Prof. A. Rajagopal, Of the Indian Statistical Institute de coded to industry4o.com, the concept of “ Waking Up The Sleeping Data From PCS To Process Decision and more”
Apart from mentoring MSME’s and crunching critical data, Prof. A.Rajagopal has recently guided Dr.Shidram Kamate, who won the Rekha Memorial TANKER Award, which was awarded by the Indian Society of Nephrology (ISN). Professor says Dr. Shidram worked on a complex paper titled “Pattern of Urinary Sodium Excretion in Healthy Volunteers and Chronic Kidney Disease Patients.”
Now one thing that clearly came out in the conversation was a method, any industry could use in order to take specific business or technological decision. There is a clear mindset sans emotion, which effectively leads into what we call process oriented decisions. Read on to find out more
When we talk about Artificial Intelligence for Business Intelligence, for manufacturing what are we referring to?
Business intelligence means taking business decision for several challenges. You cannot treat a problem or a challenge in isolation. Rather you should not. Sometimes it may involve confused state of taking alternative decisions on the choices “This or That” or in variably caught by severe impact of wrong decisions to regret later.
What challenges are we talking about here, professor?
Any business challenges where decision is critical. It could be deciding a price or a quote for a product or a treatment time taken for a patient to recover. In this case even domain expert don’t find data by which they can possibly inform the patient of his discharge date. I believe every time your inform the customer with the accurate data, you are helping in product development.
Are you seeing a trend in MSME that they are unfamiliar about this?
I classify it in boxes, unawareness, not known and unknown to many.
Your reasoning ?
Warranty time for any components can be looked in through their own data. Where is the design type of compliance. I find they are technically sleeping on their data. As this knowledge is not there, they work as a isolated case. Find your faculty in machine, train the machine that is the is Principle of Artificial Intelligence.
May I quiz you for a few examples please ?
Automobile industry is a good example. Any accident that occurs is followed by a brake test and check out points for any other technical failure. How do you measure the wear and tear in a brake pad. You need to run real time, lab stimulated data and see how many stops were done. Post the number of stops we will easily arrive at wearing out moment on the brake. Using this in R & D, one can inform the driver that at a particular period, he needs to change the brake pad. This data can easily be used for prediction and thus the business owners get to access reservoir of real business data.
Similarly, an expert doctor in the case of limp damages of accident victims could assess decisions on amputation or salvage with least time of delay and cost, and a model could be developed verifying and validating the data sets for prompt decision by junior doctor developing web based apps using classification algorithm of Artificial Intelligence.
For a Domain Expert to understand how Business Intelligence works, it will require an up skill. Isn’t it?
People in business have to develop the maturity to accept gracefully what cannot be changed, but will and effort to change, what can be changed. Such wisdom in decision making is necessary that what is learnt in many years of experience by domain experts, subject matter experts to be trained to machines for prompt decision making in right manner carrying out in right time.
If you had to fix a variable in Business Intelligence, it could be “x” or “Y”, specially for manufacturing, would you please explain how would you streamline the whole math in straight alignment?
Management in general fix targets for Sales, Production, Waste, Price, Inventory and Waiting Time, release of New Product Development. But they are called output “Y” variables.
However, input “x” variables factors effects, correlation, classification and clustering accordingly, Training data sets have been done in a sporadic way. Here comes the Data Science. Aligning business intelligence (BI) infrastructure with strategy processes not only improves your organization’s ability to respond to change, but also adds significant value to your Business Intelligence infrastructure and development investments.
Kindly tell me a little bit about Data Mining and its relevance to Business Intelligence?
Data mining is knowledge Discovery from Data Sets deriving Information, Analytics, Insight, Solutions, Verification, Validation Implementations, and Institualization. This become the foundation for training of datasets by Artificial Intelligence (AI) to enable Business Intelligence (BI).
In conclusion what according to you, does the future hold?
Algorithms in Decision support Modeling “Artificial Intelligence” by training data sets are possible in the advent of Computer science. Hence you will see , Data science, computer science deciding domain decisions in future. Such intellectual Analytics will facilitate intelligent Decision in Business.
For the manufacturing, the root causes behind poor performance management can be outlined in the steps needed to get your Business Intelligence project started correctly.
