May 31, 2021

Understanding Your Products & Processes Through IoT and Data Analytics



IoT Analytics Service Offerings from Indium

IoT Data Ingestion into a platform (Data Warehouse or Data Lake depending on data volume)
Data Streaming & Refresh – Real-time or Near Real-time
Data Processing – Loading, Cleansing, Transformation & Aggregation
Build Analytical Models based on Machine Learning
Create Visualization & Consumption Layers – Reports/Dashboards & Applications/Portals

The insights gained through our analytics are presented via:

Ad hoc reports: This helps drill down on any parameter and allow for custom reports to be built on the fly.

Alerts: These alerts are based on predefined rules and are generated automatically.

Standard reports: A predefined range of parameters are set, on which insights are provided at regular intervals data preparation




IoT and IIoT Case Studies

TVS Motor

Wiscon Products
https://iiot-world.com/industrial-iot/connected-industry/new-iiot-case-study-with-wiscon-products/


2016

Using IoT Data to Understand How Your Products Perform

HBR June 2016

General Electric has announced that it will spend $1 billion dollars onn IoT.

Survey of 795 large companies (average revenue of $22 billion) in North America, Europe, Asia-Pacific, and Latin America by TCS found that  average per-company spending on IoT initiatives — was $86 million in 2015.  It is projected to grow to $103 million by 2018.

 Use of IoT is increasing. Producers are installing sensors in their products.

But, only 6% of companies selling products with less than $100 price tags had embedded wireless sensors in their offerings. In contrast, 54% of companies whose products’ average sales price was between $1 million and $10 million did have digital sensors that communicated product performance back to them.

Four key elements to use  IoT to get the ultimate truth on product performance:


1. Getting customers to agree to have their products monitored, which in turn means giving them something of value in return.  Customer must have some benefit.

2. Product performance data must be processed and acted upon quickly. Real time data analysis capability has to be developed.

3. A culture that accepts the truth, however bad. Companies have to accept bad performance of their products and communicate to the customer the anticipated problems. Do rectification of them.

4. “Reimagining the business” must become the mantra.  It is like reengineering. Now branded as reimagining. Understand the services of IoT offers and redesign your business around the power and potential of IoT. Creative effort to use IoT in innovative way is required to see new things before others or understand what others have done through newspaper or digital media news.


Ud 1 June 2021
18.6.2016

May 19, 2021

Productivity Science, Productivity Engineering and Productivity Management




Productivity Science - Principle of Industrial Engineering

Develop a science for each element of a man - machine system's work related to efficiency and productivity.

The productivity science developed is the foundation for industrial engineering in productivity engineering and productivity management phases.
http://nraoiekc.blogspot.com/2017/06/productivity-science-principle-of.html


Productivity Engineering - Principle of Industrial Engineering

Industrial engineering is concerned with redesign of engineering systems with a view to improve their productivity. Industrial engineers analyze productivity of each  resource used in engineering systems and redesign as necessary to improve productivity.

It has to be ensured that the increase in productivity due to the use of low-cost materials, processes and increasing speed of machines and men, should not lead to any decrease in quality of the output.

Similarly, operators should not feel any discomfort, not have any health problems or safety issues in the redesigned more productive processes.
https://nraoiekc.blogspot.com/2017/06/productivity-engineering-principle-of.html

IISE 2020 Conference Paper

Framework of Productivity Science

Productivity Management- Principle of Industrial Engineering

Every industrial engineer is a productivity manager.
He has to plan for productivity and achieve productivity improvement year after year.

As a part of productivity management, he has to assess management actions of the organization for effect on productivity and has to recommend changes if they have an adverse effect on productivity or if there is scope for increasing productivity by modifying them.
https://nraoiekc.blogspot.com/2017/06/productivity-management-principle-of.html

Industrial Management Published Paper

productivity management

Productivity Thinking - An Explanation

Productivity thinking of a manager includes awareness of body of knowledge of  productivity science, productivity engineering and productivity management.
https://nraoiekc.blogspot.com/2019/11/productivity-thinking-explanation.html






________________________
________________________

IISE Annual Conference Program 2019


Work Systems & Services
Oral Presentation (20 minutes)

580470 - Productivity Science and Systems - Developments During 1895 to 1945

Tuesday, May 21
11:20 AM - 11:40 AM
Location: Gatlin A2

Learning Objectives:

  • F.W. Taylor described the system, he had used to increase productivity in 1895. More productivity improvement practices were described in his papers, "Shop Management" and "Scientific Management." Scientific management’s first principle advocates development of productivity science. Frank Gilbreth developed the science of productive human effort and proposed number of principles which became popular as "Principles of Motion Economy." In the paper, an attempt is made to highlight some important research works carried out in the area of productivity up to the year 1945 to trace the development of productivity science and systems after the need for developing them was indicated by F.W. Taylor. The purpose of the paper is to present an illustration of a collection of some scientific developments in productivity improvement that may motivate systematic development of body of productivity science in various issues related to productivity so that productivity engineering and productivity management activities are provided with scientific foundation for productivity improvement. Presently, an attempt to consolidate research output into productivity science is not taking place. Industrial engineering will have a more powerful knowledge framework if a well structured productivity knowledge base is made available as foundation for the phases of productivity engineering and productivity management.
  1. F.W. Taylor described the system, he had used to increase productivity in 1895. More productivity improvement practices were described in his papers, "Shop Management" and "Scientific Management." Scientific management’s first principle advocates development of productivity science. Frank Gilbreth developed the science of productive human effort and proposed number of principles which became popular as "Principles of Motion Economy." In the paper, an attempt is made to highlight some important research works carried out in the area of productivity up to the year 1945 to trace the development of productivity science and systems after the need for developing them was indicated by F.W. Taylor. The purpose of the paper is to present an illustration of a collection of some scientific developments in productivity improvement that may motivate systematic development of body of productivity science in various issues related to productivity so that productivity engineering and productivity management activities are provided with scientific foundation for productivity improvement. Presently, an attempt to consolidate research output into productivity science is not taking place. Industrial engineering will have a more powerful knowledge framework if a well structured productivity knowledge base is made available as foundation for the phases of productivity engineering and productivity management.
https://www.eventscribe.com/2019/IISE/fsPopup.asp?efp=SVNBQlBFWkY3NDMw&PresentationID=549392&rnd=0.9559785&mode=presinfo
_______________________

Learning Objectives:

  • F.W. Taylor described the system, he had used to increase productivity in 1895. More productivity improvement practices were described in his papers, "Shop Management" and "Scientific Management." Scientific management’s first principle advocates development of productivity science. Frank Gilbreth developed the science of productive human effort and proposed number of principles which became popular as "Principles of Motion Economy." In the paper, an attempt is made to highlight some important research works carried out in the area of productivity up to the year 1945 to trace the development of productivity science and systems after the need for developing them was indicated by F.W. Taylor. The purpose of the paper is to present an illustration of a collection of some scientific developments in productivity improvement that may motivate systematic development of body of productivity science in various issues related to productivity so that productivity engineering and productivity management activities are provided with scientific foundation for productivity improvement. Presently, an attempt to consolidate research output into productivity science is not taking place. Industrial engineering will have a more powerful knowledge framework if a well structured productivity knowledge base is made available as foundation for the phases of productivity engineering and productivity management.
  1. F.W. Taylor described the system, he had used to increase productivity in 1895. More productivity improvement practices were described in his papers, "Shop Management" and "Scientific Management." Scientific management’s first principle advocates development of productivity science. Frank Gilbreth developed the science of productive human effort and proposed number of principles which became popular as "Principles of Motion Economy." In the paper, an attempt is made to highlight some important research works carried out in the area of productivity up to the year 1945 to trace the development of productivity science and systems after the need for developing them was indicated by F.W. Taylor. The purpose of the paper is to present an illustration of a collection of some scientific developments in productivity improvement that may motivate systematic development of body of productivity science in various issues related to productivity so that productivity engineering and productivity management activities are provided with scientific foundation for productivity improvement. Presently, an attempt to consolidate research output into productivity science is not taking place. Industrial engineering will have a more powerful knowledge framework if a well structured productivity knowledge base is made available as foundation for the phases of productivity engineering and productivity management.
_______________________



Engineering Management
Oral Presentation (20 minutes)

580223 - Evolution of Productivity Management - Present Scope, Opportunity and Challenges

Tuesday, May 21
8:20 AM - 8:40 AM
Location: Wekiwa 7

Learning Objectives:

  • Frederick Taylor started productivity management theory development with his 1895 paper on piece rate system, and developed it further in "shop management" and "scientific management" papers. His methods were adopted in industrial engineering and operations management disciplines. Productivity, efficiency improvement and cost reduction as objectives of industrial engineering were indicated by many authors and scholars. AIIE also indicated the same by specially mentioning that performance from systems will be evaluated and predicted in industrial engineering. Scott Sink and David Sumanth came out with textbooks on productivity management. But a review of the curricula of industrial engineering and a survey reveal that productivity management is not yet an important area in teaching and practice. In this paper, an attempt is made to highlight the development of important productivity management theories and practices through literature review, curricula review, opinion of IE faculty and profit center managers. The current scope, opportunity and challenges for productivity management are brought out in the paper. The purpose of the paper is to point out the need for further development of the subject to convince academicians and practitioners of the utility of teaching and using productivity management systems and practices.
  1. Frederick Taylor started productivity management theory development with his 1895 paper on piece rate system, and developed it further in "shop management" and "scientific management" papers. His methods were adopted in industrial engineering and operations management disciplines. Productivity, efficiency improvement and cost reduction as objectives of industrial engineering were indicated by many authors and scholars. AIIE also indicated the same by specially mentioning that performance from systems will be evaluated and predicted in industrial engineering. Scott Sink and David Sumanth came out with textbooks on productivity management. But a review of the curricula of industrial engineering and a survey reveal that productivity management is not yet an important area in teaching and practice. In this paper, an attempt is made to highlight the development of important productivity management theories and practices through literature review, curricula review, opinion of IE faculty and profit center managers. The current scope, opportunity and challenges for productivity management are brought out in the paper. The purpose of the paper is to point out the need for further development of the subject to convince academicians and practitioners of the utility of teaching and using productivity management systems and practices.




https://www.eventscribe.com/2019/IISE/fsPopup.asp?Mode=presenterInfo&PresenterID=643406

----------------

Learning Objectives:

  • Frederick Taylor started productivity management theory development with his 1895 paper on piece rate system, and developed it further in "shop management" and "scientific management" papers. His methods were adopted in industrial engineering and operations management disciplines. Productivity, efficiency improvement and cost reduction as objectives of industrial engineering were indicated by many authors and scholars. AIIE also indicated the same by specially mentioning that performance from systems will be evaluated and predicted in industrial engineering. Scott Sink and David Sumanth came out with textbooks on productivity management. But a review of the curricula of industrial engineering and a survey reveal that productivity management is not yet an important area in teaching and practice. In this paper, an attempt is made to highlight the development of important productivity management theories and practices through literature review, curricula review, opinion of IE faculty and profit center managers. The current scope, opportunity and challenges for productivity management are brought out in the paper. The purpose of the paper is to point out the need for further development of the subject to convince academicians and practitioners of the utility of teaching and using productivity management systems and practices.
  1. Frederick Taylor started productivity management theory development with his 1895 paper on piece rate system, and developed it further in "shop management" and "scientific management" papers. His methods were adopted in industrial engineering and operations management disciplines. Productivity, efficiency improvement and cost reduction as objectives of industrial engineering were indicated by many authors and scholars. AIIE also indicated the same by specially mentioning that performance from systems will be evaluated and predicted in industrial engineering. Scott Sink and David Sumanth came out with textbooks on productivity management. But a review of the curricula of industrial engineering and a survey reveal that productivity management is not yet an important area in teaching and practice. In this paper, an attempt is made to highlight the development of important productivity management theories and practices through literature review, curricula review, opinion of IE faculty and profit center managers. The current scope, opportunity and challenges for productivity management are brought out in the paper. The purpose of the paper is to point out the need for further development of the subject to convince academicians and practitioners of the utility of teaching and using productivity management systems and practices.

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Industrial Engineering ONLINE Course

Online Handbook of Industrial Engineering


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________________________



Process Industrial Engineering - Video Presentation

https://www.youtube.com/watch?v=yIpkLPpsA18


Updated on  20 May 2021

12 November 2019, 16 September 2019

May 16, 2021

Processes Improvement Strategy - Manufacturing Strategy Component

 Chapter 7 Improvement Strategy (Slack & Lewis)

A large body of work has grown around how processes  can be developed, enhanced and generally improved.  

Processes improvement strategy explicitly declares the organisation's aspiration  to develop and improve processes on a more routine basis with important objectives, goals and methods specified as strategy. 

Importance of Improvement

Process Improvement gives competitive advantage.

‘The companies that are able to turn their . . . organisations into sources of competitive advantage are those that can harness various improvement programs . . . in the service of a broader [operations] strategy that emphasises the selection and growth of unique operating [capabilities].’

Process improvement

Two strategies are now recognized: breakthrough improvement and continuous improvement.

Breakthrough improvement

Breakthrough, or ‘innovation’-based, improvement assumes that the main vehicle of improvement is major and dramatic change in the way the operation works – the total redesign of a machining from general purpose machine tools to flexible manufacturing system. 

The impact of these improvements is relatively sudden, abrupt, and represents a step change in practice (and hopefully performance). Such improvements are rarely inexpensive, usually calling for high investment, and frequently involving changes in the product/service or process technology. 

A frequent criticism of the breakthrough approach to improvement is that such major improvements are, in practice, difficult to realise quickly.

Continuous improvement

Continuous improvement, as the name implies, adopts an approach to improving performance that assumes more and smaller incremental improvement steps. Industrial engineering studies are concerned with continuous improvement.  This has now became more popular as kaizen under the mania for using Japanese terminology. 

Continuous improvement does see small improvements as having one significant advantage over large ones – they can be followed relatively painlessly by other small improvements. Continuous improvement becomes embedded as the ‘natural’ way of working within the operation. 

Some emphasize that, in continuous improvement (exclusively bottom up improvement) it is not the rate of improvement that is important; it is the momentum of improvement. It does not matter if successive improvements are small; what does matter is that every month (or week, or quarter, or whatever period is appropriate) some kind of improvement has actually taken place.              

Both breakthrough improvements and continuous improvement are to be implemented by organizations. Break through improvements are spearheaded by product and process engineering departments. Continuous improvement related to productivity is directed and managed by industrial engineering department. Quality improvement is managed by quality department. Delivery reliability is the responsibility of production planning department. Improvement teams interact and collaborate.

Improvement strategy in Manufacturing Strategy. Three ways are to be planned. Improvements by Process Engineers - Improvements by Industrial Engineers - Improvements by Operators and their Supervisors and Managers. - Narayana Rao (4 Feb 2021. Shared on Social Media)


Improvement cycles

Continuous improvement occurs in cycles repeatedly  – a literally never-ending cycle of repeatedly questioning and adjusting the detailed workings of processes.

Degree of process change

Modification -  Extension - Development - Pioneer

Direct, develop and deploy


The strategic improvement cycle we describe  employs the three  elements of direct, develop and deploy, plus a market strategy element.

Direct. 

Some authorities argue that the most important feature of any improvement path is that of selecting a direction (Total Productivity Management). A company’s intended market position has to provide direction to  how the operations function builds up its resources and processes.

Even micro-level, employee-driven improvement efforts must reflect the intended strategic direction of the firm.

Develop. 

Within the operations function those resources and processes that are expected to contribute to competitive advantage are increasingly understood and developed over time so as to establish the capabilities of the operation. Essentially this is a process of learning.

Deploy. 

Operations capabilities need to be leveraged into the company’s markets. These capabilities, in effect, define the range of potential market positions which the company may wish to adopt. But this will depend on how effectively operations capabilities are articulated and promoted within the organisation.

Market strategy. The potential market positions that are made possible by an operation’s capabilities are not always adopted. An important element in any company’s market strategy is to decide which of many alternative market positions it wishes to adopt. Strictly, this lies outside the concerns of operations strategy. 

Process of Setting the Direction

Performance Planning Monitoring, and Control

Performance measurement

Performance measurement based on performance plans gives the inputs for planning improvements.

Performance measurement, concerns four generic issues:

● What factors should be included as performance targets?

● Which are the most important?

● How should they be measured?

● On what basis should actual against target performance be compared?


Which are the most important performance targets?

So, for example, an international company that responds to oil exploration companies’ problems during drilling by offering technical expertise and advice might interpret the five operations performance objectives as follows:

●● Quality. Operations quality is usually measured in terms of the environmental impact during the period when advice is being given (oil spillage, etc.) and the long-term stability of any solution implemented.

●● Speed. The speed of response is measured from the time the oil exploration company decides that it needs help to the time when the drilling starts safely again.

●● Dependability. Largely a matter of keeping promises on delivering after-the-event checks and reports.

●● Flexibility. A matter of being able to resource (sometimes several) jobs around the world simultaneously, i.e. volume flexibility.

●● Cost. The total cost of keeping and using the resources (specialist labour and specialist equipment) to perform the emergency consultations.


Some typical partial measures of performance



Quality  


Number of defectives of the process per unit time

Level of customer complaints

Scrap level

Warranty claims

Mean time between failures of the product

Customer satisfaction score


Speed


Order lead time

Actual versus theoretical throughput time

Cycle time


Dependability

Percentage of orders delivered late

Average lateness of orders

Proportion of products in stock

Schedule adherence


Flexibility

Time needed to develop new products/services

Range of products/services

Machine change-over time

Average batch size

Time to change schedules


Cost  

Productivity of resources

Utilisation of resources

Labour productivity

Added value

Efficiency

Cost per operation/process hour



Benchmarking

Another very popular, method  to drive organisational improvement is to establish operational benchmarks. By highlighting how key operational elements ‘shape up’ against ‘best in class’ competitors, key areas for focused improvement can be identified. 

Types of benchmarking


There are many different types of benchmarking  some of which are listed below.

● Competitive benchmarking is a comparison directly between competitors in the same, or similar, markets.

● Non-competitive benchmarking is benchmarking against external organisations that do not compete directly in the same markets.

● Performance benchmarking is a comparison between the levels of achieved performance in different operations. 

● Practice benchmarking is a comparison between an organisation’s operations practices, or way of doing things, and those adopted by another operation.


Importance–Performance Mapping

From market requirements the following are established:

● the needs and importance preferences of customers; and

● the performance and activities of competitors.

Both importance and performance have to be brought together before any judgement can be made as to the relative priorities for improvement. Both importance and performance need to be viewed together to judge improvement priority.

The importance–performance matrix

The priority for improvement that each competitive factor should be given can be assessed from a comparison of their importance and performance. This can be shown on an importance–performance matrix which, as its name implies, positions each competitive factor according to its score or ratings on these criteria. 

The sandcone theory

Some writers  believe that there is also a generic ‘best’ sequence in which operations performance should be improved. The best-known theory of this type is sometimes called the sandcone theory. The theory originally proposed by Kasra Ferdows and Arnoud de Meyer is as follows.

The sandcone model incorporates two ideas. 

The first is that there is a best sequence in which to improve operations performance; the second is that effort expended in improving each aspect of performance must be cumulative. In other words, moving on to the second priority for improvement does not mean dropping the first, and so on.

Developing operations capabilities

Underlying the whole concept of continuous improvement is the idea that  small changes, continuously applied, bring big benefits as cumulative total. Small changes are relatively minor adjustments to resources and processes and the way they are used. It happens through  the way in which humans learn to use and work with their operations resources and processes. Their understanding, ingenuity and creativity is the basis of capability development. 

Learning, therefore, is a fundamental part of operations improvement. Here we examine two views of how operations learn. The first is the concept of the learning curve, a largely descriptive device that attempts to quantify the rate of operational improvement over time. The second is  operations’ learning driven by the cyclical relationship between process control and process knowledge.

Process knowledge

Central to developing operations capabilities is the concept of process knowledge. 

The more we understand the relationship between how we design and run processes and how they perform, the easier it is to improve them.

For most operations manufacturing persons have at least some idea as to why the processes behave in a particular way. The path of process improvement is along  operations managers attempt to learn more. It is useful to identify some of the points along this path. 

One approach to this has been put forward by Roger Bohn. He described an eight-stage scale ranging from ‘total ignorance’ to ‘complete knowledge’ of the process.

● Stage 1, Complete ignorance. There is no knowledge of what is significant in processes. 

● Stage 2, Awareness. There is an awareness that certain phenomena exist and that they are probably relevant to the process, but there is no formal measurement or understanding of how they affect the process. Managing the process is far more of an art than a science, and control relies on tacit knowledge (that is, unarticulated knowledge within the individuals managing the system).

● Stage 3, Measurement. There is an awareness of significant variables that seem to affect the process with some measurement, but the variables cannot be controlled as such. The best that managers could do would be to alter the process in response to changes in the variables.

● Stage 4, Control of the mean. There is some idea of how to control the significant variables that affect the process.  Managers can control the average level of variables in the process, even if they cannot control the variation around the average. Once processes have reached this level of knowledge, managers can start to carry out experiments and quantify the impact of the variables on the process.

● Stage 5, Process capability. The knowledge exists to control both the average and the variation in significant process variables. This enables the way in which processes can be managed and controlled to be written down in some detail. 

● Stage 6, Know how. By now the degree of control has enabled managers to know how the variables affect the output of the process. They can begin to fine-tune and optimise the process.

● Stage 7, Know why. The level of knowledge about the processes is now at the ‘scientific’ level with a full model of the process predicting behaviour over a wide range of conditions. At this stage of knowledge, control can be performed automatically, probably by microprocessors. The model of the process allows the automatic control mechanisms to optimise processing across all previously experienced products and conditions.

● Stage 8, Complete knowledge. In practice, this stage is never reached, because it means that the effects of every conceivable variable and condition are known and understood, even when those variables and conditions have not even been considered before. Stage 8 therefore might be best considered as moving towards this hypothetically complete knowledge.

Source: from Bohn, R.E. (1994) ‘Measuring and managing technical knowledge’, MIT Sloan Management Review, Fall 1994, article no. 3615.


The  importance of  routines of process control

One of the most important sources of process knowledge is the routines of process control. Process control, and especially statistically based process control results in knowledge. Knowledge it is vital to establishing an operations-based strategic advantage.


Ch. 10. Sharpening the Edge: Driving Operations Improvement

in Operations, Strategy, and Technology: Pursuing the Competitive Edge

Robert Hayes, Gary Pisano, David Upton and Steven Wheelwright

2005

https://bcs.wiley.com/he-bcs/Books?action=contents&itemId=0471655791&bcsId=2135



Sections

Introduction

A Framework for Analyzing Organizational Improvement


"Within" vs. "Across" Group Improvement


Learning by Doing vs Learning Before Doing


Transferring Learning from Outside the Organization


Breakthrough vs Incremental Improvement


A Framework for Improvement Activities - with Two Examples


Organizational Implications of Different Approaches






Updated on 17 May 2021,  28 Feb 2021,  4 February 2021

Published on 31 Jan 2021

May 6, 2021

Design and Development of Knowledge Management for Manufacturing: Framework, Solution and Strategy - Ganesh et al. Book Information



Design and Development of Knowledge Management for Manufacturing: Framework, Solution and Strategy (Google eBook)


K. Ganesh, Sanjay Mohapatra, S. Nagarajan
Springer Science & Business Media, Nov 19, 2013 - 213 pages
0 Reviews

This book examines the modules/elements required before implementing knowledge management solutions in typical manufacturing and service industry. The objective is to develop a framework, design and model suitable for all requirements and a strategy to properly implement. Related case studies from organizations are included, with the results provided to use as a solution to problems experienced when implementing knowledge management in the industry.Implementing a knowledge management system can be complex and dynamic, no matter how well planned and developed. Inevitably a degree of organizational inertia is focused on the current state rather than the new. Within an enterprise, personal and group involvement and interests process status and technology landscape can deflect the commitment needed to successfully implement such a system. Cumulative evidence from past research in knowledge management suggests that effective implementation of KM solution in any organization requires a robust designs and models for various critical elements of process, people and technology. Using the techniques provided in this book, readers should be able to design knowledge management strategies, to align objectives of the KM initiatives with their business goals.

https://books.google.co.in/books?id=VkvABAAAQBAJ

Knowledge Management: Theoretical Foundations
Johannes Britz · 2008
Full view book














March 22, 2021

Digital Transformation of Business and Industrial Organizations - Subject Update


What is Management? A New Look:

Management Definition – Narayana Rao


Management of an organization is the process of establishing objectives and goals of the organization periodically, designing the work system and the organization structure, and maintaining an environment in which individuals, working together in groups, accomplish their aims and objectives and goals of the organization effectively and efficiently. (3rd December 2008)

Implications of the  Definition:

(i) Management is a process.
(ii) Management applies to every kind of organization, government, profit making, or nonprofit making.
(iii) It applies to managers at all levels in the organization.
(iv) Management is concerned with effectiveness and efficiency.

Video Explanation of  the  Definition

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2021

Mar 15, 2021  Montornès / Spain

Recognition for Industry 4.0 Leadership

World Economic Forum recognizes Henkel as frontrunner in the 4th Industrial Revolution for the second time

2020
The state of digital transformation in 2020
The lessons learned from a decade of digital transformation provide a roadmap for those embarking on their own initiatives. CIO, Computerworld, CSO, InfoWorld, and Network World team up to dissect that legacy from every angle.   
Eric Knorr, Editor in Chief, CIO | 13 JANUARY 2020
https://www.cio.com/article/3513849/the-state-of-digital-transformation-in-2020.html


2019



2019 McKinsey - Top 10 Digital
https://www.mckinsey.com/~/media/McKinsey/Email/Top-Ten/2019/2019-12-23a-TopTen.html

EDITORS' PICK- Dec 16, 2019,
100 Stats On Digital Transformation And Customer Experience
Blake Morgan
https://www.forbes.com/sites/blakemorgan/2019/12/16/100-stats-on-digital-transformation-and-customer-experience/#68dd47b83bf3

A winning operating model for digital strategy

January 2019


  • Four important areas in how companies with the best economic performance approach digital strategy,
  • The best performers have increased the agility of their digital-strategy practices, which enables first-mover opportunities.
  • They have taken advantage of digital platforms to access broader ecosystems and to innovate new digital products and business models.
  • They have used M&A to build new digital capabilities and digital businesses.
  • They have invested ahead of their peers in digital talent.

https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/a-winning-operating-model-for-digital-strategy


Five moves to make during a digital transformation

April 2019


  • Ruthlessly focus on a clear set of objectives
  • Be bold when setting the scope
  • Create an adaptive design
  • Adopt agile execution approaches and mind-sets
  • Make leadership and accountability crystal clear



Looking ahead

  • Raise the bar on leadership alignment and commitment.
  • Build in flexibility with clearly defined handoffs.
  • Enforce survival of the fittest among digital initiatives.

https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/five-moves-to-make-during-a-digital-transformation

Digital transformation: Improving the odds of success
October 2019
Our latest research shows that exceptionally effective digital transformations are distinguished mostly by the practices that executives choose to follow.  Emerging from that analysis were five thematic groups of practices that particularly move the performance needle.


  • Laying out clear priorities.
  • Investing in talent—especially at the top. 
  • Committing time and money. 
  • Embracing agility.
  • Empowering people.

https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/digital-transformation-improving-the-odds-of-success

Top 10 influencers in digital transformation you should follow in 2019

Dez Blanchfield – Chief Data Scientist, Gara Guru
Ken Bonifay – North America Sales Leader – Financial Services Market, IBM
Sally Eaves – Member, Forbes Technology Council
Antonio Grasso – Founder & CEO, Digital Business Innovation Srl
Dr. Robin Kiera – Speaker, Thought Leader, Storyteller, DigitalScouting.de
Joe McKendrick – Analyst and Contributor, CBS Interactive, Forbes, Information Today
Mike Quindazzi – Managing Director US Digital Alliances, PwC
Jacqui Shawley – Global Market and Offering Manager, IBM Z
Ronald van Loon – Director, Adversitement
Helen Yu – Founder & CEO, Tigon Advisory Corp.
https://www.ibm.com/blogs/systems/top-10-people-digital-transformation-2019/

Are your operations adapting as fast as your markets are changing?
https://www.ey.com/en_gl/tmt/are-your-operations-adapting-as-fast-as-your-markets-are-changing

2018

January

Digital Transformation - Principles, Strategies and Rules


Digital Supply Chain at Corning - Lora Cecere
https://www.slideshare.net/loracecere/corning-presentation-from-the-supply-chain-insights-global-summit-2018

Advice and Suggestion from Jeff Immelt -
Leaders must act as Systems Thinkers in Digital Transformation.


Leaders have to understand how cyber - physical systems in combination deliver value to customers.

Marketing chain and Supply Chain are to be integrated and cyber - physical systems exist in both the chains. Digital and Physical systems and people have to work in combination and deliver value to customers. Leaders and managers have to first visualize and design the work system.


https://www.linkedin.com/pulse/leaders-must-act-systems-thinkers-digital-jeff-immelt

Transform Your Business to Compete in the Digital Age

Columbia Business School
uploaded 19 January 2018
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#207: Digital Transformation and disruption in the Insurance Industry, with UNIQA Insurance Group



2017

KPMG declared leader in digital transformation consulting
Jul 12, 2017

KPMG was recently named a leader in the IDC MarketScape awards titled “Worldwide Digital Transformation Consulting and Systems Integration Services 2017 Vendor Assessment”. The award process noted that under times of turbulent change and economic challenges many firms around the world were calling upon KPMG to help them through the process challenges. KPMG have been identified for their strength in aiding organizations to begin the transformation process, and the disruptions this can bring, while maintaining their place within a competitive market.
http://www.digitaljournal.com/business/kpmg-declared-as-a-leader-in-digital-transformation-consulting/article/497459


Evolve Or Die: Why Digital Transformation Is More Important Than Ever
Four areas of focus for Digital transformation
http://www.brandquarterly.com/evolve-die-digital-transformation-important-ever

Davos 2017 - Preparing for the Fourth Industrial Revolution

World Economic Forum
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Published on 19 Jan 2017
http://www.weforum.org/
What is needed from the public and private sectors to ensure that the Fourth Industrial Revolution benefits all of humanity?

- Mukesh D. Ambani, Chairman and Managing Director, Reliance Industries, India
- Mary Barra, Chairman and Chief Executive Officer, General Motors Company, USA
- Marc R. Benioff, Chairman and Chief Executive Officer, Salesforce, USA; Young Global Leader Alumnus
- Shu Yinbiao, Chairman, State Grid Corporation of China, People's Republic of China
- Vishal Sikka, Chief Executive Officer, Infosys, USA

Chaired by
- Ngaire Woods, Dean, Blavatnik School of Government, University of Oxford, United Kingdom



Davos 2017 - Press Conference: The Digital Transformation of Industries

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__________________
World Economic Forum,  Published on 19 Jan 2017
http://www.weforum.org/

The World Economic Forum launched the multi-year Digital Transformation of Industries Project in 2015 in partnership with Accenture. The project looks at how digital technologies like machine learning, cloud computing, and the internet of things are changing existing industries and creating entirely new ones. It makes recommendations to incumbents on how they can thrive in the new digital environment.

There are now hundreds of startups attacking traditional markets as a result of the democratization of technology, increased access to funds and a rising entrepreneurial culture. Start-ups are achieving scale far quicker than analogue companies ever did. Average Fortune 500 companies took 20 years to reach a market cap of $1 billion, Google managed it in eight years, and the likes of Uber, Snapchat and Xiaomi in three years or less. 88% of Fortune 500 companies from 1955 – 2015 no longer exist. Digital disruption has been a major driver of this, consequently Fortune 500 companies must use their considerable resources to fight off the attacks of leaner challengers and remain relevant in a digital age.

- Bruce Weinelt, Head of Digital Transformation, World Economic Forum
- Peter Lacy, Global Managing Director, Strategy and Sustainability, Accenture
- Stephanie Linnartz, Global Chief Commercial Officer, Marriott International Inc.
- Jean Philbert Nsengimana, Minister of Youth and Information Communication Technology,        Ministry of Youth and Information and Communication Technology of Rwanda

Moderated by
- Alem Tedeneke, Media Specialist, World Economic Forum LLC

2016


3 Industries That Will Be Transformed By AI, Machine Learning And Big Data In The Next Decade
Bernard Marr
SEP 27, 2016
Healthcare - Finance - Insurance

https://www.forbes.com/sites/bernardmarr/2016/09/27/3-industries-that-will-be-transformed-by-ai-machine-learning-and-big-data-in-the-next-decade/#2104a850183e


Acting on the Digital Imperative - BCG

SEPTEMBER 12, 2016
By Ralf Dreischmeier , Karalee Close , Thomas Gumsheimer , Peter Hildebrandt , and Adal Zamudio
https://www.bcg.com/publications/2016/technology-digital-strategy-acting-on-digital-imperative.aspx

Digital Transformation Playbook

Prof. David Rogers, Columbia Business School
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June 2016, Columbia Business School

https://cup.columbia.edu/book/the-digital-transformation-playbook/9780231175449



The Digital Imperative  - Article by BCG

By Ralf Dreischmeier , Karalee Close , and Philippe Trichet
MARCH 2, 2015
https://www.bcg.com/publications/2015/digital-imperative.aspx



Updated on 2021 - 23 March
2020 - 15 June
2019  27 December 2019,   11 August 2019, 31 May 2019
2018 - 3 February 2018,   25 January,  19 January


 25 November 2017,  26 August 2017,   22 August 2017, 7 June 2017, 14 April 2017

March 3, 2021

Transportation in the Supply Chain - Chopra and Meindl - Review Notes



Chopra and Meindl's book, Supply Chain Management: Strategy, Planning, and Operation, a comprehensive introduction on supply chain management.

The product is to be moved from one location to the other and at the end it has to be in hands of the premises of the customer. Rarely production and consumption of an item takes place at the same location. Transportation cost is a significant item in the cost sheets of a supply chain.

Key Players in Transportation Activity

Shippers and Carriers are the two key players in the transportation activity. Shippers require the movement of products from place to place. Carriers provide the transportation service.
Modes of Transport
1. Air
2. Truck
3. Rail
4. Water
5. Pipeline
6. Intermodal or multimodal
7. Package Carriers



Costs of Carrier



1. Vehicle related cost
2. Fixed operating cost
3. Trip related cost
4. Quality related cost
5. Overhead cost


Cost Items Considered by Shippers in Transportation Decisions



1. Transportation cost
2. Inventory cost
3. Facility cost
4. Processing cost
5. Service level cost

Design Options for a Transport Network

A  complex supply chain may have number of suppliers supplying a variety of components and sub assemblies to a multiple manufacturing facilities of a final assembler. From these multiple assembly facilities a number of products may be produced and distributed to a large number of retailers. A large number of suppliers and a large number of receiving points create various options for design of transport networks.


1. Direct shipment network
2. Direct shipping with milk runs
3. Shipments via  central distribution centre
4. Tailored network
  • Tailored Transportation
  • Tailored Transportation by Customer Density and Distance
  • Tailored Transportation by Size of Customer
  • Tailored Transportation by Product Demand and Value

Routing - Scheduling Decisions in Transportation

Savings Matrix method
Steps
1. Identify the distance matrix: distance between each pair of locations to be visited.
2. Identify the savings matrix: The savings that results from using only one truck to two locations
3. Assign locations to trucks.
4. Sequence customers within routes
For details visit

Generalized Assignment Method
1. Assign seed points for each route
2. Evaluate insertion cost for each customer
3. Assign customers to routes
4. Sequence customers within  routes
For details visit


Some More Guidelines for Making Transportation Decisions in Practice


Align transportation strategy with competitive strategy.
Consider both in-house and outsourced transportation.
Use technology to improve transportation performance.
Design flexibility into the transportation network
Under the facilities available to enable e-commerce by your company




References

Sunil Chopra and Peter Meindl, Supply Chain Management: Strategy, Planning and Operations, Prentice Hall, 2001. Supply Chain Management: Chopra and Meindl - Book Information and Review
Original knol - http://knol.google.com/k/narayana-rao/transportation-in-supply-chains/2utb2lsm2k7a/   1376

Updated on 3 March 2021
pub on 9 Dec 2011

Information Technology and the Supply Chain



Based on Chopra and Meindl's book, Supply Chain Management: Strategy, Planning, and Operation - A comprehensive introduction to  supply chain management.




INFORMATION TECHNOLOGY AND SUPPLY CHAIN



The supply chain management (SCM) is concerned with the flow of products and information between the supply chain members that encompasses all of those organizations such as suppliers, producers, service providers and customers. In the supply chain, these organizations linked together to acquire, purchase, convert/manufacture, assemble, and distribute goods and services, from suppliers to the ultimate and users.


The cost and availability of information resources allow easy linkages and eliminate information-related time delays in any supply chain network. Organizations are adopting Electronic Commerce, where transactions are completed via a variety of electronic media, including electronic data interchange (EDI), electronic funds transfer (EFT), bar codes, fax, automated voice mail, CD-ROM catalogs, and a variety of others. The old “paper” type transactions are becoming increasingly becoming obsolete. Leading-edge organizations no longer require paper purchase requisitions; purchase orders, invoices, receiving forms, and manual accounts payable “matching” process. All required information is recorded electronically right at the origin, and associated transactions are performed with the minimum amount of human intervention.  With the application of the appropriate information systems, monitoring inventory levels, placing orders, and expediting orders will soon become totally automated.

IMPORTANCE OF INFORMATION



The information systems and the technologies utilized in the supply chain represent one of the fundamental elements that link the organizations into a unified and coordinated system. In the current technology and process environment, little doubt remains about the importance of information and information technology to the ultimate success, and perhaps even the survival, of any supply chain management initiative. Cycle time reduction, implementing redesigned cross-functional processes, utilizing cross-selling opportunities require information. Timely and accurate information is more critical now than at any time.

Three factors have strongly impacted this change in the importance of information.

1) Satisfying customers have become something of a corporate obsession. Serving the customer in the best, most efficient and effective manner has become critical, and information about issues such as order status, product availability, delivery schedules, and invoices has become a necessary part of the total customer service experience.

2) Information is a crucial factor in the managers’ abilities to reduce inventory and human resources requirements to a competitive level.


3) Information flows play an essential role in the strategic planning for and deployment of resources.


The need for virtually seamless bonds within and between organizations is a key notion in the essential nature of information systems in the development and maintenance of successful supply chain. That is, creating intra-organizational processes and link to facilitate delivery of seamless information between marketing, sales, purchasing, finance, manufacturing, distribution and transportation internally, as well as inter organizationally, to customers, suppliers, carriers across the supply chain will improve fill rates of the customers service, increase forecast accuracy, reduction in the total inventory and savings in the company’s’ transportation costs - goals which need to be achieved.

In fact, inaccurate or distorted information from one end of a supply chain to the other can lead to tremendous inefficiencies such as excessive inventory investment, poor customer service, lost revenues, misguided capacity plans, ineffective transportation, and missed production schedules. Bullwhip effect, which is big variability in orders at factory level  is commonly experienced by the consumer goods industries due to lack of uniform information in the entire supply chain. Suitable technologies such as bar codes and scanners have been developed and applied in the supply chain to remove inaccuracy, time delays and gaps in communications.

Information Required to Manage Supply Chain at Global Scope Level


Supplier/Supply Information


What products can be purchased, at what price, with what lead time, and where they can be delivered. Supplier information also includes real time pending order status, purchase order amendments, and payment arrangements. This information can be used in product industrial engineering also.

Manufacturing Information


What products can be made, how many, by what facilities, with what lead time, with what trade-offs, at what cost, and in what batch size. This information can be used in process industrial engineering also.

Distribution and Retailing Information

Demand Information

Information must have the following characteristics to be of value in decision making.

1. It must be accurate.
2. It has to be accessible in a timely manner.
3. It has to be of right kind.


Existing IT Systems in Supply Chain

Legacy Systems

ERP Systems

Analytical Applications

Procurement and Content Cataloging Applications

Advanced Planning and Scheduling

Transportation Planning and Content Systems

 Demand Planning and Revenue Management

Customer Relationship Management (CRM) and Sales Force Automation (SFA)

Supply Chain Management Systems

Supply Chain Management (SCM) Systems are a combination of many of the preceding applications. They are delivered in a tightly integrated modules that span all the activities of supply chain: strategy/design, annual planning and operations over shorter periods.

Applications Focused on Operational Issues

Inventory Management Systems
Manufacturing Execution Systems
Transportation Execution 
Warehouse Management System


Some More Guidelines on IT Systems in Supply Chain

Select an IT system that addresses the company's key success factors
Align the level of sophistication with the need for sophistication
Think about the future.


e-business and the Supply Chain. - Review Notes

Global Complexity is driving Supply Chain Information  Systems into Cloud Wharton Knowledge Article January 2011


Software for Supply Chain Analytics, Communication, Information, Planning & Control  and Management


2021

Infor Products - Infor Nexus™


Gartner Magic Quadrant Leader for Multienterprise Supply Chain Business Networks
https://www.infor.com/products/infor-nexus

Oakland, Calif.-based GT Nexus,  runs the largest cloud-based collaborative platform for logistics, trade and transportation managers. It was acquired by Infor.



Siemens Supply Chain Suite (SCS)

The Supply Chain Suite (SCS) lets you design, monitor, manage, and understand your entire supply chain. The holistic, data-driven view of the supply chain lets you design processes more efficiently and cut costs, leaving you ideally positioned to meet the challenges of next-generation industry.

SCS gives you a state-of-the-art IT platform, so you can harness the power of data to analyze, simulate, and optimize any complex logistical tasks.


MONITORING YOUR SUPPLY CHAIN
SCS brings extended supply chain visibility to your business. It does so by consolidating data from all sources and formats (ERP data, unstructured data), refining it, then expanding it in a flexible, configurable data model.

Easily glean logistical information from distributed, inconsistent data.


UNDERSTANDING YOUR SUPPLY CHAIN
The standardized data models in SCS offer powerful simulation and analysis tools to run business and logistical evaluations.

Use key performance indicators to evaluate your entire supply chain.


DESIGNING YOUR SUPPLY CHAIN
SCS lets you simulate and optimize the logistical processes in your supply chain. Here you’ll find many areas for tapping into your strategic and tactical potential, and you’ll be able to identify conflicts before they occur.

Model and simulate changes, tap into your potential for optimization.


MANAGING YOUR SUPPLY CHAIN
SCS lends a hand with the operational aspects of warehouse and transport processes—in system-supported calls for tenders and contracts, and in the day-to-day work with warehouse strategies and transport planning. SCS also lets you leverage the operational potential of your supply chain.

Transfer operational improvements from the system directly onto the road.



2019

London UK, 14th June - Siemens Digital Industries Software announced today the immediate availability of Siemens Opcenter™ software, a cohesive portfolio of software solutions for manufacturing operations management (MOM).

Siemens Opcenter integrates MOM capabilities including advanced planning and scheduling, manufacturing execution, quality management, manufacturing intelligence and performance, and formulation, specification and laboratory management. The new portfolio combines products including Camstar™ software, SIMATIC IT® suite, Preactor, R&D Suite and QMS Professional into a single portfolio that unifies these widely recognised products and leverages synergies between them. A fully web-based, modern, consistent, adaptive and comfortable user interface implemented throughout the Siemens Opcenter portfolio offers a situationally adapted user experience and facilitates the implementation of new capabilities and additional components while reducing training efforts.

http://www.connectingindustry.com/DesignSolutions/siemens-launches-siemens-opcenter-a-new-unified-portfolio-of-manufacturing-operations-management-solutions--.aspx


Updated 3 March 2021,  22 June 2019,  10 Apr 2016
9 Dec 2011