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Industry 4.0 Digital Twins in supply chain management.

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Digital twins

Digital twins are becoming an ever-increasingly popular choice to optimise operations within certain sectors of the supply chain. A digital twin is a digital replica of a physical system, facility, or route for example a warehouse’s digital twin is created using data from sensors as well as other sources.

In the context of the supply chain, a digital twin can be a virtual model of a distribution centre, warehouse, or other supply chain assets such as transport routes these models are backed up by the data associated with their real-world counterparts.

The digital twin allows you to visually monitor in real time what events are playing out on the ground and optimise operations or simulate specific conditions using the data associated with the real-world counterpart.

Below we will explore how to set up a digital twin, the benefits of digital twins in the context of various sectors of the supply chain and discuss the five ways in which digital twins can be useful to supply chain management.

The creation process of the digital twin.

Now that we have a better idea of what a digital twin is let’s move on and analyse the steps involved in creating a digital twin in the context of warehousing.

1. Defining your core objectives

The very first step to setting up your digital twin should be to define the goals and objectives of the project. To do this, you will need to identify key performance parameters otherwise known as KPI. These KPIs are essential as the digital twin will use the KPI to monitor and measure events and create models. For example, in the case of a warehouse, the KPI can be labour productivity, inventory levels, order fulfilment time, or other metrics crucial to the specific sector.

2. Data gathering

Once our KPIs have been identified the next step is to gather the necessary data to create the digital twin in the case of the warehouse this data may include location tracking data, RFID tags, and data from various sensors.

3. Building on your digital twin’s model

Possibly one of the most important steps in this process is building the model for the digital twin in the case of a warehouse this includes building a 3D model of the warehouse. The completeness of this model combined with the quality and detail of the real-world associated data you have collected will determine how accurately the digital twin can optimise operations.

While having a large amount of high-quality data is important, it is equally important to ask yourself based on the objectives you have set up, how much data you need, and how detailed the data needs to be.

For example, when optimising transport routes, it is unnecessary to scan every truck that enters and exits your facility. Instead, you can use the GPS tracking data of your vehicles to monitor the time taken for each route. This data can be utilised by the digital twin to make predictions about the best-suited route for achieving your objectives based on the initial goals and associated data.

4. Validating your digital twin.

The model should accurately represent the physical characteristics, behaviour, and interactions of the real-world object or system. This requires defining the model parameters, such as dimensions, material properties, and operating conditions. For example, compare the digital twin’s data against your company’s historical data.

What this means is you should be using the digital twin at this point to run simulations for example simulate demand suddenly increasing and what effects this would have cross-reference the results with data you have from experience of a similar event to validate if the results are within an expected range or not.

This can be incredibly helpful in identifying problems with the data and model and understanding why there may be a major difference in results. This can be used to fix the root cause of the issue before your business takes the digital twin’s predictions seriously.

5. keep your digital twin well-fed.

By this stage, your digital twin should be running, and you will have confirmed the validity of the results it’s producing. Live data should be consistently flowing to the digital twin model at this point so that live results can be monitored, and any problems can be quickly addressed. Now that you have a live overview of your facility or system you can keep track of the situation on the ground and optimise operations with the assistance of your new digital twin.

Benefits to transportation

In the case of transportation, you can use your digital twin to optimise transportation routes as well as visualise these routes, in addition, your digital twin can also be used to simulate disruptions on routes and visually assist in the planning of alternative routes should these simulated situations take place in future.

You can see a real-world example of route optimisation using a digital twin in both Singapore and Shanghai’s digital twin systems where it’s used to optimise the flow of traffic as well as energy consumption.

Warehousing

In the case of the warehousing sector, your digital twin can be used most effectively in several ways. The first is to optimise energy efficiency as your digital twin can inform you if a particular system is using a large volume of electricity or if unnecessary electricity is being used in a certain sector of the warehouse allowing the warehouse to cut down on wasted electricity costs in the future. Preventing supply chain disruptions is another big advantage of a digital twin as discussed, the digital twin can optimise route planning however it is also capable of optimising the flow of movement and goods within the warehouse to insure items get from point A to point B without disruption.

Supply chain management

In the context of supply chain management not only does the digital twin allow various sectors to optimise routes, inventory, product movement, and energy efficiency it also allows its users to run simulations to make predictions on future issues or worst-case scenarios that could cause disruptions to the overall success of the supply chain.

These scenarios are often referred to as stress tests, and with the assistance of the digital twin different sectors of the supply chain can run simulated stress tests in the context of their specific conditions to put a plan in place to mitigate a multitude of situations. This makes digital twins a versatile tool for the various sectors wishing to make data-driven decisions quickly in a more reliable way.

Conclusion

Digital twins provide a wide variety of benefits to the supply chain, the most beneficial of which is the optimisation of the quality of the supply chain’s efficiency. While digital twins have yet to see a high adoption rate moving into the future, they could become a key part of day-to-day operations across many sectors of the overall supply chain network.

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Logistics

Why Procurement Shapes Customer Experience

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The words ‘Procurement Process’ sketched on a notebook surrounded by pens and highlighters

Customers rarely think about procurement.

When they walk into a shop, place an online order or receive a delivery at their door, they’re focused on one thing: whether the experience lives up to their expectations.

Is the product available?

Did it arrive when it was supposed to?

Was it the quality they expected?

Most people never stop to consider that those moments often begin months before they become customers. Long before an order is packed or a delivery vehicle leaves the warehouse, someone has already decided who will supply the product, how it will be sourced and how it will make its way through the supply chain.

Those decisions quietly shape almost everything the customer experiences.

The Customer Only Sees the End Result

Businesses spend a great deal of time discussing procurement, inventory, warehousing and logistics.

Customers don’t.

They simply judge what happens at the end.

If a product is out of stock, they don’t wonder whether a supplier experienced production delays. If a delivery arrives late, they’re unlikely to think about transport schedules or inventory planning.

They remember the business they bought from.

That’s what makes procurement so important. The decisions made long before a customer places an order often determine whether that customer leaves satisfied or frustrated.

Every Supplier Changes the Experience

Choosing a supplier isn’t simply a purchasing decision.

It’s a decision about how the business wants to operate.

One supplier may offer a lower price. Another may have a stronger record for delivering on time. A third might be more responsive when plans suddenly change or demand increases without warning.

None of those qualities appears on the shelf beside the product.

Yet every one of them can influence whether customers find what they’re looking for when they need it.

By the time procurement teams sit down to compare suppliers, they’re often thinking about much more than the quotation in front of them. They’re considering how dependable each supplier has been, how easily they communicate and how confidently the business can rely on them when circumstances become less predictable.

The Best Customer Experiences Start Earlier Than Most People Think

One delayed shipment doesn’t necessarily create a problem.

Neither does one supplier running behind schedule.

Supply chains are remarkably good at absorbing small disruptions.

The real challenge comes when those small disruptions begin happening more often. Deliveries start slipping by a day here and there. Inventory takes longer to replenish. Warehouses begin adjusting schedules to accommodate late arrivals.

Eventually, those small changes reach the customer.

From the customer’s perspective, it feels as though the business has become less reliable.

In reality, the first signs may have appeared much earlier inside the supply chain.

Procurement is Really About Trust

The strongest supplier relationships aren’t built on price alone.

They’re built on confidence.

Confidence that products will arrive when they’ve been promised. Confidence that suppliers will communicate when circumstances change. Confidence that both businesses will work together when unexpected challenges appear.

Those qualities don’t always stand out during a tender process.

They usually become obvious months later, when the supply chain comes under pressure.

Customers Remember How You Made Them Feel

Most customers will never know who supplied the product they purchased.

They’ll never see the purchase order, negotiate a contract or visit the warehouse where their order was packed.

What they will remember is whether the experience felt effortless.

That’s why procurement reaches much further than purchasing products.

Every decision made at the beginning of the supply chain has the potential to shape the experience waiting at the very end.

Long before a customer forms an opinion about a business, procurement has already helped write that story.

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Logistics

Why ChatGPT, Gemini and Perplexity Recommend Different Logistics Providers

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Futuristic illustration of a warehouse with digital packages stacked in a warehouse.

A procurement manager in Durban needs a bonded warehouse. A few years ago she would have searched Google, scanned the first page and made three phone calls. Today there is a reasonable chance she types the question into ChatGPT instead, reads a paragraph of considered-sounding advice, and works from the handful of companies it names.

That shift is quietly rewriting how supply chain businesses get found. And it comes with a complication most operators have not yet noticed: the answer she gets depends heavily on which tool she opens. Ask ChatGPT, Google’s AI Overviews and Perplexity the same sourcing question and you will often get three different sets of cited companies, sometimes with barely any overlap.

This is not one of the systems getting it wrong. As a recent analysis by Johannesburg digital agency IMS sets out, each platform runs a genuinely different process for deciding which sources are trustworthy enough to name. Understanding those differences matters more to a freight forwarder or 3PL than knowing the acronym for it.

ChatGPT: Being Read Is Not the Same as Being Named

When ChatGPT searches the web, it reads far more than it credits. Independent analysis of its browsing behaviour has found that only a fraction of the pages it retrieves end up cited in the final answer. The rest inform the response without ever being named. For a logistics company, that is the difference between a shipper seeing your name and a shipper seeing a competitor’s while your website quietly supplied the background.

ChatGPT also does not search the live web on every question. It is far more likely to go looking when the query carries commercial or comparative intent; “best cold chain provider,” “Transnet vs road freight costs,” “cheapest customs clearance Cape Town 2026”, than when someone asks it to explain what a bill of lading is. Which is convenient, because commercial intent is exactly where supply chain buyers sit.

The pages that survive the cut tend to share the same traits: they answer the specific question directly, they carry concrete figures rather than general claims about service excellence, and they are structured so a passage can be lifted out cleanly.

Google AI Overviews: A Page One Ranking Is Not Enough

AI Overviews sits on top of Google’s existing search index, but qualifying for the AI panel is a separate process from ranking. A logistics site can hold position two for a competitive term and still be skipped, while a page sitting at position eight gets quoted because it states the answer in two clean sentences with a number attached.

Researchers studying AI Overview outputs describe the process as a funnel: a large pool of candidate pages narrowed through semantic relevance matching, then authority and expertise filtering, then a final re-ranking before a small number of sources are stitched into the summary. Depth counts here in a way it does not for ChatGPT. Google appears to favour sites showing sustained expertise across several interlinked pages on a subject, rather than one strong article standing alone.

For an operator, that has a practical translation. A single well-written page about your temperature-controlled fleet is worth less than a cluster of connected pages covering cold chain compliance, load monitoring, last-mile handover and the regulations that govern them.

Perplexity: Built to Cite From the Ground Up

Perplexity is the outlier, usefully so. Citation is not a feature added to a chat product; it is the product. It runs a retrieval-augmented pipeline with multiple ranking layers scoring relevance, freshness, factual accuracy and structural clarity before anything reaches the answer.

It is also the most selective of the three. Perplexity will typically visit around ten pages for a query and cite only three or four. Structural trust signals carry real weight: named authors, visible editorial standards, and claims corroborated across more than one independent source rather than appearing on a single company page.

This is where trade publications, industry associations and conference coverage become genuinely valuable to a logistics business, not as vanity placements, but as the independent corroboration these systems are specifically looking for.

Why the Three Disagree

Put the pipelines side by side and the disagreement stops being mysterious. ChatGPT is deciding whether a page is worth naming after it has already read it. Google is applying an authority-and-extractability filter on top of an index built for a different purpose. Perplexity is built around sourcing and rewards signals the other two barely weigh.

The divergence is not random, either. A 2024 audit of ChatGPT, Bing Chat and Perplexity by researchers Alice Li and Luanne Sinnamon, published in the Proceedings of the Association for Information Science and Technology, found that generative search systems lean heavily on news, media and business publications for their sources, and showed measurable commercial and geographic bias in which sources they use to support claims.

Geographic bias deserves attention in this market. A system weighted toward North American and European business media is a system that may not surface the South African freight forwarder that is genuinely the right answer to a South African question.

What the Research Says Actually Works

The most rigorous evidence available comes from “GEO: Generative Engine Optimization,” a study by Pranjal Aggarwal, Vishvak Murahari and colleagues at Princeton, Georgia Tech and IIT Delhi, presented at KDD in 2024. The researchers built a benchmark of roughly 10,000 real queries and tested nine content optimisation strategies against generative engines.

The strongest performers were not keyword tactics. Adding statistics and adding direct quotations were among the most effective changes tested, improving visibility by roughly 30 to 40 percent against an unoptimised baseline. Authoritative language and explicit sourcing also helped. Keyword stuffing did close to nothing.

That finding sits comfortably with how the logistics sector already communicates. On-time delivery percentages, claims ratios, dwell times, fleet utilisation figures, tonnage handled, accreditation numbers; the industry is unusually rich in exactly the kind of concrete, quotable detail these systems reward. Most of it never makes it onto a company website, where it is replaced by phrases like “world-class logistics solutions.”

What This Means for the Industry

Three consequences follow.

The first is that vague marketing language is now actively costly. A page claiming end-to-end excellence gives an AI engine nothing to quote. A page stating that a facility holds 12,000 pallet positions, operates to a stated temperature tolerance and cleared a specific volume of customs entries last year gives it something to work with.

The second is that being visible on one platform tells you nothing about the others. Because the pipelines differ, and because independent research confirms real bias in what they select, a company cited confidently by Perplexity may be invisible in ChatGPT and Google’s AI Overviews. Each has to be checked on its own terms.

The third applies to the buying side. Supply chain managers using these tools to shortlist providers should treat the results as a starting point shaped by a particular set of preferences, not a market survey. A carrier’s absence from an AI answer says something about its web presence. It says very little about its trucks.

The industry has spent a decade learning to be found on Google. The engines that increasingly sit between a shipper and a supplier now work differently from each other, and differently from search. That is worth understanding before the next tender or RFP goes out.

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Logistics

The Data Behind Modern Supply Chains

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Holographic interface of supply chain management data dashboard

For years, logistics was measured by what people could see. Trucks left the depot, containers arrived at the port and pallets moved through warehouses. Success depended on getting goods from one place to another safely and on time.

That hasn’t changed, but something else has. Every one of those movements now creates information. A delivery being delayed, inventory running low or a vehicle taking longer than expected to complete its route all leave behind data that businesses can use to make better decisions.

Increasingly, it’s that information – not just the movement of goods – that is shaping modern supply chains.

Every Movement Tells a Story

A truck doesn’t simply complete a delivery anymore. It records where it travelled, how long the journey took, where delays occurred and when it arrived. Inside the warehouse, inventory systems track how quickly products move, which items are picked most often and where bottlenecks begin to develop.

On their own, those numbers don’t mean much. Put them together over weeks or months, however, and patterns start to emerge. Businesses can see where time is being lost, which routes perform consistently well and where small changes could improve efficiency.

Turning Information Into Action

Most businesses already have access to vast amounts of operational data. The real challenge isn’t collecting more information—it’s knowing what deserves attention and what can be ignored.

A delayed delivery, slower picking times or a recurring bottleneck in the warehouse might seem like isolated incidents. Over time, though, those patterns can reveal where processes are slowing down, where costs are creeping in or where customer service is starting to suffer. The businesses gaining the greatest value from data aren’t necessarily collecting more of it. They’re using it to make everyday decisions with greater confidence.

Spotting Problems Before They Grow

Not long ago, supply chain reports were largely used to explain why something had gone wrong. By the time the numbers reached someone’s desk, the disruption had already happened and teams were focused on recovering rather than preventing it.

Today, businesses have a much clearer view of what’s happening as goods move through the supply chain. A warehouse beginning to fall behind, unexpected congestion on a transport route or stock running lower than expected can often be identified early enough for teams to step in before those issues become much bigger problems.

It’s Not About Having More Data

The amount of information flowing through the supply chain continues to grow, but that doesn’t automatically make a business more efficient. Poor-quality data can be just as frustrating as having no data at all, especially when different teams are working from conflicting information.

For many organisations, the focus has shifted from collecting more data to making existing information more accurate, consistent and accessible. When everyone is working from the same reliable picture, decisions become quicker, communication improves and the supply chain becomes far easier to manage.

The supply chain will always be built around moving goods, but understanding what happens between each stage has become just as important. Businesses that can turn everyday operational information into practical decisions will be far better placed to respond as the industry continues to evolve.

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