Data science is dominating most industries today, as most of them operate on the basis of data. It has revolutionized the way data is perceived by different industries. Considering the vast field of data science and its diverse applications, it is only expected that it will find its sweet spot in manufacturing too.
The manufacturing industry is undergoing a huge transformation supported by today’s digital age that requires greater agility for the customers, business partners, and suppliers. The increasing scale and speed can be challenging for manufacturers, and this is where data science comes in.
Big data analytics promotes smart manufacturing. As per a prediction by IDC, by the year 2021, at least one-fifth of the largest manufacturers will rely on embedded intelligence built on cognitive data applications (like Machine Learning and AI) and Internet of Things or IoT so there is a need of learning Artificial Intelligence & Machine Learning. It will result in the automation of large scale processes speeding up the execution time by nearly 25%. If you are a beginner and want to gain expertise in data science, check out our data science certifications from top universities.
The Role of Big Data in Manufacturing
As big data paves its way for manufacturers across the world, data scientists are quite aptly regarded as the new factory workers. Leading companies like GM and Ford integrate data in huge quantities from both external and internal sources, from processors and sensors, in order to improve their production, reduce energy costs, and boost production. Even smaller businesses have discovered the benefits associated with the use of big data.
- Big data is not only cheaper to use but also cheaper to store.
- The increasing sophistication of analytics software has made it widespread.
- Parallel processing machines become easily accessible to manufacturers.
However, this is also an environment where there is hardly any room for error!
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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. It is primarily 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.
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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 creating the possibility for manufacturers to avoid critical failure.
Numerous criteria and factors have to 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. System integrators and 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.
Supply Chain Optimization
The management of supply chain risk is not an easy feat 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, market scarcity and tariffs to local weather 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. Another term used for supply chains is value chains and it is not without a reason. It works as a clockwork system that includes all the manufacturers of different parts and materials working to deliver required elements to assembly plants.
This relationship of different elements at various stages of production emanating from a certain manufacturer and being produced at a certain place, coming together to form the final product is complex. Simple contingencies 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.
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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 massive 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 simply 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.
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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. Learn more data science applications in this beginner’s guide to data science and its applications.
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 and AI-powered technologies. 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 then 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.
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Today, without doubt, manufacturing firms, as well as service-oriented businesses, are moving towards data science to have fully-integrated collaborative systems provide real-time responses to meet the changing conditions and demands of the customers’ needs in the factory and supply network.
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What are the core subjects in Data Science?
With data becoming an essential necessity, data science is governing most of the fields. This leads to immense responsibilities as a Data Scientist. The following are the core fields and skills that every company seeks in a candidate. Mathematical fundamentals such as statistics, probability, and linear algebra constitute the most important portion of Data Science. You will be responsible for decision-making at various labels, which is why you should be well versed with the latest BI tools. Python and R are considered to be the most effective and powerful languages for Data Science. Regression techniques, Naive Bayes algorithm, and regression trees are some of the major ML algorithms that you need to focus on.
Which programming language is best suited for Data Science and why?
There are dozens of programming languages for data Science out there but a majority of the data science community believes that if you want to excel in data science then Python is the right choice to go with. Below are some of the reasons that support this belief: Python has a wide range of modules and libraries like TensorFlow and PyTorch that makes it easy to deal with data science concepts. A vast Python developer community constantly helps newbies to get through to the next phase of their data science journey. This language is by far one of the most convenient and easy to write languages with a clean syntax which improves its readability.
Describe the major components that a Data Science project should have.
The following components highlight the most general architecture of a Data Science project: Training Models involves training your model against various inputs and predicting the output. This component decides the accuracy of your project. Using proper training techniques can produce better outcomes. Problem Statement is the fundamental component on which the whole project is based. It defines the problem that your model is going to solve and discusses the approach that your project will follow. Dataset is a very crucial component for your project and should be chosen carefully. Only large enough datasets from trusted sources should be used for the project.