Robbie Agustin

Every Experiment is a Success IF You Learn from it!

Remember when you were a child? The world seemed so huge! And every time you laid eyes on something new, curiosity got the best of you.

You want to touch everything, feel the texture, put things on your mouth, find out what it tastes like, and when you grew a little older you start trying to stand up and walk instead of just crawl.

Did you learn all that the first time around? Of course not. You had to experiment until you got it right, and you kept experimenting until you were able to run, until you could ride a bike, until you could drive a car.

You learned all these new skill through a lot of experiments, and you got good at it because you kept learning something new.

But why is it that now that you’re older, you are afraid of failure? Why are you afraid of rejection? Why are you afraid of losing?

Remember, Every Experiment is a Success IF You Learn from it!

So if you’ve been holding back from learning that new skill, what are you so afraid of?

It’s time to become a better version of you.

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DEMYSTIFIED: What’s the difference between Classification and Regression?

For today’s #futurefridays I’m going to answer a question that confuses a lot of people trying to learn Data Science and Machine Learning.

The question is “What’s the difference between Classification and Regression?”

Let me give a shot at this with a simple explanation and example.

Think about the output that you want to achieve.

If you have a dataset, and the output you want to get are labels or categories, then it’s classification.

However if the output you want to get is a numerical value based on computations done on the dataset, then it’s regression.

Let me give you a specific example.

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With Respect to This Thing is Concerned

For today’s #thankfulthursdays I would like to give gratitude to the people who have taken me under their wing.

These people have paved the way to me becoming the Business Optimization Expert I am today.

To my first boss in this field – Kalyan, who hired me because he saw something in me through my Robinson Crusoe story, thank you for investing in me and ensuring I have everything I need to become successful in my role, including my learning towards becoming a Lean Six Sigma Black Belt.

To my former colleagues – Remi, Aubrey, and Mel, thank you for teaching me what you know. Your generosity in sharing knowledge nuggets, and patience in me for constantly bugging you for questions.

If not for these people, I would not have been able to win my first Hall of Fame Award for generating over $1 Million Dollars.

And lastly, to the person who succeeded Kalyan – Marian, thank you for making me feel empowered. I have learned so much from your meticulous level of detail, as well as your coaching and mentoring techniques.

I would like to pay it forward by teaching what I learned from these mentors to others. I will do this by:

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10 Process Improvement Mistakes you Don’t Want to Make

It’s #wisdomwednesdays so it’s time to share things I learned.

Here are 10 Process Improvement Mistakes you Don’t Want to Make.

I was actually going to create a list myself, but my friends Karlo and Tracy at GoLeanSixSigma.com have already put this together, and I agree 100% to all of what they said!

So I thought I would just share it with you!

My personal favorites are:

1. The Glory Hoarder

An example of is when Project Leads take all the recognition, and the team members are not given any credit whatsoever.

Sometimes, Project Leads tend to do this unknowingly too. Like when it’s time to present and demonstrate the project results, the Leads feel they should always be the one to take stage, while somehow leaving the rest of the team out of the spotlight.

How about letting the team members who actually developed the solutions take center stage?

2. The Pet Solution

This is where people already have a preferred solution to the problem.

In my experience, what happens here is that people tend to reverse-engineer their Pet Solution into the DMAIC approach just for them to be able to complete a Lean Six Sigma project.

That’s just wrong!

3. Passing the Buck

This happens when you try to eliminate steps within the process you’re looking to improve, only for another department upstream or downstream to end up taking on that piece of work.

Continue on to see the rest of the mistakes you don’t want to make, along with some great examples.

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You Won’t Believe what Dale and Tracey said about the Online Green Belt Course at GoLeanSixSigma

Yesterday, I shared how I learned my core skillset – Lean Six Sigma.

This is the skill that allowed me to broaden my mindset and branch out to learn and gain experience in various other Continuous Process Improvement related skills, including automation, and even Artificial Intelligence, which I’ve been posting a lot about lately.

I truly believe that this skillset is foundational for you to be able to look at business processes in a way that no one else in your company has seen before.

And this will definitely open up so many doors in your career, as it has for me, and you will be known as the one who can make things done.

Without this skillset, I don’t think I could have achieved the same level of success as I have now.

And for today’s #transformationtuesdays, apart from what I have just shared with you regarding how this has transformed my life and my career, I’d like to share with you 2 more from people just like you – Tracey and Dale.

Like you, they’ve researched about how they can get themselves Lean Six Sigma Green Belt Certified for them to take their career to the next level.

They’ve also looked at several other options and providers, both in-classroom and online, and have weighed the pros and cons of each. And have even experienced some of these trainings, only to end up either more confused, overwhelmed, or constrained by time.

But in the end, you won’t believe what they had to say about GoLeanSixSigma.com’s Online Green Belt Training and Certification course.

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Good Judgment comes from Experience, and Experience comes from Poor Judgment

Time for some #motivationmondays.

Ever heard of the saying “Judgment comes from Experience?”

Well the thing is it’s a matter of how you gain that experience, that will allow you to make better judgments or decisions.

And you will only be able to talk from experience if you know what works and what doesn’t.

Have you ever sought advice from an expert? How did they respond?

It would usually go something like “Well based on my experience, this is what works, and this is what doesn’t.”

How do you think they know what doesn’t work?

It’s because they have made poor judgment calls and decisions in the past, and they failed, but never gave up.

Instead, they kept on trying, and made new judgment calls and decisions, until they found out what works, and succeeded.

And from then on, they know what to do, and they are able too make good judgment calls and decisions for that particular subject matter.

Let me give you a more specific example.

Someone once asked me what is the best way to improve Customer Experience or Customer Satisfaction in a Contact Center type of business.

Inexperienced people would give shallow answers or various opinions.

“You should give more empathy, or you should do more rapport,” is what you would typically hear.

But those with experience would be able to talk about what has worked and didn’t work for them in the past, such as:

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Artificial Intelligence Basics: What is Natural Language Processing?

It’s #futurefridays and today we’ll be talking about the basics of Artificial Intelligence.

Previously, I talked about Computer Vision. But today we’ll tackle another field of Artificial Intelligence called Natural Language Processing.

Wikipedia defines Natural Language Processing as:

Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Let me show you some examples of how Natural Language Processing is being used in real-world scenarios.

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Who else does this to Stay Motivated?

For #thankfulthursdays I would like to give gratitude to John Lee for reminding me about setting short, medium, and long term goals.

It’s about breaking-down your long-term goals into medium-term milestones, and further on until your baby step goals.

That way, you can keep getting constant feedback to ensure you’re going in the right direction, or if you need to make course corrections as early as possible.

I won’t say more as I would not want to water-down John’s message, so I would prefer you hear it straight from him.

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RPA vs AI: What’s the Difference?

For today’s #wisdomwednesdays I’ll talk about the difference between Robotic Process Automation (RPA) and Artificial Intelligence (AI).

In a sense, they can both be categorized as forms of software automation. But the major difference is their purpose.

The short explanation is that RPA is used to automate repetitive rules-based tasks, such as repetitive clicks and keystrokes, copying and pasting, if-then types of processes.

On the other hand, AI is used to automate tasks that require judgment, such as reading unstructured documents, identifying patterns, making decisions based on what you see in photos, or getting context based on what you hear in conversations.

Read on to find out the long explanation as well as some examples. This will also touch on Machine Learning.

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Sorting Lego Bricks using Computer Vision AI (Use Case)

For #transformationtuesdays I’m sharing another use case for Artificial Intelligence using Computer Vision.

This is the world’s first universal Lego sorting machine by Daniel West, and here you’ll see a demonstration of it in action, as well as the details as to how it works.

What makes this interesting is that the typical image classifiers you’ll see work on 2D images. But this sorter works on 3D real-life Lego bricks, which makes it more challenging.

Moreover, it has the ability to classify even Lego pieces that it has not seen before!

In fact, that’s what makes it AI, as otherwise, perhaps you could just simply hardcode every Lego brick piece into the program as if it were just a rules-based task.

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