The new economics of localised manufacturing

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Article by Bob Buttermore, Senior Vice President and Chief Supply Chain Officer, Rockwell Automation

For decades, decisions about manufacturing location were heavily influenced by labour availability and labour costs. While those considerations remain important, they are no longer the primary determining factor for many manufacturers.

Today, localised manufacturing is increasingly a decision of organisational capability, no longer one driven by labour constraints.

The organisations successfully producing closer to customers are not simply relocating production. They’re leveraging a new combination of workforce strategies, operational models and manufacturing technologies that make localised production more practical than ever before.

Manufacturing Work Has Fundamentally Changed

Automation, advanced software, and connected systems have transformed the factory floor into a safer, cleaner, and more technologically enabled workplace. The pace of change has been rapid. Modern manufacturing facilities look different than they did even a decade ago, and roles that were once repetitive and physically demanding now require specialised decision making and knowledge. Human expertise is essential, but technology amplifies what skilled workers can achieve.

This evolution has an important implication.

The new realities of manufacturing expand the available talent pool. Manufacturing roles are more accessible and more attractive to a broader range of workers when the work itself is more engaging and skill oriented.

The question is no longer simply, “Where can we find enough labour?” It is increasingly, “How do we design work so that people and technology can operate together effectively, anywhere?”

Supply Chain Volatility Is Rewriting the Economics of Distance

While the nature of work is changing on the factory floor, the economics of moving goods are shifting just as dramatically.

Historically, global manufacturing networks depended on relatively stable and predictable transportation costs. Low and consistent shipping costs made the model viable even when goods were made far from end markets.

That stability no longer holds.

Freight costs today are more volatile thanks to disruption from geopolitical events, shifting trade dynamics, and fluctuations in oil prices.

This introduces a new kind of risk into global supply chains: distance is now a volatile financial and operational variable.

Proximity is one solution to consider.

Producing goods closer to customers can help manufacturers reduce exposure to transportation volatility, improve responsiveness, and better align supply with demand. What was once considered a premium strategy may now be viewed as a lever for resilience and responsiveness.

Localising manufacturing operations, in this sense, is more about managing risk and improving agility in an environment where certainty is harder to come by.

Modern Technology Brings Localised Manufacturing Within Reach

If the first shift expands where manufacturers can operate, and the second increases the value of proximity, the third makes localised manufacturing operationally viable.

Advances in artificial intelligence (AI), software-defined automation, and robotics allow manufacturers to design production environments that can adapt quickly to changing conditions.

AI Turns Data into Action

Manufacturers have generated enormous amounts of data through painstakingly optimised “digital silos,” or operations that have been digitised without connecting to the larger operation, but data alone does not improve performance.

Augmenting operations with AI helps operators identify bottlenecks, anticipate maintenance needs, and accelerate decision-making in real time. Leveraging machine learning for data analysis can surface insights faster and help operators focus on decisions that matter rather than manually reviewing every variable across the production process.

The result is responsive operations that maintain productivity amidst changing demand and supply chain conditions.

Software-defined Automation Creates Flexibility

Where AI elevates the operational performance of digital silos, software-defined automation connects operations throughout the enterprise.

Conceptually, software-defined automation is a modernisation effort across the entire enterprise and the backbone of the next era of manufacturing. By connecting the “digital silos” across the enterprise, manufacturers can adapt to change more easily, scale faster, and perform more safely.

Digital twins and simulation technologies, for example, allow manufacturers to test scenarios, model production changes, and evaluate outcomes before implementing them on the factory floor – and without expensive downtime. Teams can identify potential issues earlier, reduce implementation risk, and perform changeovers while maintaining productivity.

The ability to plan, test and adapt digitally across the entire enterprise gives teams confidence when introducing new processes, responding to the market, or even expanding production geographically closer to the end user.

Integrated Robotics Expand What’s Possible

Autonomy comes to life through connected machines. Manufacturers can optimise end-to-end material movement in the micro, like warehouse operations, or in the macro by evaluating entire value chains.

Autonomous mobile robots (AMRs) can move materials throughout facilities with greater flexibility than traditional infrastructure, and independent cart technology enables highly adaptable production flows, while increasingly intelligent drives and sensors provide the data inputs necessary to coordinate complex production.

Together, these technologies help manufacturers operate efficiently across the enterprise.

The manufacturers making progress are building the workforce strategies, digital foundations and operational flexibility required to produce efficiently wherever opportunity exists.

There’s a real argument to be made that leaders no longer need to choose between producing closer to their customers and operating efficiently. Modern technology allows them to pursue both.

What Leaders Should Consider Next

Localised manufacturing will not be the right strategy for every organisation, but the reasons not to pursue it are ripe for reconsideration.

Evolutions in the composition of the workforce and volatility of the supply chain have increased the value of production proximity. Taken alongside the strides of modern manufacturing technology, manufacturing leaders have new levels of flexibility and responsiveness at their fingertips.

The manufacturers making progress are building the workforce strategies, digital foundations and operational flexibility required to produce efficiently wherever opportunity exists.

The future of manufacturing may be defined less by where production happens and more by an organisation’s ability to adapt, scale and respond wherever it does.

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AI Is Moving From “Know” To “Do”

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Article by Steven Fong, Corporate Vice President, APJ Embedded Business AMD

Artificial intelligence is entering a new phase—one that extends beyond digital productivity into the physical world.

For the past several years, AI has largely been defined by systems that understand, generate, and recommend information. Large language models (LLMs), AI assistants, and copilots have fundamentally changed knowledge work by helping people search, summarise, create content, and improve decisions. This first wave of AI has transformed digital productivity.

The next wave will be fundamentally different. Rather than simply understanding information, AI is increasingly expected to interact with the physical world.

Robots will work alongside people on factory floors. Autonomous vehicles will continuously perceive and respond to their surroundings. Medical systems will assist clinicians with real-time diagnosis and decision-making. Intelligent infrastructure will monitor, optimise, and respond without constant human intervention. This transition represents far more than another AI application. It marks a fundamental shift in what AI is expected to do.

As AI moves from the cloud and digital environments into physical systems, intelligence must no longer simply know. Physical AI systems like autonomous robots must also sense, decide, and ultimately act in real-time, with functional safety and while operating within strict power, thermal and space constraints. The need for this transition is being driven not only by advances in AI technology, but also by structural changes across the global economy.

This is precisely where physical AI begins. Physical AI is already reshaping industries, and AMD believes this is the defining transition of the next decade of AI. It is the transition from systems that know to systems that do.

“Doing” Fundamentally Changes The Compute Problem

Once AI begins interacting with the physical world, the compute problem fundamentally changes. Unlike cloud AI, which has traditionally been optimised for scale, throughput, and centralised processing, physical AI introduces a fundamentally different set of requirements.

A chatbot can tolerate occasional latency. However, a robot operating on a factory floor cannot. An autonomous vehicle cannot afford inconsistent system behavior. A medical device cannot simply retry a decision when timing is critical.

These systems must deliver:

  • Real-time responsiveness
  • Deterministic behavior
  • Functional safety and reliability
  • Operation under power and thermal constraints
  • Resilience when connectivity is limited or unavailable

In the physical world, intelligence only matters if systems can act within bounded time constraints. Success is no longer determined solely by AI model performance.

Physical AI extends beyond running AI models. Instead, intelligence must flow seamlessly from sensing and perception through inference, planning, control, and ultimately physical action. The conversation is no longer just about building smarter AI models. It is about building intelligent systems that can safely and reliably act in the real world.

This is why AMD believes physical AI is fundamentally a systems engineering challenge, not simply an AI model challenge.

No Single Processor Can Power Physical AI

Understanding physical AI becomes much easier when we compare it with the human body.

The human brain performs reasoning and decision-making. Our nervous system enables rapid responses. Our muscles and joints execute precise physical actions.

Physical AI systems operate in much the same way. Complex reasoning and perception require high-performance compute. Real-time responses require low-latency processing close to where decisions are made. Precise movement requires accurate and deterministic control.

No single processor architecture can efficiently perform all of these functions. Instead, different types of compute must work together.

High-performance CPUs and GPUs provide the “brain,” responsible for complex reasoning, perception, and AI workloads. Adaptive SoCs and NPUs function like the nervous system, enabling low-latency, power-efficient processing close to where decisions must be made. FPGAs support precise control of sensors and actuators, allowing intelligent machines to safely and accurately interact with the physical world.

This is why physical AI requires heterogeneous computing. Rather than relying on a single compute engine, physical AI systems can combine CPUs, GPUs, NPUs, adaptive compute, and FPGAs, with each optimised for a different role across the complete sensor-to-action pipeline.

The Physical AI Era Plays To AMD’s Strengths

The emergence of Physical AI is not changing AMD’s technology strategy. It is validating a strategy the company has been building for decades.

Physical AI requires heterogeneous computing, and AMD delivers the industry’s broadest compute portfolios, spanning CPUs, GPUs, NPUs, Adaptive SoCs, and FPGAs, to proviode the right compute for the right workload.

Rather than optimising a single stage of AI processing, these technologies work together to support the complete physical AI pipeline—from perception and reasoning to real-time control and physical action.

Equally important is AMD’s long heritage in embedded computing. For decades, AMD technologies have powered systems across automotive, industrial automation, telecommunications, healthcare, aerospace, and other embedded markets where deterministic operation, long product lifecycles, functional safety, harsh operating environments, and customer co-development have always been essential.

These are precisely the same characteristics now becoming critical as physical AI moves from research into real-world deployment.

But physical AI requires more than silicon alone. Building intelligent physical systems demands AI models, robotics software, middleware, sensors, simulation, validation, and production-ready platforms working together.

That is why AMD is extending its long-standing open ecosystem strategy through initiatives such as the AMD Robotics Partner Network and AMD Kria™ AI Solutions, helping customers move more quickly from prototype to production.

Rather than adapting to the physical AI era, AMD is extending technologies, software, and ecosystems it has already been building for many years.

The Next Decade Of AI Will Be Defined By Systems That “Do”

Physical AI is not simply the next stage of AI. It represents a fundamental shift in how intelligence creates value.

The first era of AI was largely defined by systems that could understand information. The next era will be defined by systems that can safely sense, decide, and act in the physical world.

This shift is particularly meaningful for manufacturing-driven economies facing structural demographic change. Australia is one such example. Ageing populations, tightening labour markets, and growing pressure to improve industrial productivity are accelerating investment in automation, robotics, and intelligent infrastructure.

For these economies, physical AI is not simply another technology trend. It is becoming an important tool for sustaining long-term productivity, industrial competitiveness, and economic resilience.

Physical AI will not solve these challenges on its own. But by enabling intelligent systems to safely interact with the physical world, it has the potential to fundamentally reshape how industries operate.

AMD believes the next decade of AI will be defined not only by systems that know, but by systems that do.

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Autodesk expands AI capabilities across manufacturing workflows

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Manufacturing teams could use expanded artificial intelligence (AI) capabilities to automate repetitive tasks and connect product data across the lifecycle, according to Autodesk.

The company outlined the developments at Autodesk University 2026, highlighting new capabilities across its Fusion, Inventor and Vault platforms designed to support design and manufacturing workflows.

Autodesk said AI applications in manufacturing need to account for geometry, engineering intent and product data rather than relying solely on prompts. In Fusion, new AI capabilities are being introduced to automate repetitive design and manufacturing tasks, while a new agentic product lifecycle management (PLM) experience is intended to connect product data and decisions across teams.

Autodesk Assistant is also expanding in Inventor and Vault, with the company saying the tool can help engineers work with existing data and tools more effectively.

The developments form part of Autodesk’s broader push to connect people, data and workflows through its industry clouds and Autodesk AI. 

The company said this connected approach is intended to provide what it calls “project intelligence” — information that can help teams understand project conditions and determine potential next steps.

Autodesk is also previewing an expanded, agentic version of Autodesk Assistant that is designed to bring relevant capabilities and information across products, projects and teams based on the task a user is trying to accomplish.

The company said customers will be able to bring their own agents, tools and trusted partner services into the experience where supported, while maintaining control over how AI is used in their workflows.

Autodesk said its AI developments are aimed at helping professionals increase productivity while keeping people at the centre of design, manufacturing and other industry processes.

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Engineers Australia calls for engineering expertise in Australia’s AI strategy

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Engineers Australia has called on the Federal Government to place engineering and technical expertise at the centre of Australia’s approach to artificial intelligence (AI), arguing that the country’s AI ambitions will require investment in people, infrastructure and technical oversight alongside the technology itself.

In a submission to the Joint Select Committee on Artificial Intelligence, Engineers Australia outlined four areas for government action: strengthening technical expertise in AI governance, building greater sovereign capability, investing in engineering skills, and supporting the safe adoption of AI across the economy.

Engineers Australia National President and Board Chair Tom Goerke said AI presented significant economic opportunities, but Australia would need to develop the capability to use the technology safely and productively.

“Human judgement, professional responsibility and technical assurance must remain at the centre of decision-making. AI can support decisions, but it cannot be accountable for them,” Goerke said.

“That means policymakers need access to people who understand how these systems operate in the real world, where failures can have significant consequences.”

The organisation is calling for engineering and technical expertise to be embedded in AI policymaking, alongside clear human accountability for consequential decisions.

According to Engineers Australia, 90 per cent of engineers believe AI will normally require human oversight. The submission also argues that AI systems cannot hold legal or professional responsibility for decisions.

Goerke said the infrastructure supporting AI also required engineering expertise.

“AI is not just a software challenge. Behind every AI system sits physical infrastructure, energy, water, communications networks, cyber security and people with the technical expertise to make those systems work safely and reliably,” he said.

“If Australia wants to capture the economic benefits of AI rather than simply consume technologies developed elsewhere, we need to invest in the engineering capability that underpins it.”

Engineers Australia has also proposed that the government consider establishing a National Chief Engineer role to provide independent technical advice on technology, digital transformation and major project delivery.

The organisation said workforce capability would be an important part of Australia’s AI development, alongside physical infrastructure.

“AI is changing engineering, but the evidence suggests it will augment engineers rather than replace them,” Goerke said.

“More than 70 per cent of engineers expect AI capability to become an essential professional skill, so our education and training systems need to evolve alongside the technology.”

“We cannot have a serious conversation about Australia’s AI future without having a serious conversation about the engineering workforce needed to deliver it.”

Engineers Australia is also seeking greater support for small and medium-sized businesses adopting AI, noting that only one in three practising engineers has received formal generative AI training.

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Wurth Australia advances fulfilment capacity with Dematic automation

Image supplied by Dematic.

Wurth Australia is investing in automated order fulfilment at its Yatala, Queensland and Keysborough, Victoria distribution facilities through a new partnership with supply chain automation provider Dematic.

The solution will use Dematic’s AutoStore Goods-to-Person (GTP) system to provide high-density automated storage and support split- and full-case picking, according to Dematic. The automation will operate alongside dedicated manual picking areas for products with different storage and handling requirements.

Wurth Australia distributes more than 22,000 products to over 56,000 active customers across sectors including automotive, transport, earthworks, fabrication, maintenance, repair and operations (MRO), construction and mining.

Wurth Australia Chief Executive Officer and Senior Vice President of the Wurth Group Oceania, Serge Oppedisano, said the investment was intended to improve the speed and reliability of order fulfilment.

“The customer experience is incredibly important to us here at Wurth, and a big part of that is ensuring our customers receive accurate and complete orders when they expect them,” Oppedisano said.

He said the automation investment would also support the company’s approach to inventory management.

“This investment reflects those same values in our own operations – strengthening how we manage inventory, fulfil orders and move products through our national distribution network,” Oppedisano said.

Under the solution, orders will move between automated and manual picking areas before progressing through automated conveyor transport, document insertion, carton optimisation and sealing, labelling and despatch sortation.

Dematic’s Warehouse Control System will integrate with Wurth’s warehouse management system to coordinate picking tasks and the movement of goods across the automated equipment.

Dematic Business Development Manager Terry Jamieson said the combination of automated and manual processes was intended to accommodate Wurth’s broad product range.

“Wurth’s extensive product range means there isn’t a one-size-fits-all approach to fulfilment,” Jamieson said.

“Bringing together AutoStore with dedicated picking areas and downstream automation allows different products and orders to move through the most appropriate process,” he said.

According to Dematic, the Yatala solution is expected to become operational in August 2027, with the larger Keysborough facility scheduled to follow in December 2027.

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New digital solution links cocoa products to traceable supply chain records

The Hashgraph Group, Merck and PwC Germany are piloting an integrated solution combining physical authentication, Hedera-powered traceability and Digital Product Passports. Image credit: The Hashgraph Group

The Hashgraph Group (THG), science and technology company Merck and PwC Germany have developed a cocoa traceability solution that combines physical product authentication, blockchain-based records and enterprise process design.

In a media release, THG said the pilot is designed to help cocoa processors, manufacturers and brands track product information across the supply chain, including origin, authenticity, quality, compliance and chain-of-custody data. 

According to THG, the solution also demonstrates how digital product passports can connect physical products with verifiable digital records.

The solution integrates THG’s TrackTrace Digital Product Passport platform, Merck’s M-Trust™ physical authentication technology and PwC Germany’s consulting and implementation expertise. TrackTrace records supply-chain, quality and compliance events on the Hedera network, while M-Trust™ is used to verify the authenticity of physical products or packaging.

THG said the approach is intended to address fragmented data and increasing regulatory requirements affecting cocoa supply chains. The solution is designed to support preparations for the European Union Deforestation Regulation (EUDR) and the broader adoption of Digital Product Passports under the Ecodesign for Sustainable Products Regulation (ESPR).

At defined verification points, product information can be captured through scans, system integrations or process inputs. The resulting digital record can include origin information, quality records, certificates, due diligence documentation and authentication events, with selected information potentially shared with consumers through QR codes or other scanning experiences.

Stefan Deiss, CEO and Co-Founder of The Hashgraph Group, said the initiative demonstrates how traceability can extend beyond documentation and self-declared claims.

“By integrating TrackTrace with Merck’s M-Trust™ technology and PwC’s process expertise, we can link any physical product, not limited to cocoa, to a trusted digital record,” Deiss said.

Thomas Endress, Executive Director and Head of M-Trust™ at Merck, said physical authentication is an important component of digital traceability.

“Digital traceability only delivers its full value when it is connected to physical proof,” Endress said.

PwC Germany Enterprise Blockchain Lead Husen Kapasi said the solution could also support compliance investigations and food recalls by providing a verifiable record of raw materials and finished products.

“Ultimately, the solution empowers companies to shift their perception of compliance from a cost burden to a driver of value creation,” Kapasi said.

While cocoa is the initial application, THG said the architecture is intended for other industries where provenance, authenticity, quality and regulatory compliance are important, including pharmaceuticals, luxury goods, electronics and industrial components. 

The companies also noted that broader deployment would require operating procedures, partner onboarding, training, data governance and change management alongside the technology.

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Automation offers new path to warehouse productivity, Dematic says

Image supplied by Dematic.

Supply chain automation company Dematic has argued that Australia’s productivity challenge extends beyond office-based work, with manufacturing, logistics and other industries handling physical goods facing opportunities to improve productivity through warehouse automation.

The comments follow the Productivity Commission’s June 2026 quarterly bulletin, which found labour productivity fell 0.6 per cent in the March quarter and increased just 0.3 per cent over the year. In the market sector, which includes manufacturing, wholesale, retail, transport and warehousing, productivity fell 0.7 per cent while hours worked increased 2.2 per cent.

“We talk about productivity as though it is something that happens at a desk. In reality, close to a quarter of every hour worked in Australia is worked in an industry that makes, stores, moves or sells physical product,” said Phillip Makowski, Marketing Director at Dematic Australia.

Dematic said manufacturing, wholesale, retail, and transport, postal and warehousing generated about $493 billion in industry value added in 2024/25 and accounted for 1.44 billion of the 6.10 billion hours worked in the March quarter.

“The most telling detail in the latest data is where the weakness sits. It is not evenly spread. It is concentrated in the sectors that handle the physical movement and distribution of goods,” Makowski said.

The company also pointed to changes in retail demand, citing Australia Post’s 2026 eCommerce Report, which found Australians spent a record $82.6 billion online in 2025, up 14 per cent year on year. Online spending accounted for about 24 per cent of total retail spending, while the average online basket fell to $90.

According to Dematic, the combination of more individual transactions and smaller average baskets is increasing the amount of physical handling required across warehouse and distribution operations.

“A business can be growing revenue while its cost to serve quietly climbs, because the number of individual transactions it has to physically handle is rising faster than the money coming in,” Makowski said.

Dematic said order picking can account for up to 65 per cent of labour in a typical manual warehouse, with workers spending significant time moving around facilities.

“Australian businesses are not short on effort. They are short on capacity,” Makowski said. “Automation gives operators a fourth option, which is getting more out of the footprint and the workforce they already have.”

The company also cited CBRE data showing Australia’s national industrial vacancy rate stood at 3.2 per cent in the first half of 2026, alongside what CBRE described as a structural shortage of serviced and appropriately zoned industrial land.

“Space is scarce and slow to deliver. Labour is expensive and hard to find. Neither of those constraints is going to loosen quickly, so the productivity gain has to come from the operation itself,” Makowski said.

Dematic said warehouse automation could provide businesses with a measurable way to increase output from existing facilities and workforces, while acknowledging that factors including artificial intelligence, skills and regulation also form part of the broader productivity debate.

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Research finds growing use of digital workers to ease manufacturing workforce pressures

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Manufacturing organisations are increasingly looking to agentic AI and digital workers to address workforce capacity pressures, as skilled workers remain stretched by persistent skills shortages and increasingly complex operations, according to new research commissioned by IFS.

IFS said new research from The Futurum Group found industrial workers lose 41% of their time to manual, repetitive tasks, contributing to a capacity gap between the work organisations need to complete and the available workforce.

Paul Butterworth, Managing Director ANZ at IFS, said Australia had spent decades responding to an ageing workforce and skills shortages across heavy industries.

“Engineers, technicians and trades remain stretched across major infrastructure, energy, defence and industrial projects,” Butterworth said.

He said while investment in attracting, training and retaining workers remained important, AI could help organisations address capacity constraints alongside workforce investment.

“AI allows us to tackle the problem on all sides,” Butterworth said.

The research found 77% of decision-makers had delayed or avoided a strategic initiative because their teams lacked the capacity to pursue it. At the same time, interest in digital workers is growing, with 66% of decision-makers saying they were likely to invest in the technology within the next 12 months.

However, adoption remains at an early stage. Only 10% of enterprises surveyed were running mostly autonomous AI, while just 5.7% of decision-makers said they trusted AI to act fully autonomously.

IFS said the findings point to a human-in-the-loop approach, where digital workers undertake routine, high-volume tasks while employees remain involved in decisions requiring judgement or intervention.

“We’re already working with industrial organisations to use digital workers for routine operational processes such as supplier orders, inventory management, and field service, while keeping people involved where judgement or intervention is required,” Butterworth said.

IFS said its IFS Loops Agentic Platform enables enterprises to deploy digital workers that automate 60% of agentic transactions end-to-end, with the remaining 40% incorporating human review and approval checkpoints.

The company said manufacturing customer Kitron Group is rolling out purchase-to-order digital workers across all 13 of its sites by the end of 2026, while maintaining existing business rules and approval processes. IFS said a digital worker also identified a decade-old part-number error during the rollout that had previously gone undetected.

Somya Kapoor, CEO of IFS Loops, said digital workers could help industrial organisations create additional capacity without removing human oversight.

“Digital workers built for the job, integrated into the systems already running the business, are what let teams reclaim capacity instead of just working around the shortfall,” Kapoor said.

Butterworth said shifting repetitive work away from skilled employees could allow them to focus on more complex operational tasks.

“By shifting repetitive work away from skilled teams, organisations can create more capacity for the complex operational work where experience really matters,” he said.

IFS said the pace of digital worker deployment varies across industries, with trust, human oversight and the nature of individual operational processes influencing adoption.

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Warehouse automation design needs to start with data, Swisslog says

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Intelligent software like Swisslog's SynQ can control entire processes and maximise warehouse automation efficiency. Image credit: Swisslog

Australian businesses investing in warehouse automation should prioritise solution design and operational data before selecting equipment, according to Swisslog.

In a news release, Swisslog Australia and New Zealand Director of Sales Steve Dimitrovski said businesses should assess how automation would operate within their broader warehouse environment, rather than focusing solely on individual technologies.

“Automation is a significant investment, so businesses need to look beyond what the system can deliver on day one,” Dimitrovski said.

He said effective design should take into account historical order profiles, SKU velocities, inventory levels, peak demand and potential changes in fulfilment requirements.

“Designing around an average day can give you a very different result from designing around the reality of your operation,” Dimitrovski said. “You need to understand what happens when volumes increase, when the order profile changes and when multiple channels are being fulfilled at the same time.”

Swisslog said simulation modelling can also help identify potential bottlenecks before equipment is ordered. For automated systems such as AutoStore, modelling can be used to assess different combinations of robots, bins and ports against changing throughput requirements.

“Simulation gives you an opportunity to identify problems before they become expensive problems on the warehouse floor,” Dimitrovski said.

The company also said future expansion should be considered during the initial design process. While modular automation can allow capacity to be increased over time, Swisslog said scalability depends on decisions made at the outset.

“Growth needs to be designed in, rather than treated as an afterthought,” Dimitrovski said.

Software integration is another consideration as warehouses combine robotics, automated storage, conveyors and sortation systems. Swisslog said its SynQ software platform is designed to provide an integration and orchestration layer across automated material flows.

Dimitrovski said businesses should look beyond equipment specifications and headline pricing when comparing automation proposals.

“Two proposals can look very similar on paper, but the depth of the design work behind them can be very different,” he said.

“The right questions are: what data has been used, what scenarios have been tested, where are the constraints, and how can the system expand in the future?”

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Manufacturing packaging redesigns linked to 4% average volume lift, NIQ finds

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NIQ Design Impact Awards 2026. Image credit: NIQ

Manufacturing packaging redesigns have been associated with an average 4% increase in product volume across nearly a decade of NielsenIQ (NIQ) Design Impact Award winners, according to the consumer intelligence company.

NIQ announced eight winners of its 2026 Design Impact Awards, recognising packaging redesigns that it said combined improved shopper experiences with measurable commercial performance.

“The Design Impact Awards reinforce the critical role packaging plays in influencing shopper decisions and driving business growth,” said Andrea Fraboni, vice president and global leader of Pack & Design Solutions at NIQ.

“Packaging has seconds to do its job. The strongest redesigns make products easier to notice, understand and choose while preserving the distinctive brand cues consumers recognize,” Fraboni said.

The 2026 winners included Aval French Cider, Liquid I.V., Root & Splendor, Hostess Cakes, Back to Nature, John West Tuna Chunks, Country Fresh Ice Cream and Rustica Pizza, spanning markets including the US, UK and South Africa.

NIQ said its Pack & Design experts assessed submissions against packaging principles and the role packaging plays throughout the shopper journey. The company also used its Retail Measurement Services data to identify brands that recorded measurable sales growth after introducing updated packaging.

According to NIQ, the winning redesigns shared a focus on making products easier for shoppers to notice, understand and select. The company pointed to factors including shelf visibility, clearer information hierarchy, simplified communication and distinctive branding.

“Packaging design isn’t simply aesthetic,” Fraboni said. “When done well, it can be a meaningful driver of shopper engagement and business growth.”

NIQ said its Pack & Design Solutions practice works with manufacturers to evaluate and optimise packaging, combining neuroscience-based research with behavioural and survey-based approaches to support packaging design decisions.

The company did not state that packaging redesign alone caused the reported volume increases, but said the award-winning redesigns were associated with measurable commercial performance following their launch.

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Latest News

The new economics of localised manufacturing

Article by Bob Buttermore, Senior Vice President and Chief Supply Chain Officer, Rockwell Automation For decades, decisions about manufacturing location were heavily influenced by labour...

AI Is Moving From “Know” To “Do”

Article by Steven Fong, Corporate Vice President, APJ Embedded Business AMD Artificial intelligence is entering a new phase—one that extends beyond digital productivity into the...

Autodesk expands AI capabilities across manufacturing workflows

Manufacturing teams could use expanded artificial intelligence (AI) capabilities to automate repetitive tasks and connect product data across the lifecycle, according to Autodesk. The company...

Engineers Australia calls for engineering expertise in Australia’s AI strategy

Engineers Australia has called on the Federal Government to place engineering and technical expertise at the centre of Australia’s approach to artificial intelligence (AI),...

Wurth Australia advances fulfilment capacity with Dematic automation

Wurth Australia is investing in automated order fulfilment at its Yatala, Queensland and Keysborough, Victoria distribution facilities through a new partnership with supply chain...

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