How Artificial intelligence can help to achieve supply chain visibility?
We love to hype things that give us our returns and our salaries and our standing in the market. Experimenting is very important for anybody who wants to do supply chain visibility any company to do it must start somewhere. For example a company almost for one and a half to two years had a consulting team help them define what a control tower should be like. The control tower never went up because we're just thinking and complexity can be added as much as we want to any problem. So, while technology can do many things for us and it will promise a lot and of course companies in the technology world are always ready to sell you the next hot thing out there but the companies must take a pragmatic approach they should look at where they stand. So, to put up a technology first of all which is easily deployable into your supply chain, it gives us some result on day one. Today, technology is available and we can deploy it off the shelf it will give us some results. Now we may not be happy with that result hundred per cent that's not the idea, the idea is we started somewhere we went from zero to one. Once we've been from zero to one, add more and more technology on top of it. We talk about visibility we talk about sensors available or not available, is the data available end of the day? Always remember that if we are going to have too many bells ringing around we will go deaf we will not be able to listen to any bells even though all these are alarm bells. We also need to have a process in place to kind of take out what is most important for us at a given point of time and solve that particular problem. For e.g. Is the vehicle reaching in time, is my loading of the vehicle is the biggest problem, my biggest problem is that the driver is not meeting the vehicle. What is your problem number one, two, or three? We put technology to solve that particular problem, is your vehicle driver is the biggest problem then we have a solution for that first. If we take that as a pragmatic approach and then apply various technological tools which are available to us. That would be a good way forward to start with and especially for SMEs and larger companies. Sometimes, we need that larger vision and a certain game plan because they can't just keep on doing things all the time even though they may experiment for some time but many of the companies can fall in the category of something they can do today and tomorrow but many times they fall in the trap of doing the big thing and frankly end up going nowhere. So that will be the approach will be very useful to all.
How machine learning can help improve visibility in the Supply chain?
We are going to talk about today's real-time visibility in supply chain. Real-time visibility in the supply chain is the holy grail of the modern digital supply chain. Better visibility is a key prerequisite for improving your supply chain efficiency, conducting your risk assessment upfront in your chain, building resilience and ultimately having the goal of managing sustainability. The technology is going very fast today and more than the technology, the businesses and the customers are demanding faster deliveries and greater transparency. Today's demand and supply chains are defined by higher complexity customers in customer centricity as well as massive pressures to optimize your cost and also including optimize your regular timeline.
What is the role of advanced technologies such as AI and ML and analytics to improve supply chain responsiveness and agility for operations?
We are going to talk about today's real-time visibility in supply chain. Real-time visibility in the supply chain is the holy grail of the modern digital supply chain. Better visibility is a key prerequisite for improving your supply chain efficiency, conducting your risk assessment upfront in your chain, building resilience and ultimately having the goal of managing sustainability. The technology is going very fast today and more than the technology, the businesses and the customers are demanding faster deliveries and greater transparency. Today's demand and supply chains are defined by higher complexity customers in customer centricity as well as massive pressures to optimize your cost and also including optimize your regular timeline.
What are the conclusions for AI, ML or RPA infrastructure in Supply chain visibility?
Now, AI, ML are there to take care of the discrepancies that we will always have in the infrastructure physical as well as data infrastructure and all these years people struggle with the problem that I don't have perfect data. If we ask anybody who is doing a business intelligence dashboard will tell us the dashboard will always give us something funny and the dashboard is useless if the data is not accurate and clean. The data behind is not clean and data cleaning requires effort and we always remain dissatisfied. AI and ML are coming in as small robots who are sitting on top of data, doing the cleaning is the coolest part of the technology. Almost seventy to eighty per cent of AI and ML is cleaning up the data. The tools which are available today from company like Google, Amazon, Microsoft etc they do a lot of that work for us of cleaning up the data and making it ready for us to make sense of it. If our views are very right, there is a lot of use around the world of AI, ML and many of the technology today, but over time it will settle and as people learn about it, people know how to use it. We will use them as good friends to sort out the discrepancies that we always have in supply chains.