Producing goods isn’t enough for manufacturers to compete in today’s market. They must innovate, adjust to changes in the market and customer preferences, optimize operational efficiency, and leverage cutting-edge technology.
Organizations in the manufacturing industry can benefit significantly from applying data science to achieve these objectives and increase revenue.
Things to know about data science in manufacturing:
- What is Data Science?
- Applications of Data Science in Manufacturing
- Benefits of Data Science in Manufacturing
What is Data Science?
Data science uses statistics, advanced analytics, artificial intelligence (AI), machine learning (ML), and specialized programming to extract meaningful insights from data to drive strategic planning and empower decision-making.
5 Applications of Data Science in Manufacturing
Some of the most impactful opportunities for leveraging data science in the manufacturing industry are:
- Predictive maintenance
- Price optimization
- Inventory management
- Supply chain optimization
- Customer satisfaction improvement
Organizations can lose millions of dollars when their equipment fails and creates unplanned downtime. This unplanned downtime results in lost revenue, delays in getting to market, and dissatisfied customers.
Data science, AI, machine learning, and advanced analytics can be used to proactively identify issues with equipment in the manufacturing process. For instance, organizations can use vibration monitoring to evaluate vibration patterns in machinery and detect anomalies.
They can also use infrared thermography to monitor heat signatures in the equipment to detect excessive heat from machines under extreme stress or malfunction
By using data science and real-time monitoring to predict when equipment is most likely to fail, manufacturers can alert technicians to perform maintenance before a breakdown or failure actually occurs, reducing downtime and revenue losses and maximizing productivity.
Determining the price of a product is critical to its success and sales; it needs to be competitive against other options in the market and seem reasonable to targeted customers.
The pricing of a manufactured product is based on numerous external and internal factors, including the cost of raw materials, labor, production time, distribution costs, and customer demand.
Pricing can also fluctuate based on changes in the market, customer demands, and the cost of materials. Data science can be used to aggregate and analyze data on the cost of materials and production as well as historical and current prices of market competitors in order to optimize your product pricing.
This enables your organization to stay competitive in the market, adapt accordingly to changes in the market, empower your business’s leaders, and maximize your profits.
Manufacturers can use data science to analyze their inventory data, as well as the data from supply chains, in order to maintain optimal inventory levels and reduce waste.
By leveraging big data to inform your decision-makers, track inventory, and forecast demand, your organization can manage your inventory levels so they neither exceed the required amount nor fall short of the amount that is needed.
This reduces the cost incurred by storing raw materials or products you don’t need, allows you to adapt to forecasted changes in demand, and optimizes cash flow.
Supply chain optimization
An efficiently managed supply chain is key to the timely supply of manufactured goods to your customers. Data science can be used to collect and analyze real-time data on factors and inputs including:
- Supply schedules
- Fuel and shipping costs
- Stock quantities
- Fluctuations in pricing
- Location of goods in dispatch
- Scarcity of raw materials
The relationship between these different elements that affect the supply chain and production process is complex, and if not identified or anticipated far enough in advance, can have a drastic impact on your supply chain and ability to manufacture products.
By leveraging data science tools and processes, you can predict patterns of inputs and outputs, as well as anticipate market changes, to minimize risk, avoid unnecessary expenses, and ensure a smooth supply chain.
Customer satisfaction improvement
To ensure the maximum amount of revenue and sales, manufacturers need to ensure their products are meeting or exceeding customer expectations.
Organizations can use data science to collect and analyze customer data, behavior, and feedback from a variety of sources. These sources include digital touchpoints like complaint forms, social media, and customer review sites.
Analyzing this volume of data enables your organization to identify your greatest strengths that your customers are happy with, as well as your greatest opportunities for improvement and innovation. By capitalizing on these opportunities, your manufacturing business can drive greater sales and customer satisfaction.
Benefits of Data Science in Manufacturing
Your manufacturing organization can realize significant benefits from the use of data science models, including:
- Improved efficiency
- Increased revenue
- Reduced downtime
- Improved product quality
- Increased innovation
- Improved customer satisfaction
- Increased agility
Applying Data Science in Your Manufacturing Business
Many organizations do not have the right tools and processes in place to fully leverage their data to derive meaningful insights, identify hidden patterns, and make predictions about the future.
At AIM Consulting, we help companies strategize, design, build, and operationalize data and analytical platforms through our high-performing teams of data engineers, architects, data scientists, and analysts.
We ensure your organization has the right insights to mitigate risk, maximize customer satisfaction, inform decisions, and optimize your operations.
Need Help Turning Insights Into Action?
We leverage proven analytics methodologies, best practices and tools to help you stay ahead of the competition, predict and plan for the future, and take your services and products to new heights.