Assembling a smart factory? Start with your boardroom

Smart factories are smart business. According to a 2019 joint study (PDF, p. 7) by Deloitte and the Manufacturer’s Alliance for Productivity and Innovation (MAPI), early adopters of smart-factory technology have seen average performance gains of 10%–12% within three years.

Manufacturers looking to transform their factories—and capture these gains—must commit to the long haul: Selecting and applying advanced technologies to existing processes, as well as refining the implementations, can take years. Although choosing the right set of use cases is key, even more important is choosing the right person to lead the smart-factory strategy. So where should you start to find the right person? The boardroom.

Walk into a smart factory, and on the surface, it might look much like factories did a generation ago: people, robots, and machinery making things and objects moving along conveyors, along with warehousing and dispatch operations and staff hierarchies to manage workflows.

The differences lie in what’s underneath it all, which is described in one word: information.

Information flows through the smart factory as machines and sensors report on performance and maintenance. It gives staff an alternate view of systems and products, often using using augmented reality (AR). Information drives the reprogramming of robots—sometimes on its own using machine learning. Finally, information secures itself using cybersecurity protocols.

In short, the smart factory is a storehouse of information that is processed as data.

The primary advantage of all that data is that you can improve everything—you’ll have a clearer picture of all your systems than ever before.

Asset efficiency

According to research firm McKinsey, a smart factory (PDF, p. 2) can increase productivity by 3%–5% and reduce the time to market by 20%–50%.

The data will reveal performance or maintenance issues that need corrective action, avoiding full-scale equipment failure. It optimizes production capacity, lowers downtime, and reduces the time traditionally needed to change direction.

Product quality

When data sets report on performance, they also help detect and identify poor-quality manufacturing in the making long before a human operator could spot it. Environmental factors, machinery deficiencies, or even a tiny human error can cause a chaos effect resulting in deficient output at the other end.

Interrogating and reporting on the data that emerges from your process can reveal these flaws much further back in the chain when they’re small, easy, and less expensive to address without disrupting everything.

Sustainability and safety

Optimizing production through the smart use of data can make a host of positive environmental changes. Porsche’s digital smart factory for its electric Taycan sports car in Stuttgart, Germany, for example, is a “zero-impact facility” that takes a holistic approach to resource consumption and waste, including the use of a green roof, biogas, photovoltaic systems, and other renewable-energy sources.

This enlightened data use will also give manufacturers the opportunity to make products more tailored to the needs of customers—and because customers have the option to choose sustainability as a brand loyalty practice, the contribution to saving the environment will be a feedback loop.

Automating repetitive processes will also reduce human errors that can lead to injury—as well as removing the dull, uninteresting jobs from the labor pool and giving people more discretion to manage the overall processes. This could lead to more interesting work, with the result that workers (and all their experience) stay with you longer.

The bottom line

But most of all (especially for making the case to your board), the results are proven. Production output in smart factories has been calculated to be 10% higher, capacity usage 11% higher, and labor productivity 12% higher.

Support from the boardroom can determine how far along you’re likely to be in smart manufacturing. There are three types of companies based on this metric.

First is the trailblazer company, where the chief technology officer (CTO) takes the point position. This CTO will have the CEO, chief information officer (CIO), and chief financial officer (CFO) on board and will actively seek input and guidance from production/operations on how to best adopt smart manufacturing for the unique needs of their factory floor and workflows.

Next is the explorer company, usually led by production/operations staff who have identified a need to further automate processes on the floor and must convince the higher-ups it’s a good idea. Their closest ally is the CTO, who will bring the necessary clout to executive colleagues and will be prepared with input about how smart manufacturing will affect every other business unit, from risk assessment and cybersecurity to the plant manager.

Third is the follower company, again led by production/operations staff and with the CTO, CIO, and a few other department heads on board, but without the requisite case studies from other departments about how the smart factory will integrate with business goals at every level.

The difference among the three smart-factory leadership styles is both quantitative and compelling. Trailblazers spent up to 65% of their budget on the initiative and enjoyed a KPI increase of 20%. Explorers spent 19% for a KPI increase of 10%, and followers spent 13% for a gain of only 8%.

The components behind a smart factory aren’t one-size-fits-all—it depends on your products, your inputs, your market, and your business goals. But certain common principles are a good place to start.

Command center

You need a base of operations for your data functions—a silo that synthesizes and presents your data through a dashboard or interface that makes it easy to visualize. Analytics, performance information, and output tracking are just some of the insights that let you pivot quickly and improve on the fly.

Plant consumption and energy management

Smart, end-to-end utility and resource tracking (visible through a data interface, as above) helps you keep a close eye on all your material inputs and waste outputs, optimizing water, waste, and energy and calculating your environmental impact in real time.

Factory asset intelligence

By collecting and presenting data from all systems, you can repurpose it for applications that will make even more improvements. Machinery reporting on itself lets you manage and schedule maintenance long before there’s a breakdown, and objective measurements about products can be overlaid using AR to give you a deeper view on your work.

Factory synchronization and real-time asset tracking

Sensors and tracking devices affixed to all assets give you an at-a-glance picture of where everything is, not only inside the factory but also across the entire supply chain. This lets you pinpoint the location of an asset 24/7 and adjust provision and production schedules accordingly.

Quality sensing and detecting

Because every step along the workflow (and every device that takes part) generates information about its performance, you can keep tabs on how your products and the tools that make them are performing as they work, letting you make adjustments without stopping production or slowing down workflows—or you can leave it to smart algorithms to make the changes for you.

Smart conveyance

Machine learning and similar systems based on algorithmic programming can manage the most efficient and business-appropriate asset conveyance through the smart factory without any human input, whether it’s vehicles, the assembly line, incoming materials, or more.

The global shop floor

But this is only the beginning. Once your own factory is fully automated, with systems reporting continuously on performance and high quality continually assured, you can connect it all to suppliers, partners, and customers.

That means all the streamlining of environmental impact, efficiency, and quality—scaled up industrywide—will upend manufacturing for the better.

This infographic was originally published in January 2021. Special thanks to technology writer Drew Turney for the accompanying article.