Automation and AI

Automation can be defined as the technology by which a process or procedure is performed without human assistance.

The dictionary defines automation as “the technique of making an apparatus, a process, or a system operate automatically.”

We define automation as “the creation and application of technology to monitor and control the production and delivery of products and services.”

Using our definition, the automation profession includes “everyone involved in the creation and application of technology to monitor and control the production and delivery of products and services”; and the automation professional is “any individual involved in the creation and application of technology to monitor and control the production and delivery of products and services.”

Automation Applications

Automation provides benefits to virtually all of industry. Here are some examples:

• Manufacturing, including food and pharmaceutical, chemical and petroleum, pulp and paper
• Transportation, including automotive, aerospace, and rail
• Utilities, including water and wastewater, oil and gas, electric power, and telecommunications
• Defense
• Facility operations, including security, environmental control, energy management, safety, and other building automation
• And many others

Automation crosses all functions within industry from installation, integration, and maintenance to design, procurement, and management. Automation even reaches into the marketing and sales functions of these industries.

Automation involves a very broad range of technologies including robotics and expert systems, telemetry and communications, electro-optics, Cybersecurity, process measurement and control, sensors, wireless applications, systems integration, test measurement, and many, many more.

AI

AI or artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

AI was coined by John McCarthy, an American computer scientist, in 1956 at The Dartmouth Conference where the discipline was born. Today, it is an umbrella term that encompasses everything from robotic process automation to actual robotics. It has gained prominence recently due, in part, to big data, or the increase in speed, size and variety of data businesses are now collecting. AI can perform tasks such as identifying patterns in the data more efficiently than humans, enabling businesses to gain more insight out of their data.

Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016, there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple.

Technologies

Five emerging technologies will increase the amount of procurement work that can be automated:

• Robotic process automation for simple, repeatable tasks such as scanning invoices into ERP systems.
• Machine learning for tasks that require some degree of judgment such as assigning transactions to spend categories.
• Smart workflow linking separate tasks into a coherent process such as supplier qualification and on-boarding.
• Natural language processing to help guide staff to specific information or through elements of the sourcing process.
• Cognitive agents to search a knowledge base and provide answers to users on matters such as purchasing policy, or even to recommend particular suppliers.

Robotzation

Process robotics works by automating the entire supply chain from end to end—not just individual tasks—enabling all different sections to be managed in tandem. The adoption of software robotics allows professionals to focus less time on day-to-day processes and, instead, provides more time to drive value for the entire business.

This is a highly important evolutionary next step that supply chain executives simply can’t ignore. This is especially important when you consider that, as skilled professionals, they must spend much of their valuable time on repetitive processes, such as planning, monitoring and coordination when they could be focusing on finding new and more profitable avenues for progression, growth and meeting service-level agreements (SLAs).

How Robotization Works – The majority of supply chain professionals already use efficiency tools, most of which only work on one specific task. Due to the complexity of many supply chain tasks, these tools can become a burden to managers—often taking longer to audit than they save in time. Enterprise process robots are different from traditional automation tools in that they automate an entire business process (such as the supply chain), rather than a limited, individual task approach.

Functionally, this removes the silos between various processes and allows an entire process, such as managing procurement, shipping, warehousing and inventory management, to be handled in one centralized process.

This is effectively achieved by teaching the software robot how a job is completed, which is called embedded process know-how. The tasks are completed on a job-by-job level, but coordinated as an entire unified process, allowing the interdependent sections to work in tandem. For example, if the robotics solution detects that a warehouse is full due to a lack of inventory movement, it automatically alerts/halts procurement, or adjusts to a new storage location if one is available.

Whilst automating many tasks is a much more efficient and convenient method of managing a supply chain, managers may still want to be able to track and monitor actions and output. For this reason, many solutions offer a comprehensive dashboard—a tool that supervisors can use to monitor and track all activities at a bird’s eye view.

For example, supply chain managers can use a dashboard to see that the current warehouse stock of a particular SKU decreased below necessary reorder levels. The dashboard allows them to drill down by geography or SKU, facilitating a much deeper and instant breakdown of the overall process chain. The manager can then immediately identify the exact problem, such as cessation of freight from a major supplier’s factory, which can then be traced back to storms in the area.

Dashboards not only allow the instant identification of problems, but also provide the ability to see how these problems affect the rest of the supply chain and, therefore, the ability to act upon them immediately. In the example above, the temporary cessation of production would cause the robotic solution to alert a supervisor to the change and, with approval, automatically increase purchasing from other suppliers, thereby shifting transport resources to overcome these areas. If an approval is not required, robots can be configured to replace that manual part of the work also.

Robotics process automation

Robotics process automation (RPA), as defined by Wikipedia is, “an emerging form of clerical process automation technology based on the notion of software robots or artificial intelligence (AI) workers.” It goes beyond physical systems and provides the glue that when looked at from a supply chain perspective integrates multiple systems dedicated to order taking and fulfillment.

RPA works by automating the end-to-end supply chain, enabling the management of all tasks and sections in tandem. It allows you to spend less time on low value, high frequency activities like managing day-to-day processes, and provides more time to work on high value, exception-based requirements, which ultimately drives value for the entire business.

PwC estimates businesses could automate up to 45% of current work, saving $2 trillion in annual wages. “In addition to the cost and efficiency advantages, RPA can take a business to the next level of productivity optimization,” the firm says. Those ‘lights out’ factories and warehouses are becoming closer to a reality.

Four key elements need to be in place for you to take full advantage of robotic process automation in your supply chain:

• robots for picking orders and moving them through the facility;
• sensors to ensure product quality and stock;
• cognitive learning systems;
• and, artificial intelligence to turn processes into algorithms to guide the entire operation.

In addition, you’ll need strong collaboration internally and among suppliers and customers to tie all management systems back to order management and enterprise resource planning platforms.

Automation in supply chain planning

As noted earlier, the focus of automation in supply chain has largely been on the execution side – manufacturing, logistics, order fulfillment. But the practical application of automation in supply chain planning has largely been overlooked. Once again, Amazon is on the cutting edge. The company reportedly already employs 1,000 people in artificial intelligence, with many likely working on Echo, a wireless speaker that listens to you and speaks back. It can turn off the lights, report on traffic and order things, but backed by artificial intelligence could become something far more than a novelty device.

As Kevin O’Marah, the chief content officer at SCM World, explains, “What Amazon is positioning itself to do is far more ambitious and involves what AI experts call ‘contextual awareness’. This means knowing not only the what, but also the when, why, where and how of consumer need. The long game is all about selling us not just what we want, but what we need, and probably before we realize we need it.”

Amazon has moved well beyond just sensing and responding to demand. It’s developing a complete picture of each customer, and the personal data collected will help future AI applications to know the difference between what you want, and what you need.

AI and prescriptive analytics in supply chain could lead to revolutionary breakthroughs, including automating the decision process. This concept goes far beyond just having software that runs scenarios and shows you ranked results of their outcomes, but lets the machines (in this case computers) actually make the decision entirely, and then filter that control command down through the rest of the supply chain. It’s opening the door for a conversation around optimization versus human judgment.