Nov 11, 2016Connected devices, wearables, cloud computing, sensors, machine learning and algorithms are all capabilities and technologies transforming business and society. These are the core components enabling the Internet of Things, which economist and author Michael Porter claims is the backbone for a third wave of technology-led innovation and digital disruption.
Recently, I released a book titled The Amazon Way on IoT: 10 Principles from the World's Leading Internet of Things Strategies, the second in my The Amazon Way series. The book guides readers through the maze of emerging IoT technologies, customer experiences and business models to help them develop a recipe right for their organization.
The Amazon Way on IoT is for business leaders looking to understand how the Internet of Things is transforming business and society, and how they might reinvent their own business. Readers will discover business cases, key concepts, technologies and tools to help them develop, explain and execute their own IoT approach by understanding the sophisticated IoT technologies and strategies of Amazon and other leading companies. An excerpt of the book is available here.
Connected devices are a powerful enabler for monitoring and improving your operations to make your company more efficient, competitive and profitable.
Amazon's employee evaluation process is built on a standard that tracks each worker's commitment to continuous improvement. An exemplary employee "always looks for ways to make Amazon.com better," the standard reads. He or she also "makes decisions for long-term success. Investigates and takes action to meet customers' current and future needs. [Is] not afraid to suggest bold ideas and goals. Demonstrates boldness and courage to try new approaches."
Of course, Amazon is just one of many companies that focus on continuous improvement. You are likely at least familiar with one or more business methodologies built on the concept of continuous improvement:
• Lean—the philosophy of creating more customer value with fewer resources
• Toyota Production System—a management approach intent on eliminating all waste, which includes key strategies such as "just in time" inventory and demand signals
• Statistical Process Control (SPC)—a system of attaining and maintaining quality through statistical tools that emphasizes root-cause elimination of variation
• ISO 9000 Quality Management—a set of quality-certification standards based on eight management principles, including continuous improvement and fact-based decision making
• Six Sigma—a data-driven methodology for eliminating defects, reducing costs, and eliminating waste
All of these strategies empower employees at the companies that use them to gather data, and to act on the insights that information provides. They are encouraged to drive change and improvement from within. But each of these strategies was also created before the IoT.
The introduction of ubiquitous connected devices has changed the rules of the data game, creating the possibility for real-time feedback loops that power continuous-improvement programs.
Instead of living in a world of manual data collection, which creates limited, slow and stale data sets, the IoT creates an exponential stream of affordable real-time information. That flood of data empowers companies to focus on the continuous improvements to their internal systems, saving them time and money while increasing productivity and consistency.
How Amazon Took Operations from Good to Great
These days, Amazon's operations—the way it fulfills, ships, tracks and delivers your orders—are world-class. But it didn't start out that way. The company measured, refined and executed its way to greatness, embracing continuous improvement as a way of life.
In the early 2000s, the leaders of Amazon's fulfillment and operations capabilities decided to implement Six Sigma, a data-driven five-step approach for eliminating defects in a process: define, measure, analyze, implement and control (DMAIC). This is the root improvement cycle in Six Sigma and sets up the methodical, measured steps and mindset to squeeze out defects, costs and cycle times.
One challenge in completing a Six Sigma initiative is that so much of the effort—generally up to 25 percent—lies in collecting data. Depending on the project, manual data collection can be not only difficult but inaccurate. The information itself is often of questionable quality, skewed by bias, or cut short due to time and effort.
Because of these challenges, Six Sigma certifies professionals in a set of empirical and statistical quality-management methods to help them execute on the process successfully. These professionals are installed in an organization during a Six Sigma process to make sure everything is completed successfully. These kinds of individuals are also highly sought after and well compensated. Creating a team of Black Belts within your organization is one of the biggest cost drivers of Six Sigma initiatives.
That's where the IoT comes in.
Using connected devices to collect data frees up an organization's Black Belts to tackle more projects. It also leads to faster Six Sigma initiatives and a much richer, more reliable data set.
Connected devices can bring visibility to your company's operating conditions, giving you real-time insight into the flow, status and state of key items in your process. Not only does this enhance your understanding of needed improvements, but it builds a way to scale operations with active quality measurement built into the process.
At the time that Amazon integrated Six Sigma into its operations, the company was experiencing a disconnect in a process it calls SLAM, which stands for its "ship, label and manifest" process. Every time a printer or other item is ordered on Amazon, that product is placed into a box at an Amazon fulfillment facility, then is labeled, sorted and shunted through the facility until it is eventually placed in an outbound truck. That's the SLAM process.
When Six Sigma was introduced, packages were labeled and moved down conveyor belts before being manually sorted and delivered to the proper docking station. This worked well most of the time, but there was no final confirmation that each package had actually made it onto the correct truck. What's more, there was no visibility—for the company or for customers—regarding where a particular package was in the outbound process. As a result, packages were occasionally missorted.
An occasional missort doesn't sound like a big deal, but throughout the course of a year, such issues can cost a company like Amazon millions of dollars. More importantly, even a single missort breaks Amazon's underlying promise to its customers that all orders will arrive in their hands on time.
For Amazon, the solution was to create a positive automated confirmation, or "visibility," that every package had moved correctly through all logistics checkpoints after its shipping label had been applied. The change was simple in concept, but incredibly complicated in implementation.
To execute this change, Amazon installed sensors and readers across its conveyor system. The sensors would automatically scan a package's bar code as it moved through the SLAM process. Since packages were scanned to destination-specific staging areas, the sensors allowed Amazon to track the whereabouts of specific packages at any given time in the process. Furthermore, as Amazon employees loaded those packages onto the outbound trucks, scanners mounted on the bay doors would alert them if a package were about to be loaded into the wrong vehicle.
By creating a positive-confirmation system for its packages, Amazon lowered its missorts to within Six Sigma's 0.0004 percent accuracy range. That's fewer than four packages missorted out of every million.
Integrating IoT-Driven Continuous Improvement into Your Operations
There are several questions that you can ask yourself to help identify situations that might benefit from an IoT-driven continuous-improvement process:
• What operating condition information would be valuable to your company?
• What manual data entry or logging does your business currently perform?
• What incomplete and inaccurate data exists in your business?
• What inspections and audits are being carried out?
• What shrinkage, damage or underutilization is occurring?
• What are the operating risks?
• What are the quality issues and drivers of customer contacts?
John Rossman is the author of The Amazon Way on IoT: 10 Principles from the World's Leading Internet of Things Strategies, and a managing director at Alvarez & Marsal, a global professional services firm. Prior to working at A&M, Rossman was an executive at Amazon, where he launched its third-party selling platform and ran its merchant services business. He can be reached at jrossman@alvarezandmarsal.com.
