Manoj Trivedi, Business Mentor in Economists and Finance, Production & Manufacturing, LinkedIn Founder | Director | Business Mentor • iGlobal Research and Analytics Nov 13, 2018 · 3 min read · +500

Artificial Intelligence and Manufacturing Limitations

Artificial Intelligence and Manufacturing Limitations

            Artificial Intelligence and Manufacturing Limitations


                                                             Manoj Kumar Trivedi

                                                                   Founder | Director | Business Mentor | Implementer

                                                   iGlobal Research and Analytics

In a recent survey by New Vantage Partners as reported by #IBM, 81% of Business houses across globe have no idea of requirement of Data in the age of Artificial Intelligence. Out of total Fortune 1000 Executives surveyed, 73% have said they got measurable results,while, 24% acknowledged transformative & innovative results.

AI draws upon knowledge and techniques from mathematics, statistics, computer science and domain-specific expertise to create models, software programs and tools. These software programs and tools can undertake complex tasks with outcomes that are comparable, if not better, to traditional manual approaches.

Growth is directly proportionate to shrinking leadership ability.Disruptions and growing business complexities are the biggest organisational dilemma. No leader can wear all the hat to claim he has full knowledge of business process across entire organisational domain to define about the data requirement and integrate in the system for necessary analysis and best results.

A basic level of human capital, such as literacy and numeracy, is needed for economic survival while addressing following fundamental challenges for being competitive enduring growth and stay relevant under new normal. The growing role of technology in life and business means that all types of jobs (including low-skill ones) require more advanced cognitive skills (The Changing Future of Work by World Bank Group).

  • Arranging real-time matrix to monitor, control & ensure dignified return in cost of capital employed
  • Leveraging Operation, they have to arrange econometrics for real-time monitoring of operational cost weighing decisions against real-time cost while neutralizing macro economic implications at micro level of operation.Productivity analysis and analysing each activity of the business using tools of AI is mandatory and obligatory on the part of Business Leaders for being competitive and stay relevant under new normal and VUCA world.
  • Digitization with real-time data accessibility, both structured and unstructured data.
  • Ensure high quality reliable data.
  • Clear vision of various reports required to analyse the operation on real-time basis, for cost and financial analysis, customer analytics etc
  • Requirement of relevant talent. This may add up to cost several times.

Technology is blurring the boundaries of the firm, as evident in the rise of platform marketplaces. Using digital technologies, entrepreneurs are creating global platform–based businesses that differ from the traditional production process in which inputs are provided at one end and output delivered at the other. Technology is also reshaping the skills needed for work. The demand for less advanced skills that can be replaced by technology is declining. At the same time, the demand for advanced cognitive skills, sociobehavioral skills, and skill combinations associated with greater adaptability is rising. Already evident in developed countries, this pattern is starting to emerge in some developing countries as well.

Hiring Data Scientists for this purpose will in no way help industries. They will have in no way relevant Business Knowledge of respective domains. There cannot be One-Size-Fit-All solutions for every industries. It varies depending upon product and process.Data Scientists are broadly covered under broad two categories- Modelling Scientists with relevant IT Skill and Decision Scientists with relevant domain expertise to help Modelling Scientists.

Upskilling existing IT professional with required IT skills to build necessary AI infrastructure in-house is the best option. This is in view of maintaining #DataPrivacy. Additionally, upskilling white collars & non-IT professionals of respective domain in line with requirements as enumerated above is the best available option to contain cost.
The reason behind, such skills, though not available, are being valued in the market between US$1,23,000 - 2,28,000. Such employment cost is sure to create bankrupt situation.

With our four decades of industrial experience across diverse industries, we help creating leadership/potentials in defining NEW ROLE across functions/domains with required business knowledge/skill of #FourthIndustrialRevolution to stay relevant under new normal. We also help in-house digitization as may be required.In practical application of Economic Science with business main stream of operation using Econometrics, we are SECOND-TO-NONE.
We offer our services through:

  • Two days educative discourse on ECONOMIC ENGINEERING & ECONOMETRICS suitable for Policy Makers, Decision Makers & Executors
  • We are also available to implement and help digitization at respective industries/organizations, as may be desired, while upskilling white collars across heirarchy.

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#5 As the title says, instant article is for Manufacturing houses and its limitations.With leadership across four decades, probably I am fully aware of the gap. Data mining and data analytics has always find its place in business houses to derive various strategy and needs no mention. Now with facilities and concentration of data, such analysis will be more. To analyse from various perspective, domain expertise is obligatory, particularly for being competitive in VUCA world. The purpose of this article is totally different. Unless we have domain expertise across various functions we cannot build AI infrastructure defining our requirement. I wonder how without stats, algorithm and required software AI tools will work !!!. Not being novice, I am amply clear of the requirement and need no research. There is no one-size-fit-all solution for every business houses, I reiterate success will depend upon domain expertise only to deploy digital support assistant.

Zacharias 🐝 Voulgaris Nov 14, 2018 · #5

#4 Indeed, the human must always be in the loop! No matter how much A.I. advances, humans will always be necessary in complex processes, even if it's just for the different perception we have. However, let's get the definitions right first. The fact that you mention ML without any reference to data analytics, as if it's just another term for A.I. goes on to show that perhaps some more research on this matter is needed before we can have a fruitful discussion. Cheers


#2 I could have agreed with you @Zacharias Voulgaris provided AI tools are having their own mind and consciousness. Machine learning is neither inborne nor inherited. It has to be educated by human consciousness & intelligence only. To do so, human must have required knowledge of each business domain.I say this with conviction, no person or leader can wear all the hat to build AI infrastructure. Reference is IBM itself (stating 81% of business houses across globe have no idea of data requirement) beside several articles by World Economic Forum to concede "The world will always need human brilliance, human ingenuity and human skills" This is no fiction please. If we think machine will operate of itself to add value to organizations by its own, probably we are building castles in the air for fatal accident ahead. Organizations will have to build their own competence. Machine will be machine after all as support assitant only improving execution level facilitating complex decision but will always fall short of deciding to execute. Global business will not be subject to any individual perception and limited practical experience to state "spreading misinformation". Instead such a word is dangerous to undermine one's own image while undermining others.

Zacharias 🐝 Voulgaris Nov 13, 2018 · #2

I'm sorry, but I just couldn't take this article seriously after I read this part here:
"AI draws upon knowledge and techniques from mathematics, statistics, computer science and domain-specific expertise to create models, software programs and tools."

To make such a statement you either need to have some darn good reference to support it or just label this text as fiction and rely on people's willingness to not pass judgment of it and focus on the plot instead. Here is what's wrong with this exactly:
* A.I. doesn't draw anything from Stats. There have been some people who managed to make associations with Stats by exploring how the behavior of an ANN can be modelled by stats, but A.I. was developed with no Stats references even if many A.I. professionals are adept at Stats (as they should be)
* Domain-specific expertise, although invaluable in data science and any data analytics endeavor has nothing to do with A.I. In fact, Prof. Hinton's team made the headlines when they won a Kaggle competition using their A.I. system as a prediction model, even though they had no domain expertise whatsoever.
* The aim of A.I. is to add value to an organization, not create models, software, etc. The latter are the means for this objective which may or may not involve these particular manifestations. For example, an A.I. system may provide just insight by analyzing specific models, or in the case of cyber-security, ensure that a certain level of security is maintained throughout the whole data analytics process.

Anyway, although I'm fascinated by this topic and I'm glad to see other people write about it, I'm a bit wary towards misinformation being spread, even if the intentions of the writer are good. Cheers!


Business & domain knowledge is mandatory. While no leader or person can wear all the hat to build AI infrastructure across manufacturing entity/s availibility of required talent and skill is of prime importance and of fundamental challenge to organisations & business leaders.

Instead of hiring costly talents relevant to requirement, it is prudent to upskill/re-skill existing talent through cognitive ability and required business skill to add value across value chain of dynamics of supply chain.

With four decades of leadership across diverse industries and being second to none we are available at the service across the globe