Today, we’ll explore how the Internet of Things (IoT) transforms the 7 M’s of business — key elements that drive an organisation’s operations and strategy.

These 7 M’s are Manpower, Material, Method, Machine, Market, Money, and Management. Let’s break down each one and see how IoT impacts them.

1. Manpower

IoT helps businesses optimise human resources by reducing costs, improving safety, and increasing productivity.

Impact of IoT:

• Cost Reduction: Automating repetitive tasks reduces the need for manual labour.
• Worker Safety: IoT devices, such as wearables, can monitor health and alert workers to potential hazards.
• Productivity: By enabling remote work and real-time communication, IoT allows employees to focus on high-value tasks.

Example: A construction company using wearables to monitor worker fatigue and ensure safety.

2. Material

IoT ensures better management of materials, improving supply chain efficiency and reducing waste.

Impact of IoT:

• Just-In-Time Delivery: Sensors track inventory levels and automatically reorder materials when needed.
• Asset Condition Monitoring: IoT devices monitor the condition of materials, ensuring quality and preventing spoilage.

Example: A warehouse using IoT sensors to track stock levels and ensure optimal storage conditions.

3. Method

IoT makes business processes more agile and efficient by simplifying methods.

Impact of IoT:

• Reduce Red Tape: Automating workflows eliminates unnecessary administrative steps.
• Agility: IoT enables businesses to respond quickly to changing conditions.
• Efficiency: Processes become faster and more streamlined with IoT integration.

Example: A manufacturing plant automating quality checks with IoT sensors to speed up production.

4. Machine

IoT maximises the performance of machines, ensuring reliability and reducing downtime.

Impact of IoT:

• Uptime: Predictive maintenance ensures machines are operational when needed.
• Predictive Maintenance: IoT sensors detect issues before they become critical, preventing failures.
• Error Reduction: Machines can self-correct or alert operators when errors occur.

Example: A factory using IoT-enabled machinery to monitor performance and schedule maintenance.

5. Market

IoT helps businesses expand into new markets and improve their customer reach.

Impact of IoT:

• New Market Segments: IoT enables innovative products and services, opening new revenue streams.
• Global Reach: Businesses can monitor and manage operations worldwide through IoT platforms.

Example: An IoT-enabled home security company entering international markets with smart security systems.

6. Money

IoT creates new revenue opportunities and reduces costs.

Impact of IoT:

• New Revenue Streams: IoT drives innovation, leading to new services and products.
• Cost Savings: Automating processes and improving efficiency reduces expenses.

Example: A logistics company saving fuel costs by using IoT to optimise delivery routes.

7. Management

IoT improves decision-making through data-driven insights.

• Impact of IoT:
• Data-Driven Decisions: Real-time data helps managers make informed choices.
• Transparency: IoT provides visibility into all areas of the business.
• Better Decision-Making: Analytics from IoT systems offer actionable insights.

Example: A retail chain using IoT to monitor sales trends and optimise inventory.

Key Takeaways

IoT has a transformative impact on the 7 M’s of business:

1. Manpower: Reduces costs and improves safety.
2. Material: Ensures quality and efficiency.
3. Method: Simplifies workflows and increases agility.
4. Machine: Enhances reliability and performance.
5. Market: Expands opportunities globally.
6. Money: Generates new revenue and reduces costs.
7. Management: Improves decisions with real-time insights.

Discussion Question: Which of the 7 M’s most benefits from IoT in your industry? Let’s share ideas and examples!

{You can download the FREE eBook IoT Notes by Mazlan Abbas]

Today, we’ll discuss types of analytics and their importance in turning raw data into actionable insights.

This diagram shows four types of analytics, ranked by their difficulty level and the value they provide. Let’s go through them step by step.

1. Descriptive Analytics: What Happened?

At the base of the analytics hierarchy is descriptive analytics. This is the simplest form of analytics and helps us understand what happened by interpreting historical data.

• Purpose: To summarise past events and identify patterns.
• Example: A smart thermostat that shows last week’s energy usage patterns.
• Methods: Charts, graphs, and dashboards that clearly show past performance.

This type of analytics is great for reviewing the past, but it doesn’t tell us why something happened or what will happen next.

2. Diagnostic Analytics: Why Did It Happen?

Moving up, we have diagnostic analytics, which looks at why something happened. It’s more complex than descriptive analytics because it requires diving deeper into the data.

• Purpose: To discover relationships and identify the causes behind past events.
• Example: Analysing why a specific day’s energy usage was higher than average by correlating data with external factors like weather.
• Methods: Data discovery, drill-down techniques, and correlation analysis.

This stage helps us make sense of the past by understanding the root causes of trends and anomalies.

3. Predictive Analytics: What Will Happen?

Next is predictive analytics, which focuses on forecasting future outcomes. This is where analytics becomes proactive rather than reactive.

• Purpose: To predict what might happen based on current and historical data.
• Example: A smart thermostat forecasting energy usage for the upcoming week based on weather patterns and past behaviour.
• Methods: Statistical modelling and simulations.

By identifying trends and patterns, predictive analytics helps us make informed predictions.

4. Prescriptive Analytics: How Can We Make It Happen?

At the top is prescriptive analytics, the most advanced type. This involves predicting outcomes and recommending actions to achieve desired results.

• Purpose: To decide the best course of action based on predictions.
• Example: A smart thermostat automatically adjusting settings to save energy while maintaining comfort.
• Methods: Machine learning and AI to analyse probabilities and make decisions.

Prescriptive analytics provides the highest value by enabling automated and data-driven decisions.

IoT and Analytics

This diagram also highlights how analytics works in an IoT platform:

1. Sensors: Collect data from various sources like temperature, humidity, or movement.
2. IoT Platform: Acts as a central hub to process and store the data.
3. Analytics Engine: Applies these four types of analytics to generate insights and drive decisions.

Final Thoughts

Each type of analytics builds on the previous one, moving from simple data interpretation to actionable decisions. The value increases as we move up the hierarchy, as does the complexity.

Question to consider: Which type of analytics is most valuable in your industry, and how can you implement it effectively? Let’s discuss it!

[Note: Download IoT Notes by Mazlan Abbas ]

Today, let’s explore why the Internet of Things (IoT) is experiencing a surge in awareness and adoption in recent years. The diagram provides five apparent factors driving this growth, so let’s walk through each step.

1. Hardware Advancements

The first driver of IoT adoption is the rapid development of hardware. Devices are now:

• Cheaper: The cost of sensors, processors, and connectivity modules has dropped significantly.
• More Powerful: Today’s smartphones, for instance, are as powerful as the computers that send astronauts to the moon.
• Smaller: Miniaturisation has made it easier to embed technology into all sorts of devices, from wearable health trackers to smart home appliances.

These advancements make IoT devices accessible to more people and industries.

2. Network Expansion

IoT depends on connectivity, and networks have become more pervasive and diverse:

• We now have Wi-Fi, 4G/5G, LoRa, NFC, and even satellite networks connecting devices across the globe.
• This widespread coverage ensures that IoT devices can communicate, no matter where they are located.

Imagine this: You can monitor a sensor in a remote farm or track a shipping container in the middle of the ocean because of this pervasive network infrastructure.

3. Easier and Faster Software Development

Creating IoT solutions has become simpler because:

• Software tools and platforms are now more user-friendly.
• Developers can build and deploy solutions quickly with pre-built frameworks, cloud computing, and open-source libraries.

What used to take months or years to program can now be done in days or weeks, speeding up innovation in IoT.

4. Moore’s Law: The Power of Computation

You may have heard of Moore’s Law, which states that the number of transistors on a chip doubles approximately every two years. This leads to:

• Higher computational power: Devices can handle more complex tasks, such as AI and data processing, on smaller chips.
• Lower costs over time: IoT solutions can scale quickly with more powerful chips becoming affordable.

This exponential growth in computing power has made IoT a reality.

5. The Network Effect

The network effect explains how IoT becomes more valuable as more connected devices. Here’s why:

• Everything is connected: Sensors, devices, and systems communicate and generate data.
• Data generation: The more devices there are, the more data we have. This data can be analysed to gain insights, optimise processes, and improve decision-making.

For example, a smart city network with connected traffic lights, sensors, and cameras can reduce congestion and improve safety by analysing real-time data.

Historical Context

• The term “IoT” was first coined by Kevin Ashton in 1999.
• It took years of technological progress for IoT to become mainstream. By 2020, IoT reached a tipping point, integrating with industries worldwide.

Why Now?

The convergence of cheaper hardware, pervasive networks, faster software development, computational power (thanks to Moore’s Law), and the network effect have created the perfect environment for IoT to flourish.

IoT is no longer a futuristic concept — it’s a reality shaping industries like agriculture, healthcare, and manufacturing.

Let’s discuss: Which of these factors do you think has had the biggest impact on IoT adoption? How can we use these advancements to innovate further in our fields?

[Note: Download full IoT Notes eBook]