Predictive analytics in manufacturing: top use cases & adoption tips
April 5, 2023
The role of predictive analytics in the manufacturing industry
Predictive AI-driven analytics gives you valuable insights from the complex and diverse manufacturing data you collect to forecast future outcomes and notice potential disruptions before they affect production. The AI-empowered solutions we provide enable you to find dependencies that are difficult to detect with traditional analytics.
the estimated value of the manufacturing predictive analytics market by 2026
Allied Market Research
Asia-Pacific region to show the highest CAGR between 2018-2026
Allied Market Research
Top 7 use cases of predictive analytics in manufacturing
Demand forecasting
Demand forecasting relies heavily on historical data on supply levels, raw material costs, purchasing trends, consumer buying habits, and delivery conditions. Demand forecasting will help you to:
- Calculate the number of products to manufacture
- Forecast possible sales and deliveries
- Determine out-of-stock itemsÂ
- Identify trendy products in a given period
- Allocate resources
- Plan budgets
Predictive maintenance
Data from various production vehicles, tools, and devices can be used to predict when a system needs maintenance and schedule preventive maintenance and quality control operations. Therefore, it will reduce equipment downtime, enabling you to:
- Predict equipment performance and anomalies in operations
- Recognize recurring errors
- Prevent breakdowns and quality issues
- Avoid production line halts
- Schedule repairs and equipment replacements
- Identify the most effective maintenance workflow for certain kinds of failures
Inventory management
Predictive analytics, using machine learning algorithms, provides a deeper understanding and better accuracy of part usage and production capacity, enabling you to manage inventory effectively:
- Ensure optimal inventory levels (reduce overstocking and avoid stockouts)
- Create an optimized warehouse work plan
- Get insight into the inventory-to-sales (IS) ratio
- Optimize product placement based on the demand forecast
- Predict gross margin for current warehouse supplyÂ
- Reduce products with an expired shelf life
Workforce management
Predictive analytics improves HR services by enhancing staffing support, boosting employee engagement, and building effective performance tracking. You can:
- Define staffing needs and build a hiring plan
- Plan training activities to improve employee productivity
- Develop loyalty programs to improve employee engagement
- Create personalized employee development plans to increase motivation
- Reduce employee turnover and shortages
Product development strategy optimization
The product development team can use predictive analytics to predict consumer preferences and future needs by analyzing products performance metrics and customer feedback and conducting competitor analysis to:
- Predict emerging market trends
- Create a roadmap for product modernization
- Optimize product features to meet evolving customer needs
- Streamline R&D to create new product lines
- Identify a new product’s launch timeline
Marketing and sales optimization
AI-enabled predictive analytics can help you optimize your marketing and sales activities to boost product popularity and generate more revenue. You can:
- Conduct market and competitor analysis
- Set dynamic prices based on demand and competition
- Offer products at a discount
- Develop and launch promotional campaigns
- Predict prompt and long-term revenue based on market conditions and current sales
Supply chain management
Companies can use historical and real-time manufacturing data analytics to manage the supply chain, optimize transportation and ensure on-time product delivery. Predictive analytics can help you:
- Сalculate the time required to build and ship every product
- Predict optimal shipping frequency and vehicle needs to deliver productsÂ
- Direct and redirect resources to speed up or slow down deliveries
- Analyze the impact of unplanned events such as strikes, weather conditions, accidents, or roadwork
- Order reserve supplies and buffer stocks when new demand arises
- Optimize routes to reduce overall transportation costs
Predictive analytics services we offer
Our artificial intelligence experts provide comprehensive assistance to companies that want to implement AI-enabled predictive analytics solutions for manufacturing or want to improve an existing one. Our extensive services range from analyzing business needs to selecting the best strategy and technology to implement the solution.
Development
We develop ready-to-implement MVP and enterprise AI-enabled predictive analytics tools to help our clients make data-driven decisions quickly, solve business problems and gain an edge over the competition. Itransition services include requirement identification, data management, AI model development as well as customization, integration, and further support.
Ready to leverage predictive analytics capabilities?
Client spotlight: our success stories
Incident analytics
around the globe
BI for incident management
We helped a risk management and security company create a universal BI solution that processes and visualizes different types of incident reports around the globe. The solution increased the satisfaction of existing customers and attracted a new major corporate client.
Automated
risk assessment
Risk management for a nuclear plant
Itransition developed a cross-platform risk assessment and management system for a nuclear power plant. By detecting risky events in a timely fashion, the solution enabled experts to prevent their escalation and significant impact on the enterprise manufacturing operations.
Our service delivery pipeline
1
Problem definition
Identifying business needs and user expectations
Assessing customer’s technical environment
Defining the solution’s functional and non-functional requirements
2
Data analysis
Conducting exploratory analysis of available data sources, both customer-owned and from public databases
3
Design
Designing the solution’s architecture
Defining the implementation strategy and optimal technology stack
Setting the project’s timeline and budget
4
Implementation
Data preprocessing, including data cleaning, annotation, and transformation
Defining the solution’s evaluation criteria
Developing the solution in-line with the defined implementation strategy
5
Integration and deployment
Integrating the solution into the customer’s infrastructure
Launching the solution into operation
6
Support and maintenance
Further retraining of the predictive analytics solution using user feedback and new data from the production environment
Top 5 predictive analytics platforms for manufacturing
Key features
- Customizable dashboards
- Easy sourcing and data transformation with Power Query
- Extracting insights from large datasets
- Navigation pane
- Re-using datasets across different reports and dashboards
- Integration with other Microsoft Products
Pros
- Excel integration
- Low costs
- Constant updates and innovations
- Clear learning curve
Limitations
- Advanced data cleansing options are limited
- Limited information sharing
- Big data analysis is challenging for beginners
Pricing
Free trial
for two months
Power BI Pro
$9.99 per user/month
Power BI Premium
$20 per user/month
Power BI Premium
$4,995 per capacity/month
Key features
- Linking to real-time and in-memory data
- Enhanced visualization capabilities
- Intuitive dashboard creation
- Connection to a variety of data sources
- Integration with existing technology
- Role-based permissions
- Availability of a mobile version
Pros
- Mobile-friendly
- Extensive customer resources
- Thriving community
- Ease of use
Limitations
- Unable to work with uncleaned data
- Lacks data modeling and data dictionary capabilities
- Lacks version control when building data dashboards
Pricing
Tableau Viewer
$12.00 per user/month
Tableau Explorer
$35.00 per user/month
Tableau Creator
$70.00 per user/month
Key features
- Smart visualizations and analytics
- Drag-and-drop functions
- Centralized sharing and collaboration
- Touch-free gesture-based interface
- Intelligent and in-context commentary
- Data preparation and integration support
Pros
- Easy to use for end-users
- Portability
- Can be deployed on Windows, Docker + Kubernetes
- Easily connect to most data sources
Limitations
- Inflexible data extraction
- Complicated pricing model
- Sluggish when working with large data sets
Pricing
Free trial
Qlik Sense Business
$30 per user/month
Qlik Sense Enterprise
price on request
Key features
- Preconfigured and customizable templates
- Dashboards and application building
- Role-based dashboards
- Powerful search functionality
- Embedded analytics in Microsoft PowerPoint
- Self-service visualization tools
Pros
- Robust feature set
- Seamless graphical user interface
- Microsoft Office compatibility
- Data source integrations
Limitations
- Difficult to upgrade
- Delayed and missing reports on performance and quality
- Customer support and platform administration issues
Pricing
- No free trial
- Price starts from $14,000 per year
Key features
- Advanced collaboration tools
- Automated detection of outliers, flaws, and data discrepancies
- Dashboards and analytic applications
- Location analytics, geocoding, and multi-layered maps
- Data discovery and visualization
- Interactive dashboards
- Integration with IoT and social messaging
Pros
- Works with third-party platforms
- Flexible
- Easy to handle
- Mobile access on various platforms
Limitations
- Lack of documentation and learning capabilities
- Some features can be difficult to use
- Limited in terms of types of data connections
Pricing
Free trial
TIBCO Spotfire Desktop
$650 per year
TIBCO Spotfire Cloud
$2000 per year
TIBCO Spotfire Platform
price on request
Benefits of predictive analytics in manufacturing
1 Reduced costs
Predictive analytics optimizes manufacturing processes, identifies quality failures in advance, and enables faster remedial action to minimize consequences. Advanced analytics and condition monitoring can help businesses minimize downtime and lost productivity by alerting them to potential equipment problems.
2 Spotted inefficiencies
Predictive analytics sifts through vast amounts of historical and real-time data much faster and more accurately than humans do. As a result, AI-enabled quality analytics can spot recurring errors and predict potential anomalies or equipment failures to help a manufacturing organization avoid production line halts by scheduling timely repairs and equipment replacements.
3 Streamlined growth
The maximized usage of available resources improves product quality, strengthening the company’s competitive advantage. Predictive software solutions identify actionable ways to optimize product development strategy, efficiently manage operations, make informed decisions, and maintain growth.
4 Increased profitability
AI-enabled analytics uses data to predict critical future results and revenue based on market conditions and current sales. Companies incorporating predictive analytics can significant improve their bottom lines, identifying emerging opportunities, and quickly responding to changing trends.
5 Enhanced performance
Predictive analytics can help you streamline various production processes, from inventory management to sales and marketing operations. AI-driven predictive analytics also enables organizations to optimize workforce management, improving employee engagement and motivation and, thus, boosting their performance.
6 Real-time insights
Predictive analytics allows you to obtain and compare data from historical production activities with actual production activities. It gives manufacturers a comprehensive source of real-time recommendations and alerts to improve operations, enabling data-driven decision-making.
Learn how to get more out of your manufacturing data with Itransition
Adoption bottlenecks & how to overcome them
Challenge
Solution
Collecting the right manufacturing data
Collecting and using inaccurate or incomplete information will lead to poor results that are not useful for end users and managers. To prevent this problem, you can establish robust manufacturing data collection targeted for a particular purpose and streamline quality assurance procedures. If you have just started implementing predictive analytics, the first step is to select data points, define a period, and collect as much data as possible. Then, analyze all the collected data and select the data sets applicable to your use cases.
Lack of a clear strategy for using predictive analytics
Many organizations want to put predictive analytics to work but aren't 100% sure how to use it. Before choosing a solution, clearly define your company's goals and objectives, and determine the estimation metrics. Analyze your business's needs and specific pain points to identify the most relevant use cases for a future solution.
Lack of in-house expertise
Predictive analytics can be complex for inexperienced employees. You might need help selecting, installing, customizing, and maintaining the solution. A professional technological partner can help you seamlessly integrate predictive analytics tools with applications used in your firm, such as an ERP platform, and organize training to enable your employees to adjust to predictive analytics quickly.
Predictive analytics drives the future of manufacturing
Predictive analytics helps manufacturers to reduce maintenance costs and improve operational efficiency and product quality. In addition, it enables leveraging the existing data by integrating and visualizing it to predict trends and act on opportunities before they manifest. If you want to select and apply predictive analytics in your manufacturing business successfully, Itransition's data science experts are ready to help.
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