Manufacturers require deeper insight into different processes to gauge business performance and take measures to refine operations and plans. Considering the stakes are high, it is crucial to know the company inside out and drive future predictions based on analysis.
To succeed, manufacturing companies must determine the cause of existing “trends” or “patterns” critical to their business. Diagnostic analytics facilitates this by helping businesses dive deeper into a trend to determine its cause – whether it is a sudden rise in monthly subscriptions or a dip in sales. This information enables manufacturers to make more data-driven and informed decisions.
Introducing Diagnostic Analytics
Diagnostic analytics is the process of breaking down data to identify the root cause of a specific problem, behavior, or event. It is the most prevalent strategy to prevent catastrophes and drive future success. The process involves drilling down and pursuing data discovery, data mining, and data correlation techniques for accurate analysis. Manufacturing companies can leverage this to identify production inefficiencies, inventory shortages, shipment delays, etc.
Also called Root Cause Analysis, the technique emphasizes the cause and effect of organizational concerns. The process depends on historical data, but it seeks to identify and explain the inconsistencies and outliers. Using different methods like probability theory, clustering analysis, regression analysis, time-series analysis, filtering, and others, diagnostic analytics identifies the cause of the trend. Here’s what the process looks like:
An initial glance into an issue or trend may give you a vague picture. It is pertinent to diagnose the occurrences to factor in the anomalies and their cause.
Dig Deeper into Datasets
Diagnostic analytics manages valuable datasets. To get deeper into the system, analysts must investigate the existing datasets that explain comparable inconsistency to determine how to diagnose the phenomenon. Only then is it possible to identify the necessary data.
Establish Informal Connections
Establishing causal connections requires relevant data subjected to proper data analysis to detect hidden links contributing to the inconsistencies. As mentioned above, regression analysis, probability theory, time-series data analytics, and filtering are some of the techniques applied for this purpose. Data mining also remains critical in detecting unknown patterns, which explains the cause of an event. This explains why diagnostic analytics is preferred when unforeseen trends or events occur.
How Does Diagnostic Analytics Contribute to Manufacturer Insights?
Diagnostic analytics depends on three major techniques – data drilling, data mining, and correlation analysis – to deliver sure-shot results.
- Data drilling is a technique used to reveal detailed information related to data driving the disruption. For instance, analysts drill down data to determine the dipping membership subscriptions of specific regions or demography based on other relevant factors like age, race, ethnicity, etc.
- Data mining is the concept of searching through a massive amount of data to determine associations and patterns within. The process, backed by machine learning technology, is either conducted automatically or manually.
- Correlation analysis explores the strong connection between different interconnected variables. For instance, the sale of sunscreen lotions increases during summer.
Role of Diagnostic Analytics in Improving Quality and Efficiency of Manufacturing
Manufacturing units, whether big or small, involve a lot of roles and responsibilities. From machinery and equipment to human resources, a fault in any of the technology management tools unsettles the entire system. Most often, the plausible causes of malfunction are the lack of software upgrades or failed software installations. But be it hardware or software – it is critical to detect the cause and solve the issue at the earliest behest.
Diagnostic analytics addresses the same by improving the performance of a manufacturing company at distinct levels. It evaluates data to determine connections, detect anomalies, and assess casualties and gives an accurate report of the manufacturing company’s activities. Furthermore, it highlights new, undiscovered prospects for optimization and growth.
Here are some of the most prominent benefits of diagnostic analytics for manufacturers
Defect Prediction & Preventative Maintenance
Manufacturing companies depend heavily on preventive maintenance. Using time-based and use-based programs, these companies ensure fewer unpredicted breakdowns. Applying diagnostic analytics, companies also leverage real-time data for day-to-day functions. It helps predict the probability of a breakdown and the time with precision. As a result, the cost of maintenance and overall downtime is reduced while productivity increases.
Inventory Management & Demand Forecasting
Modern manufacturers bank on demand forecasting to have better control of their supply chain and inventory. However, demand planning is highly unpredictable and complicated. With the inclusion of data science techniques, end-to-end supply chain control can be utilized together with real-time floor data to manage purchases, inventory control, and logistics. It also assists in generating precise demand plans by identifying the latest trends that could have otherwise gone unnoticed.
When manufacturers have a proper understanding of the product, the time taken to manufacture the parts, the length of job runs, the expected costs and profit margin, and other vital details, they can make wise business decisions.
Cycle time dictates cost. Hence, it is imperative to be cognizant of the precise time to create parts and determine the allied costs to produce them. If you set the prices too low, it will reduce profitability; however, if you set them high, it will hurt the demand. Advanced diagnostic analytics for manufacturing brings data forward and ensures appropriate pricing.
Warranty support is a real cause of concern for manufacturers. Since there is no generic approach for warranties, this creates unexpected problems and uncertainties. Data science plays a significant role in manufacturing as it helps capture data from active warranties. This data can be used to improve or change the course of action to reduce failures and costs. It also contributes to informed iterations and complaint management.
Product development is an expensive manufacturing process. It demands proper R&D to build new lines of products, enhance existing models, and build innovative value-added services. Today, advanced diagnostic analytics and data science allow easy simulation of the process using real-time conditions to predict performance while lowering the costs of R&D.
Supply Chain Risk Assessment
Just as data comes from different production machines, it is also possible to capture data from production materials transmitted from vendors or in transit. The aim is to offer end-to-end supply chain visibility. With diagnostic analytics, manufacturers are equipped to manage the supply chains in a controlled manner by directing resources and further redirecting them to enhance the process. They can discern demand and order buffer supplies and backup stocks before a disruption occurs.
Diagnostic analytics is a pragmatic way for manufacturers to uncover data insights and take action. It not only highlights anomalies and trends but also helps companies understand why and how they occurred. It provides vital insights into the causes to facilitate manufacturers to make informed decisions and resolve problems, further enhancing their business performance.