Infrastructure engineering software company Bentley Systems is now calling for entries for the 2026 Year in Infrastructure (YII) Awards.
The annual program seeks to recognise outstanding projects that use Bentley’s engineering software to create innovative infrastructure solutions. Submissions will be accepted until May 3, at 11:59 p.m. EDT, Bentley said in a recent announcement.
The Bentley awards have evolved significantly over the years, according to Monica Schnitger, president and analyst at Schnitger Corporation.
“Now they assess how massive projects are building and leveraging rich data assets to create value throughout the entire lifecycle of a project — from initial financial decision-making through design and construction,” Schnitger explained.
All submissions are evaluated by independent expert panels based on digital innovation, measurable outcomes, including improvements in efficiency, cost performance, resilience, and sustainability.
During its 20–year history, the awards program has attracted more than 5,500 major infrastructure projects.
Previous winners include notable initaitives, such as the digital twin for structural monitoring of St. Peter’s Basilica in Vatican City, the Seine Nord Europe Canal in France; the Thames Tideway Tunnel in the UK; Siemensstadt Square in Germany; Sydney Airport in Australia; the Fairmont Udaipur Palace in India; the Beijing Zhangjiakou highspeed railway in China; the Ontario Line subway in Canada; and EchoWater, one of the largest agricultural water recycling facilities in the United States.
“Around the world, infrastructure professionals rely on Bentley software to design, build, and operate infrastructure that is more resilient, efficient, and sustainable,” said Cate Lochead, chief marketing officer at Bentley Systems. “The YII Awards celebrate real-world results from teams that are innovating in areas that include ground-informed design, connected data, and AI. It is important to promote this work as best practice as these achievements set a new standard for what’s possible across the infrastructure ecosystem.”
Finalists will be revealed in August 2026 and will be invited to showcase their projects at the Year in Infrastructure conference in Singapore, which will be held from October 6 to 7. Winners will be announced at the event.
For additional information or to enter the competition, visit Bentley Systems’ YII awards page here.
Manufacturing has entered a new phase in its digital transformation, with Industrial AI moving beyond pilot programs into core operational systems.
Vendors and manufacturers alike are increasingly focused on measurable outcomes – such as uptime, efficiency and supply chain performance – rather than experimentation, reflecting a broader shift in how technology investments are assessed across the sector.
Shift from AI experimentation to scaled deployment
Manufacturing companies are accelerating the adoption of Industrial AI as the technology transitions from experimentation to scaled deployment, according to new results from IFS and insights from its regional leadership.
The company reported strong financial performance for FY2025, with annual recurring revenue rising 23 per cent year-on-year and cloud revenue increasing 30 per cent, as more manufacturers embed AI into core operations rather than treating it as a standalone innovation initiative.
In an exclusive interview with Australian Manufacturing, Paul Butterworth, Managing Director for Australia and New Zealand at IFS, said the shift reflects growing pressure on manufacturers to demonstrate tangible results from AI investments.
“Over the past few years manufacturers have been experimenting with AI in some form or another. What changed last year was the level of scrutiny and expectation around outcomes,” Butterworth said. “The conversation shifted from innovation to operational performance.”
IFS said its Industrial AI platform is now being deployed at scale across manufacturing, asset maintenance and supply chain environments, with customers focusing on measurable operational gains such as improved uptime, scheduling and asset availability.
Operational outcomes drive adoption
For manufacturers, particularly in asset-intensive industries, confidence in how AI operates has become a key factor in adoption.
“AI must be explainable, governed, safe, and operating on trusted system data,” Butterworth said, adding that once organisations see the technology working reliably in production, “scaling becomes a disciplined expansion exercise rather than a leap of faith.”
The company’s results show a net retention rate of 114 per cent, indicating that existing customers are expanding their use of the platform. Butterworth said this growth is partly driven by manufacturers extending initial deployments across multiple sites after achieving early success.
“In most cases, manufacturers begin with a targeted deployment focused on a specific operational challenge within one facility,” he said. “Once they see measurable improvements, such as reduced backlog or improved schedule performance, the internal case for broader rollout becomes much stronger.”
IFS also highlighted use cases delivering faster returns, including predictive maintenance and production planning, which help reduce unplanned downtime and improve throughput without requiring new capital investment.
“Reducing unplanned downtime on critical equipment has a direct and measurable financial impact,” Butterworth said. “Even modest improvements in asset availability can significantly improve margins.”
Embedded AI reshapes manufacturing systems
IFS argues that Industrial AI differs from more general enterprise AI tools in how it is integrated into manufacturing environments.
“Manufacturing operates in the physical world, where decisions directly affect safety, uptime and cost,” Butterworth said. “Industrial AI is built to function inside the systems that run the business, rather than sitting alongside them.”
The company’s FY2025 performance was also supported by acquisitions aimed at expanding capabilities across supply chains and warehouse operations, alongside partnerships with technology providers including Microsoft and Siemens.
Industry analysts say this reflects a broader shift in the market. IDC Group Vice-President Micky North Rizza said the sector is reaching an inflection point, where organisations are prioritising purpose-built AI platforms that deliver measurable outcomes over more generic tools.
Challenges and regional outlook
Despite the growth, challenges remain for manufacturers earlier in their AI adoption journey. Butterworth identified fragmented data systems as a common barrier, noting that asset, engineering and supply chain information is often siloed.
“A realistic path begins with a clearly defined operational problem rather than a broad transformation agenda,” he said. “Organisations that approach AI incrementally tend to move faster and more sustainably.”
In Australia and New Zealand, manufacturers are taking a cautious but pragmatic approach, with a strong focus on return on investment and risk mitigation.
“There is healthy skepticism toward technology that promises transformation without clear operational benefit,” Butterworth said. “Adoption may begin cautiously, but once AI demonstrates value in live production, scaling tends to proceed with confidence.”
Looking ahead to 2026, IFS expects Industrial AI to become more deeply embedded in day-to-day manufacturing operations, supporting real-time decision-making across planning, maintenance and logistics.
“Workforces will be augmented rather than replaced,” Butterworth said. “The focus is not automation for its own sake, but measurable improvements in performance and reliability.”
This article contains information provided by IFS and is intended for general use only. It does not take into account your personal, professional, or business circumstances.
Minitab President Josh Zable. Image supplied by Minitab.
As manufacturing processes become more complex and competitive pressures intensify, companies are increasingly turning to structured experimentation and advanced analytics to optimise production, reduce waste and accelerate innovation.
Against this backdrop, global analytics provider Minitab has announcedthe acquisition of advanced experimental design platform Effex, a move the company says will help manufacturers connect experimentation more closely with operational decision-making.
According to Minitab, Effex brings a large catalogue of proprietary experimental designs, optimal design algorithms and optimisation techniques used to help engineers design experiments more efficiently. The company said the acquisition responds to increasing demand from manufacturers – particularly in sectors such as pharmaceuticals, semiconductors and chemicals – for tools that support more systematic experimentation.
In an exclusive interview with Australian Manufacturing, Minitab president Josh Zable said Design of Experiments (DOE) is becoming an essential capability as manufacturing environments grow more complex.
“To improve something, you first need to understand it,” Zable said. “DOE provides a structured approach to understanding your process, not by guessing or relying on hindsight, but by designing small, effective tests that reveal how inputs truly influence outcomes.”
Experimentation gaining importance in manufacturing
Zable said manufacturers are facing tighter tolerances, more variables and increasing expectations around product quality and efficiency.
DOE allows manufacturers to systematically test process variables and identify optimal production conditions, helping improve productivity while reducing waste and time spent troubleshooting.
“By learning more quickly than your competitors, you can innovate faster, minimise waste and consistently deliver quality, and that’s the essence of genuine competitiveness,” Zable said.
Linking experimentation to production decisions
Minitab said the acquisition of Effex is designed to make advanced experimentation tools more accessible while ensuring experimental insights can be applied directly to manufacturing operations.
Zable said the company plans to integrate Effex into the Minitab Solution Center platform to create a continuous flow between data collection, experimentation and production decisions.
“Our goal with the Effex acquisition was to make experimentation simpler, more customisable and more actionable,” he said.
“By integrating Effex into the broader Minitab platform, we are creating a seamless flow from data collection to experimentation to learning to decision-making. This enables operators and engineers to use experiment results directly to set process targets and make production decisions,” Zable explained.
AI-assisted DOE in digital factories
Artificial intelligence is also playing an increasing role in the design and analysis of experiments in manufacturing environments.
According to Zable, AI-assisted DOE tools can help engineers identify more efficient experiment designs, particularly in complex systems with many interacting variables.
“AI’s role in DOE isn’t about replacing engineers; it’s about augmenting them,” he said.
Zable said the combined experimental design libraries from Minitab and Effex include millions of potential experiment configurations.
“With AI-powered DOE, manufacturers can simulate changes, assess trade-offs and implement improvements with much less risk,” he said. “It’s a practical way to combine experimentation with real-time decision support.”
Operational improvements for manufacturers
Zable said manufacturers across several sectors have used advanced experimentation to achieve measurable improvements in production performance.
“One pharmaceutical customer used smarter experimental designs to reduce weeks from their testing phase and save kilograms of expensive raw material while still meeting quality standards,” he said.
In another example, a team working on sustainable processing increased yield from about 37 per cent to more than 57 per cent while also reducing extraction time.
“These are not just theoretical improvements,” Zable said. “Advanced DOE can have direct impacts on cost and throughput.”
Different industries apply DOE in different ways, he added. Chemical manufacturers often focus on understanding reactions and formulations with minimal experimental runs, while semiconductor manufacturers use experimentation to improve yields where even small gains can translate into significant financial benefits.
“In semiconductors, even a tiny yield improvement can mean millions of dollars in savings,” Zable said. “In automotive, DOE helps balance performance, durability and cost by aligning design and manufacturing.”
Trend toward analytics consolidation
Minitab said the acquisition also reflects a broader industry trend in which manufacturers are consolidating analytics, experimentation and optimisation tools into unified platforms.
Zable said many manufacturers are seeking to reduce the number of disconnected software tools used across production and engineering teams.
“Most manufacturers tell us the same thing: they don’t want six different tools to do six different things and six different versions of the truth,” he said.
“If your experiment results are stored in one tool, your optimisation in another and your analytics elsewhere, you waste time connecting them and risk losing context.”
Integrating these capabilities into a single platform can reduce data translation errors and accelerate decision-making in production environments, he said.
Implications for future manufacturing innovation
Looking ahead, Zable said experimentation and advanced analytics will play an increasingly important role in shaping the future of manufacturing, including in Australia.
He pointed to sectors such as advanced materials, defence technology, agritech and high-value processing as areas where structured experimentation could support innovation and competitiveness.
“An organisation that integrates structured experimentation and advanced analytics into its daily decision-making will secure a competitive advantage,” Zable said.
“Experimentation offers risk-managed ways to innovate, and analytics give you the confidence to act on what you discover.”
Minitab said the integration of Effex forms part of its broader effort to expand its process improvement platform as manufacturers increasingly adopt digital technologies to optimise production and navigate complex global supply chains.
This article contains information provided by Minitab, LLC and is intended for general use only. It does not take into account your personal, professional, or business circumstances.
A new report from Deloitte shows how physical AI (PAI), the combination of artificial intelligence with physical machines, is quickly moving from testing phases to widespread implementation across industries.
The report, titled “Physical AI: The moment of acceleration,” highlighted industrial robotics as a key area where this technology is proving its value, and how early adopters in manufacturing and logistics are building the foundations needed to scale these systems across the value chain.
According to the Deloitte paper, while only 5% of companies report that PAI is currently transforming their organisation, a much larger portion (41%) expect it will do so within the next three years.
Similarly, only 3% of businesses have fully integrated PAI into their operations today, but this number is expected to reach 18% within two years.
The report pointed to a steady growth in the adoption of PAI among businesses worldwide. In 2024 alone, more than 500,000 industrial robots were deployed, with annual installations projected to reach 700,000 by 2028.
Collaborative robots, or machines designed to work alongside humans, made up nearly 65,000 of these installations in 2024.
In a media statement released today, Chris Lewin, AI lead for Deloitte Asia Pacific, emphasised the significance of this shift. “Physical AI marks the moment when intelligence moves off the screen and into the real world, transforming factories into learning systems that sense, decide and improve continuously. Organisations that are acting now will shape the operating models, skills and standards that define industrial leadership for the next decade.”
In its report, Deloitte identified several barriers to adoption, including cost and resource requirements (41%), challenges identifying use cases (36%), talent and skills gaps (33%), and technology or data availability (31%).
Adoption rates are projected to be highest in consumer goods, healthcare, telecommunications and industrial sectors.
The tech company stressed that successful PAI implementation requires more than installing technology.
“There are internal readiness factors that determine how effectively businesses can deploy and scale any PAI solution, today or tomorrow and critically, they are in the organisation’s control. Successful PAI implementation is as much about adapting as it is adopting,” Lewin said.
The report suggests three key questions for business leaders: Where are you on the technology maturity ladder? How ready are your operational fundamentals for PAI? And how are you shaping your human architecture for PAI?
Energy price volatility has become a structural feature of modern electricity markets rather than a temporary disruption. For energy-intensive manufacturers and large commercial operators, fluctuating wholesale prices are reshaping how energy costs are managed, making reactive approaches increasingly inadequate.
In its Unlocking the power of energy | FlexPilot white paper, GridBeyond said the transition toward variable renewable generation and more dynamic pricing conditions is driving greater price uncertainty. While market mechanics are complex, the operational impact is simple: prices can change significantly within hours, affecting cost per unit, throughput economics and budgeting certainty.
According to GridBeyond, managing energy risk is now as much an operational and planning challenge as it is a procurement one.
From reactive response to predictive planning
Demand response programs have traditionally helped businesses manage extreme price events by setting a “strike price” – the maximum price per unit of energy an operator is willing to pay before reducing consumption.
GridBeyond noted that while this approach can help avoid peak costs, it may also disrupt production schedules or affect delivery commitments if not carefully managed.
The company said the next stage of energy management is predictive energy intelligence, which combines real-time operational data with forward price forecasting to anticipate risk rather than respond after prices rise.
Linking price signals to operational reality
GridBeyond said industrial forecasting can be complex because production systems must balance energy costs with technical performance, commercial obligations and quality requirements.
Processes involving equipment such as kilns, mills and bulk storage cannot be adjusted arbitrarily, as sudden changes may affect thermal stability, equipment wear, production consistency and customer supply schedules.
To help address these constraints, GridBeyond’s FlexPilot platform combines energy market signals with operational data specific to each site using advanced modelling techniques and virtual simulation tools.
The platform enables operators to explore different operational strategies before making adjustments in live environments. Through scenario testing, businesses can evaluate the consequences of modifying production schedules, managing energy-intensive stages of processing, or shifting certain activities to align with periods of more favourable pricing.
GridBeyond said the approach supports more strategic energy use by helping companies determine when production can be safely adjusted while still meeting output commitments.
Potential applications include scheduling energy-heavy operations during forecast low-price windows, building inventory in advance of anticipated price increases, or moderating selected processes during price spikes while maintaining overall production continuity.
Automation and continuous optimisation
As price volatility becomes more frequent, GridBeyond said manual scheduling alone is increasingly unable to respond to rapid market changes. Predictive modelling and automation are designed to help businesses limit exposure to extreme price spikes while keeping operations stable. Through integration with plant control systems, predictive platforms can automatically adjust flexible assets as market conditions evolve.
GridBeyond emphasised that energy optimisation is not a one-time exercise. Continuous feedback loops allow forecasting models to learn from real-world performance, refine operational strategies and identify additional flexibility opportunities over time.
For multi-site operators, portfolio-level coordination can unlock further value. Production can be shifted to lower-cost facilities where practical, demand response participation can be synchronised across regions, and overall energy intensity across operations can be improved.
According to GridBeyond, the outcome is more predictable operating costs, improved returns from demand response participation and stronger resilience in production planning despite ongoing market volatility.
Predictive intelligence as a strategic capability
GridBeyond said predictive intelligence and automation are increasingly moving from optional tools to core operational capabilities as energy markets evolve.
Executive teams are now looking beyond short-term procurement strategies toward integrated approaches that connect price forecasting, operational data and automated control systems.
The company said the central challenge for energy-intensive businesses is no longer whether volatility will occur, but how effectively it can be anticipated and managed.
The session will explore how forecasting, modelling and automation can help businesses manage energy cost risks more proactively.
This article contains information provided by GridBeyond and is intended for general use only. It does not take into account your personal, professional, or business circumstances. Please seek professional advice and review the product’s terms and conditions before making any decisions based on this information.
Stock image. Image credit: IM Imagery/stock.adobe.com
The global manufacturing sector is uniquely positioned to capitalise on artificial intelligence technologies, according to Tim Long, global head of manufacturing at cloud-based data platform company Snowflake.
In a statement, Long outlined three key predictions for how AI will transform the sector by 2026, addressing key critical challenges while creating competitive advantages for early adopters.
Prediction #1: AI to combat skilled labour shortages
The manufacturing industry is seeing a global shortage of skilled workers, as major industrial economies struggle to fill critical positions in various fields, from pipe filters to essential technical roles.
Long predicts that by 2026, manufacturing companies will deploy AI solutions that augment skilled workers in complex tasks while automating routine processes.
“As labour costs rise worldwide and skilled workers become increasingly scarce, AI-driven efficiency in both production and supply chain operations will separate competitive manufacturers from those struggling to maintain output and manage costs,” the exec says.
“Companies that master AI-powered productivity gains will capture market share from competitors still relying on traditional labour-intensive approaches.”
Prediction #2: Manufacturing’s controlled environment: Perfect for AI investment validation
While many industries struggle to measure AI return on investment, Long believes manufacturing’s unique ability to create controlled experiments gives it a critical advantage in validating AI investments.
By 2026, manufacturing teams will increasingly leverage this natural testing capability to demonstrate clear performance improvements before scaling AI deployments. Long explains that the industry will shift from “experimental pilots to production applications only after controlled trials prove measurable outcomes—whether in defect reduction, output improvements, or operational efficiency gains.”
This disciplined, evidence-based approach will position manufacturing as a leader in demonstrating concrete AI value, potentially providing a model for other industries, the Snowflake exec explains.
Prediction #3: Agentic AI to drive operational optimisation
Long’s third prediction focuses on agentic AI, autonomous AI systems that can make decisions independently, becoming a significant factor in manufacturing logistics and production optimisation. According to Long, by 2026, manufacturing companies will deploy AI agents to make autonomous operational decisions that directly impact efficiency and cost reduction.
These applications will include “expediting product lots to meet delivery deadlines, optimising inventory routing based on real-time demand signals, and automatically routing products for quality inspection or determining optimal manufacturing sequences.”
This business-outcome-driven approach will accelerate the adoption of agentic AI, with early adopters gaining significant operational benefits as these systems prove their value in controlled production environments.
MIT researchers have developed a new AI system that allows people to create personalised 3D-printed objects that are both unique and durable.
The tool, called MechStyle, combines generative AI with mechanical simulations to ensure designs can withstand real-world use, MIT said in a news release.
Generative AI can easily create customised 3D models, but many fail physically when printed. MIT’s Faraz Faruqi, a PhD student at CSAIL, said, “We want to use AI to create models that you can actually fabricate and use in the real world.” MechStyle checks how changes to a design affect its strength, protecting vulnerable areas from breaking.
Users can upload a 3D model or select a preset item, such as a vase or hook, and give text or image prompts for a personalised look.
The AI modifies the object while simulating its durability, allowing for items like cactus-shaped hooks, textured pillboxes, or customised assistive devices.
Tests showed that MechStyle could produce up to 100 per cent structurally viable objects. It includes a freestyle mode for quick design ideas and a detailed mode that ensures durability.
External researchers have also commented on the work. Google Research Scientist Fabian Manhardt, who was not involved in the study, said 3D stylisation presents challenges not seen in 2D applications.
“Changing the object’s geometry can harm its structure, rendering it unusable in the real world,” Manhardt said.
He added that MechStyle “helps solve this problem, allowing for 3D stylisation without breaking the object’s structural integrity via simulation.”
According to MIT, the system aims to make 3D design more accessible, letting both novices and experts focus on creativity while AI handles structural challenges.
The research was supported by the MIT-Google Program for Computing Innovation. It was presented at the Association for Computing Machinery’s Symposium on Computational Fabrication in November.
A 3D-printed implant using polylactic acid, a biodegradable polymer, with irregular and randomly designed patterns to better replicate the internal structure of bone to give cells a place to attach and regrow to rebuild tissue. Image credit: unsw.edu.au
UNSW Canberra researchers have developed a new 3D-printed bone implant that could transform the manufacturing of personalised medical devices and improve treatment for fractures and bone injuries.
According to UNSW,the implants, known as bone scaffolds, are porous structures that support bone regrowth.
Made from biodegradable polylactic acid (PLA), they provide a temporary framework for cells to attach and regenerate tissue, dissolving safely once healing is complete. This eliminates the need for additional surgeries to remove implants, the researchers noted.
“Bone can be damaged in many locations, and its structure changes depending on where it is in the body,” said PhD student Kaushik Raj Pyla, who led the research at UNSW Canberra.
“We wanted to see if matching these patterns could help restoration. Our idea was to take existing bone patterns and check if they could be rebuilt through printing.”
UNSW researchers used irregular, stochastic lattice designs to more closely replicate natural bone structure.
Laboratory tests showed the scaffolds performed well under sudden impacts, absorbing energy efficiently, while maintaining fluid permeability critical for tissue regeneration.
The university noted that innovative bone repair solutions are urgently needed, particularly in the ACT, where poor bone health affects more than 98,000 people.
“These figures highlight the growing burden of osteoporosis and fracture-related injuries – and the importance of developing safer, more effective treatments like the 3D-printed bone scaffolds,” Kaushik said.
UNSW researchers cautioned that the technology is not yet ready for clinical use. Future steps include biological testing, long-term studies, and regulatory approval.
The team said it is also exploring applications for cartilage and soft tissue scaffolds, with early clinical testing possible within five years.
“Biodegradable scaffolds will likely play a key role in reducing both medical risks and overall treatment costs,” Kaushik said. “We’re moving toward safer, more personalised implants that work with the body, not just in it.”
ACCIONA says a new collaboration with Australian surf fin maker Bolero Surf is highlighting how recycled renewable energy materials can support local manufacturing, with the launch of what the company describes as the world’s first commercially available surfboard fins made from recycled wind turbine blades.
The fins, unveiledat the 2026 Aussie Boardriders Battle (ABB), were designed and manufactured in Australia as part of ACCIONA’s Turbine Made initiative, which was launched in February 2025 to explore how materials from decommissioned wind turbine blades can be repurposed into new products.
Professional surfer Darcy Crump from Bungan Boardriders is set to debut the fins in competition during the event on the Gold Coast.
According to ACCIONA, the project aims to demonstrate how circular economy approaches can create new manufacturing opportunities by recovering valuable materials from renewable energy infrastructure.
Bolero Surf founder Banjo Hunt said the concept emerged after observing that many surfboards continue to use fins produced through manufacturing processes that are not environmentally sustainable.
“The fibreglass inside a wind turbine blade has so much inherent value, which for us means it can be turned into high-quality surfboard fins,” Hunt said.
“By reusing this material, we avoid one of the most expensive and labour-intensive parts of traditional fin manufacturing. That advantage is helping us bring production back to Australia. Crucially, the material is super strong and stiff, which perfectly lends itself to high-performance surfboard fins.”
The fins are designed to meet the performance requirements of modern surfing while demonstrating the potential for recycled wind turbine materials to be used in commercial-scale manufacturing.
Professional surfer Darcy Crump said performance remained the key factor when testing the product.
“As a surfer, performance comes first,” Crump said. “The fins feel solid, and quick around turns, and knowing they’re made from recycled turbine blades makes it even better.”
ACCIONA said the product launch builds on earlier awareness generated by its Turbine Made surfboard prototype, which used material from a retired turbine blade.
Gavin Reymond, Managing Director at ACCIONA Energía in Australia, said the fins represent the next step in demonstrating how recycled renewable energy materials can be integrated into new products and supply chains.
“Last year, we proved the Turbine Made concept by making the world’s first surfboards from a retired wind turbine blade,” Reymond said.
“This year, we’re showing how we can scale the initiative by creating a retail product, helping surfers not just surf better, but also more sustainably.”
Reymond said the collaboration also illustrates how circular economy thinking can support new forms of manufacturing activity in Australia.
“By bringing a Turbine Made product to market, ACCIONA and Bolero Surf are demonstrating that the renewables circular economy is not a future ambition, but something already happening in Australia today,” he said.
“This is a live case study for Australian designers, innovators, manufacturers and entrepreneurs of what is possible when we rethink end-of-life materials as the start of the next product.”
The Turbine Made surfboard fins are available to Australian and international consumers through Bolero Surf.
The global packaging industry is seeing a shift toward 2D barcodes, which can carry significantly more data than traditional linear barcodes, and an industrial printing technology provider is calling it a “strategic evolution” rather than a simple technical update.
In a recent industry briefing, German printing company Leibinger said manufacturers that act early to integrate these data-rich codes are positioning themselves for higher efficiency, improved regulatory compliance and a clear competitive advantage over slower-moving competitors.
However, the company also acknowledged that while the move to 2D codes represents one of the most ambitious transformations in modern packaging, the journey comes with several technical, organisational, and external challenges that require careful navigation.
According to Leibinger, the technical aspects of 2D code implementation present risks for manufacturers.
“2D codes are dense, data-rich, and highly detailed — which means printing precision is critical. Small deviations in droplet size, ink adhesion, or substrate reflectivity can cause scanner errors. Materials such as glossy film or transparent plastic require excellent contrast and stability to maintain readability,” the company explained.
The variable data component adds another layer of complexity. As batch numbers or expiry details change, so does the code pattern, requiring consistent calibration to prevent print quality deterioration.
Beyond the technical aspects, the German company also points out that many projects struggle because of unclear data ownership and management structures.
“If systems like ERP (enterprise resource planning), MES (manufacturing execution system), and printers aren’t fully synchronised, the digital workflow breaks down. The result: wrong data on the right code — or the right data printed too late.”
Leibinger added that even if manufacturers achieve internal readiness, the broader retail environment faces its own challenges.
“Not all retailers’ POS scanners are 2D-ready yet, so during the transition phase, dual coding — printing both a 1D barcode and a 2D symbol — will remain necessary in many cases,” the company said.
The company notes that user acceptance presents another challenge, as operators, retailers, and consumers adapt to new ways of interacting with packaging.
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