The Tool Desk
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How widely are companies using supply-chain analytics?
Adoption is substantial but uneven, and survey measures capture different things: current use, experimentation, investment or organizational readiness. PwC’s 2025 Digital Trends in Operations survey found that 53% of respondents use AI in at least a few areas or widely to anticipate and mitigate supply-chain disruptions, while 31% are testing or piloting it for that purpose. These figures describe respondents’ reported use, not the share of all supply chains or proof that AI prevented disruptions.
| Measure | Finding | Source and context |
|---|---|---|
| AI use for anticipating and mitigating disruption | 53% use it in at least a few areas or widely; 31% are testing or piloting it | PwC, 2025 Digital Trends in Operations survey |
| Formal supply-chain AI strategy | 23% of surveyed supply-chain leaders reported having one | Gartner, 2025 survey on formal AI strategy |
| Analytics spending | 95% had increased spending, and 95% planned to increase investment over the following two years | Gartner, Supply Chain Analytics for CSCOs, 6 February 2025 |
| Reported analytics-driven improvement | Fewer than 25% reported high levels of improvement | Gartner, Supply Chain Analytics for CSCOs, 6 February 2025 |
| Expected impact of advanced analytics | 65% selected big data and advanced analytics as the trend expected to have the greatest supply-chain impact over the next three years | APQC, Advanced Analytics in Supply Chain: 2024 Current State, 18 July 2024 |
| Future readiness | 29% of supply-chain organizations had at least three of five future-readiness characteristics | Gartner, Future Performance Capabilities survey, 18 February 2025 |
These are survey findings with different questions and respondent groups, so they should not be combined into a single adoption rate. Together, they show that interest and investment are widespread while formal strategy and realized gains are less universal.
Which supply-chain decisions change first?
Demand and supply planning
Forecasting models can combine historical orders and sales with information such as supplier conditions, logistics data and weather. The practical value is not simply a more precise forecast: planners can identify exceptions sooner, test assumptions and adjust purchasing or production plans before a shortage or excess becomes harder to correct.
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Inventory and replenishment
Analytics can help planners weigh uncertain demand against target service levels when setting replenishment and safety-stock decisions. That can make stockout risk and excess inventory more visible, but recommendations depend on accurate inventory records, lead times and product relationships. A model cannot compensate for stale or incomplete inputs.
Transportation, tracking and visibility
Scanning, Internet of Things (IoT) data and other shipment updates can feed dashboards and predictive tools that flag delays, exceptions or route choices. RRD’s 2024 Future-Ready Supply Chain Report found respondents reporting AI use for supply forecasting (59%), visibility and tracking (56%), and optimizing operations (56%). These are reported use cases, not measured improvement rates.
Supplier risk and disruption response
Early-warning systems can monitor signals such as supplier financial information, weather, traffic and other external developments. Scenario planning can help teams compare possible disruptions and prioritize recovery actions. Such signals are prompts for assessment, not certainty: teams still need to verify alerts and decide what response is proportionate.
Management and sustainability decisions
Shared dashboards and embedded analytics can shorten the time between a change in conditions and a management decision when teams use consistent definitions and workflows. The OECD’s 2025 discussion of supply chains connects AI and analytics with environmental requirements, resilience and trusted data for safe trade. Analytics can inform sustainability or compliance decisions, but the tool alone does not establish that a supply chain is sustainable or compliant.
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What does “enhanced analytics” mean in practice?
Supply-chain analytics ranges from reporting what happened to recommending an action. More sophisticated methods are not automatically more useful; the right level depends on the decision, data and ability to act on a result.
| Approach | Question it answers | Example in a supply chain | Key consideration |
|---|---|---|---|
| Descriptive analytics | What happened, or what is happening? | A dashboard showing late shipments, inventory levels or forecast-versus-actual results | Useful only if measures are defined consistently and updated in time to inform a decision. |
| Predictive analytics | What is likely to happen? | A forecast or alert for a likely demand change, delay or supplier risk | Predictions can be wrong, become less reliable as conditions change, or reflect gaps and bias in the underlying data. |
| Prescriptive analytics and optimization | What action could best meet a goal under constraints? | A replenishment, route or allocation recommendation balancing cost, capacity and service targets | Recommendations need valid constraints, clear objectives and appropriate human review; a model may optimize the wrong goal if these are poorly set. |
How can a company tell whether analytics is working?
Evaluate a tool against the operating decision it is meant to improve, not against the novelty of its model or the size of its dashboard. Set a baseline before a pilot, then compare outcomes under a defined process and time period. Relevant measures depend on the use case and can include:
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- Planning: forecast error and how quickly planners identify meaningful exceptions.
- Inventory and service: stockouts, excess or obsolete stock, and whether target service levels are met.
- Logistics: delay detection, on-time performance, route or transport cost, and the time required to respond to an exception.
- Resilience: how early a disruption is detected and how effectively recovery priorities are carried out.
- Adoption and control: whether users act on recommendations, how often they override them, and whether overrides reveal a model or process problem.
These measures involve trade-offs. Cutting inventory may lower carrying costs but weaken availability; minimizing transport cost may conflict with speed or resilience. Define the service, cost and risk objectives together rather than rewarding one metric in isolation. Gartner analyst Ken Chadwick described productivity as a key driver of future success and pointed to the value of intangible assets in unlocking it (Gartner press release, 20 February 2024).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why do analytics investments fail to deliver?
Technology is only one part of the operating change. PwC’s 2025 operations survey identifies integration complexity and data issues among common reasons technology investments fail to meet expectations. Supply chains often rely on information distributed across enterprise resource planning (ERP), warehouse, transportation and supplier systems; mismatched identifiers, definitions, update schedules or ownership can undermine a model before users see its recommendation.
- Fragmented or low-quality data: missing, delayed or inconsistent records weaken forecasts and risk signals.
- Weak integration: a useful insight that does not reach the planning or execution workflow may not change a decision.
- Unclear accountability: teams need owners for data definitions, model performance, access and decisions.
- Skills and adoption gaps: employees need to interpret outputs, challenge them and know when to escalate or override.
- Model risk: bias, changing conditions or model drift can make outputs unreliable if performance is not monitored.
- Security and privacy: wider data sharing increases the importance of access controls and appropriate handling of sensitive information.
- Pilot trap: a promising demonstration can fail to scale if it is not embedded in routine work, supported by process owners and maintained over time.
Gartner Senior Principal, Research Benjamin Jury cautioned that pressure for short-term return on AI investment should not create future constraints (Gartner press release, 11 June 2025). That is a practical warning against optimizing a narrow pilot in ways that lock the organization into poor data practices, inflexible systems or workflows that cannot be sustained.
How should a supply-chain team get started?
- Choose a decision with measurable business value. Start with a bounded problem such as forecast exceptions, replenishment or shipment delays, and define who will act on the result.
- Audit the supporting data. Check completeness, timeliness, ownership and definitions across the ERP, warehouse, transport and supplier systems involved.
- Set governance before deployment. Define security, privacy, user access, model monitoring and when a human can or should override a recommendation.
- Pilot an interpretable model or embedded workflow. Record baseline metrics and test the tool in the setting where the decision is actually made.
- Assess operational outcomes. Compare results with the baseline and look at trade-offs, user behavior and exceptions, not only model accuracy.
- Integrate and expand selectively. Move successful work into existing planning and execution applications, and expand only when users, data stewards and process owners can sustain it.
What does this mean for customers and household budgets?
Better forecasting, inventory decisions and disruption response could help businesses manage costs and keep goods available. But analytics does not guarantee lower prices, fewer stockouts or a direct saving for consumers: results depend on the supply chain, the decisions made from the analysis and how any cost or service gains are passed through. For a personal-finance reader, the practical point is that analytics is an operating capability—not a promise of cheaper shopping or a reliable way to predict an individual product’s price.
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