Industrial manufacturing software design: problems and solutions

Early identification of core problems in manufacturing software design mitigates future risks, serving as a crucial preventive measure. This article outlines key recommendations to help designers deliver efficient and reliable industrial software solutions.

Industrial manufacturing is uniquely challenging because it bridges the gap between massive, high-risk physical machinery and digital software. In this industry software errors, especially UX&UI-related, can lead to workplace injuries, late problem identification, costly downtime, faulty batches or environmental harm. When developing new manufacturing solutions, product designers face certain problems and challenges.

Overloaded interface

When a screen is cluttered with too much data (text, buttons, competing visual elements, etc.), it triggers cognitive overload and feature fatigue. When users have to navigate multiple apps, tabs or sub-menus to piece together the full operational state, it causes frustration. Overwhelming pages lead to confusion, decision paralysis, higher error rates and loss of trust.

The following recommendations are proposed to solve the problem:

• Overview
  Consolidate key metrics, critical information or critical data points into a single, high-level view so users can digest the overall health of a system in seconds. Depending on the context, designers can leverage dashboards with various visualizations, structural and schematic overviews, summary cards, status-driven data tables, activity feeds, audit logs, etc.
  For example, the tool overview helps the Equipment Maintenance Engineer see the plant’s current status in a simplified form to identify which tools need attention.
• Progressive disclosure
  Show only a few available options among many and hide the rest under a menu, an accordion, a tab or on a different page. Usually, the visible options are the most important and likely to be relevant to the majority of users. As users move on into a task or become more familiar with the interface, they can access other features by performing an additional action: clicking a button, opening a menu, expanding an accordion. Another typical application of progressive disclosure is to show only information that is relevant for the current step of a task. This way, the user can go through each step one at a time without being distracted by potentially irrelevant information. This approach focuses the user on the current step and is particularly helpful when the process is very complex and seeing all the details of all the different steps would be overwhelming [1].
• Persona-based design
  Industrial manufacturing relies on workflow continuity with interfaces bridging handoffs between different personas — from line operators managing hourly throughput to process engineers reviewing quality notifications and plant managers monitoring overall equipment effectiveness.
  Persona-based design uses personas to guide product development, with each of them having its own goals, tasks, pain points, experience, skill level, and the conditions and physical environment in which they operate, which require different designs.

Introduce customizable views for different roles, for example:

• For the Plant Manager persona, show the status of all tools across the plant for a selected time range so they can monitor performance metrics such as production panel count, production panel area and so on.
• For the Process Engineer persona, show current data for a selected tool (active batches, active faults, current line status and the equipment health status).
• For the Equipment Maintenance persona, show the plant’s current status in a simplified form to help them identify which tools need attention.

Poor user experience and interface design that users have become accustomed to

While upgrading a poor UI/UX interface might seem like an obvious win, in high-stakes industrial manufacturing environments, changing an established user interface — even a deeply flawed one — introduces operational risks, cost burdens and human-error vectors that often outweigh the benefits of redesign.

• Muscle memory and operational speed
  Operators perform repetitive tasks hundreds of times a day, developing automatic muscle memory. They know precisely where to tap or click without reading labels. Redesigning a screen breaks this flow, forcing cognitive overhead back into routine tasks, slowing down throughput and increasing operator fatigue.
• Safety and zero-error tolerance
  In manufacturing environments (e.g., electronic components production lines, chemical plants, heavy machinery, etc.), hesitation or a misclick caused by unfamiliar navigation can lead to severe safety hazards, expensive material waste or damaged equipment. An imperfect system that operators can navigate blindly with zero errors is often preferred over an intuitive modern system that introduces temporary uncertainty.
• Training and onboarding costs
  Factory workforces often operate on complex shift schedules with large numbers of personnel. Rolling out a new interface requires re-training hundreds of operators, updating standard operating procedures and suffering a temporary drop in overall factory productivity during the transition period.
• Legacy system integration and reliability
  Industrial interfaces are often tightly coupled with legacy Programmable Logic Controllers, edge devices and back-end databases. Changing the UI layer can destabilize underlying integrations, risk unexpected software bugs or require costly hardware upgrades to run modern graphical elements.

A solution to this problem would be to follow a context-driven design approach, which means prioritizing the user’s immediate context — their mental model, goals and specific task at hand — over rigid design systems and consistency. Designers can violate consistency rules if a specific task demands a different layout to be intuitive. Designers can pass over flawed design if it makes a specific screen work perfectly for its unique purpose.

Upgrading flawed design makes sense if the value gained by the user is significantly higher than the cognitive cost of learning a new pattern. If the design improvement makes the task 10x easier, update the design. If it only makes it slightly better, stick to the existing solution.

When changes are necessary, stick to gradual incremental updates rather than full redesigns.

Fragmented data

Manufacturing runs on dozens of disconnected applications. When users have to navigate multiple apps, tabs or sub-menus to piece together the full operational state, the product feels chaotic, leading to the following problems:

• Delayed decision-making and delayed reaction time
  Critical anomalies or operational bottlenecks go unnoticed until they become severe issues.
  To solve the problem, highlight status changes in a real-time dashboard (using clear visual hierarchy, color cues, and status badges) to trigger immediate, targeted action.
• Loss of context
  Looking at isolated data points doesn’t reveal whether a metric is normal, improving, or degrading.
  To remove guesswork, contextualize isolated numbers within a dashboard, using baseline trends, historical comparisons and progress bars.
• Lack of prioritization
  Equal visual weight across all data makes it hard to distinguish urgent tasks from routine status checks.
  To address this issue, implement a dashboard overview, establishing a clear visual hierarchy — placing high-level status summaries at the top (“at-a-glance” scanning) and detailed trend charts or lists below.

Thus, when users need to monitor a complex system, creating a dashboard overview solves the core problem of fragmented data by transforming raw, disconnected metrics into actionable situational awareness.

Constraints of specific company tools and legacy systems

Oftentimes, manufacturing systems run on specific tools and outdated software, which are hard to customize.

When companies combine self-developed applications with complex open-source platforms like Grafana into a single software suite, they encounter a major architectural trade-off between customization flexibility and long-term maintainability.

In-house software offers total control, allowing engineering teams to easily modify features and adjust design to match business needs. However, heavy customizations of complex external open-source platforms make future version upgrades complex, risk-prone and time-consuming.

From a UX&UI standpoint, it results in different representations of the same data across systems, inconsistent navigation patterns, different terminology for identical concepts and, overall, a lack of shared design language. Inconsistent interaction design across different tool ecosystems increases human error during emergency overrides or high-stress operational adjustments.

In addition, many plants rely on old legacy systems with severe platform limitations, such as small (e.g., 800x600), fixed-resolution displays, where dense data is often packed onto a single screen without visual hierarchy, causing critical alerts to get lost in visual noise (“alarm fatigue”).

The following recommendations are proposed to solve the problem:

• Identify what can be changed. Freely customize in-house products, but leave external tools as close to their original setup as possible.
• Focus on consistency, where possible.
• Create cross-product UX&UI standards.
• Develop product-specific design systems based on the core product suite design system.

Low UX maturity level in the organization

Sometimes industrial manufacturing companies operate without a dedicated designer for quite a long time, leaving the design function to product managers and engineers. Thus, bringing in a designer disrupts the company’s established workflow, as the team is used to handling all UX&UI tasks on its own.

As a result, some engineers and product managers may selectively share functionality details, keeping designers from gaining a thorough understanding of the product and focusing them purely on visual execution. When a designer asks how it works, to build good UX, the reply is often: “ Why do you need to know that? ”

Without understanding how the product functions, designers cannot deliver an optimal UX solution.

It appears that design is created based on the assumptions of product managers and engineers who believe they know how to do it right and what is best for users, yet strongly resist usability testing. When requesting users for research and usability testing, designers are presented with reasons why this is complicated or not possible (no money, no time, no need, security risks, etc.) or are provided with non-representative “users” — such as sales personnel or software engineers — instead of equipment operators and process engineers. Conducting interviews or testing with these candidates will ultimately lead to flawed insights and ineffective product designs. Needless to say, the ultimate value of industrial software hinges heavily on how usable it is for the end user. This is precisely why conducting usability testing is critically important, particularly when developing complex industrial manufacturing software.

Consequently, upper management is repeatedly reinforced in the belief that, in complex high-technology products, designers are ineffective, that only product managers and engineers can create good UX and that designers’ capability is strictly limited to creating visual design (UI). When design is perceived as an aesthetic surface layer rather than a strategic driver, executives defer to the functions that appear to own execution and business survival, distrusting designers and defaulting to product managers and engineers.

General recommendations:

• Facilitate over dictate
  Resistance often stems from anxiety or a fear of losing control. Rather than imposing design solutions top-down, act as a facilitator — help engineers and product managers articulate their needs while guiding them to lead decisions within their respective domains (such as technical constraints, product strategy, business logic). This approach reduces friction, mitigates information gatekeeping and encourages open cross-functional teamwork.
• Educate engineering and product teams on design
  Gradually teach product managers and engineers about design through structured, step-by-step guidance.
• Prioritize gradual design process improvement
  Avoid introducing drastic alterations all at once. Instead, roll out modifications incrementally. Prioritize supporting existing workflows first, then gradually guide toward best practices.
• Foster engagement
  Involve product managers and software engineers frequently throughout the design process. Encouraging active participation builds a sense of shared ownership, allowing them to directly recognize the value and importance of design.
• Advocate for design integration
  Formally embed a design phase into organizational processes and infrastructure (e.g., operational documentation, Jira workflows, RACI matrix, etc.).
• Focus on value capture
  Shift emphasis toward continuous value capture alongside value creation to ensure design contributions deliver measurable impact.

Designing software for industrial manufacturing is an intricately demanding process, yet addressing critical problems early effectively prevents recurring errors. Applying the recommended practices will guide the creation of an efficient and reliable industrial software solution.

Bibliographic reference

1. Nielsen Norman Group: Progressive Disclosure.

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