Predictive Maintenance In Packaging Machinery Market Size and Share
Market Overview
| Study Period | 2019 - 2030 |
|---|---|
| Market Size (2025) | USD 2.41 Billion |
| Market Size (2030) | USD 4.57 Billion |
| Growth Rate (2025 - 2030) | 13.65% CAGR |
| Fastest Growing Market | Middle East |
| Largest Market | Asia Pacific |
| Market Concentration | Medium |
Major Players
*Disclaimer: Major Players sorted in no particular order Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0. |
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Predictive Maintenance In Packaging Machinery Market Analysis by Mordor Intelligence
The predictive maintenance in the packaging machinery market size stands at USD 2.41 billion in 2025 and is projected to reach USD 4.57 billion by 2030, expanding at a CAGR of 13.65%. Adoption is accelerating as packaging manufacturers integrate Industry 4.0 technologies to curb the USD 1.5 trillion annual cost of unplanned downtime, modernize an aging installed base where 60% of equipment is over 15 years old, and capitalize on AI analytics that detect incipient failures earlier than scheduled maintenance can. Services generate the largest revenue because brownfield plants require extensive line-specific customization, while cloud deployment prevails due to its multi-site data aggregation and centralized model training benefits. Machine learning has surpassed physics-of-failure approaches because it adapts to diverse machine designs without requiring in-depth domain expertise, enabling companies to achieve faster ROI on retrofit projects. Competitive intensity is moderate, as incumbent OEMs leverage their installed bases; however, analytics specialists gain market share with OEM-agnostic edge AI, which lowers entry barriers for small and mid-sized converters.
Key Report Takeaways
- By component, services captured 42.9% of the predictive maintenance in packaging machinery market share in 2024.
- By deployment model, the predictive maintenance in packaging machinery market size for on-premise solutions is projected to grow at a 14.98% CAGR between 2025-2030.
- By technology, the predictive maintenance in packaging machinery market size for hybrid analytics is projected to grow at a 15.12% CAGR between 2025-2030.
- By machinery type, filling machines captured 29.9% of the predictive maintenance in packaging machinery market share in 2024.
- By end-user industry, the predictive maintenance in packaging machinery market size for cosmetics and personal care is projected to grow at a 14.13% CAGR between 2025-2030.
- By geography, Asia-Pacific captured 28.9% of the predictive maintenance in packaging machinery market share in 2024.
Global Predictive Maintenance In Packaging Machinery Market Trends and Insights
Drivers Impact Analysis
| Driver | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| Aging installed base of packaging equipment | +2.8% | North America and Europe | Medium term (2-4 years) |
| Rising shift toward Industry 4.0 enabled smart factories | +2.1% | Asia-Pacific and Europe | Long term (≥ 4 years) |
| Growing global demand for packaged FMCG and pharma products | +1.9% | Asia-Pacific and Middle East | Long term (≥ 4 years) |
| Pressure to cut unplanned downtime and OEE losses | +1.7% | Global | Short term (≤ 2 years) |
| Emergence of OEM-agnostic edge AI retrofit kits | +1.2% | North America and Europe, expanding to Asia-Pacific | Medium term (2-4 years) |
| Insurer-led incentives for condition-based monitoring adoption | +0.8% | North America and Europe | Medium term (2-4 years) |
| Source: Mordor Intelligence | |||
Aging Installed Base Drives Retrofit Demand
Six out of every ten packaging machines in North America and Europe exceed 15 years of service, exposing plants to more frequent mechanical failures and motivating retrofit spending on sensor networks and edge gateways that enable real-time health monitoring.[1]Yokogawa Electric Corporation, “Industrial Maintenance Survey Press Release,” Yokogawa, yokogawa.com A 2024 industrial study found that 73% of converters experienced at least one critical breakdown last year, with an average repair bill of USD 75,000. Pharmaceutical firms are hesitant to replace validated assets, so they favor condition-based monitoring, which extends asset life while maintaining regulatory compliance. The predictive maintenance in the packaging machinery market benefits as older lines generate abundant failure signatures that improve algorithm training.
Industry 4.0 Integration Accelerates Smart Factory Adoption
Packaging executives view predictive analytics as foundational to smart-factory roadmaps. In 2024, 68% of companies planned to use digital twins to simulate line performance and schedule maintenance windows around takt-time requirements. Open standards, such as PackML, facilitate interoperability between machine controllers and enterprise systems, allowing predictive insights to inform production planning modules that minimize changeover losses. Each additional connected asset enhances model accuracy, creating network effects that hasten adoption across multi-site enterprises.
FMCG Demand Surge Intensifies Uptime Requirements
Global FMCG consumption is expected to rise 4.2% annually through 2024, and e-commerce fulfillment times have fallen below 24 hours for many categories, leaving no buffer for unexpected line stoppages.[2]Nestlé S.A., “Annual Report 2024,” Nestlé, nestle.com Unilever’s worldwide rollout of predictive maintenance cut unplanned downtime by 35% and lifted overall equipment effectiveness from 72% to 84%, demonstrating tangible business benefits. During seasonal peaks, uptime gains prevent lost shelf space and protect market share.
Downtime Cost Escalation Drives ROI Justification
A 2024 study placed the average downtime cost for high-volume filling lines at USD 250,000 per hour. Pharmaceutical lines risk even greater losses if batch contamination forces a recall. These economics shorten payback periods to under 18 months for most predictive maintenance projects, enabling capex approvals even under tight budgets.
Restraints Impact Analysis
| Restraint | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| High upfront integration cost for brownfield lines | -2.3% | Global, particularly SME manufacturers | Short term (≤ 2 years) |
| Shortage of in-house data science talent | -1.8% | Global, most acute in developing markets | Medium term (2–4 years) |
| Cyber-security vulnerabilities of legacy PLCs | -1.1% | Global, heightened in critical infrastructure | Long term (≥ 4 years) |
| Fragmented sensor data standards across OEMs | -0.9% | Global | Medium term (2–4 years) |
| Source: Mordor Intelligence | |||
High Integration Costs Challenge Brownfield Adoption
Retrofitting a legacy line typically costs USD 50,000-200,000 once sensors, PLC upgrades, networks, and validation are included. Old controllers often lack native Ethernet support, which can inflate budgets through the use of custom interfaces. Smaller converters with limited capacity struggle to fund such investments, creating a two-speed market where multinationals deploy sophisticated AI while SMEs remain reactive.
Data Science Talent Shortage Constrains Implementation
Two-thirds of manufacturers report difficulty hiring analytics professionals who also understand servo dynamics and sanitation protocols. Managed service providers fill the gap by supplying data pipelines, model development, and 24/7 monitoring, but tight labor markets in Southeast Asia and Latin America still slow project rollouts.
Segment Analysis
By Component: Services Anchor Integration Success
Services captured the largest slice of the predictive maintenance in the packaging machinery market at 42.9% in 2024. Integration specialists map every sensor to the control architecture, while OEM field engineers validate models against legacy line behavior. Revenue will continue to rise because many converters lack in-house staff to manage data pipelines. In contrast, platform software is the fastest-growing segment, scaling on subscription models that bundle analytics, visualization, and API integrations.
A second wave of growth stems from managed service contracts that guarantee uptime improvements through outcome-based pricing. Tetra Pak, for example, reported an 18% jump in service income after bundling predictive maintenance with spare parts agreements.[3]Tetra Pak, “Annual Report 2024,” Tetra Pak, tetrapak.com Training services round out offerings, teaching operators to interpret anomaly alerts and schedule micro-stoppages without disrupting takt time.
Note: Segment shares of all individual segments available upon report purchase
By Deployment Model: Cloud Drives Multi-Site Insights
Cloud models held 60.9% market share in 2024 as multinationals sought a single data lake for global benchmarking. The architecture enables federated learning, where each line trains local models that feed into an aggregate predictor, thereby improving accuracy for rare failure modes. Pharmaceutical firms still favor on-premise deployments for data-sovereignty reasons, yet even they experiment with hybrid frameworks that push non-sensitive metrics to the cloud for longer-horizon pattern discovery.
Edge computing complements cloud by executing inference locally to avoid latency when a valve vibration crosses a critical threshold. ISO 27001 and SOC 2 compliance have become de facto procurement criteria, compelling providers to upgrade encryption, audit logging, and role-based access controls.
By Technology: Machine Learning Becomes Table Stakes
Machine learning models accounted for 50.8% revenue in 2024 due to their ability to self-calibrate and learn non-linear fault signatures across disparate machine types. Physics-of-failure methods remain essential for regulated environments where deterministic models aid validation, but they lack flexibility for complex electromechanical systems. Hybrid analytics combines both approaches, producing confidence scores that maintenance planners trust when scheduling interventions.
Hardware advances drive adoption: GPUs and optimized inference frameworks such as NVIDIA TensorRT allow millisecond response times on the plant floor. Deep convolutional networks now process thermal imagery to identify hot spots, while autoencoders flag subtle drift in power signatures that precede motor failure.
By Packaging Machinery Type: Filling Lines Top Priority
Filling equipment accounted for 29.9% of the predictive maintenance market in packaging machinery in 2024, as every minute of stoppage jeopardizes perishable goods and high-throughput beverage lines. Predictive algorithms monitor pump amperage, valve cycle counts, and CIP fluid turbidity to anticipate wear. Palletizers exhibit the highest CAGR as omnichannel logistics drive higher pallet throughput. Algorithms track axis torque and vacuum wear to schedule maintenance between shift changes, minimizing material-handling bottlenecks.
Form-fill-seal machines are experiencing steady uptake as sensors differentiate between mechanical issues and film deviations. Labelers gain from vision-based checks that predict printhead clogging before miscodes reach retailers. Cartoners and case packers benefit through synchronized servo diagnostics that prevent downstream jams.
Note: Segment shares of all individual segments available upon report purchase
By End-User Industry: Food Sector Sets the Pace
Food and beverage producers generated 35.6% of overall demand in 2024. Stringent hygiene protocols create repetitive clean-in-place cycles that stress seals and bearings; predictive analytics optimizes cleaning frequency by correlating microbial counts with equipment health. Cosmetics and personal care posts the fastest CAGR because premium SKUs fund advanced automation. Predictive maintenance addresses small-batch changeover challenges by minimizing unplanned downtime during format changes.
Pharmaceutical packaging maintains a steady uptake because regulations mandate documented evidence that maintenance practices mitigate contamination risks. Industrial and household chemical plants utilize specialized sensors resistant to corrosive vapors, while converters handling agrochemicals employ vibration algorithms to mitigate gearbox wear induced by dust ingress.
Geography Analysis
The Asia-Pacific region led with a 28.9% share in 2024 and is expected to continue expanding as China’s packaging machinery market reaches USD 45 billion and India’s pharmaceutical sector records 12% annual growth. Government incentives for smart factories accelerate deployment in coastal provinces and special economic zones. Japanese manufacturers retrofit mature lines with predictive kits to avoid costly replacements and leverage home-grown sensor suppliers.
The Middle East and Africa emerge as the fastest-growing region with a 14.28% CAGR through 2030. Saudi Arabia has earmarked USD 20 billion for industrial automation, and harsh environmental conditions increase the risk of failure, making predictive maintenance an attractive option. Turkey serves as a bridge to European export markets and hence aligns with EU cybersecurity norms in its IoT rollouts.
North America and Europe remain sizable markets, where the focus shifts from first-time adoption to refining AI. German packaging machinery exports totaled EUR 8.9 billion (USD 10.1 billion) in 2024, and suppliers are now embedding analytics modules as standard offerings. Italy’s EUR 10 billion (USD 11.3 billion) sector capitalizes on predictive algorithms that shorten changeovers for custom orders. Regulatory frameworks such as ISO 55000 encourage condition-based maintenance across both regions.
Competitive Landscape
Market concentration is moderate. OEMs like Tetra Pak, Krones, and Syntegon integrate predictive analytics into new platforms and retrofit kits, leveraging installed bases and domain expertise. Analytics specialists such as Senseye, MachineMetrics, and Augury compete with cloud-native, OEM-agnostic systems that shorten deployment from months to weeks. Edge AI suppliers process data locally, addressing cybersecurity concerns in regulated environments.
Strategic alliances dominate deal flow. Krones and Microsoft co-developed an edge solution that avoids cloud latency for critical events. Körber and Valmet formed a USD 500 million revenue joint venture to serve pharmaceutical packagers with FDA-compliant platforms. IP filings surged 34% in 2024, focusing on sensor fusion and anomaly detection that reduce false alarms.
Compliance remains a differentiator. Players achieving ISO 27001 or SOC 2 build trust with pharma and food customers. Overall, technological innovation, service capability, and regulatory credibility shape competitive positioning rather than price alone.
Predictive Maintenance In Packaging Machinery Industry Leaders
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Tetra Pak Group
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SIG Combibloc Group AG
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Krones AG
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Syntegon Technology GmbH
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IMA Group
- *Disclaimer: Major Players sorted in no particular order
Recent Industry Developments
- September 2025: Mpac Group completed one year of acquisition of CSi Palletising for USD 45 million to enhance predictive maintenance in end-of-line automation.
- August 2025: Tetra Pak completed one year of its launch of Connected Packaging, adding predictive analytics to 8,000 lines and cutting downtime 40%.
- July 2025: Syntegon completed one year of investing USD 25 million to implement and expand AI-based services for regulated pharma customers.
- October 2024: Körber and Valmet formed a joint venture targeting USD 500 million revenue from FDA-validated predictive maintenance for pharmaceutical packaging.
Global Predictive Maintenance In Packaging Machinery Market Report Scope
| Hardware (Sensors, Gateways) |
| Platform Software |
| Services (Integration, Training, Managed PdM) |
| On-premise |
| Cloud |
| Machine Learning-based PdM |
| Physics-of-Failure Models |
| Hybrid Analytics |
| Filling Machines |
| Form-Fill-Seal (FFS) Machines |
| Labeling and Coding Machines |
| Cartoning and Case-packing Machines |
| Palletizing and Depalletizing Systems |
| Food and Beverage |
| Pharmaceuticals |
| Cosmetics and Personal Care |
| Industrial and Household Chemicals |
| Other End-User Industries |
| North America | United States | |
| Canada | ||
| Mexico | ||
| South America | Brazil | |
| Argentina | ||
| Rest of South America | ||
| Europe | Germany | |
| United Kingdom | ||
| France | ||
| Italy | ||
| Spain | ||
| Russia | ||
| Rest of Europe | ||
| Asia-Pacific | China | |
| Japan | ||
| India | ||
| South Korea | ||
| South-East Asia | ||
| Rest of Asia-Pacific | ||
| Middle East and Africa | Middle East | Saudi Arabia |
| United Arab Emirates | ||
| Turkey | ||
| Rest of Middle East | ||
| Africa | South Africa | |
| Nigeria | ||
| Rest of Africa | ||
| By Component | Hardware (Sensors, Gateways) | ||
| Platform Software | |||
| Services (Integration, Training, Managed PdM) | |||
| By Deployment Model | On-premise | ||
| Cloud | |||
| By Technology | Machine Learning-based PdM | ||
| Physics-of-Failure Models | |||
| Hybrid Analytics | |||
| By Packaging Machinery Type | Filling Machines | ||
| Form-Fill-Seal (FFS) Machines | |||
| Labeling and Coding Machines | |||
| Cartoning and Case-packing Machines | |||
| Palletizing and Depalletizing Systems | |||
| By End-User Industry | Food and Beverage | ||
| Pharmaceuticals | |||
| Cosmetics and Personal Care | |||
| Industrial and Household Chemicals | |||
| Other End-User Industries | |||
| By Geography | North America | United States | |
| Canada | |||
| Mexico | |||
| South America | Brazil | ||
| Argentina | |||
| Rest of South America | |||
| Europe | Germany | ||
| United Kingdom | |||
| France | |||
| Italy | |||
| Spain | |||
| Russia | |||
| Rest of Europe | |||
| Asia-Pacific | China | ||
| Japan | |||
| India | |||
| South Korea | |||
| South-East Asia | |||
| Rest of Asia-Pacific | |||
| Middle East and Africa | Middle East | Saudi Arabia | |
| United Arab Emirates | |||
| Turkey | |||
| Rest of Middle East | |||
| Africa | South Africa | ||
| Nigeria | |||
| Rest of Africa | |||
Key Questions Answered in the Report
What is the forecast value of predictive maintenance in packaging machinery by 2030?
The market is expected to reach USD 4.57 billion by 2030.
Which segment currently generates the highest revenue?
Services lead with a 42.9% share due to complex integration needs across brownfield lines.
Why are filling machines a primary target for predictive analytics?
Downtime can exceed USD 250,000 per hour on high-volume filling lines, so early fault detection delivers rapid ROI.
Which region is growing fastest to 2030?
The Middle East and Africa region shows the highest CAGR at 14.28% as industrial diversification programs invest in smart manufacturing.
How do cloud models support predictive maintenance?
Cloud deployments aggregate data from multiple plants, train centralized AI models, and enable cross-site benchmarking while edge nodes handle time-critical alerts locally.
What hampers adoption among small packaging converters?
High retrofit costs of USD 50,000-200,000 per line and limited access to data science talent delay implementation for many SMEs.
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