Robotic Palletizer Market by Axis (4-Axis, 6-Axis), Automation Level (Collaborative, Fully Automated, Semi-automated), Application - Global Forecast 2026-2032
Description
The Robotic Palletizer Market was valued at USD 1.78 billion in 2025 and is projected to grow to USD 1.90 billion in 2026, with a CAGR of 7.89%, reaching USD 3.03 billion by 2032.
Framing the strategic role of robotic palletizers in modern supply chains and manufacturing lines where integration, reliability, and adaptive automation drive competitive advantage
Robotic palletizers have transitioned from niche automation assets to strategic enablers of throughput, safety, and consistency in contemporary production and distribution environments. Early adopters demonstrated clear reductions in manual handling and improved pallet integrity, but the current generation of systems emphasizes integration, modularity, and data-driven orchestration that align with broader Industry 4.0 objectives.
Across manufacturing lines and fulfillment centers, decision-makers now evaluate robotic palletizers not merely on cycle speed but on their ability to integrate with warehouse management systems, conveyance networks, and upstream packaging cells. As a result, procurement and engineering teams are prioritizing architectures that offer predictable reliability, minimized downtime, and compatibility with evolving safety standards. Consequently, robotic palletizers are being specified as part of holistic automation strategies that address labor availability, sustainability targets, and the demand for rapid SKU changeovers. This shift reframes the palletizer from a stand-alone machine to a connective node within smarter, more resilient material handling ecosystems.
How advances in sensing, control, and collaborative safety have reshaped deployment strategies and accelerated adoption of palletizing solutions across diverse operational footprints
The landscape for robotic palletizers has experienced transformative shifts driven by advancements in control systems, collaborative robotics, and adaptive vision, as well as by evolving workforce dynamics and regulatory emphasis on safety. Increased sensor fidelity and real-time motion planning have enabled more compact cell footprints and faster SKU changeovers, allowing integrators to deploy palletizers in tighter, higher-mix environments that were previously unsuitable for heavy automation.
Simultaneously, the proliferation of collaborative modes has altered how teams design human-machine workflows. Rather than segregating robots behind safety cages, engineers increasingly leverage power and force limiting, safety-rated monitored stops, and speed and separation monitoring to create shared workspaces that enhance flexibility without sacrificing safety compliance. This trend has lowered barriers for medium-sized operations to adopt palletizing robots and has driven a premium for solutions that offer straightforward commissioning, intuitive programming interfaces, and predictable maintenance pathways. Together, these shifts are redefining capital deployment strategies, enabling faster return on adoption for operational leaders who prioritize agility and continuous improvement.
Assessment of 2025 tariff-driven procurement shifts and supply chain resilience strategies that have emerged in response to changes in U.S. import policy and industrial sourcing dynamics
Tariff shifts and trade policy adjustments in the United States throughout 2025 have introduced new cost considerations and supply chain recalibrations for companies procuring robotic palletizers and their components. Changes in import duties for specific industrial equipment categories have prompted procurement teams to reassess supplier selection, logistics routing, and inventory positioning to mitigate near-term cost impacts while preserving project timelines.
As a result, several manufacturers and system integrators have emphasized localized assembly, expanded regional spare-parts networks, and selective sourcing of critical subcomponents to reduce exposure to cross-border tariff volatility. Procurement cycles have elongated as buyers seek contractual protections, greater price transparency, and flexible delivery terms. These adjustments have also encouraged closer collaboration between OEMs and end users to redesign packaging, consolidate shipments, or qualify alternative suppliers with robust quality assurance processes. In this context, operational resilience-achieved through diversified sourcing, supplier redundancy, and stronger contractual risk-sharing-has become a primary criterion for capital projects involving robotic palletizers.
Deep-dive segmentation analysis showing how payload classes, axis architecture, automation modalities, and application types together determine optimal palletizer configurations and deployment choices
Insightful segmentation clarifies how design choices and application requirements determine the optimal configuration and value proposition of robotic palletizers. When evaluated by payload capacity, systems targeted at the 500–1000 kg band often balance robustness and flexibility for general manufacturing and distribution use cases, lighter systems under 500 kg emphasize speed and collaborative interaction for small- to medium-sized items, and models above 1000 kg prioritize heavy-duty endurance for industrial and bulk packaging scenarios.
Axis configuration reveals a trade-off between reach and dexterity; 4-axis robots provide reliable, high-throughput pick-and-place performance for standardized pallet patterns, whereas 6-axis solutions deliver enhanced articulation and complex trajectory control suitable for irregular loads or tightly constrained cells. Considering automation level, collaborative palletizers enable human-robot shared workspaces and include implementations based on power and force limiting, safety-rated monitored stop, and speed and separation monitoring, which permit safer interaction and simplified integration with manual operations. Fully automated cells focus on maximum throughput and closed-loop process control with minimal human intervention, while semi-automated setups blend mechanized handling with human oversight to manage exceptions and changeovers.
Application-specific segmentation further refines solution design; bag-handling challenges such as slumping and variable shapes demand tailored end-of-arm tooling and adaptive gripping strategies, carton and case palletizing emphasize cycle consistency and pattern optimization for mixed SKU pallets, and tray-based workflows require precision placement and compatibility with downstream wrapping and stretch-wrapping stations. Together, these segmentation lenses inform specification decisions, influencing robot selection, end-effector design, vision and sensor architecture, and cell layout to meet distinct operational priorities.
Regional demand drivers and deployment preferences across the Americas, Europe Middle East & Africa, and Asia-Pacific that shape procurement, service models, and integration strategies
Regional dynamics shape demand drivers, systems architecture preferences, and service models for robotic palletizers in distinct ways. In the Americas, labor cost pressures and a strong emphasis on nearshoring have driven interest in solutions that provide rapid commissioning and robust local aftersales support, with integrators offering financing and service contracts to accelerate projects in manufacturing and distribution centers.
The Europe, Middle East & Africa region emphasizes regulatory compliance, energy efficiency, and integration with established automation standards, which favors modular platforms that can meet stringent safety certifications and interoperable communication protocols. In Asia-Pacific, dense manufacturing clusters and high-volume consumer goods production favor high-throughput, compact cells and aggressive innovation cycles; integrators often focus on scalability and rapid SKU changeover to serve dynamic e-commerce and packaging demands. Across these regions, differences in aftermarket expectations, preferred commercial terms, and talent availability influence how OEMs and system integrators structure partnerships and manage deployment lifecycles, with successful providers tailoring their service footprints and training programs to regional customer requirements.
Competitive dynamics and partner ecosystems that influence procurement calculus, where OEM strength, integrator expertise, and software capabilities collectively determine deployment success
The competitive landscape for robotic palletizers is characterized by a mix of established robot manufacturers, specialized end-of-arm tooling suppliers, systems integrators, and software providers delivering orchestration and vision capabilities. Leading robot OEMs bring decades of motion-control expertise and global service networks that support heavy-duty palletizing, while smaller specialists often differentiate through niche tooling, domain-specific vision algorithms, or rapid customization for particular application segments.
Partnerships between robot manufacturers and integrators accelerate time-to-deployment by combining proven robot platforms with application-specific tooling and validated palletizing patterns. Meanwhile, software vendors are increasingly competing on the ability to provide intuitive programming environments, pattern optimization engines, and cloud-enabled diagnostics that reduce commissioning time and simplify remote troubleshooting. For end users, the primary purchase considerations extend beyond unit price to include total cost of ownership factors such as uptime guarantees, spare-parts availability, training, and the vendor’s ability to support lifecycle upgrades and incremental automation expansions.
Actionable guidance for procurement and operations leaders to secure interoperable, serviceable, and scalable palletizing solutions that deliver durable operational benefits
Industry leaders should prioritize interoperability, lifecycle service models, and scalable architectures when specifying palletizing solutions. By insisting on open communications standards and modular cell designs, organizations can reduce integration time, future-proof investments against changing SKU profiles, and enable phased automation strategies that align with capital planning horizons.
Leaders must also adopt rigorous supplier qualification processes that evaluate not only technical capability but also regional service capacity, spare-parts logistics, and contractual protections against trade-policy volatility. Investing in operator training and establishing internal centres of excellence for automation can accelerate adoption and ensure knowledge retention. Additionally, trialing collaborative modes where practical can yield quick productivity wins while preserving human oversight for exception handling. Finally, integrating advanced diagnostics and condition-based maintenance into procurement specifications will improve uptime and allow engineering teams to shift from reactive repairs to predictive support, thereby stabilizing operational metrics and enabling continuous process improvements.
Integrated research approach combining primary stakeholder interviews, technical literature review, and cross-validation against vendor specifications and field demonstrations to ensure robust conclusions
The research underpinning these insights combined primary interviews with engineering leaders, operations managers, and system integrators, alongside secondary analysis of technical literature, standards guidance, and vendor product documentation. Primary engagements included structured discussions to understand selection criteria, deployment challenges, and service expectations across varied industry verticals. Secondary research focused on control architectures, safety standards, and published case studies that illuminate best practices for cell design and tooling choices.
Findings were triangulated by cross-referencing interview observations with vendor technical specifications and independent third-party reliability data to ensure consistency. Where applicable, scenario analysis was used to test the resilience of procurement strategies against tariff changes and supply chain disruptions. Quality assurance processes included peer review by automation engineering experts and validation of technical claims through vendor demonstrations and field reports, providing a robust foundation for the conclusions and recommendations contained in this summary.
Concluding perspective on how technological capability, supplier resilience, and disciplined procurement together determine successful scaling of palletizing automation across industries
Robotic palletizers have evolved into strategic assets that influence throughput, safety, and the agility of modern material handling operations. Technological advances in sensing, collaborative control modes, and software orchestration are enabling more flexible deployments while reducing the total effort required to commission and maintain palletizing cells. Consequently, organizations that align procurement strategies with operational resilience and integration readiness realize the greatest operational benefits.
Decision-makers should view palletizers as components of a broader automation architecture, paying careful attention to interoperability, regional serviceability, and supplier risk management. Thoughtful specification of safety modes, end-of-arm tooling, and vision systems-matched to application-specific needs such as bags, cartons, cases, and trays-will determine long-term performance. In sum, the combination of technological capability, supplier ecosystem strength, and disciplined procurement practices will define which organizations successfully scale palletizing automation to meet evolving market demands.
Note: PDF & Excel + Online Access - 1 Year
Framing the strategic role of robotic palletizers in modern supply chains and manufacturing lines where integration, reliability, and adaptive automation drive competitive advantage
Robotic palletizers have transitioned from niche automation assets to strategic enablers of throughput, safety, and consistency in contemporary production and distribution environments. Early adopters demonstrated clear reductions in manual handling and improved pallet integrity, but the current generation of systems emphasizes integration, modularity, and data-driven orchestration that align with broader Industry 4.0 objectives.
Across manufacturing lines and fulfillment centers, decision-makers now evaluate robotic palletizers not merely on cycle speed but on their ability to integrate with warehouse management systems, conveyance networks, and upstream packaging cells. As a result, procurement and engineering teams are prioritizing architectures that offer predictable reliability, minimized downtime, and compatibility with evolving safety standards. Consequently, robotic palletizers are being specified as part of holistic automation strategies that address labor availability, sustainability targets, and the demand for rapid SKU changeovers. This shift reframes the palletizer from a stand-alone machine to a connective node within smarter, more resilient material handling ecosystems.
How advances in sensing, control, and collaborative safety have reshaped deployment strategies and accelerated adoption of palletizing solutions across diverse operational footprints
The landscape for robotic palletizers has experienced transformative shifts driven by advancements in control systems, collaborative robotics, and adaptive vision, as well as by evolving workforce dynamics and regulatory emphasis on safety. Increased sensor fidelity and real-time motion planning have enabled more compact cell footprints and faster SKU changeovers, allowing integrators to deploy palletizers in tighter, higher-mix environments that were previously unsuitable for heavy automation.
Simultaneously, the proliferation of collaborative modes has altered how teams design human-machine workflows. Rather than segregating robots behind safety cages, engineers increasingly leverage power and force limiting, safety-rated monitored stops, and speed and separation monitoring to create shared workspaces that enhance flexibility without sacrificing safety compliance. This trend has lowered barriers for medium-sized operations to adopt palletizing robots and has driven a premium for solutions that offer straightforward commissioning, intuitive programming interfaces, and predictable maintenance pathways. Together, these shifts are redefining capital deployment strategies, enabling faster return on adoption for operational leaders who prioritize agility and continuous improvement.
Assessment of 2025 tariff-driven procurement shifts and supply chain resilience strategies that have emerged in response to changes in U.S. import policy and industrial sourcing dynamics
Tariff shifts and trade policy adjustments in the United States throughout 2025 have introduced new cost considerations and supply chain recalibrations for companies procuring robotic palletizers and their components. Changes in import duties for specific industrial equipment categories have prompted procurement teams to reassess supplier selection, logistics routing, and inventory positioning to mitigate near-term cost impacts while preserving project timelines.
As a result, several manufacturers and system integrators have emphasized localized assembly, expanded regional spare-parts networks, and selective sourcing of critical subcomponents to reduce exposure to cross-border tariff volatility. Procurement cycles have elongated as buyers seek contractual protections, greater price transparency, and flexible delivery terms. These adjustments have also encouraged closer collaboration between OEMs and end users to redesign packaging, consolidate shipments, or qualify alternative suppliers with robust quality assurance processes. In this context, operational resilience-achieved through diversified sourcing, supplier redundancy, and stronger contractual risk-sharing-has become a primary criterion for capital projects involving robotic palletizers.
Deep-dive segmentation analysis showing how payload classes, axis architecture, automation modalities, and application types together determine optimal palletizer configurations and deployment choices
Insightful segmentation clarifies how design choices and application requirements determine the optimal configuration and value proposition of robotic palletizers. When evaluated by payload capacity, systems targeted at the 500–1000 kg band often balance robustness and flexibility for general manufacturing and distribution use cases, lighter systems under 500 kg emphasize speed and collaborative interaction for small- to medium-sized items, and models above 1000 kg prioritize heavy-duty endurance for industrial and bulk packaging scenarios.
Axis configuration reveals a trade-off between reach and dexterity; 4-axis robots provide reliable, high-throughput pick-and-place performance for standardized pallet patterns, whereas 6-axis solutions deliver enhanced articulation and complex trajectory control suitable for irregular loads or tightly constrained cells. Considering automation level, collaborative palletizers enable human-robot shared workspaces and include implementations based on power and force limiting, safety-rated monitored stop, and speed and separation monitoring, which permit safer interaction and simplified integration with manual operations. Fully automated cells focus on maximum throughput and closed-loop process control with minimal human intervention, while semi-automated setups blend mechanized handling with human oversight to manage exceptions and changeovers.
Application-specific segmentation further refines solution design; bag-handling challenges such as slumping and variable shapes demand tailored end-of-arm tooling and adaptive gripping strategies, carton and case palletizing emphasize cycle consistency and pattern optimization for mixed SKU pallets, and tray-based workflows require precision placement and compatibility with downstream wrapping and stretch-wrapping stations. Together, these segmentation lenses inform specification decisions, influencing robot selection, end-effector design, vision and sensor architecture, and cell layout to meet distinct operational priorities.
Regional demand drivers and deployment preferences across the Americas, Europe Middle East & Africa, and Asia-Pacific that shape procurement, service models, and integration strategies
Regional dynamics shape demand drivers, systems architecture preferences, and service models for robotic palletizers in distinct ways. In the Americas, labor cost pressures and a strong emphasis on nearshoring have driven interest in solutions that provide rapid commissioning and robust local aftersales support, with integrators offering financing and service contracts to accelerate projects in manufacturing and distribution centers.
The Europe, Middle East & Africa region emphasizes regulatory compliance, energy efficiency, and integration with established automation standards, which favors modular platforms that can meet stringent safety certifications and interoperable communication protocols. In Asia-Pacific, dense manufacturing clusters and high-volume consumer goods production favor high-throughput, compact cells and aggressive innovation cycles; integrators often focus on scalability and rapid SKU changeover to serve dynamic e-commerce and packaging demands. Across these regions, differences in aftermarket expectations, preferred commercial terms, and talent availability influence how OEMs and system integrators structure partnerships and manage deployment lifecycles, with successful providers tailoring their service footprints and training programs to regional customer requirements.
Competitive dynamics and partner ecosystems that influence procurement calculus, where OEM strength, integrator expertise, and software capabilities collectively determine deployment success
The competitive landscape for robotic palletizers is characterized by a mix of established robot manufacturers, specialized end-of-arm tooling suppliers, systems integrators, and software providers delivering orchestration and vision capabilities. Leading robot OEMs bring decades of motion-control expertise and global service networks that support heavy-duty palletizing, while smaller specialists often differentiate through niche tooling, domain-specific vision algorithms, or rapid customization for particular application segments.
Partnerships between robot manufacturers and integrators accelerate time-to-deployment by combining proven robot platforms with application-specific tooling and validated palletizing patterns. Meanwhile, software vendors are increasingly competing on the ability to provide intuitive programming environments, pattern optimization engines, and cloud-enabled diagnostics that reduce commissioning time and simplify remote troubleshooting. For end users, the primary purchase considerations extend beyond unit price to include total cost of ownership factors such as uptime guarantees, spare-parts availability, training, and the vendor’s ability to support lifecycle upgrades and incremental automation expansions.
Actionable guidance for procurement and operations leaders to secure interoperable, serviceable, and scalable palletizing solutions that deliver durable operational benefits
Industry leaders should prioritize interoperability, lifecycle service models, and scalable architectures when specifying palletizing solutions. By insisting on open communications standards and modular cell designs, organizations can reduce integration time, future-proof investments against changing SKU profiles, and enable phased automation strategies that align with capital planning horizons.
Leaders must also adopt rigorous supplier qualification processes that evaluate not only technical capability but also regional service capacity, spare-parts logistics, and contractual protections against trade-policy volatility. Investing in operator training and establishing internal centres of excellence for automation can accelerate adoption and ensure knowledge retention. Additionally, trialing collaborative modes where practical can yield quick productivity wins while preserving human oversight for exception handling. Finally, integrating advanced diagnostics and condition-based maintenance into procurement specifications will improve uptime and allow engineering teams to shift from reactive repairs to predictive support, thereby stabilizing operational metrics and enabling continuous process improvements.
Integrated research approach combining primary stakeholder interviews, technical literature review, and cross-validation against vendor specifications and field demonstrations to ensure robust conclusions
The research underpinning these insights combined primary interviews with engineering leaders, operations managers, and system integrators, alongside secondary analysis of technical literature, standards guidance, and vendor product documentation. Primary engagements included structured discussions to understand selection criteria, deployment challenges, and service expectations across varied industry verticals. Secondary research focused on control architectures, safety standards, and published case studies that illuminate best practices for cell design and tooling choices.
Findings were triangulated by cross-referencing interview observations with vendor technical specifications and independent third-party reliability data to ensure consistency. Where applicable, scenario analysis was used to test the resilience of procurement strategies against tariff changes and supply chain disruptions. Quality assurance processes included peer review by automation engineering experts and validation of technical claims through vendor demonstrations and field reports, providing a robust foundation for the conclusions and recommendations contained in this summary.
Concluding perspective on how technological capability, supplier resilience, and disciplined procurement together determine successful scaling of palletizing automation across industries
Robotic palletizers have evolved into strategic assets that influence throughput, safety, and the agility of modern material handling operations. Technological advances in sensing, collaborative control modes, and software orchestration are enabling more flexible deployments while reducing the total effort required to commission and maintain palletizing cells. Consequently, organizations that align procurement strategies with operational resilience and integration readiness realize the greatest operational benefits.
Decision-makers should view palletizers as components of a broader automation architecture, paying careful attention to interoperability, regional serviceability, and supplier risk management. Thoughtful specification of safety modes, end-of-arm tooling, and vision systems-matched to application-specific needs such as bags, cartons, cases, and trays-will determine long-term performance. In sum, the combination of technological capability, supplier ecosystem strength, and disciplined procurement practices will define which organizations successfully scale palletizing automation to meet evolving market demands.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
195 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Definition
- 1.3. Market Segmentation & Coverage
- 1.4. Years Considered for the Study
- 1.5. Currency Considered for the Study
- 1.6. Language Considered for the Study
- 1.7. Key Stakeholders
- 2. Research Methodology
- 2.1. Introduction
- 2.2. Research Design
- 2.2.1. Primary Research
- 2.2.2. Secondary Research
- 2.3. Research Framework
- 2.3.1. Qualitative Analysis
- 2.3.2. Quantitative Analysis
- 2.4. Market Size Estimation
- 2.4.1. Top-Down Approach
- 2.4.2. Bottom-Up Approach
- 2.5. Data Triangulation
- 2.6. Research Outcomes
- 2.7. Research Assumptions
- 2.8. Research Limitations
- 3. Executive Summary
- 3.1. Introduction
- 3.2. CXO Perspective
- 3.3. Market Size & Growth Trends
- 3.4. Market Share Analysis, 2025
- 3.5. FPNV Positioning Matrix, 2025
- 3.6. New Revenue Opportunities
- 3.7. Next-Generation Business Models
- 3.8. Industry Roadmap
- 4. Market Overview
- 4.1. Introduction
- 4.2. Industry Ecosystem & Value Chain Analysis
- 4.2.1. Supply-Side Analysis
- 4.2.2. Demand-Side Analysis
- 4.2.3. Stakeholder Analysis
- 4.3. Porter’s Five Forces Analysis
- 4.4. PESTLE Analysis
- 4.5. Market Outlook
- 4.5.1. Near-Term Market Outlook (0–2 Years)
- 4.5.2. Medium-Term Market Outlook (3–5 Years)
- 4.5.3. Long-Term Market Outlook (5–10 Years)
- 4.6. Go-to-Market Strategy
- 5. Market Insights
- 5.1. Consumer Insights & End-User Perspective
- 5.2. Consumer Experience Benchmarking
- 5.3. Opportunity Mapping
- 5.4. Distribution Channel Analysis
- 5.5. Pricing Trend Analysis
- 5.6. Regulatory Compliance & Standards Framework
- 5.7. ESG & Sustainability Analysis
- 5.8. Disruption & Risk Scenarios
- 5.9. Return on Investment & Cost-Benefit Analysis
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Robotic Palletizer Market, by Axis
- 8.1. 4-Axis
- 8.2. 6-Axis
- 9. Robotic Palletizer Market, by Automation Level
- 9.1. Collaborative
- 9.1.1. Power & Force Limiting
- 9.1.2. Safety-rated Monitored Stop
- 9.1.3. Speed & Separation Monitoring
- 9.2. Fully Automated
- 9.3. Semi-automated
- 10. Robotic Palletizer Market, by Application
- 10.1. Bag
- 10.2. Carton
- 10.3. Case
- 10.4. Tray
- 11. Robotic Palletizer Market, by Region
- 11.1. Americas
- 11.1.1. North America
- 11.1.2. Latin America
- 11.2. Europe, Middle East & Africa
- 11.2.1. Europe
- 11.2.2. Middle East
- 11.2.3. Africa
- 11.3. Asia-Pacific
- 12. Robotic Palletizer Market, by Group
- 12.1. ASEAN
- 12.2. GCC
- 12.3. European Union
- 12.4. BRICS
- 12.5. G7
- 12.6. NATO
- 13. Robotic Palletizer Market, by Country
- 13.1. United States
- 13.2. Canada
- 13.3. Mexico
- 13.4. Brazil
- 13.5. United Kingdom
- 13.6. Germany
- 13.7. France
- 13.8. Russia
- 13.9. Italy
- 13.10. Spain
- 13.11. China
- 13.12. India
- 13.13. Japan
- 13.14. Australia
- 13.15. South Korea
- 14. United States Robotic Palletizer Market
- 15. China Robotic Palletizer Market
- 16. Competitive Landscape
- 16.1. Market Concentration Analysis, 2025
- 16.1.1. Concentration Ratio (CR)
- 16.1.2. Herfindahl Hirschman Index (HHI)
- 16.2. Recent Developments & Impact Analysis, 2025
- 16.3. Product Portfolio Analysis, 2025
- 16.4. Benchmarking Analysis, 2025
- 16.5. ABB Ltd.
- 16.6. Alvey Palletizers
- 16.7. Arrowhead Systems LLC
- 16.8. Brenton LLC
- 16.9. C&D Skilled Robotics Inc.
- 16.10. Chantland-MHS Company
- 16.11. Columbia/Okura LLC
- 16.12. Concetti S.p.A.
- 16.13. Dematic Group
- 16.14. FANUC Corporation
- 16.15. Fuji Yusoki Kogyo Co. Ltd.
- 16.16. FUNAC Inc.
- 16.17. Gebo Cermex
- 16.18. Honeywell International Inc.
- 16.19. Kawasaki Heavy Industries Ltd.
- 16.20. KION Group AG
- 16.21. KUKA AG
- 16.22. Mitsubishi Electric Corporation
- 16.23. Mollers North America Inc.
- 16.24. Newtec Group
- 16.25. Schneider Packaging Equipment Co. Inc.
- 16.26. Von Gal Palletizers
- 16.27. Wulftec International Inc.
- 16.28. Yaskawa Electric Corporation
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