Skip to main content

Internet Of Things - IOT - Scenarios

In my last contribution we discussed – what is IoT and how this third wave of internet will change the world. Next, what are the scenarios that can be envisioned at this point where IoT will make an impact. On a high level there are 4 that can be listed down.

Machine to Dashboard

When a device senses something, it will send that data to a dashboard – which in turn will alert certain system to respond. The other system responds depending on a priority and the operation ends there. The most common example of this scenario would be – server monitoring dashboard. Server process fails – a red alert goes to a dashboard – IT person responds to it or server restarts the process. This is not something new. It happens already. In IoT scenario we will see enhanced version of this that will change the speed with which these devices talks to each other. In real life a train collision can be averted in seconds, a plane mishap can be taken care better. The response time and automation will make the difference.

Machine to Machine

One machine talking to another machine. That is the integral part of IoT. Machines do talk to each other in today’s industrialized world, but mostly in a serial fashion. So one device completes the job, passes instructions to another and move on. In the IoT world – location of machine or type of machine will not be an issue. Multiple devices will communicate with each other – wired or wireless.

Machine to Data

Machine to Machine will create huge data to handle, what we call Big Data today. So there will be a need where connected devices can become consumer of these big data systems, and then to make run-time decisions or predictive analysis based on it. The speed, accuracy and volume will be consideration here.

Machine to Business process

This one will be the most important aspect which will probably touch every company. Supply chain processes and procurement processes will have IoT impact. So a truck of perishable food will have sensors to predict “best to use” time based on chemical levels rather than just thumb rule of number of days from manufacturing. A demand or supply planning can be done at real-time basis rather than just seasonal variable predictive algorithms. A supplier can sense and manage inventory based on actual shelf movement.
Next question is about the data generated in this wave. Will it add new aspect to Big Data – as the social media wave did? Will all these devices communicating with each other generate so much data that things will explode beyond imagined capacity? Let’s look at that in my next contribution – IoT and Big Data

Original Blog Post - http://supplychainminded.com/internet-of-things-scenarios/

Comments

Post a Comment

Popular posts from this blog

Master Data Management – Product or Process ?

I have 2 SAP systems and I want to fix my material master, Services Master. I want all that data to be clean, standardized, classified, enriched and load it back to my SAP in next 6 months. What do you suggest ? Chris - one of my key client was explaining during a “solution understanding” call. My sales manager Tom, enthusiastically started talking about new version of the MDM platform by ERP company, tools, technologies, product landscape, licenses etc. After 30 minutes of sales pitch, I could see confusion on Chris’s face clearly. He said - but I don’t want to add any new product in my infrastructure for all this. Can you just implement MDM for me without I adding any new software ?   Both are using MDM implementation as a keyword, but in a completely different context. Chris wants to implement MDM as a process while Tom was trying to sell MDM as a new software. Whats the difference ? Lot I will say. MDM as a product – when you sell a   software license to a...
4 comments
Read more

Data Management - Terminologies & Definitions

As a third step in my Data Management article series – lets look at commonly used terminology in the domain. Now these are very standard definitions I am quoting from a standard available glossary. The next step – next article would be to explain the relevance and usage of these terminology in business world. E.g. How to look at data standardization in supplier data context or material data context – when it comes to optimizing your procurement processes. That’s next. In my first article in this data management series –I compared data management with the story of elephant and seven blind men. http://manageyourdata.blogspot.in/2012/09/data-management-elephant-seven-blind-men.html The second post is more about – why its important to speak same language when you are running any data management initiative.   http://manageyourdata.blogspot.in/2012/09/data-management-are-we-all-speaking.html Data analysis : Analysis of data is a process of inspecting, cleaning, transfo...
1 comment
Read more

Journey of procurement transformation begins with..….. Part II

 Original Blog post - https://www.linkedin.com/pulse/journey-procurement-transformation-begins-part-ii-prashant-mendki Procurement transformation journey is complex, cross functional, time consuming and even frustrating at times. The very basic but a strategic step to start this journey is “Spend Analysis”. Again – this has to be done in a right way to get the potential benefits. We talked about that in first part of this article https://www.linkedin.com/pulse/journey-procurement-transformation-begins-part-i-prashant-mendki By definition – Spend Analysis is an analysis of your spend (invoice paid), what items you are spending on (product), who you are paying to (supplier). It looks really simple – no? When I worked with one of the large Media Entertainment company few years back, they had thousands of suppliers, millions of transactions, good amount of Maverick spend. It’s a global business with more than $2Bn in Spend, 12 different global systems. Thousands of trans...
Post a Comment
Read more