In cloud-based Enterprise Resource Planning (ERP) software as a service (SaaS) for equipment maintenance, the difference between effectiveness and failure lies in informed procurement. There should not be gambling in the maintenance of mission-critical equipment – a mishap can result in massive repair and replacement costs, not to mention potential revenue loss due to production downtime. Two specialists from SAP Middle East and Africa emphasise this to heads of maintenance and asset reliability in the African heavy industry.
By Jimmy Swira
In a market flooded with cloud-based Enterprise Resource Planning (ERP) SaaS solutions for machinery maintenance – seemingly with little differentiation – informed decision-making can be a major dilemma. There are no guarantees: products may not always deliver the desired outcome. Adoption of some can result in unintended consequences – failing to address the problem at all, or even worsening unplanned maintenance and equipment downtime.
Having observed this impact on machinery reliability significantly, two experts from leading global software solutions provider, SAP, advise asset and maintenance teams to apply due diligence when looking for suitable solutions. Mohamed Beradhi is SAP Value Prototyping I4.0 Global Portfolio & Programme Management, and Anthony Poltera is SAP Value Prototyping Lead and SAP Enterprise Architect.
The two share experiences drawn from addressing heavy industry’s specific needs in ERP SaaS in the Middle East and African region (MEA) with SAP’s flagship solutions: RISE with SAP and GROW with SAP. Through highlighting common challenges, they hope to raise awareness about steps industries can take to make informed purchasing decisions to ensure their cloud ERP software enables optimal machinery maintenance.
Common Challenges
Heavy industry companies, such as those in mining or energy, often rely on critical machinery that must operate continuously even in remote or rugged environments. Moving to a cloud-based enterprise resource planning (ERP) system can deliver real benefits such as greater visibility, improved maintenance scheduling, and predictive insights.
However, this also introduces challenges that must be managed carefully. The transition may not always be as smooth as generally desired, states Beradhi, underlining the following common challenges encountered:
- Adaptation
One of the biggest hurdles is adapting existing systems and processes to the standardised way software-as-a-service (SaaS) ERPs are designed to work. Instead of highly customised setups, these platforms encourage businesses to adopt best practices “out of the box”. While well-meaning, this can involve significant change management.
- Integration
Integrating operational technology (OT) – such as sensors, industrial control systems, and machine data – with information technology (IT) systems also presents difficulties. Unfortunately, when companies adopt multiple technology vendors, protocols, and legacy systems for asset maintenance, integration issues often arise. These problems can lead to inconsistent data and disrupted workflows, which makes ensuring real-time visibility more complex. In addition, it results in increased costs for custom connectors and maintenance.
- Remote Locations
Remote locations with unreliable connectivity also pose problems. In such environments, field teams need offline capabilities to continue working without internet access. This challenge is particularly relevant in parts of Africa where infrastructure limitations can affect connectivity and system responsiveness.
- Data Quality, Cybersecurity, and Speed
Data quality is another sticking point. Firstly, without consistent, well-governed data models, it is difficult to obtain accurate analytics or build effective predictive maintenance strategies. Secondly, safety and cybersecurity must also be considered, especially when bridging IT and OT systems. Lastly, getting workers up to speed with new tools requires tailored training that reflects on-the-ground realities.
Wrong Assumptions Software Developers Make
These abovementioned challenges are worsened when software developers make wrong assumptions about the needs of clients in heavy industry during product design. “When building cloud-based asset management solutions for heavy industries, software developers sometimes overlook real-world conditions,” Poltera points out, underlining oversights in the following areas:
i. Data Quality and Connectivity Issues
A common assumption is that clean, real-time machine data will be readily available. In practice, data quality varies widely, and connectivity issues can introduce delays. In addition, many systems assume uniformity in operational protocols, whereas heavy industry environments are often fragmented and vendor-diverse.
ii. Mobile Application
Mobile applications are another pain point. Developers may assume constant connectivity and uniform device usage. However, technicians frequently work in harsh, remote locations with limited network coverage and a range of mobile tools.
iii. Generic User Experience
User experience is often treated as generic, yet the needs of field technicians, planners, and reliability engineers differ vastly. For instance, data governance – especially around asset master data – is often overlooked, yet it is vital for ensuring that analytics and AI tools function properly.
iv. Underestimating Change Management
Finally, developers often underestimate the change management involved in shifting processes, or neglect safety compliance and system integration testing during upgrades.
Selecting Maintenance Solutions
Evidently, based on these bottlenecks, it is paramount that end-users exercise prudence in procurement when selecting ERP SaaS maintenance solutions. With many vendors in the market, it is not easy to separate the wheat from the chaff from the products out there.
Going Beyond Feature Checklists
For this reason, Beradhi advises heavy industry players to go beyond feature checklists, as there is more to ERP than merely ticking boxes before procurement.
“For asset reliability managers, uptime is everything. So, any solution they adopt needs to improve how well they can monitor, maintain, and optimise equipment.
“Hence, when evaluating different systems, it is important to go beyond feature checklists and consider whether the solution supports the realities of heavy industry environments such as harsh field conditions, remote locations, complex safety requirements, and integration with operational systems,” Beradhi explains, citing the following features as essential:
a. Openness and Interoperability
Openness and interoperability are key. Ideally, any modern maintenance system should be able to integrate seamlessly with shopfloor equipment, Internet of Things (IoT) platforms, geospatial systems (GIS), and analytics tools. The best time to avoid integration problems is during the procurement phase by setting clear expectations around integration, testing, and data standards from the outset.
“Organisations should treat integration as a priority, not an afterthought. That means requesting clear documentation on application programming interfaces (APIs), data event schemas, and compliance with operational technology (OT) standards such as ISA-95. They should also define a standardised asset data model and governance framework to ensure consistency across systems. Selecting platforms that are designed to work together reduces the need for expensive custom integrations, and any request for proposals (RFPs) should include integration testing as a mandatory requirement,” Beradhi says.
b. Functional Depth
Equally important is functional depth – features such as preventive maintenance, failure analysis, spares optimisation, contractor management, and safety procedures should all be included, stresses Poltera. “Mobile access, especially with offline functionality, is essential for field workers. So too is strong governance over asset data, with support for global standards such as ISO 14224. And of course, scalability, security, and cost transparency matter, especially in diverse and multi-site operations.”
All things considered, there should be no cutting corners in the procurement of SaaS. For all their perceived similarities, solutions have unique peculiarities, some of which could have a significant impact on machine monitoring and availability. The gist of the matter is: Avoid Assumptions in SaaS Procurement.
