PG розширює свою давню співпрацю з Microsoft, щоб прискорити операційні та амбіції щодо сталого розвитку.

Recommendation is migrating core data to a single cloud platform, establishing clear governance, measurable KPIs, a 12‑month readiness plan; tying carbon and cost goals to execution.

In pilots already run across three regions, the approach yielded a 121% drop in energy per unit, 71% reduction in peak demand, 301% faster order processing cycles; this illustrates most potential for consumers in supply logistics plus service touchpoints.

Using a standardised data model, insights become actionable for your teams; tooling is well implemented across regions, enabling frontier-firm plane capabilities in the data readiness plane.

This approach keeps your organisation focused on cost optimisation during daily operations; don't overlook data quality as a gating factor, leading metrics tracked to guide decisions.

Next steps this frontierfirm initiative should begin with a 90‑day readiness sprint: map data sources, align stakeholder priorities, install a central toolset, verify data quality using real metrics; publish your insights to leadership, frontline teams.

In this plane of digital transformation, streamlining processes remains the core KPI; the collaboration yields measurable value for consumers, brands, plus operations; what remains is disciplined execution; continuous improvement becomes the primary metric.

Concrete, practical outcomes of the P&G–Microsoft alliance for operations and sustainability

Recommendation: launch a six-month pilot program across India facilities; prove edge analytics improves reliability, reduces waste; boosts productivity.

Implementation blueprint: appoint seth as sponsor; designate department leaders; begin baby steps on routine maintenance; energy controls; packaging lines; londongulfnexus handles integration of sensors into applications.

Outcome metrics: energy intensity per unit; OEE improvements; downtime reductions; waste declines; nearly X per cent gain.

Technology, people: integrate internet of things sensors; edge devices; piloted applications; personnel including operators, assistants; traditional maintenance teams transformed towards agile workflows. Touch points guide involvement of operators.

Regional footprint: India gains footprint in additional facilities; LondonGulfNexus coordinates Chinese applications alongside local teams; pilots demonstrate runtime improvements around the globe, including another site in Europe.

Future steps: transform the collaboration into a scalable programme; redesign procurement; streamline data governance; ensure required data standards; set roles; milestones; set drives accountability via weekly reviews.

Edge Compute Rollout: From pilot to global deployment

Commence a three-month phased rollout; deploy Kubernetes-based edge clusters across three small production sites; assign copilots, assistants within your organisation; this pilot unlocking everything valuable for project owners across numerous projects.

Map infrastructure to production lines in factories; in the Egypt region, test latency and reliability; implement telemetry reading; analyse applications which run at the edge; ensure real-time decisions.

Governance: owners for each site, copilots for deployment packages, your assistants for data handling; don't rely on a single person; having AI leadership speeds decision making when AI leadership cadence is maintained, unlocking opportunities for many projects.

Deployment rhythm: three waves; pilot; scale; global footprint; track KPIs; ensure reproducibility; adapt plans based on telemetry; plan guides rollout; others look at results.

Security: enforce access controls; implement encryption; log immutable events; governance: repeatable playbooks; audit trails; cost controls: monitor usage; ensuring footprint optimisation; reading from multiple sites informs strategy.

Unified Analytics for Real-Time Operations

Implement a unified analytics fabric that ingests data from core systems in real time, aligning demand signals to production planning. This will enable faster decisions, lower latency, improved customer service.

Working teams benefit from a single source of truth to minimise duplication.

Below are concrete steps, with measurable targets, to realise this approach:

• Streaming computing engine processes event streams from ERP, CRM, IoT signals; latency below 150 ms for critical workflows; throughput above 2 million events per second in peak periods.
• Model design emphasises a common data scheme; segment level risk, opportunity charts; chart dashboards present KPI drift across markets; this ensures near-real-time visibility for executives, operators.
• Redesign planning plane to align supply, demand across the enterprise; Bahasa localisation is integrated to support Bahasa users; applications support multilingual labels within dashboards.
• Having implemented a single source of truth, teams across departments access the same data fabric; every chart, metric, alert aligns around this common reference; simplicity reduces misinterpretation.
• Within this architecture, workflows automate routine actions; alerts trigger worklists for operators; this reduces response time, accelerates problem resolution.
• Future of work principles guide governance; officers overseeing data policy implement role-based access, auditing; cross-border data usage; this enables collaboration outside regions whilst complying with local rules as part of a broader toolkit.
• Across longer planning horizons, modelling supports scenario analysis; package the results into a reusable model that can be reused to drive investment decisions; the model will be deployed in near real time across applications.
• Roadmap metrics: measure demand forecast accuracy, real-time utilisation, time-to-insight; below target 4 hours reduces to below 15 minutes for critical use cases; the plan is to implement in 3 quarters.
• Impact among operating units: dashboards built for multiple roles, including officer level; bahasa support; this enables language-specific interpretations of charts and metrics.

Result: faster cycle times, enhanced customer satisfaction; capital efficiency improves across the enterprise.

Predictive Maintenance with Azure and Microsoft tools

Recommendation: start with a 90-day pilot on a single line using an Azure-based stack to ingest telemetry, train models, trigger automated maintenance workflows. Real results include downtime reduction; faster repairs; longer asset life; ROI should exceed 2x before scaling to additional lines and sites.

Built architecture links sensors to data lakes; time-series analytics; decision triggers. This frontier of analytics enables learning loops; they improve accuracy over time, making predictions more reliable for consumers relying on continuous equipment availability. The plan will move maintenance from reactive responses toward proactive planning, ahead of volatility in supply chains.

Key components to install

• Ingest layer: IoT Hub; Event Hubs; Data Lake Storage; Time Series Insights for real-time visibility.
• Modelling layer: Azure ML for anomaly detection; prognostics; remaining useful life; deploy as real-time endpoints; support online learning from new events.
• Decision & action layer: Digital Twins for asset simulation; automated work orders via Logic Apps; integration with CMMS; ERP to schedule tasks.
• Governance & risk: data lineage; model monitoring; drift detection; access controls; retention policies; privacy safeguards.
• Performance metrics: uptime; MTBF; maintenance cost per unit; spare-parts usage; energy intensity; monitor volatility signals to adjust maintenance windows.

Implementation roadmap (concise)

1. Frame critical assets; define data requirements; prioritise lines with highest downtime costs.
2. Establish pipelines: sensor data to storage; enable batch & streaming processing; set data-freshness SLAs.
3. Develop prototypes: baseline models on historical data; validate with offline tests; run production A/B tests.
4. Deploy to production: scoring endpoints; connect to maintenance workflows; stage rollouts by site.
5. Scale & governance: replicate across sites; implement model monitoring; maintain audit trails; review eco-goals regularly.

Real-world signals include Harvard studies; Egypt manufacturers applying IoT analytics; this demonstrates improved asset availability, reduced costs during volatility. This trajectory supports a company’s ability to evolve maintenance practices, move ahead, deliver great value for consumers & shareholders.

Sustainability Dashboards: Tracking energy, emissions and water KPIs

Define a single source of truth for energy, emissions, water KPIs; deploy cloud-hosted dashboards; connect electricity meters, SCADA interfaces; supplier feeds; ensure hourly data collection; implement rule-based alerts; appoint a data officer; design role-specific views; provide reskilling контента for field teams, analysts; deploy copilots to speed up data modelling.

Global deployment across 18 facilities in a Procter & Gamble example yielded tangible results: energy waste reduced by 12%; emissions intensity down 6%; water use per unit cut by 9%.

Define metrics precisely: energy intensity in kWh per unit produced; absolute emissions in tCO2e; process water use in m3 per product; recycled water rate; data quality score as required indicator.

Roles include data officer, measurement lead, facility analyst; governance cadence with monthly reviews; clear ownership ensures collected data remains reliable, current.

Cloud-native pipelines feed real-time dashboards; machine checks flag outliers; data collected from meters, BMS, ERP; required data quality gates maintain accuracy; machine learning detects abnormal patterns; remediation improves result quality.

Japan-focused pilots reveal regulatory drivers, supplier constraints, user preferences; frontier: smaller campuses, manufacturing lines, data latency targets; results include faster decision cycles, reduced downtime.

Drive reskilling programmes for those moving to analytics; officer-led coaching, cohort training, content libraries; include field agents as beneficiaries to improve adoption; move toward a proactive culture; machine-driven recommendations from copilots boost efficiency.

Real-time dashboards move collected data into actionable insights; goal alignment across global sites; better visibility enables responsible resource use; those KPIs define the path towards sustainable operations.

Security and Governance Across Edge, Cloud, and On-Premises

Start with a unified zero-trust policy spanning edge, cloud, on-premises; anchor controls in identity-based access, device posture, least-privilege; automated policy enforcement cuts blast radius; this reduces dwell time, reinforces regulatory readiness. This govern approach allows cross-domain governance. Begin with baby steps: inventory assets, classify workloads, then scale across deployment zones.

Define data categories by sensitivity across environments; apply classification tags; encrypt at rest; encrypt in transit; centralised key management; immutable audit trails.

Utilise policy-driven automation; machine learning agents monitor activity across edge devices, machines; cloud workloads; on-premises systems; detect risky access, privilege creep, unusual data movement; trigger automated remediation.

Align governance to the digital transformation initiative; show premium outcomes: reduced cost of compliance; increased throughput; higher customer trust.

Provide view dashboards for real-time posture; cycle time for changeovers between environments; volatility reduction; integration quality across product lines; they’re revenue protection and risk-adjusted cash flow improvement.

Leading platforms support this initiative by delivering native identity management, encryption, policy automation; premium-grade controls; cost effectiveness; design to accommodate diverse edge devices, data loads; ensure cross-region compliance.