As manufacturing operations become increasingly data-driven, implementing business intelligence (BI) tools has become a strategic imperative.
The business intelligence market is projected to grow at a CAGR of 7.2% to reach $54.9 billion by 2032. While IT and communications, banking and finances, and retail and consumer goods make up the bulk of the BI market, but manufacturing accounts for 9% of the total spending — driven by the need for improved production control, supply chain visibility, and forecasting.
However, many manufacturers struggle to realize the full value of their BI investments and data analytics efforts. Here are five of the most common mistakes manufacturers make when implementing BI tools, including:
- Not Involving Operations Teams in Selecting and Implementing the BI Platform
- Focusing on Backward-Looking Performance Metrics.
- Overwhelming Users with Too Many Dashboards and Visualizations
- Inadequate Data Governance and Data Quality Checks
- Not Planning for Scalability
Mistake #1: Not Involving Operations Teams in Selecting and Implementing the BI Platform
Manufacturing teams have unique workflow needs and critical inputs that must be incorporated from the beginning of the project design phase. For example, machine operators may require production KPI dashboards with real-time visibility into OEE, cycle times, scrap rates, and downtime root causes. Inventory analysts need to extract signals from stock levels to improve planning. Quality engineers rely on capabilities for root cause and predictive analytics.
However, too often IT drives platform selection without operational input. This can cause problems in driving adoption rates. Getting buy-in from top leadership gets projects launched, but it takes widespread acceptance to realize the biggest benefits. Managers and production teams need to be involved in designing the BI process. It’s a crucial step that’s often overlooked. Despite the power of BI, average adoption rates hover around 20% in organizations — and that number hasn’t changed much over the past five years.
Involving a cross-section of team members, implementing an intuitive, easy-to-use BI platform, and providing training will go a long way in improving adoption and realizing benefits. When frontline workers understand the benefits of BI tools and can navigate them easily, adoption rates increase.
Mistake #2: Focusing on Backward-Looking Performance Metrics
Understanding historical manufacturing performance is essential, but relying too much on backward-looking reports can foster a reactive culture versus enabling the forward-looking insights needed for agile planning. The true opportunity lies in uncovering relationships between leading indicators and outcomes still in flux.
World-class manufacturers increasingly couple intuitive historical reporting with predictive analytics to uncover risks and opportunities while there is still time to respond. Machine learning techniques can detect early signals of anomalies leading to defects, simulate production scenarios for better planning, and recommend interventions for critical events in real-time.
Mistake #3: Overwhelming Users with Too Many Dashboards and Visualizations
Another risk manufacturers face when implementing BI platforms is overloading users with overly complex tools or an explosion of dashboard choices — leading to “analysis paralysis.” Without clear guidance on what metrics are most insightful for specific roles, relevant insights get lost in the noise.
A best practice is to continually gather user feedback and carefully tailor interactive dashboards to focus on the highest value KPIs needed for better decisions in different functions. Today’s self-service BI tools and dashboards provide great flexibility, but users can get frustrated by the abundance of decisions. It’s best to keep things simple, creating personalized dashboards that reflect the KPIs individual users need and allowing them to go on a journey of discovery by digging deeper into the metrics that matter most.
Mistake #4: Inadequate Data Governance and Data Quality Checks
“Garbage in, garbage out” is an unfortunate reality for manufacturers relying on BI platforms fueled by incomplete, redundant, or poor-quality data. Bad data leads to bad decisions and it’s incredibly prevalent in the manufacturing industry.
Manufacturing operations generate massive scale and variety of data from sensors, equipment, inventory systems, quality checks, procurement records, CRM platforms, and more. According to the International Society of Automation, manufacturers generate nearly twice as much data as any other sector.
Legacy systems, siloed data, and poor data governance can make any data untrustworthy and it’s more common than you might think. The data itself may be fine, but it’s often buried in legacy systems or suffering from poor data governance.
Addressing these pervasive problems requires both a well-designed platform to handle enormous, varied data volumes and disciplined data governance protocols actively managed cross-functionally between IT, quality, manufacturing engineering, and analytics teams.
Mistake #5: Not Planning for Scalability
Manufacturing enterprises often underestimate how quickly their needs for more BI users, larger data processing capacity, and additional functionality arise. Selecting BI tools rigidly capped in these areas will rapidly hamstring an organization’s agility.
Common scalability limitations include:
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Facing massive license cost increases when adding more users to the BI platform. Inflexible per-seat pricing models deter accessibility.
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Lacking storage and computing headroom as source data multiplies, preventing ingestion of richer inputs needed for enhanced insights.
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An inability to readily accommodate new visualization, dashboard, ML-powered, and analytics feature rollouts. Change management headaches disincentivize innovation.
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Difficulty connecting new productivity software, custom applications, and IIoT data sources into the BI environment because of hard-coded connectors versus open architecture.
These barriers manifest as production teams manually track KPIs outside the intended BI tools due to limited viewer seats. Quality engineers wrestling with data uploads timing out. Supply chain analysts copy-pasting ERP dataset extracts because integrations are maxed out.
Seeking out manufacturing BI platforms designed for elastic scalability is essential for circumventing rapid obsolescence. The best practice is pursuing server-based licensing that accommodates unlimited user expansion without per-seat fees. Architecting for scale from the start ultimately saves manufacturers from compounding costs and allows them to focus resources on using BI to drive better business performance rather than fighting adopted tools.
Overcoming These Manufacturing Business Intelligence Challenges With Wyn Enterprises
Wyn Enterprises is a real-time BI solution for manufacturers that empowers you to visualize data and make smart decisions. With no data limitations or poor user fees, you get a lower total cost of ownership (TCO) with industry-leading BI tools to power your business.
Contact Wyn Enterprise today to request a personalized demo or start a free trial today.
Brandon Boribong
Brandon is a Product Consultant for the Wyn Enterprise platform, where he works on technical demonstrations, support, and blog writing. Brandon graduated with a Bachelor's of Science in Computer Information Science and Technology from Temple University and has experience developing websites and apps, as well as client relations as a customer facing liaison. He likes to work on solving complex problems as a team and helping people achieve their goals through the use of technology. When he isn't thinking of problem solving, Brandon enjoys traveling with his wife, and going on scenic nature adventures. Brandon also loves videography and foreign cuisine.
