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In the last couple of years, data science has seen an immense increase in various industrial applications across the board. Today, we can see data science applied in health care, customer service, governments, cybersecurity, mechanical, aerospace, and other industrial applications. Among these, manufacturing has gained more prominence to achieve a simple goal of Just-in-Time (JIT).
How data science is used in manufacturing industry?
What are the challenges of data science in manufacturing?
1: Which of the following algorithms is most suitable for classification tasks?
There are various challenges for applying data science in manufacturing. Some of the most common ones that I have come across are as follows
Lack of subject matter expertise: Data science is a very new field. Every application in data science requires its own core set of skills. Likewise, in manufacturing, knowing the manufacturing and process terminologies, rules and regulations, business understanding, components of supply chain and industrial engineering is very important. Lack of SME would lead to tackling the wrong set of problems, eventually leading to failed projects and, more importantly, losing trust.
Reinventing the wheel: Every problem in a manufacturing environment is new, and the stakeholders are different. Deploying a standard solution is risky and, more importantly, at some point its bound to fail. Every new problem has a part of the solution that is readily available, and the remaining has to be engineered. Engineering involves developing new ML model workflows and/ writing new ML packages for the simplest case and developing a new sensor or hardware in the most complex ones.
8 Interesting Data Science Applications in Manufacturing Industry [2022]
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Applications of Data Science in Manufacturing
The way data science is applied in manufacturing is unique in certain ways, considering the specific requirements of the field. Firstly, it is used to provide valuable insights to the manufacturers aiming at profit maximization, risk minimization, and productivity assessments. Here is a list providing the major applications of data science in manufacturing:
Predictive Analytics or Real-time Data of Performance and Quality
The collection of data from operators and machines is used to create a set of KPIs or Key Performance Indicators like Overall Equipment Effectiveness or OEE. This enables a root cause analysis of scrap and downtime driven by data. Data science is hence utilized to offer a proactive and responsive approach to machine maintenance and optimization.
The ability to generate a quicker response to issues has a direct impact on productivity and costly downtime. The creation of a predictive model that monitors machine performance and downtime can then be used to anticipate the nature of yield gains, the impact of any external changes, scrap reduction, and quality. This will, in turn, help manufacturers discover new methods and ways to approach quality improvement and cost management.
Preventive Maintenance and Fault Prediction
Production in modern manufacturing has very few critical cells or machines to depend on. The data used for real-time monitoring can be further analyzed to prevent machine failure and improve asset management. Data scientists make use of the knowledge of the machine and take note of the reasons why it may fail in order to make these predictions.
Process data indicating varied vibration and temperature is used in big data manufacturing to predict the failure of a machine beforehand. Tracing the deviations against the settings for optimum performance of machines, engineers can be signalled to take preventive measures when required by creating the possibility for manufacturers to avoid critical failure.
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Price Optimization
Numerous factors should be taken into account when determining the cost of a product. Each stage involved in the process of manufacturing and selling the item counts. The final price of the product is an end result of taking into account the cost of each element, starting from the raw material to the distribution costs. But that is not all, for a product to be saleable, even the customer has to find the price reasonable.
This is the expertise of price optimization where the trick is to find the best possible quotation acceptable to and beneficial for both the manufacturer and the customer. Modern solutions for price optimization is modelled around profit maximization and product efficiency.
Data science uses tools for aggregation and analysis of data, including both pricing and cost from internal sources and market competitors to extract optimized price variants. The market competition, in combination with the change and fluctuations in customer needs and preferences around the world, makes data science a valuable tool in manufacturing.
Automation and Robotization in the Smart Factory
The big move towards automation involves big investment. Engineers around the world chart their path using the advancements in data science as a guide leading them to effective allocation of resources and significant productivity gains. Data scientists employ predictive and analytical tools to determine the best cost-saving opportunities yielding optimum benefits.
The insights are then used by the engineers in their mode of operation and allowing the manufacturers to make the best decision while investing their money in robotics and automation technology. This is how data science provides a new way of approaching design and optimization in some of the best production facilities operating today. The use of real-world data to understand the effect on production caused by new technology, designs and machinery have been revolutionary for the manufacturing industry.
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Supply Chain Optimization
The management of supply chain risk is not an easy thing to achieve. The complexity and unpredictability of this arena make it a job more suitable for the data scientists to be handled. Working with inputs ranging from fuel and shipping costs, pricing differences and market scarcity is the domain of data science by simply converting them into data points.
Using the right data science model, market changes can be anticipated to minimize risk, avoid unnecessary expenses and result in savings.
This relationship of different elements at various stages of production spreading out from a certain manufacturer and being produced at a certain place, coming together to form the final product is complex. Simple circumstances like scarcity of material or late deliveries can be costly mistakes in the production process. Data scientists analyze and predict patterns of inputs and outputs to minimize risks and ensure a smooth-running system.
Product Design and Development
Validation of material design and decisions can be obtained from data science by analyzing customer needs and preferences. Product development is one of the main services provided by contract manufacturers. Their product designs and features need to resonate with their customer’s choice and requirement. Data science tools are often employed to determine the best way to produce an item to suit the unique specifications of a customer or a group.
Data science can also be used in the production of a new item or improve an existing item to analyze consumer preferences and market trends. The actionable insights from customer feedback can be used by product marketers to improve products to fulfill customer requirements and profit the manufacturers.
Inventory Management and Demand Forecasting
Demand forecasting involves enormous work for the specialists and accountants as it requires analysis of big data aimed at efficient decision making. The strong relation it shares with inventory management makes the two fields literally depend on the other for smooth functioning. An insight into their interrelation can be drawn from the fact that it is the data from the supply chains that are utilized in demand forecasting.
Demand forecasting is crucial to the efficient management of the production system for a manufacturer. The opportunity to control the inventory just by analyzing data reduces the cost incurred in storing items you may never need. The beauty of application in data science in demand forecasting is that the data input can be continually updated.
Hence, the forecasts will be relevant to the current situation, taking note of any external changes in the production environment, market or material availability. This, in turn, helps maintain a better supplier-manufacturer relation where both parties can regulate their activities more efficiently.
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Efficiency and Computer Vision Applications
For leading firms, sustainability is increasingly becoming a dominating concern when it comes to their long term strategy. Manufacturers are setting ambitious goals to reduce carbon emissions and save energy as a part of their role in the environmental crisis. This includes complex calculations involving supply chain management, energy usage estimation and so on while maintaining efficient production.
Data science can be relied upon to fulfill these exceeding goals with its computer vision applications. Using modern quality control methods like object identification, detection, and classification, the process can be monitored through computer vision to achieve the desired results.
The data can be used to create images that are algorithmically compared to existing models, ideal cases, and future expectations by identifying discrepancies in the current process and making the necessary changes. Among the many advantages of using computer vision applications, manufacturers can get improved quality control, decreased labour cost, continued operability and high-speed processing capacity.
Conclusion:
Currently, applying data science in manufacturing is very new. New applications are being discovered every day, and various solutions are invented constantly. In many manufacturing projects (capital investments), ROI is realized over the years (5 – 7 years). Most successfully deployed data science projects have their ROI in less than a year. This makes them very appreciable. Data science is just one of many tools that manufacturing industries are currently using to achieve their JIT goal.
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