DPA vs RPA: What's the Difference?

Digital process automation (DPA) redesigns the flow of work in a business so that everything gets completed in the right order. Robotic process automation (RPA) is different, handling the routine steps of each workflow. With an RPA, bots update company records and trigger tasks for members of your staff when certain conditions are met.

Both processes have their value. RPA is better for high-volume workflows that require staff to use screens. DPA is better for cross-team and cross-system workflows that need approvals at various stages.

Companies can achieve significant returns on their investment from automating with DPA and RPA. The first step is scaling automation that tackles your biggest bottlenecks. Then, move on to smaller inefficiencies to find more benefits. This methodical approach is how companies driveoperational efficiency.

In this article, we explain the pros and cons of each technology. We will also show you which manufacturing processes are best suited to RPA or DPA.

Digital process automation is software that sets and manages business workflows. It connects data, decisions, and actions across different teams, systems, and machines.

For example, companies use DPA for the following tasks:

• Onboarding: Notifies different teams (e.g. IT, HR), creates user accounts, and updates payroll
• Returns: Validates the return, books the collection, triggers the refund, and notifies the buyer
• Changeover: Sequences the next job, sends new settings, and confirms the right materials are ready using online sensors

DPA manages these processes across multiple software applications while still involving staff. So, in the case of the changeover, it can handle the setup tasks and alert the shift leader when the line is ready to run.

DPA gives companies far greater control over how they manage their business processes from start to finish. This means quicker handovers, shorter job queues, fewer errors, and less duplication of effort.

Compliance improves with DPA, too. Built-in auditing features include evidence capture, timestamping, and approval/sign-offs.

Many current DPA packages work in low-code development environments. This means teams can build and adjust workflows with minimal help from IT.

Firms can roll out successful DPA deployments quickly. Once a process runs smoothly in one team, you can extend it to other lines or sites because the data and steps stay the same.

DPA will replace many of the processes your business has relied on until now. You need to know what the current process actually involves and where it breaks down. From there, you will map each workflow step by step to identify inefficiencies.

Once you’ve mapped the current process, agree on what the future version looks like. Give the DPA team clear input so they can build accurate new workflows. Start small and scale from there. This keeps early issues contained and gives your team time to adjust.

Change is also challenging for your teams. DPA is more than new software for your staff. You're upending the way they're used to working. During roll-out, train your team well, give each team member clear responsibilities, and open a feedback channel.

Integration with existing hardware and software is also a challenge. You have to establish a reliable data link between all the moving parts of a workflow. The more systems you connect, the more complex and time-consuming it becomes to maintain those links. Where you can, use standard connectors and keep tight control of software versions.

Robotic process automation is software that handles repeatable, rules-based tasks within a business.

Companies use RPA for various tasks, including:

• Accounts payable: Capturing invoices, matching them to POs and receipts, and posting them to the ledger
• Compliance: Gathering data from sources like CRM and access logs, completing the reporting template, and submitting it to the relevant portal
• Payroll: Pulling approved timesheets from team heads, transferring hours and overtime into payroll, and generating payslips

RPAs are highly flexible, operating in one of two ways:

• Calling APIs to read, edit, create, and delete files on other software apps
• Using bots to mimic the actions a person would take on a computer screen (e.g.  clicking buttons) where there is no integration

Bots can perform tasks like data entry and updates, workflow approvals, and sending emails or messages. Bots take on the admin and keep people in the loop, giving them more time to focus on higher-value work.

You can also run attended bots on a user's desktop for various tasks. For instance, they can pull up customer details and create a quote during a call. On servers, unattended bots can extract data from scanned forms. They can also update prices and availability on e-commerce platforms.

The appeal of RPA is speed and accuracy. Bots repeat the same steps every time, so you get fewer errors and more consistent results. Two of the key benefits of RPA are more accurate data and a more predictable flow of work.

RPA also bridges the gap between older and newer tools. It buys companies time when they're planning larger changes like a major system upgrade.

RPA bots can run all day, every day. They create a record of every auditing action they take and reduce staff workload on monotonous tasks.

Like DPA suites, their ability to call APIs and use low-code tools means you can fit them around existing systems. This causes less disruption and is easier to implement.

Some bots rely on screen elements to do their job. If a screen layout or label changes, the bot may no longer function until a programmer updates it with the new layout. This is because RPA uses selectors to find where to click or type.

RPA relies on strict naming conventions. For example, if a file name doesn't match the expected pattern, then the bot may choose the wrong item or stop. A bot will continue to make mistakes or fail to fulfil its tasks until you fix the issue or change the logic.

To avoid issues, set naming rules, monitor key bots, and test selectors. Also, check the process before you automate it. A bot will repeat a flawed task unless you improve the flow first. Maintain light oversight, so automations are registered, measured, and maintained.

Although smart, bots lack nuance. Even AI-powered bots struggle with human wants and emotions. So, decide from the outset what the bot shouldn't do and what still needs a human decision.

Both DPA and RPA systems deliver a range of benefits, but each works better in slightly different environments.

RPA is better for high-volume tasks that change rarely and require a bot to mimic an employee's actions. DPA is better for companies where different teams and systems handle separate parts of a workflow. DPA is also better if you need to audit your work and there are approval stages in your workflow.

Companies wanting greater engineering control often opt for DPA. It captures the initial change request and attaches any relevant notes or drawings. It then sends those drafts to the design, quality, and operations teams.

From there, DPA tracks review comments and manages approvals. It then releases the final specification to the production line. After that, it publishes the new work instructions to the line. At the same time, it alerts the shift leader and saves records of each run (e.g. effective date, line, batch numbers).

Businesses that use outbound shipping can benefit from RPA. A bot can book the courier, generate the labels, copy the tracking number into the order, and send the customer an update. 

If a field is missing or there is another discrepancy, it can hand it over to a person to complete the required details. The steps are the same most days, and the screens rarely change, so RPA is a good fit.

When comparing DPA vs RPA, start with where time is lost in your workflow. If it's tasks your team complains eat up hours but add little value, then consider prioritising RPA. Alternatively, for work that stalls waiting for the next person or department to pick it up, DPA is the better option. As your company grows, you may end up using both.

Begin with the pain point that costs you the most, prove the return on investment, and build out from there.