This article addresses:
- Why is artificial intelligence becoming important in manufacturing?
- What are common use cases for AI in manufacturing environments?
- How can I identify opportunities for AI at my plant?
- What is a fundamental requirement for successful AI implementations?
- How can I begin to prepare my organization for AI adoption?
Artificial Intelligence (AI) is on manufacturers' minds. It plays a key role in Industry 4.0, a.k.a. the Fourth Industrial Revolution. The benefits are real, but selecting the best path requires navigating a dizzying array of options and even more hype. Leaders must also consider whether they’re operationally prepared to embrace AI. 
Mobile apps for manufacturing represent a low-risk, high-reward opportunity for manufacturers to adopt AI. They can also help set up success for future projects.
Apps do this by making data collection more efficient. They also improve data quality and make it easier to store and share. Given that data is the foundational underpinning of all AI projects, getting it right is paramount.
Understand the fundamentals of AI in manufacturing. Then, take action: you can future-proof your operations by pinpointing your top use cases and gathering high-quality data to support them through the adoption of simple, low- and no-code mobile applications.
As one MIT article pointed out, start with what matters most: data.
Applying AI to Manufacturing
Manufacturers can apply AI technology into different facets of the manufacturing process. Production, supply chain management, quality assurance, and predictive maintenance can all benefit from AI technology.
Manufacturers can use data and AI models to automate repetitive tasks. This helps improve decision-making and product quality. It also streamlines operations. As a result, manufacturers can boost success metrics and grow their businesses.
Benefits of Adding AI to the Manufacturing Process
Manufacturers can benefit from AI by applying it to:
- Predictive maintenance: This involves gathering data—using sensors or other devices—to monitor equipment and predict potential failures, which is superior to simply performing maintenance on a predetermined schedule. Done right, predictive maintenance identifies defects early, increases safety, and contributes to efficiency.
- Quality assurance: AI has also proven instrumental in ensuring quality and uniformity in products. For example, computer vision manufacturing systems offer a range of benefits. They can parse multiple large visual datasets and offer real-time insights. They identify patterns and reduce defects by analyzing images and videos. AI can also analyze vast amounts of data to identify root causes and optimize production line processes. (Further reading on quality manufacturing solutions.)
- Inventory management and forecasting: Manufacturers are finding that AI revolutionizes supply chain optimization. Large datasets fuel machine learning algorithms that analyze historical data and enable AI-driven systems to improve demand forecasting and inventory management.
- Production optimization: By evaluating real-time production data, AI finds bottlenecks and their underlying causes, assisting in increasing throughput and cutting cycle durations.
- Natural language search: By making document management and product search more user-friendly, generative AI can enhance internal processes and the client experience.
By analyzing data on inventory turnover, sales trends, and seasonal variations, AI can reduce overstocking and minimize waste.
These are just a few use cases; many other possibilities exist. IBM describes a broad variety, including in adjacent areas like product search and document management—where, for instance, generative AI can help customers find the right product through natural-language search.
How to Pinpoint Your Company's Use Cases for AI
Given the breadth of technologies and tools, it’s essential for manufacturing business leaders to identify the most promising starting points for using AI. Use this process to develop a plan.
- Define clear business objectives. Make sure there is a shared agreement and focus on the metrics that truly matter. Stay laser-focused on these.
- Assess current pain points. Start with areas where inefficiencies or high costs exist, e.g., downtime, waste, or quality issues. Common areas include defect detection, equipment maintenance, supply chain bottlenecks, or energy consumption.
- Evaluate available data. Assess whether the company has sufficient and reliable data. If data is inadequate, consider implementing mobile apps to enhance data collection.
- Rank opportunities. Use an impact/difficulty matrix to assess and prioritize the most feasible use cases based on impact.
- Start with small experiments. Pick a pilot project that is constrained and has clear success metrics. For example, pilot an AI-driven inventory process on just one product rather than the whole lot.
- Consider industry benchmarks. Look to peers in manufacturing for both ideas and success metrics.
Roadblocks to Using Data in AI Projects
“Water, water everywhere and not a drop to drink” goes the line from a famous poem about a sailor stranded at sea, surrounded by undrinkable salt water. It’s a similar story with data: On the one hand, we’re drowning in it; on the other, it’s extremely difficult to harvest, manage, and use properly.
According to McKinsey, data collection and data infrastructure are to the AI revolution in manufacturing what coal was to the industrial revolution. Factories generate terabytes or even petabytes of data per week that, if harnessed properly, can be used to power efficiencies at scale. Yet according to VentureBeat, 87% of AI projects don’t make it into production – often due to data quality issues.
The hype is omnipresent, and the promise is certainly there. But the reality on the ground is that manufacturers find execution challenging. Some companies throw money at flashy AI initiatives that aren’t well thought through. It’s important to understand where your AI initiatives could get thwarted.
The Top Challenge for Introducing AI to Manufacturing Plants: Poor Data
In our work with manufacturers, we’ll often hear, “We have a lot of data, but yet it’s not always useful data.” This is a paradox many manufacturing teams face already, and it can really railroad your AI initiatives.
The problems? Low-quality data, fragmented data and disparate data are the biggest barriers to successful artificial intelligence efforts.
Inconsistent or Inaccurate Data
According to Venture Beat, only 13% of data science projects make it into production, and a key blocker is data quality and availability.
Fragmented Data and Availability of Data
Data fragmentation is another common issue. Data is gathered by different teams, potentially in organizational silos. That can make it difficult for data scientists to even procure the data across the enterprise. Privacy and security requirements are other common barriers to getting started.
Disparate Data and Formatting Issues
Adding to the complexity: data exists in a variety of formats and is housed in different systems with different standards and formats. It’s then up to data engineers to bring disparate sources into a unified repository of actionable data.
The Foundation of Any AI Effort: Timely, Accurate, and Complete Data
Another common scenario: existing data from piecemeal sources might not even be reliable to begin with. Effective AI models must have trusted data as the foundation or suffer from bad recommendations and hallucinations based on shoddy inputs.
Data movement company Fivetran surveyed organizations and found that companies estimate they lose an average of 6% of their annual global revenue due to problems with data quality in AI, including unreliable and incorrect data.
What is the foundation of successful AI in manufacturing? Real-time, high-quality data. Your AI algorithms won’t produce insights or precise forecasts without it.
Real-World Example: Digitizing Data Collection with Mobile Apps
Increasing data quality can be as simple as migrating paper processes to digital workflows, as Igloo did when their quality control manager migrated from paper audits to a 100% digital process. Igloo digitized its quality inspections with Alpha Software Quality Management Solutions for mobile devices with built-in dashboards and estimates a cost savings of $145,000, in addition to real-time, more accurate data.
Why AI in Manufacturing Requires Mobile Apps
Even if you're not prepared to implement AI right now, digital data collection is an essential step. Mobile apps will:
- Create the foundation for superior data.
- Use GPS, time stamps, speech-to-text, images, and other features to make field data entry easier.
- Facilitate the smooth integration of backend systems
- Ready your plant for automation, artificial intelligence, and digital transformation.
In its whitepaper on adding artificial intelligence to mobile apps, Alpha Software explains that traditional data collection often comes from multiple sources, including manual data entry and paper forms. It could take weeks to get handwritten forms completed in the field updated into corporate systems of record, and the results are prone to errors. If your data isn’t accurate, your AI results will be lackluster, if not entirely wrong.
A Smart First Steps for Manufacturers Considering AI: Begin with Mobile Apps
Regardless of whether an organization has started an AI initiative, digital data collection through mobile industrial apps boosts efficiency and future-proofs manufacturers for AI projects by supporting the foundational efforts to enable accurate and efficient gathering, storing, and transmission of data.
Mobile apps speed up data collection. Workers can use them on the job and gather data instantly, which helps their efficiency.
Mobile apps offer low-risk, high reward in manufacturing environments considering AI soon or in the future. Why? Because every AI project depends on high-quality data — and that starts with how you collect information.
Mobile apps improve:
- Data accuracy: Reduced human data entry errors
- Data capture: Faster data input and system synchronization
- Data availability: Enterprise-wide access to more comprehensive datasets
The best mobile apps for manufacturing work offline and are optimized for one-handed data collection. For example, an equipment inspector can easily gather quality control data using photo capture, speech-to-text, time/date stamping, etc.
From there, the digital data can be shared easily with the rest of the organization.
In short, mobile industrial apps enhance existing processes and pave the way for successful AI efforts, whether ongoing or in the future. For more information, see the report on Industry 4.0: The Role of Mobile Apps in the Manufacturing Industry.
Talk to an expert about mobile apps for your manufacturing business.
Next Step: Future-Proof Your Operations for AI
Interested in adopting mobile apps to boost efficiency or power an AI project? Alpha Software is ready to help with fast, reliable apps and solutions that deliver data you can trust. Our company has over a decade of experience building database apps and solutions for customers who need secure, timely, and accurate data.
Our tehcnology is fast and flexible, so we can have your custom solution ready for testing on your production floor in record time.
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