Africa’s temperature-controlled logistics leaders will strengthen the industry’s shared voice when they gather at the GCCA (Global Cold Chain Alliance) Africa Cold Chain Conference at the Fairway Hotel Resort in Johannesburg, South Africa on September 2-3 2026.

The annual GCCA event is a keenly anticipated feature of the continent’s logistics calendar. It is unique in bringing together third party logistics providers with supply chain partners, customers, politicians and other key stakeholders to address pressing challenges and to discover new opportunities in temperature-controlled logistics.

Conference delegates will join GCCA President & CEO Sara Stickler and GCCA Africa Chairman Dr Newton Matope (CEO of Cold Solutions Kenya) to steer discussions in line with the conference theme, “It’s Time for Dialogue”.

GCCA President & CEO Sara Stickler says: “As Africa’s cold chain navigates ongoing regional and global uncertainty, opportunities to share insights and form new relationships at this year’s GCCA Africa Cold Chain conference are invaluable. So too is the industry’s shared voice, empowering cold chain businesses to help shape Africa’s future at a time when supply chain logistics are evolving in response to regional needs and global change.

“GCCA’s work in Africa has built a new foundation for finding solutions to shared temperature-controlled logistics challenges, for collaborating to access new opportunities, and for advocating for the industry’s needs with governments and key stakeholders. This all starts with two-way dialogue and we look forward to fortifying the shared voice of Africa’s cold chain at the GCCA Africa Cold Chain Conference 2026.”

GCCA will welcome Mohammed Mahomedy, Head of Infrastructure and Rail for Africa at DP World, as the conference’s keynote speaker. He will take to the conference main stage on September 3 to share insights into DP World’s approach to integrated logistics at scale, in practice.

Main stage sessions and panel discussions will be delivered throughout the conference by a high quality rostrum of renowned experts including:

• Chris Hattingh, Executive Director, CRA (Centre for Risk Analysis) onthe business trading climate in Africa and the role of geopolitics
• Dr. Martin Cameron, Managing Director, Trade Research Advisory (Pty) Ltd on how trade policy meets reality
• Dr. Juanita Maree, CEO, SAAFF (Southern African Association of Freight Forwarders) discussing freight forwarding in a new era of dialogue
• Dr Ikechukwu Opara of the University of the Western Cape exploring sustainable food systems
• Cassandra Potteiger, Head of Strategy, Marketing and Communication at SA Harvest on people and partnerships.

Find out more and register to attend at www.gcca.org/events/gcca-african-cold-chain-conference/.

We placed two identical orders one minute apart through the SPAR2U app and Uber Eats app to compare the purchase experiences. Using the same SPAR store (SUPERSPAR Sunninghill), basket and delivery location, we compared everything from the final price to live updates, fulfilment and delivery times to see how each platform performed.

App Testing: The Order and Price

To keep the test as fair as possible, we made sure that none of the ordered items were on special – in order to keep the comparison in price as close as possible.

The basket contained a mix of everyday household and baking essentials:

Final Price: Uber Eats vs SPAR2U

The SPAR2U order came to R462.51 and the Uber Eats order cost R545.59. This left us with a difference of R83.08, making the Uber Eats order 18% more expensive than the SPAR2U order.

Real-Time Updates and Communication

After placing both orders, the apps provided live updates throughout the shopping and delivery process; from when the shopping started through to the final delivery time and driver tracking.

SPAR2U sent shopping updates and updated invoices showing the items being picked, packed, and eventually dispatched via email. On the other hand, Uber Eats sent push notifications to keep us updated on the progress of the order.

Both apps kept us in the loop throughout the shopping experience.

Delivery Times

With both orders placed, we began tracking the progress of the deliveries.

The following times were recorded:

Both orders were placed practically at the same time, just one minute apart, but Uber Eats started shopping way faster – their shopper started picking items just two minutes after the 09:31 order went through. SPAR2U took 36 minutes just to get started at 10:06.

Interestingly, SPAR2U was actually faster once they were in the aisles, taking only seven minutes to finish shopping compared to Uber’s 12 minutes.

The real gap opened up during dispatch and delivery, though. Uber Eats had the items out the door nine minutes after picking (09:54) and delivered them in 13 minutes flat at 10:07. SPAR2U sat waiting 17 minutes for dispatch, followed by a 20-minute drive.

All in all, Uber Eats crushed it: total time was 36 minutes end-to-end, while SPAR2U took 80 minutes. Uber beat SPAR2U to the doorstep by 44 minutes.

Final Time Comparison: SPAR2U vs Uber Eats

Overall, Uber Eats completed the order-to-door process in 55% less time than SPAR2U.

Order Fulfilment

When both deliveries arrived, we unpacked everything and compared the orders with our shopping list. The SPAR2U order was completed to a T, with all eight items delivered as ordered.

The Uber Eats order was a little different. We specifically ordered the same 500g butter from both apps, yet the Uber Eats order arrived with a 250g pack from a different brand instead and was more expensive. The same happened with the 750ml dishwashing liquid, which was suitably substituted with a 750ml refill bag.

Finally, the caramel, which wasn’t available on the Uber Eats app, was replaced with condensed milk.

SPAR2U Order

Uber Eats Order

Usually, the Uber Eats shopper is meant to contact you before making a substitution or replacement, but for this order they did not do so. So we only saw the replacements when the order arrived.

Our Final Take

Now that the groceries have been unpacked, here’s what we took away.

1. Shopping Time

The first big difference was how long it took for each order to get moving. The Uber Eats shopper started picking the order just two minutes after it was placed while the SPAR2U shopper only started 36 minutes later.

Interestingly, once shopping began, SPAR2U had the edge. The shopper completed the SPAR2U order in 7 minutes, compared with 12 minutes for Uber Eats.

2. Order Completion Time

The biggest difference came down to the overall delivery time. The Uber Eats order arrived 36 minutes after it was placed, while the SPAR2U order took 80 minutes to reach the door, but was still delivered within the allocated time 10–11 a.m. time slot.

The Uber Eats order arrived 44 minutes before the SPAR2U order.

3. Speed Isn’t Everything

Getting your groceries to the door quickly is great, but it’s not the only thing that matters. Price, product availability, substitutions, and updates from the app and shopper all play a massive role in the overall experience.

The Verdict Is In: Which Ordering Experience is Better?

After testing both ordering experiences, the choice of which delivery app to use for your next order ultimately comes down to your personal preference.

If speed is your priority, Uber Eats takes the cake for a quick and convenient shopping and delivery experience.

Or, if you are a bit more savvy about specials, price, promotions and combo deals, SPAR2U is your go-to platform.

What the test did show us is that the ordering experience is about much more than getting groceries to your door. From finding products and spotting promotions to watching an order move from confirmed to shopping to dispatched and finally delivered, every step contributes to the experience.

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.