Industries

5 uses of data analysis techniques in manufacturing

Blog Manufacturing

The manufacturing industry is constantly changing to remain at the forefront and be competitive. In today’s digital age, customers, suppliers and business partners require greater agility on the part of manufacturers, and this is precisely what explains the growing number of companies in the manufacturing industry that are setting their sights on the use of Data Science techniques 

Trends in the industry move like this:

  • Collaborative manufacturing systems: Integrated manufacturing systems that allow responding in real time to meet demand and adapt to customer needs.

  • Backbone of Integrated Intelligence: The competitive advantage lies in the use of data and the application of artificial intelligence and machine learning techniques to automate processes, optimize execution time and reduce costs. analysis based on real-time monitoring and predictive algorithms. 

Data science is called to dramatically change the manufacturing industry. Let’s consider these 5 data science use cases in industry that have already become commonplace and have brought benefits to manufacturers.

Predictive analytics: Analysis of current data to forecast certain situations in the future from the present. Let’s review the possible solutions provided by predictive analysis to the manufacturing industry:

1. Demand forecasting and inventory management: Two key points that intertwine and for which data analysis is involved to generate reliable models that allow manufacturing companies to plan their production strategy and better control inventory. Demand forecasting and inventory management consider numerous factors both internal and external to companies. This way, you can get a more complex view of your manufacturing business performance and better planning. Dataknow offers the Forecasting service as a dynamic mix between machine learning techniques and time series data with additional variables, which generate personalized forecasts that provide relevant and reliable information to the creation of strategies for process optimization and support business decisions.

2. Product development: Data analysis offers great opportunities for manufacturing companies regarding product development. Manufacturers capitalize on analysis and results of market segmentation models, to better understand their customers, better sectorize their sales efforts, and develop new products or add capabilities to their products to meet customer needs or demands. Using data science for product development, manufacturers can design a product with higher customer value and minimize the risks associated with introducing a new product to the market. At Dataknow we offer the clustering service that combines data analysis techniques to generate an accurate view of the market and users of our clients.

3. Failure prediction and preventive maintenance: Both prediction models are intended to predict when a team cannot perform its task based on real time series and usage data. As a result, information can be obtained on how to prevent these failures from occurring or at least reduce their number. The greatest strength of preventive maintenance is planning. With the prediction of future equipment problems close at hand, the manufacturer can schedule equipment idle times to perform cooling or repair work to avoid subsequent delays and significant unplanned failures.

4. Warranty analysis:Manufacturers prepare each year to meet the costs related to warranty claims on their products. What they have collected for years on warranty claim data is valuable information about product quality and reliability and helps reveal early warnings or product defects.

 5. Price optimization:Price optimization is the process of finding the best possible price for both the manufacturer and the customer, considering numerous factors and criteria of an internal and external nature to the company. For this, optimization models are used that use the data of the company and the market to estimate an optimal price that allows the company to remain competitive and increase its profits efficiently. In highly competitive market conditions and changing customer needs, price optimization becomes a necessity and is a continuous development process within the industry.

At Dataknow we know the power that data brings, and we offer customized solutions for the manufacturing industry, our forecasting and clustering service offer the following technical and business benefits:

1. Cloud-based solution with a focus on microservices, supporting scalability and modularity.

2. Specialized support in forecasting and clustering with personalized business knowledge for your industry.

3. Technical support in the stability and precision of the models.

4. Use of State-of-Art methods of time series analysis

5. Use of multiple methods for profiling and reconciliation by customer value

6. Optimized classification and segmentation algorithms.

7. Ready-to-use deployment with integration to the end user BI or management system.

8. Governance and centralization of the forecasting and clustering process

9. Optimization of talent and resources in the core business.

10. Independence from maintaining large-scale forecasts.

11. Improvement of forecast sources of purchasing and supplier management systems.

12. Control of irregularities or atypicalities.

13. Historical visibility 

What more use cases do you know for the industry? Any specific case?

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