Best Practices in Remote Working - Prof. Tsedal Neeley

 

https://hbswk.hbs.edu/item/succeeding-in-the-new-work-from-anywhere-world

Remote Work Revolution - Prof. Tsedal Neeley

__________________

__________________ 

Developing a Digital Mindset

Digital mindset as a set of approaches we use to make sense of, and make use of, data and technology.

Once people are able sense data and related technology and figure ways of using them, they see new possibilities and chart a path for the future. 

This set of attitudes and behaviors enable people and organizations to see new possibilities and chart a path for the future. 

https://www.linkedin.com/pulse/developing-digital-mindset-following-30-rule-tsedal-neeley/ 

https://hbswk.hbs.edu/item/why-digital-is-a-state-of-mind-not-a-skill-set

Digital Mindset by Tsedal Neeley

Regularly advises top leaders who are embarking on virtual work and large scale-change that involves global expansion, digital transformation, and becoming more agile. Recent work provides remote workers and leaders with the best practices necessary to perform at the highest levels in their organizations.

https://thinkers50.com/biographies/tsedal-neeley/

Learn - How to Become Industry 4.0 Digital Manufacturing Champion

Do You Want 55% Increase in Efficiency in the Next 5 Years?

Become Digital Champion.

https://www.pwc.in/consulting/transformation/digital-operations/digital-operations-to-drive-smart-manufacturing.html

Possible Efficiency Gains in the Next 5 Years due to Digital Manufacturing

Digital novice - 5%

Digital followers - 15 to 25%

Digital innovator  35 to 45%

Digital  champion greater than 55%

Industry 4.0 manufacturing and distribution systems utilize end-to-end digitization and data integration in all information generation, processing, analysis & decision making and communication activities. Industry 4.0 companies offer along with traditional products, digitally enhanced products and digital products/services also. In these production systems there are digital twins, physical assets and virtual assets that are connected and communicate. The connected information systems integrate all suppliers, all customers and all manufacturing and service facilities of the enterprise. Each and every machine and each and every person are connected in these digital manufacturing companies/factories.

Mastering Industry 4.0 implementation and transformation requires initial learning and subsequent deep understanding of the scientific and managerial knowledge and technologies of Industry 4.0 by the complete enterprise staring with the top management. A top management with a compelling vision for  the transformation is essential for the company to become  a champion.

PWC, one of the top management consultancy organizations did a survey in 2018 about the implementation status of Industry 4.0 and identified digital champions from 1100 companies surveyed. 10 percent of the companies surveyed were classified as digital champion companies.

Four ecosystems are essential in digital manufacturing systems: Customer ecosystem, Operating ecosystem (suppliers, manufacturing facilities, internal logistics, external logistics), Technology ecosystem and People ecosystem.

Customer ecosystem is the basis for exchange of information and value. Integrated operations, technology and people ecosystems serve the customers through the customer ecosystem that is integrated with the other three ecosystems.

Digital champions implement new digital technologies in purposeful manner and deploy them in various application in value creating mode. They are able apply the technologies in multiple applications by employing partners instead of slowing down implementation due to paucity of internal talent.

Artificial intelligence is being used in 9 per cent of companies and its use is increasing. Employees with relevant digital skills are required and they are in short supply. Hence tailored training programs development and organization are crucial. Digital champions have to invest heavily in people development training. 

How to Become Industry 4.0 Digital Manufacturing Champion? - Blueprint

1. Invest in Learning Industry 4.0 Knowledge and Skills

2. Conduct an Audit of Four Ecosystems - Customer, Operations, Technology, and People

3. Define Ecosystem Vision and Value Proposition

4. Develop a Model for Each Ecosystem

5. Set Ecosystem Project Investment Decision Board and Encourage Project Proposals

6. Approve Projects and Build EcoSystem Components. Improve Capabilities iteratively

7. Realize the Value of the New Production System

8. Reinvest and Expand Operations

Read the excerpts and  download PWC Report from https://www.strategyand.pwc.com/industry4-0

Customer Ecosystem - Subsystems and Components 

Products

Complementary Products

Performance Services

Financial Solutions

Digital Technology

Hardware and Infrastructure

Software and Apps

Platform Integration

Third Party Platforms

E Commerce Service

Advanced Customer Services

Demand Signals and Insights

Data Acquisition and Analytics

Multichannel Interaction

Customization of Product and Service

Operations Ecosystem - Subsystems and Components

The Operations ecosystem encompasses the physical activities and flows that support the customer solution offering. These might include product development, planning, sourcing, manufacturing, warehousing, logistics and services. Any external partners that are part of a company’s operations, including contract manufacturers, logistics partners and academia, are part of this ecosystem.

Digital Research and Development

Procurement 4.0

Smart Manufacturing - Connected Manufacturing System

Connected Logistics and Distribution - Smart Warehouses and Trucks

After Sales Services - Digital Channels and Digital Support to Service Professionals

Product Life Cycle Management

Digital Industrial Engineering (Computer Aided Industrial Engineering - CAIE)

(IE redesigns facilities, products and processes to increase productivity)

Persons and Firms as Partners

Research Institutes

Open Innovation Platforms and Participants

Development Partners

Development Stage Component and Material Suppliers

Materials Suppliers
Component Suppliers
Internal Production Facilities
Contract Manufacturers
Inbound Logistics Service Providers
Warehouses and Distribution Centers
Transporters and Last Mile Delivery Centers and Facilities
Repair Centers
Service Providers

Digital Champions - Bosch, Daimler Benz

Digital Transformation at Daimler Benz - Now Daimler is Digital Champion of PWC Survey

The Technology Ecosystem - Subsystems and Components

Digital Technologies

Blockchain
Sensors
IIoT
3D Printing
Robots
Artificial Intelligence
Augmented Reality
Virtual Reality
Drones
Automated Guides Vehicles
Mobile Devices
Driverless Trucks

Networks and Connectivity
Cloud Computing
Edge Computing

Integrated Platforms
Human Machine Interfaces
User Experience
Data Networks
Integration Layers

Applications

Core ERP
CRM
Integrated Business Planning Models
Manufacturing Execution Systems
Data Analytics
PLM and Digital Twins

2018 Survey

At least 90 percent of Digital Champions have already implemented, piloted, or planned some of the most critical current technologies. 

• Integrated end-to-end supply chain planning (100 percent of Digital Champions)

• Predictive maintenance of assets and products (96 percent)

• Manufacturing execution systems (94 percent)

• Industrial Internet of Things (97 percent)

• Digital twins, which essentially are virtual versions of physical assets or products, such as factories, that can be used for digitally supported planning, scheduling, and product development, among other 

possibilities (94 percent)

• Advanced robotics (90 percent)

By comparison, only about one-third of Digital Novices have adopted the most common operational technologies, like predictive maintenance (39 percent) and integrated supply chain planning (32 percent).

People Ecosystem - Subsystems and Components

Skills - Skill Sources - Mindset and Behavior - Career Development

Skills

Skill Sources

Mindset and Behavior

Career Development

Webinar of PWC on Digital Champions 2018



Getting Ready for Industry 4.0
Webinar by Scott Philips
https://www.ecisolutions.com/m1/landing/videos/recorded-webinar-scott-phillips/

Updated 11.7.2022, 11July 2021

Pub 11 July 2018

What is Data Science? - Evolution of Data Science

Excerpts from Global Data Science Forum What is Data Science?
By Paco Nathan posted Mon March 04, 2019
https://community.ibm.com/community/user/datascience/blogs/paco-nathan/2019/03/04/what-is-data-science

What is Data Science?

A popular 2012 tweet by Josh Wills:

Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.

Data Science gained traction in industry circa 2008, just as tooling for big data was on the rise, and as business use cases for machine learning (ML) became popularized. Those three grew together in contrast to an earlier era of business intelligence (BI), which was initially popularized by Gartner analyst Howard Dresner. Most of BI was defined atop data warehouse (DW) practices, based on work by Barry Devlin and Paul Murphy, Ralph Kimball, Bill Inmon, et al. BI and DW were both introduced in the late 1980s, then became widespread practices throughout the 1990s.

Data science emerged in response to demand for more advanced techniques and larger scale-out than what the best practices from the prior decade could provide. Cloud resources were becoming popular, and crucial insights could be obtained more quickly and more cost-effectively due to popular open source tools such as Hadoop, Spark, plus a whole range of Python libraries.

In 1962, a Bell Labs mathematician named John Tukey wrote a paper called “The Future of Data Analysis”.  Tukey urged a provocative new stance for applied mathematics which he called data analysis. The interesting section headings are:

“We should seek out wholly new questions to be answered.”
“We need to tackle old problems in more realistic frameworks.”
“We should seek out unfamiliar summaries of observational material, and establish their useful properties.”
“And still more novelty can come from finding, and evading, still deeper lying constraints.”

In the  books on visualizing data by Ed Tufte, references to Tukey show up throughout most all of books.

A generation later, another Bell Labs researcher named William Cleveland coined the term data science in a 2001 paper citing Tukey among others,  “Data science: An action plan for expanding the technical areas of the field of statistics”. Cleveland proposed an outline for a multi-disciplinary curriculum:

(25%) Multidisciplinary Investigations: data analysis collaborations in a collection of subject matter areas.
(20%) Models and Methods for Data: statistical models; methods of model building; methods of estimation and distribution based on probabilistic inference.
(15%) Computing with Data: hardware systems; software systems; computational algorithms.
(15%) Pedagogy: curriculum planning and approaches to teaching for elementary school, secondary school, college, graduate school, continuing education, and corporate training.
(5%) Tool Evaluation: surveys of tools in use in practice, surveys of perceived needs for new tools, and studies of the processes for developing new tools.
(20%) Theory: foundations of data science; general approaches to models and methods, to computing with data, to teaching, and to tool evaluation; mathematical investigations of models and methods, of computing with data, of teaching, and of evaluation.

This curriculum indicates what Cleveland thought the field required, namely that data science is a space in which statistics and computing needed to interact, to provide the necessary resources and scale.

That same year, a UC Berkeley professor named Leo Breiman wrote “Statistical Modeling: The Two Cultures”. One culature is of the previous era which he called data modeling and a new trend emerging which he called algorithmic modeling. That culture of data modeling was what Tukey had argued against.  The newer culture embraced much larger data rates and more computation and also leveraged machine learning algorithms to help automate decisions at scale.

The current heyday of data science began when some of these applications which required more data started to become tractable, reliable, and cost-effective (in that order).

Check out these histories by lead architects at those firms – roughly centered on Q3 1997, which turned out to be a key inflection point for the Dot Com Boom:

“Early Amazon: Splitting the website”, Greg Linden, Amazon
“eBay Architecture”, Randy Shoup, eBay
“Inktomi’s Wild Ride”, Erik Brewer, Yahoo! Search (0:05:31 ff)
“Underneath the Covers at Google”, Jeff Dean, Google (0:06:54 ff)

The timing for those projects was during the peak of data warehouses and business intelligence adoption. However, a common theme among those four architects’ reflections is that they recognized how they’d need to scale ecommerce applications but could not do so with available tooling. Instead they turned to open source tools (such as Linux) for early data science work on proto clouds, leveraging ML at scale for ecommerce. Their timing was impeccable, particularly for Amazon: just in time to monetize the first big wave of ecommerce in the holiday season of Q4 1997. The rest is history.

The gist is that ecommerce firms split their web apps using a principle of horizontal scale out, i.e., proto cloud work on server farms. Those many servers generated lots of log files (proto Big Data), which in turn were analyzed using machine learning algorithms, which in turn provided predictive analytics that improved customer experience in the web apps. A virtuous cycle emerge, with data as a product.

However, after Q4 1997 the world of data changed, predictive analytics loomed large. Breiman described that sea change quite succinctly:

A new research community using these tools sprang up. Their goal was predictive accuracy. The community consisted of young computer scientists, physicists and engineers plus a few aging statisticians. They began using the new tools in working on complex prediction problems where it was obvious that data models were not applicable: speech recognition, image recognition, nonlinear time series prediction, handwriting recognition, prediction in financial markets.

Plenty of other people also helped further the cause of “data science” and deserve credit, such as Jeff Wu who likely coined the phrase (in its contemporary usage) during his U Michigan appointment lecture “Statistics = Data Science?”

The main takeaway from this article:

Looking at decades of history, data science found its place by applying increasingly advanced mathematics for novel business cases, in response to surges in data rates and compute resources.

In the latest wave of AI applications in industry, we have the term ABC emerging to describe a winning combo of “AI”, “Big Data”, and “Cloud Computing” – as the latest embodiment of that takeaway described above.

Beyond the well-known roles of data scientist and data engineer, there’s another important role emerging which has not yet been named. We found that 23% of the enterprise organizations attempting to leverage data science, machine learning, artificial intelligence, etc., cite recognize business use case as a critically missing skill within their teams. What would you call that role? Where and how does a person learn to perform it?

Data Science - More explanations

Data Science in Manufacturing and Automation

https://www.slideshare.net/RavishankarRajagopal/data-science-in-manufacturing-and-automation

What is Data Science?

 In a 2009 McKinsey&Company article, Hal Varian, Google's chief economist and UC Berkeley professor of information sciences, business, and economics, predicted the importance of adapting to technology’s influence and reconfiguration of different industries. 2

“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”
https://datascience.berkeley.edu/about/what-is-data-science/

Chapter 1. Introduction: What Is Data Science?
Doing Data Science by Rachel Schutt, Cathy O'Neil
https://www.oreilly.com/library/view/doing-data-science/9781449363871/ch01.html

Data Science vs. Big Data vs. Data Analytics
By Shivam Arora
Jan 4, 2019
https://www.simplilearn.com/data-science-vs-big-data-vs-data-analytics-article

Oracle Artificial Intelligence (AI)—What Is Data Science?

An elaborate article giving many details of data science and related issues.

https://www.oracle.com/in/artificial-intelligence/what-is-data-science.html

Earlier Articles

What is Data Science? - An Introduction to Data Science

Data Science - Online Study Notes and Video Courses - Free Also

Data Analytics and Data Mining - Difference Explained


Updated on 10.7.2022,  31 May 2019, 26 May 2019

Product Industrial Engineering - Grenoble INP - Génie industriel, UGA

The Grenoble Institute of Technology is composed of

8 Grandes Ecoles specialised in different areas of Engineering and Management.

Grenoble INP - Génie industriel, UGA is the Industrial and Management Engineering School of the Institute.

https://genie-industriel.grenoble-inp.fr/en/about-us

 Product Industrial Engineering 1 - 3GMC0515

A+A-

Number of hours

Lectures : 3.0

Tutorials : 15.0

Laboratory works : 7.5

Projects : -

Internship : -

Written tests : 1.5

ECTS : 1.5 

Officials : Marie-Anne LE DAIN, Pierre CHEVRIER

Goals

• Carry out a piece of work in groups using a project-based approach (10 project teams with 12-13 students, each team member has a specific role to play within the project, use of project management tools...)

• Carry out an integrated, cross-sectional analysis in "industrial engineering" according to the following five approaches: Market approach, Product approach, Manufacturing Process approach, Production Management approach, Supply Chain approach.

Content

• From October to November: students organise themselves into 10 project teams and decide on the individual role of each team member. Students put forward a list of potential products and target companies to be contacted in the context of the particular project.

• From November to the end of March: descriptive, explanatory and prospective analysis of an existing product, chosen by the project teams and validated by the teaching staff. This analysis must include the five approaches, be based on bibliographic research as well as include interviews with professionals. A report must be written on this analysis.

• From April to May: after analysing the product, suggestions are to be made on how to improve the product. This piece of work will be presented orally in front of a panel of industrialists/business professionals and teaching staff.

• Tutorials (23hours). Instructions are given before each tutorial specifying the work to be carried out.

Two types of tutorials take place depending on the stage of the projects:

• Tutorial on 'the project' supervised by 2 tutors (staff members):

-Each project team chooses a project manager and a person responsible for managing data and documents.

-6 tutorials where students can talk with teachers about any difficulties encountered, the work carried out for the project and the progress of the project.

-2 tutorials to prepare for the oral presentation.

• Tutorial on 'approaches' supervised by 1 tutor (staff member):

-within each project team, students organise themselves into 5 "approach" groups and choose one person responsible for each approach.

-4 tutorials for sharing knowledge, providing information, helping out with methodology, providing assistance in developing an interview guide to follow when meeting industrialists in companies.

• 30 hours are also programmed in the timetable for general work on the project and visits to companies.

Tests

Evaluation from 1st exam period = the final project mark for each student is calculated on the following marks :

E1 = analysis approach based on documentary research (staff members)

E2 = oral presentation assessed by the panel (staff members and professionals)

Tutors will give students oral and written feedback about analysis approach and project reports. Feedback concerning the oral presentation will be given by the chairman of the panel.

No 2nd exam period except on jury decision

N1 = Final mark from 1st exam period

N2 = Final mark from 2nd exam period

N1 = 0,5*E1 + 0,5*E2

N2 = N1

Cette pondération est compatible avec une organisation des enseignements et des examens en distanciel

En cas d'évaluation à distance due à la crise sanitaire, E2 sera fait en visio conférence.

Calendar

The course exists in the following branches:

Curriculum - Engineer student Bachelor - Semester 5

see the course schedule for 2022-2023

Additional Information

Course ID : 3GMC0515

Course language(s): FR

https://genie-industriel.grenoble-inp.fr/en/studies/product-industrial-engineering-1-3gmc0515

https://genie-industriel.grenoble-inp.fr/en/studies/product-industrial-engineering-2-3gmc1115

https://genie-industriel.grenoble-inp.fr/en/studies/product-industrial-engineering-2-3guc1501

Full course list

https://genie-industriel.grenoble-inp.fr/en/studies/course-list

Meme Marketing

 

https://blog.hubspot.com/marketing/meme-examples

https://brandequity.economictimes.indiatimes.com/news/marketing/rise-of-meme-marketing-in-india/79809387

https://nealschaffer.com/meme-marketing/   - meme generator 

Practical Meme Marketing

https://imgflip.com/memegenerator/Change-My-Mind

Do it. It is Real Engineering. Industrial Engineering is Engineering Primarily.

Find 5 new engineering developments every day in elements related to facilities, products and processes in your organization and assess their use for industrial engineering. 

Best Practices in #IndustrialEngineering 

https://nraoiekc.blogspot.com/2022/06/do-it-it-is-real-engineering-industrial.html

292 hits in first 8 hours.  24.6.2022 7 pm to 3 am 25.6.2022.

550 hits by 4.30 pm on 25.6.2022

600 hits in the first 24 hours. For a blog post I consider 100 hits in the first 24 hours as good. I call it a trending post.

Post on the profile

https://www.linkedin.com/posts/narayana-rao-kvss-b608007_do-it-it-is-real-engineering-industrial-activity-6946084102445895680-Phq-

IISE community post

https://www.linkedin.com/feed/update/urn:li:activity:6946216164469420032

45,329 impressions - 325 likes - 4.7.2022

IE Network Community

https://www.linkedin.com/feed/update/urn:li:activity:6946084813086801920

22,809 impressions - 218 Likes - 4.7.2022

Business Horizons - Journal Information

 Business Horizons is the bimonthly journal of the Kelley School of Business, Indiana University. The editorial aim is to publish original articles of interest to business academicians and practitioners. Articles cover a wide range of topical areas within the general field of business, with emphasis on identifying important business issues or problems and recommending solutions that address these. Ideally, articles will prompt readers to think about business practice in new and innovative ways. Business Horizons fills a unique niche among business publications of its type by publishing articles that strike a balance between the practical and the academic. To this end, articles published in Business Horizons are grounded in scholarship, yet are presented in a readable, non-technical format such that the content is accessible to a wide business audience.

https://www.sciencedirect.com/journal/business-horizons/about/aims-and-scope

https://www.sciencedirect.com/journal/business-horizons/vol/65/issue/3

https://www.sciencedirect.com/journal/business-horizons/vol/65/issue/2

https://www.sciencedirect.com/journal/business-horizons/vol/65/issue/1

The open academic: Why and how business academics should use social media to be more ‘open’ and impactful

Ian P.McCarthy Marcel L.A.M.Bogers

The mission of Business Horizons is to publish research that practitioners can understand to help them change how they think and act. However, this mission remains an elusive ideal for many business school academics because they struggle to design and produce research capable of overcoming the “research-practice gap.” 

To help scholars address this gap, we explain why and how they should use social media to be more ‘open’ to connecting with, learning from, and working with academics and other stakeholders outside their field. We describe how social media can be used as a boundary-spanning technology to help bridge the research-practice gap. 

To do this, we present a process model of five research activities: networking, framing, investigating, disseminating, and assessing. 

Using research published in Business Horizons as an illustrative example, we describe how social media was used to make each activity more open. 

We present a framework of four social media enabled open academic approaches (connector, observer, promoter, and influencer) and outline some dos and don’ts for engaging in each approach. 

https://www.sciencedirect.com/science/article/abs/pii/S0007681322000453?via%3Dihub