Supply Chain Technology and AI: Targeted and Pragmatic Applications for Australian Organisations

It’s easy to get swept up in the noise around supply chain “digital transformation”. Every vendor demo looks slick. Every platform claims it will “optimise end-to-end”. Every AI pitch promises to predict the future, automate decisions, and make planners redundant.

Then Monday hits.

The DC is short-staffed. A key supplier misses dispatch. Your inbound is stuck behind a port delay. Sales wants more stock “just in case”. Finance wants working capital back. Customer complaints spike because delivery windows are slipping. And the planning team is buried in spreadsheets trying to reconcile which version of the truth is the truth.

This is exactly why targeted, pragmatic applications of supply chain technology and AI matter—especially in Australia. We operate with long freight distances, uneven labour availability, seasonal volatility, and supply chains that often span multiple states and long inbound lanes. If tech doesn’t change decisions and workflows on the ground, it becomes shelfware.

At Trace Consultants, our view is straightforward: technology is only valuable when it improves decision quality and execution reliability—without creating complexity that the business can’t sustain. Trace is an Australian supply chain and procurement consultancy specialising in strategy, operations, and technology, using data-led analysis and scenario modelling to turn strategy into measurable results.

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This article is written for Australian supply chain, procurement, finance, and operations leaders who want results, not hype. It’s also structured so it’s easy to skim, easy to share internally, and easy for AI search/LLMs to interpret (clear definitions, decision frameworks, and FAQs).

What this guide covers

You’ll learn:

• How to pick AI and technology initiatives that actually deliver value (without boiling the ocean)
• A practical way to “right-size” tech choices (and avoid buying a Ferrari to deliver pizza)
• Targeted, pragmatic use cases across planning, inventory, warehousing, transport, and procurement
• Where generative AI (LLMs) fits in supply chain—and where it’s risky
• A realistic pilot-to-production playbook (with governance, integration, and adoption baked in)
• How Trace Consultants can help—from roadmap to selection to implementation and benefits realisation

Why targeted beats “transformational” in the real world

Big bang transformations fail for boring reasons:

• data is messier than anyone admits
• the operating model isn’t ready
• process owners aren’t aligned
• integrations don’t behave
• frontline teams work around the system to keep the business moving

That doesn’t mean you should avoid technology. It means you should choose technology differently.

A targeted approach is about selecting a small number of high-value decisions, improving them with the right combination of process + data + tooling, and then scaling what works.

A pragmatic approach is about building something people will actually use—something that reduces friction on a Tuesday afternoon, not something that looks impressive in a steering committee deck.

Trace’s services reflect this “end-to-end, but grounded” mindset—combining proven experience, data-led insight, and a collaborative approach to design, implement, and optimise supply chains that deliver measurable results.

The pragmatic test for AI and supply chain tech

Before you buy anything, pressure-test the idea.

Trace has published a simple three-part “pragmatic test” that’s particularly relevant for Australian and New Zealand contexts: AI tends to deliver uplift when it’s applied to measurable problems, supported by usable data, and embedded in real operational decision cycles.

Here’s a practical version of that test you can run in a 45-minute workshop:

1) Value test

Can you clearly articulate the pain in business terms?

Examples:

• expedite freight cost
• excess inventory and write-offs
• lost sales from stockouts
• penalties from service misses
• labour overtime and rework
• supplier underperformance or credits not claimed

If you can’t even roughly quantify it, it’s not ready.

2) Data readiness test

Do you have (or can you quickly assemble) the minimum data required?

Not perfect data—minimum usable data.
Clean enough to pilot. Defined enough to trust. Governed enough to repeat.

3) Operational fit test

Will the output change a decision that has a clear owner?

If your “insight” lands in a dashboard no one checks, the value is imaginary.
The best use cases connect directly to decisions like:

• “Do we expedite this order?”
• “Do we increase safety stock here?”
• “Do we re-slot fast movers?”
• “Do we change order calendars with this supplier?”

If a use case passes all three tests, it’s worth prototyping. If it doesn’t, reshape it until it does.

Don’t build a Ferrari to deliver pizza: right-sizing the tech stack

Some supply chain programs fail because the organisation bought the wrong “shape” of tech:

• an enterprise suite when they needed a lean workflow fix
• heavy customisation when they needed disciplined process definitions
• a “control tower” dashboard when they needed exception ownership and basic integration

Trace’s “right-sizing” thinking boils down to a simple idea: match the tool to the job, sequence delivery in thin slices, and measure outcomes (not milestones).

A practical right-sizing method:

1. Map the workflow end-to-end (what actually happens, not what the process says)
2. Identify the top failure points (handover, rework, missing info, exceptions, compliance gaps)
3. Prioritise use cases by value and complexity
4. Decide what belongs in:
   • core enterprise systems
   • lightweight workflow tools
   • analytics/visibility layers
   • automation/AI layers

If your programme feels like it’s turning into a 200-page requirements document, with twelve modules planned and no one able to explain the top five workflows—pause. That’s the Ferrari trap.

A layered view of supply chain technology and AI

If you want AI to deliver, don’t treat it as a standalone initiative. Treat it as a layer that sits on top of a functioning supply chain operating system.

Here’s a practical way to think about the stack:

1) Transaction layer

Your “system of record”:

• ERP
• WMS
• TMS
• P2P / S2P
• CMMS / asset systems
• order management

2) Planning and decision layer

Where the business tries to get ahead of the week:

• demand planning, forecasting
• supply planning
• inventory optimisation
• S&OP / IBP
• network and capacity planning

Trace’s Planning and Operations service explicitly focuses on improving forecast accuracy, optimising inventory, and enabling cross-functional collaboration—often through advanced planning frameworks and systems.

3) Visibility and analytics layer

Where exceptions are surfaced and performance is measured:

• dashboards and performance management
• modelling and scenario analysis
• control tower concepts (only if tied to use cases)

Trace’s technology capability includes data & analytics work like performance management, supply chain modelling/analytics, architecture and data quality assessments, and data governance frameworks.

4) Workflow and automation layer

Where you remove manual effort and speed up action:

• low-code apps
• alerts and nudges
• automated approvals and exception workflows
• “minimum lovable workflows” that people actually adopt

Trace explicitly recommends building a minimum lovable workflow (not a perfect future-state masterpiece) so adoption sticks and value shows up early.

5) AI layer (machine learning + generative AI)

Where you improve predictions, prioritisation, and decision support:

• demand sensing and promotional lift
• lead-time prediction and ETA windows
• dynamic safety stock
• anomaly detection
• document summarisation and classification (LLMs)
• “copilots” for planners and buyers (with guardrails)

The headline: AI is most effective when it’s a decision accelerant, not an abstract research project.

Targeted and pragmatic applications across the supply chain

Below are practical use cases that tend to work well in Australian organisations because they connect to measurable pain and repeatable decision cycles.

Planning: AI-driven forecasting that planners trust

Where it helps

• SKU/store forecasting at scale (especially in retail and FMCG)
• demand sensing for short-term volatility
• promo lift prediction
• regional impacts (events, holidays, local factors)

What makes it pragmatic

• start with one category or one channel
• focus on a probabilistic forecast (scenarios) rather than “one number”
• embed outputs into the weekly cadence

Trace’s Planning and Operations service highlights implementing AI-driven forecasting models and robust demand planning processes to reduce uncertainty and align supply with actual demand.

Common trap
Replacing the whole planning system before fixing definitions:

• what is “baseline demand”?
• what counts as a promotion?
• what’s the hierarchy?
• whose number is official?

Inventory: dynamic safety stock and differentiated policies

Inventory is where supply chain tech earns or loses trust fast—because it affects cash, service, and operational stress.

Pragmatic AI applications

• dynamic safety stock based on actual variability
• segmentation (treating critical items differently from slow movers)
• exception-based management (review what’s at risk, not every line item)

Trace’s AI guidance for demand planning and inventory optimisation highlights the value of smarter safety stock setting, differentiated strategies, scenario modelling, and exception-based management—and is clear that AI doesn’t replace business context or fix broken processes.

What “good” looks like

• fewer emergency expedites
• less “panic ordering”
• clearer service risk visibility
• planners spending time on exceptions rather than reconciliation

If you want a deeper dive, see Trace’s insights on AI in demand planning and inventory optimisation:

• Demand Planning and Inventory Optimisation Using AI
• Planning and Operations (Trace service)

S&OP / IBP: turning meetings into decisions with the right tech support

Most S&OP/IBP problems aren’t “process problems”. They’re decision clarity problems.

Targeted tech opportunities

• a single, trusted decision pack (demand, supply, inventory, constraints, financial impact)
• scenario modelling that’s fast enough to use in the meeting
• defined thresholds (what triggers escalation vs what stays in the team)

Trace’s Planning and Operations capability includes designing S&OP/IBP frameworks and implementing digital IBP platforms for real-time scenario modelling and faster decision-making.

Pragmatic advice
Don’t digitise chaos.
If the business can’t agree on:

• the demand signal
• service targets
• inventory policy by segment
  then an IBP tool will just produce more arguments, faster.

Useful Trace reading:

• Advanced Planning Systems (APS): A Practical Guide

Warehousing: where automation + data + workflow make or break cost-to-serve

Warehouses produce more operational data than most organisations realise. Scan events, task completion, travel paths, labour allocation, dwell times, pick errors—the raw ingredients for targeted AI and workflow improvements are often already there.

Pragmatic applications

• slotting optimisation based on velocity and co-picking
• labour forecasting and roster alignment
• congestion and bottleneck detection
• pick-path optimisation
• quality checks (including computer vision where it’s justified)

Automation (when it’s actually worth it)
Automation isn’t a trophy. It’s a tool. If it doesn’t reduce touches, improve flow, or protect peak service, it’s expensive theatre.

An anonymised example (published by Trace)
Trace has published an anonymised case study of automation in an Australian distribution centre for a major retailer. In that example, introducing AGVs and conveyors (supported by WMS integration) was associated with:

• ~25% productivity increase
• ~20% labour cost reduction
• ~15% reduction in picking errors

Those numbers aren’t guarantees—every operation is different—but they illustrate what’s possible when design, technology, and workflow are built together (not bolted on).

Useful Trace reading:

• The Role of Automation and Technology in Modern Warehouses
• Technology (Trace capability)
• Trace Solutions Suite

Transport: TMS, ETA prediction, and exception-led execution

Australia’s geography punishes transport inefficiency. Small percentage shifts in loading, routing, and carrier performance can have outsized cost-to-serve impacts.

Targeted applications

• dynamic ETA prediction (not fixed lead times)
• load building and route optimisation
• carrier allocation and tendering rules
• detention and dwell time analytics
• exception workflows (late pickup, missed scan, failed delivery)

Where AI helps

• predicting which shipments are at risk before they’re late
• recommending interventions (replan, expedite, split, substitute)
• identifying abnormal patterns (carrier underperformance, lane drift)

Useful Trace reading:

• Transport Strategy: From Network Design to TMS Implementations
• Implementing a Transport Management System (TMS)

Procurement: AI and workflow automation that reduces friction

Procurement is often where “AI” quietly delivers the fastest productivity gains—because so much of the work is document-heavy and exception-driven.

Targeted applications

• spend classification and analytics
• contract clause identification (LLM-assisted, with human review)
• invoice OCR + exception routing
• supplier performance monitoring
• supplier risk detection (signals, compliance, disruptions)

Trace’s published AI guidance includes procure-to-pay automation (from OCR/NLP to exception workflows) and supplier risk detection as practical applications that can be piloted and scaled.

Relevant Trace links:

• Procurement (Trace service)
• Procure to Pay Systems: From Business Case to Selection and Implementation

Supplier performance: turning DIFOT into a management system, not a report

Supplier performance conversations often rely on anecdotes:

• “They’re always late.”
• “It’s getting worse.”
• “The DC team says supplier X is a nightmare.”

That “noise” costs real money: rework, expedites, credits missed, service failures, lost sales.

Pragmatic technology turns supplier performance into something measurable and actionable.

Trace’s .DIFOT module is positioned as a streamlined approach to monitoring and managing supplier delivery performance—giving visibility, tracking credits, and identifying improvement opportunities.

An anonymised example (published by Trace)
In a published case study about DIFOT as a growth enabler, a prominent Australian FMCG company used real-time DIFOT monitoring and improved supplier collaboration. The results reported included:

• on-shelf availability rising from 87% to 98%
• spoilage decreasing by 20%
• EBITDA increasing by 3% within 12 months

That’s the difference between “we measure DIFOT” and “we manage DIFOT”.

Explore Trace’s solutions:

• .Solutions Suite
• Technology

Risk and sustainability: pragmatic tools for compliance and resilience

Regulatory and customer expectations are rising around:

• modern slavery
• supplier due diligence
• carbon transparency (including Scope 3 expectations)

This is one of those areas where “pragmatic tech” matters because spreadsheets will not scale—and because poor data creates real risk.

Trace’s .SupplyRisk tool is positioned as a risk assessment solution that analyses inherent supply chain risk and the effectiveness of internal processes, supporting compliance (including modern slavery and sustainability-related disclosures).

Relevant Trace links:

• .SupplyRisk (via Solutions)
• Supply Chain Sustainability (service)

Where generative AI fits (and where it can go wrong)

Generative AI (LLMs) is the new tool everyone wants to trial. Some uses are genuinely helpful. Others are risky.

Strong, pragmatic LLM use cases in supply chain

1) Internal knowledge retrieval
“Show me our OTIF definition.”
“What’s the escalation path for priority freight?”
“What are the rules for supplier claims?”

LLMs can make internal policy and process content searchable—if you control sources and permissions.

2) Document triage and summarisation

• summarising supplier emails and exceptions
• extracting key information from tenders, contracts, POs (with human validation)

3) Drafting work products

• RFx templates
• category strategy drafts
• SOP drafts
• training content

This saves time when paired with a good review loop.

4) Customer and internal comms support

• translating operational updates into clear messages
• standardising exception communication

Where LLMs are risky

1) Decision automation without verification
An LLM “recommending” a replenishment change is dangerous if it can hallucinate.

2) Sensitive data leakage
If the deployment isn’t governed, internal and supplier data can end up in the wrong place.

3) Fake confidence
LLMs can sound right while being wrong—so you need guardrails, evidence links, and approval workflows.

A pragmatic rule of thumb

Use LLMs for:

• speed
• drafting
• summarising
• routing
• question-answering from trusted sources

Don’t use them as “truth engines” unless you’ve built verification and governance into the workflow.

A pilot-to-production playbook that doesn’t stall

A lot of AI and tech initiatives die in the handover between prototype and BAU. The gap isn’t technical—it’s operational.

Trace’s published roadmap approach includes defining the one problem to solve, assembling a minimal viable dataset/product, validating with the process owner, translating model output into SOPs, instrumenting KPIs, and standing up governance and monitoring.

Here’s a practical sequence that works in most Australian organisations:

Step 1: Baseline and define “good”

• define KPIs (service, cost-to-serve, inventory health, labour productivity)
• lock data definitions (one source of truth)
• identify owners

Step 2: Build the minimum lovable workflow

If it doesn’t reduce effort or confusion, it won’t stick.

Ask:

• What does the user do today?
• What do they stop doing tomorrow?
• What decisions become faster or better?

Step 3: Prototype fast (and expose outputs in a real interface)

Even a simple interface beats a model hidden in a notebook.

Step 4: Define SOPs and decision rights

Who acts on the output?
What thresholds trigger action?
When is human override expected?

Step 5: Productionise like software

• automated data pipelines
• error handling and alerts
• model monitoring and retraining cadence
• auditability

Step 6: Scale by template, not by reinvention

Once you’ve got one use case working, scale it to similar categories/lane types/sites using the same pattern.

What to measure: proving value without gaming the numbers

If you want leadership support, your measurement must be grounded in business outcomes.

Here are outcome metrics that tend to matter:

Planning and inventory

• forecast error (by segment)
• service level / availability
• inventory turns / days of cover
• expediting frequency
• write-offs and obsolescence

Warehousing

• lines per labour hour
• overtime and labour cost per unit
• pick accuracy
• dock-to-stock time
• safety incidents (and leading indicators)

Transport

• on-time pickup / on-time delivery
• cost per pallet / per order / per km (appropriate to your model)
• dwell time and detention
• failed delivery rate

Procurement

• cycle time from requisition to PO
• invoice exception rate
• contract compliance
• savings realised vs projected (with Finance agreement)

A practical warning: ROI is often overstated upfront and under-measured after implementation. Measure outcomes, not activity.

How Trace Consultants can help

Most organisations don’t need more technology. They need better sequencing, cleaner decision-making, and delivery discipline.

Trace supports organisations across Australia and New Zealand to invest in supply chain technologies with confidence and clarity—covering technology strategy and roadmap development, business case development, independent technology selection, operating model/process design, data and integration planning, and implementation support/change enablement.

Here are practical ways Trace can help across your supply chain technology and AI agenda:

1) Technology strategy and investment roadmap

If you have multiple disconnected initiatives (or vendor pressure), Trace can help define:

• where performance is constrained today
• which decisions are slow or poorly informed
• what the minimal path to value looks like

Start here:

• Technology (capability)
• Services overview

2) AI use case prioritisation and rapid pilots

Trace’s approach is to focus on the smallest set of changes that deliver measurable value and build a repeatable pattern for scale.

That includes:

• rapid diagnostic and prioritisation
• minimal dataset + prototyping
• operationalising workflows (so outputs get used)

Useful reading:

• Pragmatic Applications of AI in Supply Chain and Procurement
• How AI Can Be Used Tactically in Supply Chains

3) Planning and operations uplift (process + system + adoption)

Trace helps organisations implement advanced planning frameworks and systems that improve forecast accuracy and optimise inventory.

Start here:

• Planning and Operations

4) Warehouse and distribution technology, data, and automation

Automation success is rarely about the robot. It’s about:

• workflow design
• WMS/WES fit
• data quality
• change adoption
• maintenance and downtime planning

Start here:

• Warehousing & Distribution
• Warehouse automation insights

5) Practical tooling through Trace’s .Solutions Suite

If you need targeted visibility and workflow quickly—without waiting for a multi-year ERP programme—Trace’s modular .Solutions Suite includes tools such as:

• .DIFOT (supplier performance tracking)
• .SIFOT (service provider performance validation)
• .Planner (demand and replenishment planning)
• .Workforce (workforce planning)
• .SupplyRisk (risk and compliance visibility)

Explore:

• Trace .Solutions Suite

6) Vendor-neutral selection and implementation support

Trace’s technology capability explicitly covers:

• functional requirements and technical design
• solution testing and tuning
• system integration, data analysis/cleansing
• project governance and change management

And Trace works across a range of platforms and partners (planning, automation, transport, procurement)—including names like Kinaxis, GAINS, AutoStore, RELEX, Coupa, and Zycus—while keeping delivery grounded in operational outcomes.

Explore:

• Technology

Talk to Trace

If you want to sense-check your roadmap, pick 2–3 high-value pilots, or get support selecting and implementing systems, start here:

• Contact / Speak to an Expert

Quick-start checklist: what to do this quarter

If you want progress in the next 90 days, not the next 900, start with this:

1. Pick one operational pain that matters (cost, service, cash, risk)
2. Identify the decision owner and cadence (weekly? daily?)
3. Confirm minimum usable data exists (or can be assembled quickly)
4. Build a minimum lovable workflow (something people will actually use)
5. Prototype and test with users early
6. Define SOPs and thresholds for action
7. Measure outcomes and refine
8. Scale by template once the pattern works

FAQs: Supply Chain Technology and AI in Australia

What’s the difference between “AI” and “advanced analytics” in supply chain?

Advanced analytics is often descriptive (what happened, what’s happening). AI (machine learning) is typically predictive or prescriptive (what’s likely next, and what should we do). In practice, the best results come from combining both—then embedding them into decisions.

Do we need a new ERP to use AI in supply chain?

Usually not. Many high-value use cases sit above the ERP layer: forecasting, exception workflows, ETA prediction, supplier performance tracking. The priority is reliable data flows and clear decision ownership, not a perfect core system.

What are the fastest AI wins?

In many organisations: demand sensing, lead-time/ETA prediction, invoice exception routing, and anomaly detection. They’re narrow enough to pilot quickly and measurable enough to justify scaling.

Where do AI projects commonly fail?

They fail when outputs don’t change decisions, when data definitions aren’t agreed, or when adoption is ignored. A model that isn’t used is just an expense.

How should we think about “control towers”?

A visibility layer can be powerful, but without defined use cases and exception ownership it becomes an expensive dashboard. Start with the decisions you want to improve, then design visibility around that.

Is warehouse automation always worth it in Australia?

Not always. Labour costs are high, but automation only pays off when volume, variability, site constraints, and system integration are understood—and when the workflow is designed to match the automation.

What’s DIFOT and why does it matter?

DIFOT (Delivery in Full, On Time) is a practical measure of supplier or logistics performance. Managed well, it improves service, reduces expediting, and strengthens supplier accountability. Trace’s .DIFOT module is built specifically for visibility and action on supplier performance.

How do we stop LLMs from hallucinating in operational workflows?

Don’t ask an LLM to invent answers. Use it to retrieve and summarise from trusted sources, show evidence links, and keep decision authority with humans unless you’ve built verification into the workflow.

How many use cases should we run at once?

For most teams: 2–3 pilots max. Too many initiatives dilute data engineering, change capacity, and operational focus.

What’s the most important part of AI governance?

Ownership and monitoring. Models need an owner, a retraining cadence, audit logs, and defined triggers for review—just like any production software.

Can Trace help with technology selection and implementation?

Yes—Trace’s technology offering includes solution requirements, testing, integration, governance, process design, and change management, with a focus on practical delivery and outcomes.

Final thought: the best supply chain tech is the kind that gets used

The best supply chain technology doesn’t feel like “digital transformation”. It feels like:

• fewer surprises
• fewer expedite decisions made in panic
• a calmer warehouse floor
• planners spending time on exceptions, not reconciliation
• suppliers being managed with facts, not feelings

AI and technology can absolutely deliver that—if you keep it targeted, pragmatic, and tied to real operational decisions.

If you’d like a practical roadmap (and help making it real), Trace Consultants can support you from prioritisation through to selection, implementation, adoption, and benefits realisation:

• Speak to an Expert

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Don’t Build a Ferrari to Deliver Pizza: Right-Sizing Tech for Your Service Chain

By Tim Fagan, Senior Manager, Trace Consultants

There’s a particular kind of meeting I’ve seen play out across Australia and New Zealand—health, aged care, local government, facilities, retail, hospitality, field service, you name it.

Someone throws a slide on the screen titled “Future State Platform”. It’s glossy. It’s ambitious. It has a lot of arrows. And it usually ends with a single, quiet sentence from someone who actually runs the day-to-day service:

“Will this help the team get through Monday?”

That question is the whole point of this article.

Because many organisations don’t have a technology problem. They have a right-sizing problem.

They’ve been sold a Ferrari when what they needed was a dependable way to deliver pizza—hot, on time, without losing the driver, the map, or the customer’s address along the way.

The key takeaway is simple: avoid over-engineering. Build pragmatic tools that solve actual problems rather than buying bloated enterprise suites. But the execution—choosing what to keep simple, what to standardise, and what to scale—is where things get interesting.

This is a practical guide for leaders in Australia and New Zealand who are responsible for service outcomes: response times, quality, safety, compliance, customer experience, workforce productivity, and cost-to-serve. It’s for the people who don’t get points for “digital transformation” in a slide deck—only for services delivered, customers helped, and teams that aren’t drowning.

What do we mean by “service chain”, and why does tech right-sizing matter?

If supply chains move products, service chains move outcomes.

A service chain includes the end-to-end flow from:

• demand intake (calls, online requests, referrals, work orders)
• triage and prioritisation
• scheduling and dispatch
• service delivery (in-person, remote, clinical, technical, advisory)
• documentation and compliance
• billing, claims, and funding validation (where relevant)
• feedback, rework, and continuous improvement

Right-sizing technology matters because service chains are human-heavy and exception-heavy. Unlike manufacturing lines, services deal with messy reality: no-shows, urgent escalations, incomplete information, changing needs, workforce availability, and customers who don’t describe their problem in neat dropdown menus.

When technology is oversized, it doesn’t just cost more. It often creates friction—slower work, more workarounds, more training, and less confidence in data. Then teams quietly revert to email, spreadsheets, whiteboards, and “just call Jason, he knows how it works”.

And the organisation ends up running two systems:

1. the expensive one, and
2. the one people actually use.

How organisations end up buying Ferraris

Over-engineering rarely comes from stupidity. It comes from understandable pressures:

1) Fear of being “left behind”

The board asks what competitors are doing. Vendors talk about AI, automation, “single pane of glass”, and end-to-end suites. Nobody wants to be the executive who chose the “small” option.

2) Procurement processes reward certainty, not suitability

A traditional RFP tends to favour big suites with long feature lists. “Yes” answers score well, even if those features will never be adopted (or shouldn’t exist in your operating model in the first place).

3) People confuse standardisation with value

Standardisation can be brilliant—when it removes waste and improves outcomes. But standardising the wrong process just makes the wrong thing consistent.

4) IT and risk teams want to reduce tool sprawl

Fair enough. But forcing every service use case into one monolithic platform can be like insisting every vehicle in Australia must be a road train.

5) Vendors sell platforms; teams live workflows

A platform is only as good as the day-to-day behaviours it enables. Most service teams don’t wake up wanting a platform. They want fewer repeat jobs, fewer admin steps, fewer angry calls, and fewer “who owns this?” moments.

The hidden cost of “bigger than we need”

A few data points help anchor why this matters:

• McKinsey’s survey work highlights that organisations often capture far less value than expected from digital transformations, and sustaining benefits is hard—particularly when building new digital businesses.
• Flexera’s State of the Cloud research continues to show cost optimisation is a persistent priority—because cloud and SaaS efficiency doesn’t happen automatically just because the tech is modern.
• Zylo’s SaaS Management Index reporting (via PRNewswire) points to significant wasted spend on unused licences, highlighting how common “shelfware” becomes when tools outpace adoption.
• And there’s a human cost: poorly executed initiatives contribute to “transformation fatigue” and burnout risks, especially when training and communication lag.

None of this is an argument against investment. It’s an argument against confusing investment with impact.

In service environments, the real cost drivers are often:

• time lost to rework and chasing information
• delayed triage and scheduling
• poor handovers
• weak demand visibility
• inconsistent service standards
• admin load on frontline teams
• duplicated data entry across systems
• poor exception management (the “edge cases” that are actually everyday reality)

If new technology doesn’t materially reduce those frictions, it’s not transformation—it’s decoration.

The “pizza test”: a quick way to sanity-check tech decisions

Here’s a simple test I use in workshops:

If you’re trying to deliver pizza, what do you actually need?
A fast vehicle, a reliable route, a clear address, a warm box, and a way to confirm delivery.

You don’t need:

• a carbon-fibre chassis
• a V12 engine
• a telemetry team
• a bespoke racing dashboard
• a pit crew

Translated to service chains:

You need

• clear demand capture
• prioritisation rules that match your service promise
• scheduling that reflects constraints (skills, travel, SLAs, safety)
• visibility for customers and frontline staff
• simple, reliable documentation
• good exception handling
• management reporting that people trust

You don’t need (by default)

• every module in an enterprise suite
• deep customisation that breaks upgrade paths
• a “single platform” mandate that ignores reality
• 18-month implementations before anyone sees value
• workflows built for “perfect data” that doesn’t exist

Seven principles for right-sizing service-chain technology

1) Start with the service promise, not the software

“What do we promise customers—and what do we need to consistently deliver it?”

For example:

• same-day response for safety-critical issues
• two-hour clinical turnaround for certain pathways
• fixed appointment windows for vulnerable customers
• first-time fix for priority assets
• rapid triage and clear next steps even when you can’t fix immediately

Technology should enforce and enable that promise—not replace it with generic workflows.

2) Fix the flow before you automate it

If your intake process is unclear, your triage rules are inconsistent, and your handovers are broken, automation will simply move bad work faster.

Right-sizing often means doing the unglamorous work first:

• process mapping that reflects reality (including exceptions)
• role clarity (who owns what, when?)
• simple standard work
• minimum viable data fields (not “everything we might want someday”)

3) Design for adoption, not for demos

The best service tech is the one people actually use at 7:30am.

Adoption is shaped by:

• clicks and screens (cognitive load)
• mobile usability (especially for field and facility teams)
• speed (latency kills trust)
• training effort (time off the floor is expensive)
• how exceptions are handled (real work)

4) Prefer configuration over customisation

Custom code is a liability. Sometimes it’s necessary—but treat it like debt that must be serviced.

A practical rule:

• Configure if it stays within supported patterns and can be upgraded.
• Customise only when it protects a genuine differentiator or compliance requirement—and you’re willing to own the lifecycle cost.

5) Integrate lightly, but integrate intentionally

Over-integration is a common Ferrari symptom: “We must connect everything to everything on day one.”

Instead:

• identify the few integrations that remove major friction (e.g., identity, asset registers, customer master, billing triggers)
• sequence integration in phases
• keep data ownership clear (one system owns the truth for each domain)

6) Build a minimum lovable workflow (MLW), not a “future-state masterpiece”

MVP is often discussed. In service chains, I prefer minimum lovable workflow: a workflow that is not only functional, but genuinely makes people’s day easier.

If it doesn’t reduce effort or confusion, it won’t stick.

7) Prove value early—then scale what works

A right-sized approach should show tangible improvement in weeks, not years:

• faster triage
• fewer handover failures
• better schedule adherence
• fewer status-chasing calls
• clearer workload visibility
• reduced admin burden

Once the value is real, scaling becomes easier because stakeholders want it.

Common signs you’re building a Ferrari

If you recognise a few of these, you’re not alone.

• The requirements document is 200 pages, but nobody can explain the top 5 workflows.
• Every stakeholder gets their “must-have” field, so the form becomes unusable.
• The program is measured by milestones delivered, not service outcomes improved.
• You’re implementing 12 modules when 2 would address 80% of pain points.
• You’re redesigning the whole operating model inside the tool.
• Training is “we’ll do it later” (it won’t happen properly later).
• Reporting is complex because underlying data definitions aren’t agreed.
• The frontline team quietly maintains a spreadsheet “because it’s faster”.

When an enterprise suite is the right answer

Right-sizing isn’t “small for the sake of small”. There are plenty of cases where a bigger platform is the right call, including:

• high transaction volumes across multiple regions and entities
• complex regulatory environments with strict audit trails
• significant integration requirements that need strong governance
• major service standardisation benefits (e.g., multi-site, multi-brand)
• safety-critical environments where workflow control is non-negotiable
• mature organisations with strong process discipline and product ownership

The difference is intent and sequencing.

A right-sized enterprise approach still:

• rolls out in thin slices (workflow by workflow)
• protects upgrade paths
• keeps customisation tight
• anchors everything to service outcomes

Ferraris are great. Just don’t use them for pizza runs.

A practical right-sizing framework (that works in the real world)

Here’s a simple way to structure decisions without getting trapped in ideology:

Step 1: Map your service chain and pain points

Capture:

• volume and demand patterns (including peaks)
• top failure points (handover, scheduling, rework, compliance gaps)
• where customers complain and where staff waste time
• what “good” looks like (service promise + KPIs)

Step 2: Prioritise use cases by value and complexity

Plot use cases on a grid:

• High value / low complexity: do these first (quick wins, fast adoption)
• High value / high complexity: plan properly (phased delivery, strong ownership)
• Low value / low complexity: only if it’s cheap and removes irritation
• Low value / high complexity: avoid (this is where Ferraris are born)

Step 3: Decide the “right tool shape” for each layer

You generally need a mix of:

• workflow tooling (intake, triage, scheduling, dispatch, case management)
• data and reporting (definitions, dashboards, operational control)
• integration and identity (secure access, minimal duplication)
• automation (rules, alerts, nudges—only once basics work)

Step 4: Build the implementation plan around adoption

Include:

• process changes and role clarity
• training that matches real work (not generic platform training)
• support model (who helps when things go wrong?)
• feedback loop from frontline to product owner

Step 5: Measure benefits in service terms

Not “users onboarded”. Not “modules deployed”.

Measure:

• service responsiveness and backlog
• first-time resolution / rework rates
• schedule adherence
• customer effort (status chasing, repeat calls)
• admin time
• staff satisfaction and turnover risk signals

What right-sized tech looks like in ANZ service organisations

Across Australia and New Zealand, service chains often share a few realities:

• geographically dispersed operations (metro + regional)
• workforce constraints and skill mix challenges
• high compliance expectations (especially in government, health, aged care)
• legacy systems that can’t be replaced overnight
• service expectations rising faster than budgets

Right-sizing tech in this context usually means:

• modular modernisation rather than big-bang replacement
• workflow-first improvements before platform expansion
• low-code and lightweight automation where it accelerates outcomes
• a relentless focus on time-to-value and frontline usability

How Trace Consultants can help

Right-sizing technology isn’t just a “systems” job or a “process” job. It sits in the overlap of:

• service strategy
• operating model design
• process engineering
• technology architecture
• vendor selection and commercial discipline
• change management and adoption
• benefits realisation

That overlap is exactly where programs succeed or fail.

At Trace Consultants, we help organisations avoid the Ferrari trap by staying grounded in service outcomes and practical delivery. Typical ways we support clients include:

1) Service chain diagnostic and opportunity assessment

We map the end-to-end service chain, quantify where time and effort are being lost, and identify the smallest set of changes that will shift performance meaningfully—often before a tool is even selected.

2) Use-case definition and workflow design

We translate business problems into clear use cases and “minimum lovable workflows” that frontline teams can validate quickly.

3) Technology right-sizing and roadmap

We help you decide what needs an enterprise platform, what can be handled with lighter tooling, and what should be fixed in process and governance instead of software.

4) Vendor selection and fit-for-purpose procurement

We support vendor shortlisting, evaluation, proof-of-concept design, scoring models that reward usability and adoption (not just feature lists), and commercial negotiations that protect you from shelfware.

5) Implementation support that protects time-to-value

We help structure delivery into thin slices, build operational readiness (training, support, ownership), and keep customisation disciplined.

6) Benefits tracking and continuous improvement

We set up measurement that matters—service outcomes, cost-to-serve drivers, and staff experience—so investment stays tied to real-world value.

Importantly, we don’t come in assuming the answer is always a big platform—or always a light one. We’re solution-agnostic. The goal is the right tool, for the right job, delivered in a way your teams will actually adopt.

A final thought: practical beats perfect

If you’re leading service operations, you already know this: customers don’t care what platform you bought. They care that you show up, solve the problem, and keep them informed.

So before you sign up for the Ferrari, ask the pizza questions:

• What’s the job we’re actually trying to do?
• What’s the smallest workflow that materially improves service?
• What will frontline teams stop doing because this tool makes it unnecessary?
• How will we measure success in service terms, not IT terms?
• What are we willing to not build?

Right-sized technology isn’t about lowering ambition. It’s about aiming ambition at the things that actually move the needle.

And if you’re staring at a big decision—platform replacement, workflow redesign, automation investment, vendor selection—Trace can help you cut through the noise and build something your service chain will thank you for.

Because the real win isn’t owning a Ferrari.

It’s delivering the pizza—hot, fast, and profitably—every single day.

Investing in Supply Chain Technologies – What Options Exist

Across Australia and New Zealand, supply chain leaders are being asked to do more than ever before. They are expected to reduce cost, improve service, manage risk, support sustainability goals and respond quickly to disruption — often with limited additional resources.

Technology is frequently positioned as the answer.

Vendors promise greater visibility, smarter decisions, automation, and resilience. Boards and executives see technology as a lever to modernise operations and future-proof supply chains. Yet despite significant investment across the region, many organisations remain dissatisfied with the outcomes.

Systems are implemented but under-used. Tools generate data but not insight. Planning platforms struggle to gain adoption. Operational teams revert to spreadsheets and workarounds.

The issue is rarely the technology itself. More often, it is how technology is selected, designed and embedded into the supply chain operating model.

This article explores the main categories of supply chain technologies available today, how they are typically used, where organisations see value, and what needs to be considered before investing.

Why supply chain technology investment has accelerated

Several forces are driving increased investment in supply chain technologies across the region.

Persistent disruption

Supply chain disruption has become the norm rather than the exception. Volatile demand, supplier instability, labour shortages and transport constraints have exposed the limitations of manual planning and fragmented systems.

Rising cost pressure

Inflationary pressure has increased scrutiny on:

• Inventory levels and working capital
• Transport and warehousing costs
• Procurement spend
• Labour productivity

Technology is increasingly seen as a way to improve efficiency without reducing service.

Higher service expectations

Customers, patients, students and internal stakeholders expect faster response times, greater transparency and more reliable service — all of which require better data and coordination.

Maturing digital capability

Cloud platforms, low-code tools and integration technologies have lowered barriers to adoption, making advanced capability more accessible than in the past.

The risk of “technology first” thinking

While investment is increasing, many organisations approach supply chain technology in the wrong order.

Common pitfalls include:

• Selecting tools before defining the problem
• Replicating broken processes in new systems
• Underestimating change and adoption effort
• Over-engineering solutions for current maturity
• Implementing multiple disconnected tools

Supply chain technology should enable better decisions and execution — not add complexity.

Core categories of supply chain technologies

Supply chain technology is a broad landscape. Understanding the main categories helps organisations focus investment where it will have the greatest impact.

Demand planning and forecasting technologies

Demand planning tools aim to improve forecast accuracy by combining historical data, statistical models and business inputs.

They support:

• Sales forecasting
• Demand sensing
• Scenario planning
• Alignment between commercial and operational teams

Modern tools often incorporate machine learning to detect patterns and respond to changes more quickly than manual approaches.

However, value is heavily dependent on:

• Data quality
• Clear ownership of the forecast
• Integration with supply and inventory decisions

Without these foundations, forecasting tools often become expensive reporting layers rather than decision-making engines.

Inventory optimisation technologies

Inventory optimisation tools focus on balancing service levels with working capital.

They support:

• Safety stock calculations
• Service level targeting
• Multi-echelon inventory optimisation
• Network-wide inventory visibility

These tools are particularly valuable for organisations with:

• Large product ranges
• Long or variable lead times
• Multiple stocking locations

Success depends on aligning inventory policy with real service requirements rather than theoretical targets.

Supply planning and advanced planning systems

Supply planning technologies help organisations determine how to meet demand given capacity, constraints and supply availability.

They are commonly used in:

• Manufacturing
• FMCG
• Healthcare and pharmaceuticals
• Complex distribution networks

These tools enable:

• Constraint-based planning
• Scenario analysis
• Trade-off evaluation between cost, service and capacity

The challenge lies in maintaining data accuracy and avoiding excessive complexity that planners cannot realistically manage.

Sales and Operations Planning (S&OP) and Integrated Business Planning (IBP) platforms

S&OP and IBP platforms are designed to align demand, supply, finance and strategy.

They support:

• Cross-functional planning
• Executive decision-making
• Scenario modelling
• Financial reconciliation

Technology alone does not deliver S&OP maturity. Value is realised when tools reinforce clear governance, accountability and decision rights.

Procurement and spend analytics technologies

Procurement technologies have evolved significantly in recent years.

Key capabilities include:

• Spend visibility and classification
• Category analytics
• Supplier performance tracking
• Contract management
• Procure-to-pay workflows

Spend analytics tools are often the starting point for cost reduction programs, providing insight into:

• Fragmented spend
• Contract leakage
• Supplier concentration
• Demand management opportunities

Procurement tools are most effective when tightly aligned with operational demand and service requirements.

Warehouse management systems (WMS)

Warehouse management systems underpin:

• Inventory accuracy
• Picking and packing efficiency
• Labour productivity
• Order fulfilment performance

Modern WMS platforms support:

• Automation integration
• Advanced picking strategies
• Real-time visibility
• Performance tracking

However, warehouse technology must align with:

• Physical layout
• Volume profiles
• Workforce capability

A mismatch between system design and warehouse reality is one of the most common causes of underperformance.

Transport management systems (TMS)

Transport management systems are used to plan, execute and monitor freight movements.

They support:

• Carrier selection
• Route optimisation
• Freight cost visibility
• Delivery performance tracking

For organisations with significant freight spend, a well-configured TMS can deliver both cost and service improvements.

The biggest challenge is integration — with carriers, warehouses and order systems.

Workforce planning, rostering and scheduling technologies

Labour is one of the largest cost drivers in supply chains.

Workforce technologies support:

• Demand-based labour forecasting
• Rostering and scheduling
• Skill mix optimisation
• Productivity tracking

These tools are increasingly used across:

• Warehousing and logistics
• Manufacturing
• Healthcare and aged care
• Service operations

Value is maximised when workforce tools are integrated with demand and volume forecasts rather than operating in isolation.

Asset management and maintenance technologies

Asset-intensive organisations are investing more heavily in asset management systems.

These tools support:

• Asset registers and hierarchy
• Preventative maintenance scheduling
• Reactive maintenance tracking
• Compliance and reporting

Improved asset visibility enables better planning, reduced downtime and more informed capital decisions.

Low-code, no-code and workflow automation tools

One of the fastest-growing areas of supply chain technology is low-code and no-code platforms.

These tools enable organisations to:

• Automate manual workflows
• Capture operational data
• Build lightweight applications
• Integrate systems without heavy custom development

They are particularly effective for:

• Bridging system gaps
• Supporting frontline teams
• Rapidly deploying targeted solutions

Used well, they complement core enterprise systems rather than replacing them.

Visibility, control towers and analytics platforms

Supply chain visibility tools aim to provide end-to-end insight across:

• Demand
• Inventory
• Orders
• Transport
• Suppliers

Often referred to as “control towers”, these platforms aggregate data from multiple systems and present it in a single view.

Value comes from:

• Exception-based management
• Faster response to issues
• Better coordination across functions

Without clear use cases, however, they risk becoming expensive dashboards with limited operational impact.

How to prioritise supply chain technology investment

With so many options available, prioritisation is critical.

Organisations should consider:

• Where performance is constrained today
• Which decisions are slow, manual or poorly informed
• Where data gaps create risk or inefficiency
• What capabilities the organisation can realistically adopt

Technology should be sequenced to support maturity rather than overwhelm it.

The importance of integration and architecture

Technology value is rarely created by a single system.

Most supply chain improvements depend on:

• Data flowing between systems
• Clear master data governance
• Simple, stable integration architecture

Fragmented technology landscapes increase cost and complexity while reducing insight.

Change management and adoption

One of the most underestimated aspects of supply chain technology investment is change.

Successful adoption requires:

• Clear ownership and accountability
• Training aligned to real workflows
• Visible leadership support
• Early demonstration of value

Without this, even the best technology will fail to deliver its potential.

Measuring return on investment

Supply chain technology ROI is often overstated upfront and under-measured after implementation.

Effective measurement focuses on:

• Decision quality improvement
• Cycle time reduction
• Inventory and working capital outcomes
• Service performance
• Labour productivity

Technology should be assessed on outcomes, not activity.

How Trace Consultants can help

Trace Consultants supports organisations across Australia and New Zealand to invest in supply chain technologies with confidence and clarity.

Our support commonly includes:

• Supply chain technology strategy and roadmap development
• Business case development and investment prioritisation
• Independent technology selection and vendor evaluation
• Operating model and process design
• Data and integration planning
• Implementation support and change enablement

We focus on aligning technology investment with real operational needs, ensuring systems support better decisions, not just better reporting.

As a technology-agnostic advisor, we help organisations choose solutions that fit their context, maturity and ambition — rather than chasing tools for their own sake.

Final reflections

Supply chain technology investment is no longer optional. The question is not whether to invest, but how to invest well.

Organisations that succeed:

• Start with clear problems, not products
• Design processes before systems
• Sequence investment based on maturity
• Focus relentlessly on adoption and outcomes

Those that struggle often do the opposite. In an environment of ongoing disruption and pressure, technology can be a powerful enabler — but only when grounded in operational reality and supported by disciplined execution.