Small Automation, Big Impacts

5 min read

This is a series of automation journeys made possible by industrial 3D scanners. Written based on Hermary’s experience, the article series intends to inspire automation experts across all manufacturing sectors and can be used as a supporting use case to obtain management’s endorsement. 

Where automation journeys began

Industrial automation has been around for a long time. It wasn’t until recently that automation technologies became more affordable. Small and medium manufacturers are starting to take advantage of the many benefits of automation.

What is industrial automation

The First Industrial Revolution began when steam-powered machinery was introduced to the textile industry in the 18th century. This marked the beginning of mass-producing goods at a lower cost through machines. 

Industrial Automation, however, did not happen until almost 150 years later, when Henry Ford implemented the first moving assembly line at Ford’s Detroit plant in 1915. The rope-and-pulley system reduced the car production time from 12 hours to 1.5 hours. Reduced production time dramatically lowered production costs. For the first time in history, automobiles became affordable for the average household.

Several technological milestones in the early 1970s sparked the third Industrial Revolution. During this time, manufacturers started incorporating digital controls such as PLCs to automate manual production processes without much human intervention. 

Modern industrial automation: digital data and manufacturing agility

Technological advancements have made headway for Industry 4.0, an initiative that brings together all industries and stakeholders under one digital roof. Industrial Automation in the Fourth Industrial Revolution departs from the previous generations, which relied on mechanically automating rote tasks. 

Industry 4.0 employs digital data/digital twins to derive actionable intelligence. Dashboards displaying data can improve manufacturing processes, allowing humans to make swift decisions. Real-time data has enabled machines to make autonomous decisions with precision and accuracy, eliminating common cognitive biases. Maintenance optimization, process optimization, automated inspection, and vision-guided robot technologies are examples of smart automation techniques made possible by processing high-accuracy data. 

Over the past two decades, the steady fall in industrial robots’ price has made them more affordable to purchase for smaller firms. Industrial Automation is no longer only for companies with large capital budgets. Small-and-medium firms can secure their niche markets by quickly responding to consumers’ changing demands, making low-volume production profitable. 

In summary, modern industrial automation applies digital data to improve factory automation processes. Below is a table that highlights the main differences. 


Industry 3

Industry 4.0

Defining technologies

  • Computing power
  • Programmable Logic Controllers (PLCs)
  • Digital data
  • Cloud computing
  • Industrial Internet of Things (IIoT)

Key concepts

  • Automatization
  • Connectivity


  • Mechanically automatizing repetitive tasks
  • To increase productivity


  • Parameters set by humans
  • Results from data analysis
  • Digital intelligence

Relation with Humans

  • Humans are needed for process supervision and machine operation
  • Co-existing and symbiotic
  • Humans are relieved to performing higher-intelligence tasks like problem-solving or creative thinking

Small Automation, Big Impacts is a collection of industrial automation journeys made possible by 3D machine vision

We believe sharing these stories is a way to stay connected with our partners and positively impact the manufacturing community.

This free eBook also includes a bonus chapter to help automation professionals easily decide when to use 2D and 3D machine vision

All this is just one click away in our new eBook, Small Automation, Big Impacts.

What to start automating? 

When people talk about industrial automation, they often think of a lights-out manufacturing facility. While this is a great example, the time and capital required to set up a dark factory where no human intervention is required are tremendous. Not to mention that historically, many companies’ failed attempts at a lights-out factory – from GE, IBM to Tesla – have proven that humans’ manufacturing flexibility is hard for machines to replicate. 

Don’t be overambitious. Think long-term and start small

As with tackling any big project, breaking it down into smaller sub-projects can help. Think of automation as the company’s long-term plan with various milestones to achieve down the road. Automation cannot and should not happen overnight. Start small by finding tasks that may benefit from automation. Below is a guideline that can help organizations identify these tasks:

  • If your worker has to repeat a manual task more than three times in an hour; 
  • If it’s a quality assurance (QA) process that requires accuracy; 
  • If it’s a production process that requires consistency (precision); 
  • If the task is related to workplace wellness (ergonimics, safety, mental health, etc.), and 
  • If the task is critical to improving the bottom line

The more items being checked off the list above, the higher the automation priority. For example, while many production lines could use an upgrade, a paper mill realizes that its material forecasting is a task/position that needs to be automated using the checklist. 

Material forecasting happens at the planning stage of the paper-making process. It determines the amount of raw material (e.g., wood chips) required to fulfill the plant’s orders. The procurement department uses the amount to determine how much raw material to order. The production manager also uses the information to assign staffing to match the production schedule. 

One of the most mission-critical tasks for a paper mill is accurate material forecasting.

The calculation, however, was performed manually and was highly subjected to human errors. Procurement overshoot as frequently as they fell short. The manager also faced staffing issues when he had to let workers go home multiple times because of insufficient raw materials. All these issues directly impact the company’s bottom line.  

Resource forecasting seemed like a small task, but it was imperative to have accurate data and consistent results as a mission-critical function. Automating this step enables the Plant Manager and the procurement department to make the right decisions. The data can also be further analyzed to help fine-tune and optimize the production process. 

Turning imagination into solutions

Identifying an automation opportunity is only the beginning of an exciting journey. Transforming ideas into practical solutions can be made possible by:  

Securing management’s buy-in will ensure the project fits in with the company’s big-picture plan and receives approvals from the higher-ups.

However, changes can often be met with resistance as humans are hardwired to resist change. Aside from limiting the project scope to a manageable size, the following automation journeys can be used as a supporting use case to obtain management’s endorsement. Each automation story is based on Hermary’s experience and intends to inspire plant managers, process engineers, and automation experts across all manufacturing sectors. 

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Read about Small Automation, Big Impacts: A Sandwich Assembly Plant’s Journey.

About The Author - Terry Hermary

Co-founder of Hermary.

Terry is the customer-facing machine vision expert at Hermary with over 30 years of experience. With a background in electrical engineering, he specializes in developing 3D vision applications with system integrators and machine builders. He is passionate about solving unique automation challenges using 3D vision technologies. Over the past three decades, Terry and his team have established Hermary as the leading innovative 3D machine vision provider, revolutionizing industries from sawmilling to meat processing.


  • Co-founded Hermary Machine Vision in 1991
  • Patent holder of many 3D machine vision inventions