by Bossard
Proven Productivity


Smart Machines

Smart Vertical Center - a compact machine designed for high - performance and unsurpassed value
The Smart series are designed for high productivity, compact design and environmental considerations. They provide high efficiency machining thanks to the No. 40 taper spindle with maximum spindle speed of 12000 rpm and high speed feed rates.

Smart Chocolate Factory: increased productivity and quality - Bühler
April 2017

Smart Chocolate Factory: increased productivity and quality
It is increasingly important for chocolate producers that their plants operate at full capacity. At the Interpack trade show, Bühler is demonstrating how the use of digital services and IoT technologies offers significant gains in efficiency: "With more intelligent process control, we can further improve productivity and achieve even more consistency in product quality,"


The Ministry of Trade, Industry & Energy, South Korea (MOTIE) announced on March 10, 2016 that it has assisted in the construction of smart factories in 1,240 small and medium enterprises (SMEs). Smart factories are  defined as facilities that are fully automated based on information technology. The smart facilities have improved the SMEs’  productivity by approximately 25%.



Published on 3 Dec 2015
MES can improve your shop floor productivity by over 30%. FORCAM MES relies on real-time data processing and machine data collection in order to identify potential of optimisation. User-friendly reports and visaulisation help you monitor work orders throughout the manufacturing process and keep trace all production related data. The Smart Data generated ensures that you can stay pro-active and make sure production is on track and up to the quality standards your customers expect.

Smart Factory - A Step towards the Next Generation of Manufacturing

Lucke D., Constantinescu C., Westkämper E. (2008) Smart Factory - A Step towards the Next Generation of Manufacturing. In: Mitsuishi M., Ueda K., Kimura F. (eds) Manufacturing Systems and Technologies for the New Frontier. Springer, London

PDF Files

Smart Manufacturing Leadership Council
Building the Science of Manufacturing Enterprise, 2011

Smart Process Manufacturing
(More files on this site)

Smart Factory - Mobile Computing

Factory of Tomorrow will be Smart - Intel

Updated 2018 - 22 February, 8 January 2018,
12 December 2017, 12  July  2017

Tuesday, February 20, 2018

Artificial Intelligence - A Note for Industrial Engineers for Industrial Engineering 4.0 (IE 4.0)

Artificial Intelligence (AI) is explained by PWC as  a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they’re sensing and
their objectives.

 AI is in use today in actual devices or systems like digital assistants, chatbots and machine learning
amongst others.

The intelligence included in AI can be categorized as:

Automated intelligence: Automation of manual/cognitive and routine/nonroutine
Assisted intelligence: Helping people to perform tasks faster and better.
Augmented intelligence: Helping people to make better decisions.
Autonomous intelligence: Automating decision making processes without human intervention.

More AI innovations are likely to come out of the research lab and the transformational possibilities are staggering based on the various research and development proposals announced or indicated.

PWC Report on AI

Sizing the prize: What’s the real value of AI for your business and how can you capitalise?

A Very Short History Of Artificial Intelligence (AI)

Cambrian Intelligence: The Early History of the New AI

Rodney Allen Brooks
MIT Press, 1999 - Computers - 199 pages
Until the mid-1980s, AI researchers assumed that an intelligent system doing high-level reasoning was necessary for the coupling of perception and action. In this traditional model, cognition mediates between perception and plans of action. Realizing that this core AI, as it was known, was illusory, Rodney A. Brooks turned the field of AI on its head by introducing the behavior-based approach to robotics. The cornerstone of behavior-based robotics is the realization that the coupling of perception and action gives rise to all the power of intelligence and that cognition is only in the eye of an observer. Behavior-based robotics has been the basis of successful applications in entertainment, service industries, agriculture, mining, and the home. It has given rise to both autonomous mobile robots and more recent humanoid robots such as Brooks' Cog.

This book represents Brooks' initial formulation of and contributions to the development of the behavior-based approach to robotics. It presents all of the key philosophical and technical ideas that put this "bottom-up" approach at the forefront of current research in not only AI but all of cognitive science.

Monday, February 19, 2018

Industry 4.0 - A Note for Industrial Engineers for Industrial Engineering 4.0 (IE 4.0)

Prof, Dariusz Plinta edited a  book collection of articles, "Advanced Industrial Engineering: Industry 4.0" and it is published by in Bielsko-Biała 2016.

He indicates that Advanced Industrial Engineering (AIE) is a major direction of current and future
Industrial engineering development in line with technological developments taking place in Europe.  AIE arises from the productivity assessment and improvement  needs of applying new innovative technologies to create better position of production companies in global and knowledge-based society. Productivity solutions for the evolving industry need a new perspective and the collection of articles in an attempt to provide the early inputs.

Industry 4.0 is a concept which is being developed very intensively in Europe especially in industrial advanced countries and more and more of its components are being developed and implemented now in production systems.  Changes are taking place in  different areas of organization's operations, with the impetus for change coming from  IT developments. The redesign of production and other engineering processes and products are driving innovation (new products and services) and improvements in productivity and quality. Therefore, the industrial revolution termed Industry 4.0  provides opportunities for companies to innovate both new products and processes. Every managers of engineering company has to notice that  some companies have already introduced them in market.

Industry 4.0 Concept

The Industry 4.0 concept was presented in 2011.  In the first publications, the most important new
 technological developments that were responsible for the emergence of revolutionary changes in engineering processes and products  were indicated.  These are:

a) Autonomous Robots,
b) Simulations and Forecasting Techniques
c) Vertical/Horizontal Software Integration
d) Industrial Internet of Things – IoT
e) Direct communication between machines
f) Internet of Services
g) Big data and analytics
h) Innovative methods of collecting and processing large amounts of data, including
the use of potential activities in the cloud (Clouds)
i) Additive Manufacturing
j) Augmented Reality – AR
k) Virtual Reality – VR
l) Cyber-Physical Systems – CPS
m) Digital Twin
n) Artificial Intelligence,
o) Neural Networks
p) Cybersecurity
q) Mass Customization

There technologies have to be incorporated into new software systems used for designing, testing,
process planning, manufacturing and assembly.

The evolution of production systems  follows the development of innovative technology and its direct environment, like machines, devices, methods and tools aiding the work related to preparing technical documentation, including description of product models, processes and production resources. The evolution or innovation in production systems provides  introduction of shorter production cycles, new products and manufacturing processes development, minimization of the supplies level, more efficient logistics, and the usage of effective and innovative ideas of production realization.

The main types of software used in production enterprises are linked in PLM solutions, which control different parts of the manufacturing cycle. CAD systems define what will be produced, Manufacturing Process Management (MPM) defines how it will be manufactured, ERP informs when and where it is created, whereas MES provides shop floor control and simultaneously manufacturing feedback. The stored information generally aids communication and improves making decisions, but also removes human errors from the design and the manufacturing process.

More detailed notes on Each Technology are presented as separate notes.

c) Vertical/Horizontal Software Integration
d) Industrial Internet of Things – IoT
e) Direct communication between machines
f) Internet of Services
g) Big data and analytics
h) Innovative methods of collecting and processing large amounts of data, including
the use of potential activities in the cloud (Clouds)
i) Additive Manufacturing
j) Augmented Reality – AR
k) Virtual Reality – VR
l) Cyber-Physical Systems – CPS
m) Digital Twin
n) Artificial Intelligence,
o) Neural Networks
p) Cybersecurity
q) Mass Customization


In BCG's view nine  technology trends form the building blocks of Industry 4.0.


The collection and comprehensive evaluation of data from many different sources— Various machines, equipment, material handling trucks and communication devices, and business process systems will become provide significant extra support to increse revenues and decrease costs by supporting  real-time decision making as well as finding the best ways of doing things.


Robots are becoming more autonomous and mobile as their ability to work safely side by side with humans is increasing. Robots learning process is also changing with the developments in machine learning theory and practice. The robots are also being made available at lower and lower prices with  greater range of capabilities than those used in manufacturing today. Hence robotization of manufacturing is occurring.


Simulations can be used more extensively in plant operations by feeding  real-time data and experimenting on the  virtual model that mirrors the physical world. The model can include machines, products, and humans. This will make possible optimization the machine settings very quickly even  for the next product in line thus driving down machine setup times. The quality is also improved as root causes can be eliminated as identification can be done very quickly on the simulated model.


Industrial Internet of Things (IIOT) consists of connections between devices both consumer and production that can pass on information and instruction between themselves.  This will allow field devices to communicate and interact both with one another and with more centralized controllers, as necessary. As the processing of information can be done very fast based on fast communication without human intervention, real time responses are possible minimizing waste and increasing optimum working conditions. The IIOT can facilitate decentralize analytics and decision making and also allow centralized analytics and decision making.


The integration capability provided by communication, analysis and action technologies will make companies, departments, functions, and capabilities much more cohesive. The cross-company, universal data-integration networks evolve and they enable automated value chains also.


There are developments in security technologies that can assure users that  the increased connectivity and use of standard communications protocols that come with Industry 4.0 does not increase the risk significantly. The risk dimension is recognized by the Industry 4.0 proponents and research and engineering efforts are being made to reduce the threats.


Development in clould storage and application capabilities are providing computing facility at a less and less cost to users. Users can utlize analytics services on vast data as they pay for the facility on a use basis only. The shared facility provides them service as well as security of data. The performance of cloud technologies is improving providing reaction times of just several milliseconds. As a result, machine data and functionality are being be deployed to the cloud. Successes of pioneer companies in this environment,  enables  more organizations to take up data-driven services for production systems.


Companies have just begun to adopt additive manufacturing ( 3-D printing). It became popular in preparing prototypes and some companies started producing even regualr production components. With Industry 4.0, where designs can be transferred to additive manufacturing devices anywhere in teh world,  these additive-manufacturing methods will be more widely used to produce small batches of customized products. Spare parts etc. can be made on demand through additive manufacturing at the users premises by the user.


Augmented-reality-based systems can be used to send repair instructions and provide practice to technicians over mobile devices. These systems are currently in their infancy. But their potential is high to provide workers with real-time information to improve decision making and work procedures thus providing faster responses to various unforeseen events in engineering systems. Waiting for an expert or knowledgeable technician to come will come down as local technicians can be trained very quick using augmented reality systems as and when the need arises.

Industrial engineers have to understand each of the technologies that are forming part of the Industry 4.0 revolution. They have to understand that engineering potential and also productivity potential. In diverse combinations, they may be more effective than when they are used alone. Research needs to be done industrial engineers to understand their productivity potential and productivity pathways related to each technology and various bundles. Then only, they can carry out systematic productivity engineering and management. In the coming posts, each technology will be explained from the perspective of industrial engineering. (Accessed on 9 January 2018)  (Visited on 9 January 2018)

Chapter of the Blog Book - Industrial Engineering 4.0 - IE in the Era of Industry 4.0 - Blog Book

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Updated 2018 - 20 February 2018,  8 January 2018