In the roaring workshop, thousands of sensors are silently recording every equipment vibration,
every degree of temperature change, every second of energy consumption fluctuations; MES system
in real time scrolling the progress of the work order, quality inspection results, material consumption
details; ERP precipitated supply chain response rate, customer order patterns, inventory turnover days ... ...
These seemingly complex, isolated data torrent, is the modern manufacturing industry's most underestimated
strategic assets. However, the reality is that the massive amount of data or be put on the shelf, or only used
to generate lagging reports, which contains the deep value is far from being tapped. Big data analytics in the
manufacturing industry is the disruptive force that refines data “crude oil” into ‘fuel’ for decision-making and
drives enterprises from “experience-driven” to “data-driven”. The subversive power of “experience-driven”
to “data-driven”.
First, the pain point: sleeping data, lost profits
Manufacturing enterprises are generally trapped in the data dilemma:
Data sleep: equipment data is not networked to collect, paper records can not be analyzed, the data between
the system is fragmented, the value of data has not been activated.
Lagging reports: relying on after-the-fact statistical reports, managers are like “driving with a rearview mirror”,
unable to sense the pulse of production in real time, and missing the opportunity to intervene.
Experience dilemma: Decision-making is overly dependent on personal experience and intuition, and the risk
of inaccurate judgment increases dramatically in the face of a complex and changing environment.
Ambiguity: Inefficiency, quality fluctuations, and high costs make it difficult to pinpoint the root cause, making
it difficult to improve the situation.
Lack of prediction: Lack of ability to foresee equipment failures, demand changes, supply chain risks, and high
cost of reactive response.
Second, the core value: big data analytics-driven leap in manufacturing
When data is effectively connected, governed and analyzed, its value will run through the entire manufacturing chain:
Perspective production site, drive real-time optimization:
Equipment health portrait: real-time analysis of equipment vibration, current, temperature, oil and other operating
data, build a health model, accurately predict failures, change the “fire-fighting” repair to preventive maintenance,
reduce unplanned downtime.
OEE in-depth analysis: Beyond the overall OEE figures, penetrating analysis of the causes of downtime (planned/unplanned),
performance loss (speed/small downtime), quality loss (scrap/rework) of the root cause, targeted to improve the overall
efficiency of the equipment.
Process Parameter Optimization: Correlate and analyze massive production process data (temperature, pressure, speed,
recipe) with quality results to find the optimal process window to stabilize quality, improve yields, and reduce energy
consumption.
Intelligent scheduling of work-in-process: Track the location and status of work-in-process in real time, and dynamically
optimize the production path and sequence by combining order priority and equipment load to reduce waiting and backlog.
Build a strong quality line of defense and realize precise control:
Full-process quality traceability: Open up raw material batches, process parameters, equipment status, operators, and
quality inspection results data to realize minute-level accurate traceability and quickly lock the source of the problem.
Real-time quality warning: Based on online inspection (machine vision, spectroscopy, etc.) data and process parameters,
establish a prediction model to issue an early warning before defects occur, and stop defective products in the bud.
Root Cause Analysis (RCA): Utilizing association rules, decision trees and other algorithms, we can dig deep into the
combination of key factors that affect quality, and guide process improvement and preventive measures.
Reshape supply chain resilience and optimize global resources:
Accurate Demand Forecasting: Integrate data from historical sales, market intelligence, macroeconomics, seasonal factors,
etc., to improve the accuracy of demand forecasting and guide more accurate production planning and procurement strategies.
Supplier Risk Insight: Analyze data such as suppliers' on-time delivery rate, quality pass rate, price fluctuation, public
opinion, etc. to build supplier risk profiles, optimize supplier combinations, and ensure security of supply.
Intelligent Inventory Optimization: Dynamically calculate the optimal inventory level based on multi-dimensional data such
as demand forecast, production plan, procurement cycle, storage cost, etc., to reduce capital consumption and avoid
production stoppage due to lack of materials.
Logistics path optimization: analyze transportation cost, time, road condition, weather and other data to plan the optimal
logistics routes and methods, reduce transportation cost and improve delivery efficiency.
Enabling product innovation and insight into customer value:
Product Usage Analysis: Collect networked product operation data (e.g., energy consumption, failure mode, usage intensity),
gain insight into real usage scenarios and pain points, and drive product design improvement and iteration.
Customer Demand Mining: Analyze sales data, customer service records, and social media feedback to identify customer
preferences, unmet needs, and potential market opportunities.
R&D Simulation Optimization: Use historical R&D data and simulation results to accelerate design iteration, predict product
performance and reliability, and reduce trial-and-error costs.
Key Technologies and Implementation Paths
To realize the value of big data, we need to cross the technology and landing gap:
Technology cornerstone:
Data collection and connection: Industrial Internet of Things (IIoT) technology to open up the device layer data; ESB/API/data bus
integration of MES, ERP, PLM, SCM, QMS and other system data; processing of unstructured data (e.g., images, logs).
Data Governance and Platform: Establish a unified data warehouse/data lake/data center, implement data cleansing, standardization,
and labeling management to ensure data quality and consistency.
Analytics Engine: Use distributed computing framework (e.g., Spark) to process massive data; combine with stream processing
technology to realize real-time analysis; use machine learning/deep learning algorithms for prediction and insight.
Visualization and Interaction: Through BI tools, customized kanban boards, and mobile applications, analytics results are presented
visually and in real-time to different roles to support agile decision-making.
Pragmatic landing path:
Strategy First, Focus on Pain Points: Define analysis goals (e.g., improve OEE by 10%, reduce quality loss costs by 15%), and select
1-2 high-value, implementable scenarios (e.g., predictive maintenance, quality root cause analysis).
Strengthen data foundation: Ensure that key data sources are accessible, connectable and of reliable quality. Prioritize solving the
“availability” problem, and then gradually improve data quality and breadth.
Build the foundation of the platform and take small steps: Adopt a modular and scalable platform architecture. Start with pilot
projects to quickly validate the value and accumulate experience and confidence.
Cross-domain collaboration to break down silos: Establish joint teams across IT, OT, and business departments to ensure that
data needs are aligned with business goals and analysis results drive action.
Internalize capabilities and iterate continuously: Foster a fusion team of internal data analysts and business experts. Establish a
closed loop of “analysis-action-verification-optimization” to continuously expand application scenarios and depth.
Crossing the Challenge: The Last Mile from Data to Value
Data silos and quality: Promote the construction of system interconnection and data governance system, and formulate unified
data standards and responsibilities.
Technical complexity and selection: Evaluate your own IT/OT capabilities, prioritize solutions that are easy to use, scalable, and
have industry know-how, and make good use of external expertise.
Talent bottlenecks: Bring in and cultivate complex talents with knowledge of manufacturing processes, data thinking and
analytical skills. Strengthen the data literacy of business personnel.
Security and Privacy: Build a security protection system (encryption, desensitization, access control) that covers the entire
life cycle of data and complies with regulatory requirements.
Cultural Transformation: Promote a corporate culture of **“data talk, data decision-making ”**, break down empirical
barriers, and embrace evidence-based management.
Conclusion: Data Alchemy, forging new manufacturing competitiveness
Manufacturing big data analysis is not a show-off, but a profound cognitive revolution and management upgrade. It will be
the production site, supply chain, product, customer “data footprint” into quantifiable, predictable, optimizable insights,
giving enterprises unprecedented “perception - cognition - decision-making - execution” closed-loop capabilities.
Those enterprises that take the lead in mastering data “alchemy” are quietly pulling away from the gap: they can foresee
equipment failures before they occur, minimizing downtime losses; they can accurately lock in the culprits of quality
fluctuations, achieving near-zero-defect production; they can dynamically optimize the allocation of resources, responding
to the ever-changing market at the lowest possible cost; and they can sniff out subtle changes in customer needs from the
massive amount of data, detecting the impact on customer satisfaction. It can also sniff out subtle changes in customer
demand from massive data and drive continuous product innovation.
In the data-driven future manufacturing scenario, big data analysis is no longer the icing on the cake, but the underlying
infrastructure and core driving force for enterprises to build core competitiveness. Digging into the data, insight into the
future, is the manufacturing industry towards high-quality development of the road. Actions should not be delayed.