Many decision-makers view the cost of industrial automation as a significant barrier to adoption. How can these leaders justify the expenses while maintaining high-quality output in their facilities?
Many leaders assume they just need to purchase the most advanced robots, install them and look forward to near-immediate results. However, researchers from the University of Cambridge gathered data from 25 European countries between 1995 and 2017. Their results showed that decision-makers believed adopting automation would create competitive advantages through cost reduction. However, the realities were not so straightforward.
As organizations bring more robots into their workflows, they’ll eventually need to redesign all processes from the bottom up. More specifically, they need to work on them at the same time as integrating automation into their companies. Updating the business model to accommodate automation helped organizations avoid challenges that can reduce cost-effectiveness.
The researchers clarified that bringing automation into a business is complex and costly. It requires significant investment and careful planning. But when everyone understands that they must adapt their processes to support automated improvements, quality levels can stay high while costs remain well-controlled.
Getting feedback before and during process redesigns is also important. Some changes that executives view as advantageous are not as positive for the line workers who carry out the tasks daily. Additionally, asking employees for input emphasizes their important role in upholding product quality.
Decision-makers often realize they must adopt industrial automation to keep up with peers. However, choosing to use the technology is only the first step. Costs can quickly get out of control unless people focus on the most effective ways to use automation in an organization. Similarly, if executives try to automate too many processes at once, quality levels could drop as people adjust to the newness.
A best practice is to identify processes that are both expensive for the company and suitable to automate. One recent example involved researchers relying on machine learning to expedite drug development. The team’s work concerned long-acting injectables. These medications are typically time-consuming to engineer because people must determine the optimal amounts of drugs released over certain periods. Succeeding reduces side effects and makes the drugs work as intended.
Machine learning allowed the researchers to find the desired drug-release profile in one attempt rather than trying multiple iterations. Then, automation supported humans’ attempts to develop effective products faster. Such results are particularly impressive in the characteristically cost-intensive pharmaceutical industry.
Statistics suggest drug development costs can surpass $2 billion, making it appealing to save time and money where possible. Even if the initial expenses for creating an automated solution are substantial, the expenditures should eventually even out if they shorten development time frames. Automated options such as the one mentioned here also support quality control by giving researchers more time to put toward the possibilities most likely to work in the real world.
People also have the best chances of keeping the cost of industrial automation relatively low if they wait to see results before scaling up their efforts. It’s then easier to see which automated changes have the biggest impacts on streamlining operations.
Automation can be an excellent way to reduce business risks that could lead to long-term disruptions, dissatisfied clients or other undesirable outcomes. Company leaders interested in this approach should consider beginning their automation journeys by focusing on pieces of equipment that could harm product quality or the industrial facility if they malfunction. For example, automatic valve actuators can prevent chemical spills, building damage and accidents by going into fail-safe states when emergencies occur.
Another option is to use smart sensors that automatically capture machine data and use algorithms to detect potential abnormalities. Using that approach only with the essential machines or those that have previously failed most frequently can keep the costs of industrial automation down while maintaining high-quality output.
In-depth technician training can also prevent machine downtime by increasing people’s awareness of possible problems. When they’re more familiar with normal operating states, it’ll be easier to know when something’s wrong — and, hopefully, before a failure happens.
Even so, there will inevitably be occasions where on-site employees lack the knowledge to fix a faulty machine, and off-site technicians aren’t available soon enough. One case study indicates automation could help in those situations, too. A company developed a machine learning-based solution that analyzes machine and process data to diagnose issues. It also uses pattern recognition to compare the current problems with past faults.
Next, the system displays knowledge cards that give employees the knowledge to solve specific problems. This approach could minimize the cost of industrial automation by equipping people to act immediately once vital automated machines malfunction. Instead of waiting for someone with first-hand knowledge to address the problem during their next shift, anyone can retrieve the information to troubleshoot.
Since industrial automation can be so costly in the early stages, options that reduce expenditures are undeniably attractive. Thus, many automation providers have introduced as-a-service packages that allow clients to pay flat fees to use the products. The rates paid usually cover installation and maintenance, making it easier for potential and current customers to plan their budgets.
For example, one company purchases standard robot arms and packages them with its specialty software. Customers then sign leases to use the technology at cost-effective prices. People from companies that use the solution say it has helped them cope with labor shortages.
In many cases, the cost of renting a robot is less than hiring a human. Such arrangements also appeal to small to medium-sized companies where executives are curious about automation but worry about unexpectedly high costs.
Interested persons should also consider whether robots could tackle specific repetitive tasks that often make humans bored or tired. Physical and mental fatigue can cause declines in quality that automation could remedy.
These examples illustrate that decision-makers can pursue industrial automation while maintaining reasonable costs. Doing it might mean making relatively small sacrifices — such as scaling up slowly instead of automating a whole department at once or renting a robotic arm instead of buying it.
However, these are practical and accessible ways to explore industrial automation without dealing with ballooning expenses. Once the cost of industrial automation becomes too high, company leaders may overlook other aspects of the business while getting things back on track. That could mean quality levels slip, and customers go elsewhere for service. Luckily, the suggestions here allow people to strike a balance that facilitates tight quality control and the investigation of automated technologies.