IDC estimates that the global big data analytics market revenue would’ve reached ~$274B by 2022. This would have made manufacturing as one of the top 3 industries with largest analytics growth.
- Automate traditional manufacturing processes
- Reduce costs
- Improve efficiency
What is manufacturing analytics?
Manufacturing analytics is the practice of capturing, cleansing, and analyzing machine data in order to predict their future use, prevent failures, forecast maintenance requirements, and identify areas for improvement. Manufacturing data includes all structured and unstructured information collected manually or by using software from machines and humans during every stage of production until a product is launched to the market.
What are the use cases of manufacturing analytics?
Manufacturing processes produce a large volume of data from:
- Machines: robotics, sensors, actuators, IoT devices, etc.
- Operators: ERP, sales, logistics, etc.
This data can be collected and analytics can be applied to it for:
1. Demand forecasting
Demand forecasting relies heavily on historical data about supply levels, material costs, purchase trends, and customer behavior. Manufacturers can leverage analytics to:
- define the types of products to be manufactured in a certain period
- define out of stock products
- calculate the number of products to be manufactured
- forecast sales opportunities
2. Inventory management
Forecasting demands enables manufacturers to manage their inventory, purchase materials, and optimize storage capacities in a data-driven manner. Analytics also provides insights about:
- sales to inventory ratio which represents the average inventory over the net sales
- days in inventory which is the number of days a manufacturer holds their product before selling it)
- gross margin return on inventory (GMROI) which indicates how much gross margin a manufacturer gets back for each dollar invested in inventory.
Explore inventory management in more details.
3. Order management
Manufacturers can leverage predictive analytics to optimize the order management workflow by identifying products in demand, calculating the time required to build and ship every product, and defining the inventory needed to meet the demand for the finished product.
Explore order management in more details.
4. Maintenance optimization
Data collected from different manufacturing machines, tools, and devices, as well as data about operations and which machines they require, can be analyzed in order to:
- Predict when a machine will require maintenance based on time it’s been used and operations used in.
- Detect anomalies in operations which are caused by or will lead to machine failure.
- Prevent down time by planning machine breaks, fixes, or replacements.
Explore predictive maintenance in more details.
5. Risk management
Implementing analytics enables manufacturers to manage risks in a data-driven manner, such that they can:
- Determine recurring errors and prevent repetitive losses
- Predict insurance needs
- Monitor real-time machinery and operator work
- Identify real-time fails and system anomalies
- Plan risk management strategies
6. Price optimization
Leveraging analytics can help manufacturers understand the real price of a product based on the prices of materials, cost of operations, machines, and tools used or purchased for manufacturing. Additionally, manufacturers can leverage data about competitors, market trends, consumer behavior, and purchase history to optimize prices accordingly. Analytics can also help set dynamic prices which are based on demand, supply, competition price, and subsidiary product prices.
7. Automation and robotics
Analytics can provide an overall view of a manufacturing process, operation costs, as well as the number of operators and hours spent on a product. Large manufacturing firms can leverage these analyses to uncover automation or robotization opportunities which can reduce the time and cost of launching certain products
8. Transportation allocation
Manufacturers can leverage analytics on:
- Historical data: For predicting transportation time and vehicle requirements to deliver products to businesses or consumers.
- Real time data: For analyzing the impact of unplanned transportation events such as labor strikes or road works.
9. Product progress measurement
Based on historical data about the same or similar products, materials, machines, and tools used, as well as allocated employees for production, analytics can provide an estimation about the production process, when the product will be launched, which errors or pitfalls may be faced, and create a roadmap for the following procedures.
10. End user experience estimation
Product development teams can leverage analytics on product features, consumer behavior, and comments on online platforms, as well as competitor products, to estimate why end users buy certain products, when to launch similar products, and which features require optimization.
What other technologies are used in manufacturing?
Some of the technologies leveraged today by manufacturers include:
Robotic process automation (RPA)
RPA is a type of software capable of replicating human interactions with computers in order to automate repetitive processes. Manufacturers can leverage RPA for supply chain management and stock optimization.
To explore use cases, feel free to read our article about the benefits and top 8 use cases of RPA in manufacturing.
AI has numerous applications in manufacturing including:
- Digital twins and digital twin of an organization
- Augmented reality
- Demand forecasting
- Generative design
- Quality assurance
- Process optimization
To explore AI use cases in manufacturing, read our in-depth article top 12 use cases and applications of AI in manufacturing.
If you believe your business will benefit from manufacturing technologies, feel free to check our data-driven lists of vendors for:
And you can contact us to guide you through the process
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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