Six Major Core Challenges in Traditional Manufacturing

This article systematically identifies six major challenges facing traditional manufacturing, spanning market operations, production management, equipment infrastructure, data systems, talent and organization, and intelligent transformation implementation, helping companies thoroughly diagnose pain points and clarify upgrade paths.

I. Market and Operations: Intensifying External Competition, Sustained Profit Pressure

1. Drastic Changes in Demand Structure, Insufficient Flexibility

The market has shifted from mass standardization to multi‑variety, small‑batch, customized production, with frequent rush orders and order changes. Traditional rigid production lines have long changeover times and high switching costs; capacity allocation relies on experience, often leading to idle capacity or delayed deliveries.

2. Continuously Rising Factor Costs, Squeezed Margins

Labor, land, energy, and raw material costs rise year by year; low‑end products are heavily homogenized, forcing price‑based competition, resulting in thin gross margins and lack of premium pricing power.

3. Fragmented Supply‑Demand Information, Severe Inventory and Capital Tie‑up

Production plans, sales orders, and supplier data are not interconnected, leading to inaccurate demand forecasts; raw material obsolescence and finished goods overstock slow down capital turnover and weaken resilience.

4. Unstable Industry and Supply Chains

Supply constraints exist in upstream components, high‑end industrial software, and precision equipment; upstream/downstream small and medium‑sized supporting enterprises have low digitalization levels, making collaborative scheduling, delivery warnings, and quality traceability difficult, leading to chain breaks and production stoppages.

5. Increasing Pressure for Green Compliance

Regulations on energy consumption control, environmental emissions, carbon accounting, and safety production are tightening; traditional factories lack digital energy management, making compliance retrofits costly.

II. On‑site Production Operations: Coarse Management, Loss of Efficiency and Quality Control

1. “Black‑Box” Production Processes, Entirely Manual

Workflow progress, equipment status, and labor hour losses lack real‑time data; management relies solely on verbal reports from workshop team leaders, making it impossible to precisely identify waste points.

2. Quality Control Relies on Manual Inspection, High Cost of Defects

Appearance and dimensional inspection depend on the human eye, leading to frequent missed detections and false detections; defects cannot be traced throughout the process, making it difficult to identify root causes when batch quality issues occur, resulting in significant rework and scrap losses.

3. Coarse Equipment Management, High Downtime Losses

Equipment maintenance follows a model of scheduled overhauls and breakdown repairs, with no predictive warnings; old machine tools, injection molding machines, and welding equipment experience frequent unscheduled downtime; Overall Equipment Effectiveness (OEE) is generally below 80%.

4. Hidden Costs Difficult to Quantify

Hidden costs such as material waste, idle labor, changeover downtime, mold wear, and energy waste have long been overlooked; cost accounting is coarse, making precise cost reduction impossible.

5. Difficult Safety Management and Control

Relying on manual inspections for safety violations, equipment anomalies, and hazardous chemical leaks leads to delayed warnings and high risk of safety incidents.

III. Hardware Equipment Foundation: Many “Dumb Devices,” Weak Digital Foundation

1. Mixed Old and New Equipment, Non‑unified Protocols

Workshops contain old machines from over a decade ago without data interfaces, mid‑range PLC equipment, and new smart devices; brands and communication protocols (Modbus, Profinet, proprietary protocols) are incompatible, making data acquisition and retrofitting extremely costly.

2. Dilemma of Retrofitting Old Equipment

Direct replacement requires enormous investment, unaffordable for SMEs; adding sensors and gateways for data acquisition incurs additional costs for mechanical retrofitting, electrical debugging, and production stoppages.

3. Insufficient Underlying Network Support

Most workshops only have basic office networks, lacking industrial Ethernet or 5G private networks; high‑concurrency, low‑latency data transmission for robots, vision inspection, and AGVs experiences frequent lag and disconnections.

IV. Data and Systems: Prominent Data Silos, Digitalization Fails to Create Value

1. Multiple Independent Systems, Data Not Interconnected

ERP, MES, WMS, PLM, and SCADA come from different vendors with non‑standard interfaces and inconsistent data formats; staff must manually copy data across systems, leading to large errors and low efficiency.

2. Poor Data Quality, Unable to Support Intelligent Analysis

Missing data, entry errors, timestamp confusion, and non‑standard parameters prevent equipment, process, quality, and inventory data from being correlated; big data and AI models lack effective training data.

3. Lack of Data Governance Capability

No unified material coding, process standards, or equipment ledgers; no data platform or data governance system, so data cannot be accumulated as corporate assets.

4. Constraints on High‑End Industrial Software Supply

CAD, CAE, simulation, advanced MES, and digital twin software rely on overseas products, with high licensing fees and risks of technology restrictions and security vulnerabilities.

V. Talent and Organization: Compound Talent Gap, Strong Internal Resistance to Transformation

1. Structural Employment Contradictions

Difficulty recruiting and high turnover among frontline operators; young workers are unwilling to enter traditional workshops; at the same time, there is an extreme shortage of compound talents who understand both production processes and IT systems and automation. SMEs almost never have dedicated digital teams.

2. Low Digital Literacy Across the Workforce

Senior technicians rely on decades of hands‑on experience, resisting system entry, scanning for work reporting, and digital control; management is accustomed to experience‑based decisions, unable to interpret data reports or use data to drive improvement.

3. Organizational Structure Mismatch with Transformation Needs

Production, quality, procurement, and IT departments operate in silos with no cross‑functional digital task force; traditional performance systems only measure output, ignoring digital implementation and data utilization.

4. Strong Resistance to Change

Process reengineering, job streamlining, and changes in operating methods disrupt established work habits; grassroots workers resist the introduction of intelligent systems.

VI. Core Bottlenecks in Intelligent Transformation Implementation (Most Prominent in SMEs)

1. High Capital Investment Barrier, Uncertain ROI

Smart equipment, software, retrofitting, and implementation services require large one‑time investments; companies cannot quantify transformation returns, fearing that millions invested may yield no results, leading to hesitation.

2. Lack of Lightweight, Adaptable Solutions

Most market solutions are full‑scale customized packages for large factories, at high prices; there is an insufficient supply of standardized, low‑cost, modular products suitable for SMEs that allow small‑step iteration, leaving companies unsure where to start.

3. Uneven Service Provider Quality, Poor Delivery and Implementation

Many software vendors sell systems without understanding workshop processes; after go‑live, the system is disconnected from actual workflows, leaving it idle and useless.

4. Widespread Misconceptions About Transformation

Companies often favor hardware over software and neglect data, blindly purchasing robots and automated production lines without MES or data acquisition, failing to achieve true intelligence. Others pursue a one‑shot full‑factory overhaul, exposing themselves to high risk.

5. Data Security Concerns

Fears of leakage of core data on production processes, orders, customers, and supply chains when moved to the cloud or networked; concerns over external cyberattacks causing production stoppages, with a lack of industrial cybersecurity protection systems.

VII. Supplement: Differentiated Pain Points – Large Enterprises vs. SMEs

• Large leading enterprises: Complex system integration, difficulty unifying data across multiple plants, shortage of high‑end compound talent, difficulty in full‑industry‑chain coordination, and high implementation costs for advanced applications like digital twins.

• SMEs: Capital shortages, aging equipment, no dedicated digital teams, lack of standardized low‑cost solutions, insufficient management awareness, and weak risk tolerance for transformation experimentation.