Analyzing Supply Chain Automation with the Information Value Chain (4/5)

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Drivers for the Information Value Chain

We are living in a period of exponential technological evolution. The effects are often illustrated by pointing to Moore’s Law, which deals with the evolution of microprocessors (Mack, 2011), and Metcalfe’s Law, which deals with the growth of networks. (Metcalfe, 2013) But there are several other branches of rapidly advancing technology which are enabling new possibilities for automation. 

●     Cloud computing. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. (Mell and Grance, 2011)

●     Internet of Things (IoT). Machines that are connected to a communications network and which have capabilities for sensing and actuation are referred to individually as smart objects, or as IoT devices. (Stankovic, 2014)

●     Edge processing. Data is typically collected at the “edge” of a network. And this is also where actions need to be taken. Edge processing involves doing decentralized data processing in smart devices at the edge of the network. (Shi, et al, 2016) Edge processing eliminates the latency and bandwidth constraints that are common in centralized processing models.

●     Fiber Optics. Fiber optic communications provide significantly greater communications bandwidth than copper wires. Fiber optics are evolving from providing a backbone to the internet, and are increasingly available as direct connections to customer sites. (Alferness, Kogelnik and Wood, 2000)

●     5G. Fifth Generation cellular communication networks, which are now being deployed worldwide, will increase capacity, improve reliability, and reduce latency. (Andrews, et al, 2014) This step-change improvement in wireless communications will enable more devices to communicate faster throughout the entire supply chain.

●     Blockchain. Blockchain ledgers are enabling the digitization of financial transactions using cryptocurrencies such as Bitcoin and Ethereum. Many companies are beginning to experiment with blockchain solutions for automating transactions with smart contracts, and for tracking physical products through a supply chain. (Tapscott and Tapscott, 2016)

●     Quantum computing. Quantum computing is an emerging technology that could perform mathematical calculations far faster than digital computers. It is expected that quantum computers will be particularly useful for complex optimization problems. Quantum advocates, including teams at Google, see quantum computing emerging in optimization applications within the next decade. (Mohseni, 2017)

●     Artificial Intelligence (AI). Russell and Norvig (2016) define AI as the study of agents that receive precepts from the environment and perform actions. In supply chain terms, that essentially describes any machine that can receive inputs and then take action. This includes programming machine learning algorithms that enable computers to identify patterns and adapt their responses based on these observations.

Advances in each of these areas are radically increasing the capabilities of automation, and they are also reducing the costs of technology.

Now, let’s examine some of the factors that must be considered any time a task is to be done by a human. We can start with the cost of labor.

Triplett defines labor cost as “the employer’s cost of hiring an incremental unit of labor.” Along with this definition he points out that “labor cost” is more than simply wages, because it needs to account for all of the costs of employing a person to complete a task. For example, capital costs for safety equipment and workplace amenities are a component of labor costs referred to as “compensating wage differentials.” When analyzing the potential savings from widespread automation, it is important to consider not only the savings in wages, but rather the total labor cost savings including the compensating wage differentials. (Triplett, 2007)

It is also true that when humans perform a task, it is necessary to consider factors beyond cost and utility. For example, humans have personal values, which influence their attitudes and behaviors. (Ros, Schwartz and Surkiss, 1999) These values can affect which tasks people want to do, and how well they do them. And, of course, these values can change over time.

When comparing between the cost of human effort and the cost of automation, it is helpful to have units for comparison. While it may be difficult to quantify the cost of an incremental unit of automation, we can assume that the automation cost includes the software, hardware, and communications infrastructure that would be required to substitute for an incremental unit of labor. Where the cost of automation is lower than the cost of labor to complete a task, then a rational manager will naturally be inclined to automate the task.

For simplicity, let us summarize the above argument in very crude terms. People are expensive, slow, fragile, picky, and unreliable. Machines are getting cheaper, faster, and more resilient; they don’t take time off to care for their families, nor do they spend time searching the Internet for a new job. People do what they want to do, while machines do what they were designed to do, over, and over, and over again. In terms of their ability to complete supply chain tasks, people are standing still while machines are evolving quickly.

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Who is Mr. Supply Chain?

Daniel Stanton is a supply chain industry veteran and the best-selling author of Supply Chain Management For Dummies He is dedicated to empowering professionals through education and technology. His courses on LinkedIn Learning (formerly Lynda.com) have been viewed over 1 million times, and he's a frequent speaker at educational conferences and industry events.

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