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Industrial software development and automation is a high-stakes undertaking for manufacturing, pharmaceutical, healthcare, automotive, and practically any other business that deals with large-scale production, distribution, and logistics. With assembly lines entirely dependent on the applications and workflow automation that run it, business leaders need to ensure near-uptime, in addition to economies of scale.

In this article, we focus on five areas that are important for industrial software, and what business leaders need to keep in mind when searching for a new industrial software development partner, or when evaluating an existing one.

Need a custom software development company that can understand your business’s industrial software needs? As a leading IT consulting and outsourcing company, EFutures presents niche domain expertise in building high-performance industrial applications for clients the world over.

To know how you can transform your industrial application posture, contact us today for a free and no-obligation assessment.

Legacy systems are part and parcel of industrial software

Many industrial facilities still rely on legacy systems to keep operations running. In many cases, deprecating these legacy systems and migrating over to a newer and more cutting-edge application is a complex process that can stall operations. This is a consequence that many organisations are unwilling to risk, as it entails halting production  and subsequently, revenue.

As a result, your software development partner needs to be familiar with integrations that can enable smooth operations with various legacy systems. This also extends over to fixing bugs and ongoing maintenance; a dedicated team that focuses on the needs of your industrial application is optimal for this purpose, as they’ll harbour the tribal knowledge necessary to build and fix components on an ongoing basis.

When it comes to the resignation of and replacing certain team members, establishing a process which addresses the handover of duties as early as possible, is optimal. This way, no tribal knowledge leaves the team, and new members are familiar with what their responsibilities are.

Smart devices and IoT are now infused with AI

With AI now in mainstream use, monitoring systems across industrial facilities aren’t just smart, but also AI-powered. This calls for AI development, in addition to industrial automation. Smart sensors, for example, collect vast amounts of data round the clock, all of which can be used for predictive analysis and pattern recognition. The same goes for monitoring devices in the healthcare and manufacturing industries, so relevant adjustments can be made to improve patient health and production, respectively.

For industrial use cases, AI can also be used for intelligent recommendations. Personnel on the field can be supplied with contextually relevant summaries and other suggestions that are applicable to the task at hand, while supervising managers, in due course of evaluating overall performance across real-time dashboards, can receive automated suggestions of how to scale up (or down), depending on existing variables and constraints.

If AI-powered applications are a necessity for your industrial-level use case, it is important to:

1. Aggregate and transform big data from within your industrial facility and organisation at large, for AI model training,
2. Conduct thorough testing prior to any implementation (a beta version can be released for employees to test, with results being used to improve AI models further prior to official implementation),
3. Integrate seamlessly with existing systems, and ensure no data silos exist.

Data migration can make or break new industrial software

Even small industrial facilities can be a source of big data. When migrating over to a new system, data migration is a highly crucial part of the process. Failure to migrate data correctly can cause system corruption, ultimately leading to a failed implementation as well. As a result, botched data migration isn’t an option, when adopting newly built industrial applications.

To mitigate any risks pertaining to data migration, comprehensive testing is vital. Even following rigorous testing procedures, how your new application is implemented matters:

• Phased implementations of the software involve releasing parts. This carries less risk, as faulty migrations can be rolled back lest a failure happens even after testing.
• Parallel implementations involve utilising both the old and new versions of software. This helps determine whether results are as expected from the new software, as both applications can be compared side-by-side, and on a real-time basis.

The above forms of implementation can also ease the learning curve for employees who will be end users of the software.

Security and failover systems are more crucial than ever

Security and failover systems are indispensable for any application, irrespective of industry or the objective it intends to fulfil. In the case of industrial applications, though, momentary halts due to security breaches and technical issues could mean significant losses, in addition to reputational damage and compliance violations.

Cyber breaches, being as rampant as they are now, are best prevented with a DevSecOps approach to industrial application development. But cyber security for industrial applications isn’t confined to software-based security alone; guarding physical entry points is just as important. This would typically include (but isn’t limited to):

• Access control systems placed across entry and exit points in and around the premises,
• Round-the-clock camera surveillance,
• Intrusion Detection Systems (IDS), which includes a combination of sensors and alarms.

As for failover systems, data security forms the foundation of storing, retrieving, and backing up data for business continuity. This includes Data Loss Prevention (DLP) systems that help prevent exfiltration and unauthorised access to data. Additionally, facilitating intentional data redundancy, where copies of data are stored across multiple data centres (and preferably, across multiple geographical regions) is a key disaster recovery component that prevents complete data loss, be it due to cyber breaches, technical problems, accidents, or natural disasters.

Don’t forget compliance

Depending on the region your industrial facility is located in and what it specialises in, regulations from government or private entities may apply. Adhering to these regulations is a must, with a failure to do so leading to fines, reputational damage, and even complete closure in certain extreme situations.

As a result, every industrial workflow needs to be built with compliance in mind – and one key way to facilitate this is through industrial software and automation. Your software development partner needs to align here, in order to design and build compliant software that is then thoroughly tested to ensure it meets obligations.

The implementation of compliance procedures are also bound to increase the number of steps involved in any workflow. This means that a relevant system needs to be in place to accommodate for the extra time and resources that these steps may consume. This includes:

1. Aligning compliance bodies and your software development team, to have the latter familiarised with what’s important, and what objectives need to be met. If connecting personnel from the regulatory body isn’t possible, someone from your business’s legal team needs to step up for briefing,
2. Training employees on how to adhere to new workflows, in conjunction with new industrial applications.

Key takeaways

Software development services for industrial applications can be more complex than other use cases, owing to longer software lifespans, the need to integrate with legacy systems, compliance obligations, and higher financial risks surrounding downtime. What’s more, the adoption of AI-powered systems further raises complexity, as organisations now need to also train LLMs, transform data, and even have privacy policies in place for the use of its proprietary data.

With so many high-stakes variables involved, your industrial software development partner needs to have the domain expertise necessary to meet requirements. Additionally, they also need to have robust agreements in place that prevent sudden knowledge gaps, especially as team members leave. But it doesn’t end there; business teams need to be constantly supervising to observe compatibility, and whether standards are being met no matter how long-standing the partnership is.