Work that changed how leaders decide.

Situation

A global consumer goods company ran seven end-to-end processes, from idea to launch through order to cash, on separate systems with their own KPIs. Service, cost and inventory were reviewed in separate meetings with numbers that did not reconcile, and root causes surfaced weeks late, after expedited freight and write-offs were incurred.

Task

Our task was to give the company one control tower for all seven processes, built on shared KPIs and one trusted data model.

Action

• Defined one KPI language for plan, source, make and deliver: north-star outcomes, the driver KPIs behind them, owners and thresholds
• Built a certified data model on Microsoft Fabric covering demand, supply, inventory, production, logistics, quality and financial impact, so every team worked from one source
• Brought forecast accuracy, on-time-in-full delivery, inventory days and cost to serve into that model, replacing separate team sources whose numbers did not reconcile
• Delivered a control tower with exception triage across all seven processes, from idea to launch through record to report, so issues reached the right owner early
• Linked each exception to its financial impact, so expedited freight and write-off risk sat beside service and inventory instead of surfacing after the fact
• Set up the operating model: a business owner for every process, a weekly KPI quality review and closed-loop tracking of each issue to resolution

Result

• +8 pts forecast accuracy in pilot categories
• −12% inventory days with no loss of service
• +4 pts on-time-in-full delivery and 15% less premium freight

Situation

As demand for digital solutions across logistics, manufacturing and sales far outpaced IT capacity, a global consumer goods leader had critical processes running on spreadsheets, email approvals and one-off tools, each built differently. Leadership wanted to scale automation and adopt generative AI without creating ungoverned shadow tools.

Task

The brief was to industrialize low-code and AI automation, giving every new solution the same governed path from request to production.

Action

• Defined the Power Platform environment strategy, security roles, data-access model and data loss prevention policies, with clear separation of duties between environments
• Built reusable patterns for Power Apps, Power Automate cloud flows and approvals, so each new request started from a tested template rather than a blank page
• Integrated the patterns with core systems through Azure Functions, Logic Apps and Data Factory, using one approach to connectivity and data access
• Introduced Copilot Studio agents into day-to-day workflows, with multi-agent orchestration for work that spans several steps or teams
• Connected the agents to business data and tools through Model Context Protocol integrations, keeping them within existing security standards and access by role
• Put analytics projects under Git version control with Azure DevOps CI/CD pipelines, so every change was reviewed, tested and released the same way

Result

• 30+ digital solutions delivered per year
• Governed environments with clear separation of duties
• AI agents embedded in operational workflows, within existing security standards

Situation

As its regional business grew quickly, a global consumer goods company was working from fragmented sales reporting, and commercial teams reconciled channel figures by hand before every review. Online prices drifted from policy and the exceptions surfaced late, while new products took too long to clear registration and reach market.

Task

Within a digital transformation program, we were asked to build one trusted view of sales, pricing and market share, and to speed up product registration.

Action

• Designed and delivered a Power BI sales management system, with one model and shared definitions so every channel reports sales the same way
• Connected the channel sources to that model, so commercial teams opened each review with figures that already matched instead of reconciling them by hand
• Built price-control analytics comparing online marketplace prices against policy, with deviations shown by platform and product so exceptions surface early
• Delivered a category market-share dashboard with financial-efficiency analysis, so gains and losses in share could be weighed against what they cost
• Led a Lean Six Sigma project to map product registration end to end and measure where requests waited at each step
• Streamlined the registration steps the map exposed as slow, giving the business the end-to-end view of the process that no team had owned

Result

• One trusted view of sales performance across channels
• Price deviations visible by platform and product
• Significantly shorter product-registration lead times

Situation

At a consumer goods company, trade deductions, freight and handling costs sat in different systems. Sales knew revenue by customer, but nobody knew the margin left after the cost to serve. Deductions were cleared by hand weeks after invoicing, while expedited freight and small-order surcharges disappeared into overhead.

Task

We were asked to reveal the true cost to serve every customer, from order to cash, and to resolve deductions faster.

Action

• Built an order-to-cash control tower in Power BI that puts order fill, on-time delivery, invoice accuracy, open deductions and days sales outstanding on one page
• Modelled cost to serve by customer, channel and lane, allocating freight, handling, deductions and returns to each order instead of leaving them in overhead
• Made expedited freight and small-order surcharges visible by customer, exposing the service patterns that cost more than they earned
• Used document AI to read each deduction claim and its supporting documents and classify it by type
• Routed each dispute to its owner with a service level, and tracked open deductions on the control tower until they were cleared
• Gave sales and finance the same customer profitability view, refreshed daily, so both teams discussed each customer from one set of numbers

Result

• 100% of customers with a visible cost to serve
• −40% time to resolve deductions
• −6 days sales outstanding

Situation

A CPG company reviewed marketing spend in spreadsheets weeks after each month closed. Spend sat in the ERP by cost centre, plans in marketing’s files and sales in another system. Every review started by rebuilding the numbers, and over-spend surfaced after the budget was gone.

Task

Our job was to show the finance controller, brand by brand, where spend runs ahead of plan while sales fall behind.

Action

• Built one model of marketing spend, plan and sales by brand, activity and month, bringing together the ERP, marketing’s plan files and the sales system
• Aligned ERP cost-centre spend with marketing’s brands and activities, so actual spend and plan could be compared brand by brand
• Set the model to refresh daily from the ERP, so spend against plan was current every day rather than rebuilt weeks after close
• Designed a decision view for the finance controller and the business head, placing spend against plan next to sales against plan for every brand
• Added alerts that flag a brand when its spend runs ahead of plan while its sales trail, so the budget can be re-phased in time
• Moved the monthly operational reviews onto the same view, with month-end numbers locked so finance and marketing start every review from identical figures

Result

• Daily visibility of spend against plan, instead of weeks after close
• 2 brands re-phased within the quarter, before the budget was exhausted
• 1 review where finance and marketing debated actions, not numbers

Situation

A CPG company measured sales on shipments to distributors, and loading at every quarter end hid real demand. The sales team had no view of the 8,000 restaurants and cafés its distributors served, and leads, key accounts and distributor coverage lived in separate spreadsheets.

Task

The goal was to make distributor sell-out visible and reliable enough to base sales incentives on what distributors sell, not what they buy.

Action

• Brought distributor sell-out data, which arrived in different formats, into one standard structure feeding a single Power BI model of products, customers and outlets
• Matched distributor sell-out to the company’s product and customer codes, raising coverage from half of sales to most of it, distributor by distributor
• Built a distributor Pareto and key-account view showing who sells what and where across the restaurants and cafés served, and where penetration is low
• Tied new-lead and opportunity tracking to the CRM, so sales results are recognized automatically rather than tracked in separate spreadsheets
• Re-based sales incentives on sell-out instead of shipments to distributors, so the sales team no longer gained anything from loading stock at quarter end
• Gave sales, finance and distributors the same sell-out numbers, so incentives and distributor plans rest on one shared set of figures

Result

• 86% of sales now visible at the point of sale
• Quarter-end loading gone: shipments now track sell-out
• Top 3 distributors make 67% of sell-out and have dedicated plans

Situation

When a global consumer health company set out to design an end-to-end supply chain control tower, its data sat in disconnected systems and each market calculated KPIs its own way. Reviews were spent debating numbers, and service failures were explained after the fact.

Task

We were asked to design the control tower, its KPI framework and a roadmap to prevent service failures rather than explain them.

Action

• Designed the target BI architecture as a decision pipeline, from source data through a unified semantic layer to role-based cockpits and workflow actions
• Built a capability model across Plan, Source, Make and Deliver, connected by a cross-functional control tower so issues can be traced from end to end
• Defined a KPI framework with north-star outcomes, driver metrics, owners, thresholds and a review cadence, so every market calculates each KPI the same way
• Designed early warnings that alert planners to demand shifts, and a way to rank inventory risk by business impact, both driven by the framework’s thresholds
• Prioritized demand sensing, inventory health and on-time-in-full use cases, each framed to target stock-outs, overstock and service reliability together
• Sequenced the use cases into a three-wave roadmap, moving from foundation work to predictive capabilities and then to a global rollout

Result

• One KPI language for supply chain, with a named owner for every metric
• A sequenced roadmap from foundation to predictive, global rollout
• Use cases that target stock-outs, overstock and service reliability together

Situation

As its categories grew fast, a consumer goods business needed new products on shelf quickly, yet registering a new product code took 55 days on average, and anywhere from 11 to 155. Requests crossed six functions with no view of where they waited, and no one owned the end-to-end lead time.

Task

Our task was to cut new product set-up time and make it predictable, using Lean Six Sigma.

Action

• Mapped the registration process across all six functions and measured queue time and touch time at every step, using data from the workflow system itself
• Traced where requests waited longest and why incomplete ones bounced back, so the redesign went after the delays that mattered most
• Simplified the request form so requests would arrive complete instead of bouncing back, and cut the approval sequence to one step per function
• Introduced first-in, first-out rules, so requests moved through each function in order of arrival instead of being pushed ahead by phone calls
• Built a real-time Power BI monitor of every open request, showing which function holds it and how long it has been waiting there
• Set a service standard for every step, with a named owner per step and a weekly review of the end-to-end lead time

Result

• 55 → 18 days average lead time
• 144 → 21 days spread between the fastest and the slowest request
• 9 in 10 new products set up within 30 days

Situation

At a CPG company, unsold product returned by distributors was among the biggest losses in the value chain, and it showed up only when credit notes arrived. Stock, sell-out and return data lived separately with planners, finance and sales, so overstocked distributors went unseen until the product came back.

Task

We were brought in to reduce market returns by managing distributor stock and performance, catching overstock before the product came back.

Action

• Built one view of distributor stock in weeks, sell-out, expiry exposure and returns, by distributor and product, combining data held by planners, finance and sales
• Set a stock policy for each distributor segment, defining the maximum weeks of stock a distributor in that segment may hold at any time
• Added alerts when a distributor’s weeks of stock exceed the limit, so teams could act well before product neared its expiry date
• Re-evaluated distributor performance every month on sell-out and returns, not just on sell-in, so distributors were judged on what actually sold
• Gave every return a named owner and made returns a standing item in the monthly distributor review, alongside sell-out
• Brought finance and supply planning onto the same numbers before each order cycle, so new orders reflected real sell-out and the stock already in the market

Result

• 4.1% → 2.3% of sales returned from the market
• Under 6 weeks of stock at every major distributor
• Monthly distributor review on sell-out, with returns owned by name

Situation

A consumer goods company turned replenishment orders between plants and warehouses into truckloads by hand. Every day, planners spent hours balancing product mix, pallet positions and weight limits in spreadsheets. Loads left at 78% of capacity on average, and no one could see how full the network’s trucks were.

Task

The aim was to raise truck fill and free planners from building every load by hand, replacing each market’s own method with one standard.

Action

• Captured pallet, weight and volume limits for every lane in one place, replacing the spreadsheets each market had used to balance its loads
• Wrote load-building rules that optimize product mix against those limits, so every truck carries the best combination of product it can
• Built a load utilization cockpit showing fill rate by lane, plant and carrier, so logistics and planning could see the network’s true truck fill
• Flagged loads below standard every day, showing the lane, plant and carrier behind each one so the cause of each shortfall could be traced
• Automated the creation of optimized orders from replenishment needs, so planners review the exceptions instead of building every load by hand
• Set up a weekly review of lanes below target with logistics and planning, turning the daily flags into actions for each lane

Result

• 78% → 91% average truck fill
• −14% trucks needed for the same volume
• Hours → minutes to build the day’s loads

Situation

A CPG company had thousands of recipes on its brand websites with no description, and most of their images had no alt text, even though accessibility required it. Writing them by hand would have taken a content team more than a year, and new recipes arrived every month.

Task

We were asked to draft accurate, on-brand descriptions that would pass editorial review, add alt text to every image and keep pace with new recipes.

Action

• Built a generation pipeline that uses a large language model to read each recipe’s metadata, tags and images and draft a description from those facts alone
• Captured each brand’s voice in written guidance and example descriptions, so every draft matches the tone and vocabulary its editors expect
• Generated alt text for every image and checked each one against the recipe before publishing, so screen-reader users get an accurate description
• Set up editorial review inside the content system, where editors approve, edit or reject each draft before it goes live
• Fed every editor change back into the guidance and examples, so the drafts kept improving with each round of review
• Deployed the pipeline to production, where it picks up new recipes every month and drafts their descriptions and alt text without manual work

Result

• 3,200 recipe descriptions written in six weeks
• 100% of images with alt text
• 9 in 10 drafts approved by editors without changes

Situation

At a North American financial institution, management, finance and risk teams each reported from their own data extracts, so the same question produced different answers. Performance by business line, product and region was assembled in spreadsheets, adjustments were hard to trace, and every month-end consumed weeks of manual reconciliation.

Task

We were asked to give these teams one reconciled set of numbers, with traceable adjustments and access to sensitive data governed centrally rather than report by report.

Action

• Brought balances, income, expenses and credit data together in a governed Microsoft Fabric lakehouse, replacing the separate extracts each team had relied on
• Defined one certified set of measures for revenue, margin, efficiency and credit losses, and documented each definition so every team reads it the same way
• Published those measures in one Power BI semantic model, so every scorecard and analysis draws on the same certified numbers
• Automated reconciliation to the general ledger after every refresh, so any difference is flagged as soon as new data arrives
• Logged every adjustment on the platform, so each change to a reported figure can be traced from the report back to its source
• Built executive scorecards by business line, product and region, showing variance to plan and prior year
• Applied information security by business line in the model and managed it in one place, so each leader sees only what their role allows

Result

• One reconciled set of numbers for management, finance and risk
• A faster, more predictable month-end
• Every adjustment traceable, with access governed by role

Situation

A financial institution’s monthly management pack had grown to 40 pages and 127 visuals. It took 90 seconds to open, and finding the point took most of a meeting. Every KPI had its own card and colours, and comparisons to plan and prior year looked different on each page.

Task

We were asked to cut the pack down to what leaders read, with every KPI defined and shown the same way, so meetings could focus on the business.

Action

• Reviewed all 127 visuals against the questions leaders ask each month, then removed or merged every visual that did not answer one
• Introduced one notation for actual, plan and prior year on every page, with signed variances so a gap reads the same way everywhere
• Wrote a message into every title, so leaders read the conclusion first and the charts serve as evidence
• Replaced 107 KPI cards with a compact matrix and small multiples, so results can be compared side by side instead of card by card
• Rebuilt the data model behind the pack for speed, so leaders no longer wait for each page to load
• Built a design system with a palette, typography, templates and a report checklist, which every team adopted for its own reports
• Coached analysts to write the message first and then choose the chart, so new pages keep the same discipline

Result

• 40 → 6 pages and 90 → 4 seconds to open
• One definition and one look for every KPI
• Half as much meeting time spent establishing what the numbers are

Situation

An oil and gas producer kept drilling reports, production volumes and capital spending in separate systems and spreadsheets. Each asset team tracked wells, budgets and production its own way, engineers spent days compiling weekly reports, and cost overruns surfaced only after the fact.

Task

We were asked to give leadership one comparable view across assets and basins, in time to act on problems while wells were still being drilled.

Action

• Integrated drilling, production and capital-spend data from each asset team’s systems and spreadsheets on one governed data platform
• Aligned how asset teams record wells, budgets and production, so performance can be compared like for like across assets and basins
• Built Power BI well performance views that compare every new well with its expected production curve, so underperforming wells stand out early
• Delivered capital spend against approved budgets, well by well and refreshed daily, so cost and schedule variances appear while drilling is still under way
• Automated data validation, so missing or inconsistent records are caught before they reach a report
• Set up field data capture with low-code Power Apps, so field teams enter data once, at the source, instead of in spreadsheets
• Automated the weekly operations report, which engineers had spent days compiling from separate systems and spreadsheets

Result

• Weekly operations reporting produced automatically
• Cost and schedule variances visible while wells are being drilled
• One view of performance across assets and basins

Situation

An industrial manufacturer runs plants in three North American countries. Each plant measured equipment effectiveness its own way. Production, quality and maintenance data sat in separate systems at every site, and downtime reasons were captured on paper, if at all. Daily reviews debated the numbers instead of the causes.

Task

We were asked to give every plant the same performance measures and a reliable record of downtime, so improvement work could be compared across sites.

Action

• Agreed one definition of equipment effectiveness and its losses with all plants, so the measure means the same on every line in every country
• Turned the agreed losses into a common list of stop causes, so downtime is classified the same way at every site
• Connected production, quality and maintenance data from each site’s systems in Microsoft Fabric, giving all plants one shared data model
• Gave operators a simple app, built in Power Apps, to record each stop and its cause at the line, replacing paper records
• Delivered Power BI scorecards by plant and by line, so everyone from the line to the leadership team works from the same measures
• Set up automatic alerts for unplanned stops with Power Automate, so the right people are notified as soon as a stop is recorded
• Ranked stop causes by lost time at each plant, giving every site a fact-based list of improvement priorities that can be compared across plants

Result

• One performance language from the line to the leadership team
• Downtime captured at the source, by cause
• Improvement priorities set on facts, plant by plant

Situation

An industrial manufacturer’s annual budget took four months to build and was out of date by the second quarter. Plant and sales assumptions lived in spreadsheets that finance re-keyed by hand, and volume, price, mix, material and labour assumptions were not linked to each other.

Task

Our task was to replace the annual cycle with a rolling forecast that would show leaders why the numbers moved and let them test a scenario before deciding.

Action

• Built a driver-based model in Microsoft Fabric covering volume, price and mix by product line, plus material and labour rates by plant
• Loaded actuals from the ERP automatically each month, so every forecast starts from the latest results without manual entry
• Set up a rolling 12-month forecast refreshed every month, so the outlook always covers the next 12 months
• Saved a locked snapshot of every forecast version, so finance can audit any past forecast and see exactly what changed between versions
• Built variance bridges from budget to forecast by driver, plant and product line, showing leaders why the forecast moved
• Moved scenario inputs into a Power Apps form that feeds the model directly, instead of spreadsheets re-keyed by hand
• Routed every scenario for approval in Microsoft Teams, so leaders see its effect on the forecast before they decide

Result

• 4 months → 10 days forecast cycle
• Half the forecast error at a three-month horizon
• −60% finance time spent preparing data

Situation

A healthcare provider network kept revenue, collections and receivables data in separate exports and spreadsheets, and every department maintained its own version of the numbers. Leaders could not see where cash was stuck, and sensitive financial data had to stay restricted to the right people.

Task

We were asked to build one governed view of financial performance that showed leaders where to act, with sensitive data open only to those who need it.

Action

• Built governed Power BI semantic models for revenue, collections and receivables on the enterprise lakehouse, replacing separate exports and spreadsheets
• Codified business rules into the model, so every department works from the same definitions instead of its own version of the numbers
• Documented every rule in plain language and gave each one a business owner, so changes are decided by the people who use the numbers
• Designed views from the executive scorecard down to individual transactions, showing where cash is stuck and which issues each team can act on
• Reconciled the model’s detail line by line against the source systems, so teams can trust the figures at every level
• Embedded information security in the model, with access granted person by person and coverage reports that show who can see what
• Automated refresh orchestration, timed to the upstream warehouse loads, so reports update only after the source data has fully arrived

Result

• One view of financial performance, from executive scorecard to transaction
• Zero line-level variance when detail was reconciled to source
• Access governed user by user, with audit-ready coverage reporting

Situation

A healthcare organization’s leadership wanted every clinician to see their own results, and only their own. Detailed reports were assembled by hand, names were spelled differently across systems, and late adjustments could quietly change numbers that had already been reported.

Task

Our task was to automate this reporting, keep finance in control of exports and make month-end figures stop moving once the period was closed.

Action

• Resolved identities across systems with stable identifiers instead of names, so every result is attributed to the right clinician
• Linked each clinician’s sign-in to their resolved identity, so the right results appear for the right person every time
• Built a personal Power BI scorecard with information security in the model, so each clinician sees their own results and nothing else
• Gave each clinician self-service drill-down into their own performance detail, replacing the detailed reports assembled by hand
• Created a separate finance export, restricted to approved users and gated by a monthly lock, keeping detailed exports under finance control
• Introduced locked month-end snapshots, so late adjustments can no longer quietly change numbers that have already been reported
• Automated the snapshot refresh after each close, so the locked figures are ready without manual work

Result

• Self-service access to individual performance detail
• Locked, auditable month-end numbers, with no silent restatements
• Exports controlled by role, in line with finance policy

Situation

A multi-site healthcare organization kept headcount in its HR system, hours in time-keeping, pay in payroll and openings in recruiting. Leaders received a quarterly deck three weeks after the quarter closed, attrition became visible only after people had left, and compensation reviews compared roles by hand.

Task

We were asked to give leaders a timely view of their own teams, so they could act on attrition early, while keeping sensitive data protected outside HR.

Action

• Built one workforce model in Power BI that joins HR, time-keeping, payroll and recruiting data and refreshes every week
• Organized headcount, hires, exits, vacancies, hours and compensation by site, role and tenure, so every leader reads the same numbers
• Analyzed attrition by tenure, role and manager, showing which roles and teams lose people and how early in their tenure
• Added early-warning indicators that flag teams at risk of attrition while there is still time to act, not after people leave
• Positioned each role’s compensation against its pay band, replacing the manual comparisons used in compensation reviews
• Applied information security so each leader sees only their own teams, which allowed sensitive data to be shared beyond HR
• Planned the workforce against budget month by month, with scenarios that let leaders test openings and overtime before committing to them

Result

• −5 pts first-year attrition
• −18 days time to fill
• Weekly view with role-based access replaces the quarterly deck

Situation

A healthcare provider planned marketing spend, inquiries and clinical capacity in separate spreadsheets. Leaders saw results only after the month closed, with no forward view of how today’s inquiries would become completed treatments, and budgets were set monthly while operations ran weekly.

Task

Our task was to forecast patient demand weekly, from first contact to completed treatment, and tie each marketing channel to its outcomes.

Action

• Unified CRM, scheduling and operational data in the lakehouse, so every inquiry can be traced through to its outcome
• Built cohort-based conversion curves that show how each group of inquiries moves from first contact to completed treatment, and how long each step takes
• Applied those curves to current inquiries to project completed treatments weeks ahead, by market and channel
• Delivered weekly forward-visibility reporting against budget in Power BI, so leaders see progress every week instead of after the month closes
• Converted monthly budgets into weekly targets using working-day calendars, so pacing matches the weekly rhythm operations already run on
• Linked marketing channels to completed treatments, so each channel is judged by the outcomes it produces rather than by inquiries alone

Result

• A forward view of demand, weeks ahead
• Budget pacing by market and channel in one place
• Marketing channel performance tied to outcomes

Situation

A healthcare provider spent on marketing across channels and markets to fill its clinics. Marketing reported clicks and leads, finance reported revenue, and the two were reconciled once a quarter, in a spreadsheet. With six weeks between first contact and treatment, no one could say which channels drove completed visits.

Task

We were asked to tie spend to leads, completed visits and revenue by channel, and to give leaders a view they could act on every week.

Action

• Brought channel spend, CRM leads, consultations, scheduled and completed visits and finance revenue into one governed Power BI semantic model, refreshed daily
• Defined certified measures for cost per lead, cost per completed visit and return by channel, so marketing, operations and finance worked from the same figures
• Built the six-week lag between first contact and treatment into those measures, so each channel was credited with the visits and revenue it actually produced
• Created pacing views of spend and leads against the monthly budget and target, by channel and market, so gaps showed up while the month was still open
• Introduced a weekly briefing for marketing and operations leaders that opens with exceptions: channels off pace, rising cost per completed visit or markets behind target
• Retired the quarterly spreadsheet reconciliation, with marketing, operations and finance reading return by channel from the same model

Result

• Weekly return by channel instead of quarterly
• −22% cost per completed visit after reallocating spend
• ±3% budget pacing every month

Situation

A global medical technology manufacturer rated its suppliers on a narrow set of indicators. Quality and sourcing teams worked from static reports, and quality signals reached sourcing too late to prevent defects. Drawing-release notifications, meanwhile, reached suppliers through manual channels that introduced errors of their own.

Task

Our task was to broaden the supplier rating, get quality signals to sourcing in time to act, and take the manual errors out of drawing-release notifications.

Action

• Built control-chart trending dashboards so quality teams could tell real shifts in supplier quality from normal variation and act on them early
• Delivered supply-management and supplier-quality dashboards that replaced static reports, giving sourcing and quality teams the same current view of each supplier
• Reviewed the supplier performance rating system with quality and sourcing teams to find the gaps left by its narrow set of indicators
• Identified and implemented new KPIs in the rating system, each with an agreed definition and data source, for a broader, data-driven view of every supplier
• Mapped the drawing-release process with Lean Six Sigma methods to locate where manual notification steps introduced errors
• Moved drawing-release notifications into the supplier change-notification system, so every supplier received changes through one controlled channel instead of manual messages

Result

• A broader, data-driven supplier rating system
• Fewer defects in the drawing-release process
• Timely supplier-quality visibility for sourcing and quality teams

Situation

At a consumer goods company, each launch needed artwork, regulatory and legal approvals and retailer listings. Those approvals moved by email between marketing, regulatory, legal and sales, and as launch dates approached, nobody could see where a SKU was stuck. Launches missed their dates.

Task

Our task was to move launch approvals out of email into one queue where every step, owner and due date was visible.

Action

• Built one Power Apps request form for every launch, with required fields and attachments, so each request arrived complete with its artwork and listing details
• Mapped every approval step across marketing, regulatory, legal and sales, and agreed an owner and a service level for each one with the teams
• Set up a live queue showing every SKU in flight, with its current step, owner, due date and ageing, so no request waited unseen in an inbox
• Linked each request to its launch date, so the queue showed which launches were at risk as soon as a step ran late
• Added automatic reminders through Power Automate flows, and escalation whenever a step passed its service level, so stalled approvals surfaced without anyone chasing email
• Delivered launch status reporting for the monthly portfolio review, with approval cycle time by step and owner and the cause behind each late launch

Result

• Approval cycle time visible by step and owner for the first time
• Late launches traced to their cause instead of debated
• A single list that marketing, regulatory and sales work from

Situation

A medical technology company was launching products across several countries, each launch gated by regulatory submissions, labelling and device identification. Its launch plans showed dates, not the steps behind them, so slips surfaced late, in different countries at different times, and turned into last-minute market exceptions.

Task

We were asked to make the regulatory and labelling critical path of every launch visible, so slips surfaced early, with a named owner.

Action

• Built a readiness model in Power BI linking every submission, labelling and device identification task to the product and market launch it gates
• Mapped the dependencies between those tasks to derive the critical path of each launch, making explicit which steps set the date
• Delivered a calendar view of the critical path by product and market, showing the weeks at risk and the step driving each one
• Set up weekly exception lists for steps at risk, each routed to the responsible regulatory or supply owner with the step and launch named
• Brought regulatory, supply and commercial teams onto the same readiness model, replacing launch plans that showed only dates
• Produced portfolio reporting for the launch council, showing readiness across all launches and the decisions needed where markets were at risk

Result

• Slips visible weeks earlier, with the step and owner named
• One launch plan shared by regulatory, supply and commercial teams
• Fewer last-minute market exceptions

Situation

A consumer goods company reviewed each new product in launch meetings, but every review used a different definition of success. Marketing and sales arrived with different numbers, so decisions to support or withdraw a product came late, around week twenty, and were argued.

Task

We were asked to define one way to read the first 13 weeks of every launch, so the business could decide early and on the same evidence.

Action

• Agreed one definition of launch success with marketing and sales, built on sell-in, sell-out, distribution and repeat purchase
• Built a Power BI launch scorecard showing those measures by week since launch, so every product was read on the same clock
• Set benchmarks from prior launches in the same category, so each new product was measured against how comparable launches actually performed
• Turned those benchmarks into a corridor for each week since launch, showing the range where an on-track launch should sit
• Added alerts when a launch fell below its benchmark corridor, so marketing and sales saw a weak start while there was still time to act
• Delivered a single review page for the monthly portfolio meeting, with every launch on the same measures and the decision to support or withdraw in view

Result

• Support or withdraw decisions taken at week six, not week twenty
• Launch plans calibrated on real prior performance
• Marketing and sales reviewing the same page

Situation

A consumer goods company planned demand with a statistical model that could not see promotions, and most of its forecast error came from them. Error spiked around promotions and seasonal peaks, and every week the planning team corrected the forecast by hand, line by line.

Task

We were asked to build promotion-aware forecasts and to let planners focus on exceptions instead of correcting every line by hand.

Action

• Brought demand history, the promotion calendar and sell-out data together by SKU and channel as the training base for the models
• Mapped each promotion in the calendar to the SKUs, channels and weeks it covered, so the models could learn its effect on demand
• Trained machine learning demand models by SKU and channel in Azure Machine Learning, so each forecast reflected planned promotions as well as seasonality
• Tracked accuracy and bias every month by forecast horizon and by category, showing where the models were improving and where they still missed
• Built exception lists for the SKUs where the model and the planner disagreed, so planners reviewed those lines first instead of every line in the plan
• Set up a consensus workflow that records each override and its reason, so the value of every override could be measured against actual demand

Result

• Forecast error reduced most on promoted SKUs, where the money is
• Planners spend their week on exceptions rather than on every line
• Overrides measured, so the process improves

Situation

A manufacturer ran a monthly sales and operations planning (S&OP) meeting that worked from five decks. Each function arrived with its own deck and its own numbers, so the meeting spent its time reconciling figures rather than deciding what to do about the gaps.

Task

We were asked to replace the five decks with one page that puts demand, supply, inventory and the financial view together, with the decisions needed.

Action

• Built a single S&OP model in Power BI combining the demand plan, supply plan, inventory projection and financial outlook, so every function read the same numbers
• Agreed one definition for each figure with every function, replacing the separate numbers each one had brought to the meeting
• Designed one page per product family showing demand against supply, projected inventory and the financial view, with the gaps and the decisions required
• Added scenario toggles for capacity, lead time and promotion changes, so the meeting could test an option and see its effect on inventory and the financial outlook
• Restructured the monthly meeting to run from those pages, one product family at a time, starting with the gaps to decide
• Kept a decision log each month with the numbers behind every decision, so later meetings could compare what was decided with what happened

Result

• Meetings that decide instead of reconcile
• Inventory projections and the financial view read together
• A record of decisions and their outcomes

Situation

A consumer health company made capacity decisions plant by plant across its manufacturing network. Investment requests arrived separately from each plant, with no shared view of where, or when, demand would exceed capacity across the network. Operations and finance had no common outlook to weigh them against.

Task

We were asked to build a network-wide view of demand against capacity over a two-year horizon, showing where constraints would bind and when.

Action

• Brought demand, plant and line data into a Microsoft Fabric lakehouse as the single source for the outlook, shared by operations and finance
• Built a demand outlook by product family and plant over 24 months, so every plant planned against the same view of demand
• Modelled capacity by line with planned maintenance and changeover assumptions, so available capacity reflected real running conditions on each line
• Projected utilization by line and month, showing where and in which months constraints bind across the network
• Compared scenarios for shift patterns, volume transfers between plants and new investments, so each option was weighed across the whole network rather than plant by plant
• Delivered Power BI views of the outlook for operations and finance, so investment requests were reviewed and sequenced against one shared picture

Result

• Investments sequenced across the whole network
• Constraints visible a year ahead
• One outlook shared by operations and finance

Situation

For a retailer, two months of the year made a third of sales. The seasonal buy was planned as one national number, so some regions sold out early while others carried stock into clearance: the plan was either short in November or heavy in January.

Task

We were asked to plan the seasonal buy by region from weekly point-of-sale data, so stock matched demand in each region through the peak.

Action

• Brought point-of-sale and store stock data into one Power BI model, refreshed weekly, as the base for planners and buyers
• Tracked weekly sell-through and stock cover by store cluster and category, so planners saw early in the peak where stock was running short or heavy
• Built a seasonal profile by region from three years of point-of-sale data, showing how demand in each region builds, peaks and falls through the season
• Used the profiles to split the seasonal buy by region, replacing the single national number with regional buys
• Set replenishment and markdown triggers by week, so fast-selling clusters were restocked before they ran out and slow ones were marked down in time
• Introduced a season review comparing plan, sell-through and clearance, so each season’s results fed the next plan

Result

• Fewer stockouts in the peak weeks and less clearance after them
• Regional buys instead of one national number
• A season review that improves the next plan

Situation

A manufacturer could not say how much of its spend sat outside contracts. Spend data lived in several ERPs with inconsistent supplier names and categories, so contract coverage was a guess, maverick buying went unseen, and rebates earned against contract tiers were only known at year end.

Task

We were asked to show purchasing what sat under contract, by category, supplier and plant, and which rebates were within reach.

Action

• Harmonized supplier names and categories across the ERPs, mapping each supplier record to one master supplier and one category, so each supplier counted once whatever the source
• Built a Power BI spend cube by category, supplier and plant, with contract coverage and payment terms for every supplier
• Flagged purchases made outside contract, so maverick buying showed up by category, plant and supplier for follow-up in the monthly category reviews
• Tracked rebates against contract tiers, showing what had been earned and how close each supplier was to the next tier, before year end
• Measured spend under contract as a share of total spend, so progress could be tracked quarter by quarter
• Set up monthly category reviews with the category managers, built around the top opportunities to bring spend under contract and capture rebates

Result

• Spend under contract measured and rising quarter by quarter
• Rebates earned against tiers made visible before year end
• Category managers working from one list of opportunities

Situation

Every month, accounts payable at a consumer goods company keyed thousands of supplier invoices into the ERP by hand. The invoices came in several layouts and languages, errors surfaced only at payment, and suppliers called to ask where their invoices stood.

Task

We were asked to take manual keying out of the process: read invoices automatically, match them to orders and receipts, and leave people only the exceptions.

Action

• Deployed document intelligence models that read header and line-item data from supplier invoices in several layouts and languages, with a confidence score for every field
• Automated the three-way match of each invoice against its purchase order and goods receipt, with validation rules for price, quantity and duplicate invoices
• Built Power Automate flows that pass clean invoices straight through and send only low-confidence reads and failed matches to a person
• Set up one exception queue that shows the reason, owner and age of every exception, so none sits without someone responsible for clearing it
• Automated posting to the ERP with a full audit trail linking each entry to the original invoice and to every check it passed
• Gave accounts payable a status view of every invoice, from receipt to posting, searchable by supplier

Result

• Standard invoices processed without a touch, from capture to posting
• Every exception handled in one queue, with an owner and a reason
• Supplier status questions answered from the system

Situation

At a medical technology company with several plants, every supplier review started from recollection and from spreadsheets kept by each site. Two plants could rate the same supplier differently, so performance discussions and negotiations on terms rested on anecdote rather than evidence.

Task

Our task was to give purchasing and quality one scorecard that rates every supplier on the same evidence, in every plant, and ties weak scores to corrective action.

Action

• Built a Power BI semantic model that joins purchase orders, receipts, incoming inspections, supplier audits and payment terms for every supplier in every plant
• Defined certified measures for on-time-in-full, incoming quality, audit findings and terms, so every plant reads them the same way
• Agreed the weighting of each measure with purchasing and quality, and documented it so every score can be explained
• Combined the measures into one weighted score per supplier, with trends by quarter and the detail behind every point lost
• Generated quarterly business review pages straight from the model, so each review opens with the same evidence instead of a newly assembled spreadsheet
• Linked corrective action tracking to the score: each action gets an owner, a due date and a status, and stays visible until it is closed

Result

• Supplier reviews based on the same evidence in every plant
• Corrective actions tracked through to closure
• Terms and performance negotiated from one page

Situation

When we started, each plant at a manufacturer calculated overall equipment effectiveness with its own formula and its own loss categories. The results could not be compared, so cross-plant reviews became debates about the figures rather than about the losses behind them.

Task

We were asked to give every plant one way to measure OEE and classify losses, so plants could be compared fairly and improvement aimed where it mattered most.

Action

• Agreed one OEE definition with every plant, built from availability, performance and quality, with common rules for planned downtime and ideal cycle times
• Mapped every plant’s loss codes to one shared taxonomy, so a stop, a slowdown or a reject is classified the same way on every line
• Brought line-level data from machine systems and production orders into one Microsoft Fabric lakehouse, so every plant feeds the same model
• Published a semantic model with certified OEE measures, so the definition lives in one place and no plant recalculates it locally
• Delivered a plant scorecard that breaks OEE into availability, performance and quality losses, with drill-down from plant to line to loss category
• Set up weekly loss reviews built around the top causes by line, with an owner and a next step agreed for each

Result

• Plants compared on the same basis for the first time
• Improvement efforts aimed at the largest comparable losses
• A scorecard plant managers trust, built on one definition

Situation

At the start of the engagement, changeovers at one of a manufacturer’s plants took 20% longer than standard and varied twice as much as at its other sites. Times differed widely between crews and product pairs, and the production schedule absorbed the variation as lost capacity.

Task

Our task was to find where the variation came from and give the plant a repeatable way to bring changeovers under control.

Action

• Measured every changeover by line, crew and product pair, using start and end signals from machine data matched to production orders
• Built control charts by crew and product pair to separate routine variation from special causes that need a specific fix
• Added distribution views that show the spread of each changeover type, not just its average, so outliers no longer hide in the mean
• Set standard work targets for each product pair from the plant’s own data, so every crew works to the same expectation
• Configured alerts that flag a changeover as soon as it falls out of control, so supervisors can act during the shift, not after it
• Introduced a monthly review that ranks product pairs by recoverable capacity and decides which changeovers to standardize next

Result

• Variability traced to specific crews and product pairs
• Capacity recovered without new equipment
• A method the plant now applies to the next loss

Situation

Until this engagement, maintenance on a consumer goods company’s filling lines was reactive: technicians fixed what broke. The same assets stopped repeatedly, taking shifts of output with them, while planned maintenance followed the calendar rather than the condition of the equipment.

Task

We were asked to predict which assets would fail next, early enough for maintenance to plan the work around production.

Action

• Combined sensor readings, alarms and maintenance work orders into one history per asset, so each past failure lines up with the signals that preceded it
• Trained failure-risk models in Azure Machine Learning that estimate, for each asset, the likelihood of failure in the next two weeks
• Attached a plain-language explanation to every flagged asset, showing which signals drove the risk, so technicians can check the reasoning before acting
• Set up a weekly maintenance queue that ranks flagged assets by failure risk and by the production impact of a stop on that line
• Shifted planned maintenance from calendar intervals to equipment condition, with the ranked list feeding each weekly plan
• Tracked avoided stops against the plan, so the models are judged on stops prevented, not on accuracy scores alone

Result

• Maintenance planned on equipment condition, not the calendar
• Fewer unplanned stops on the lines that matter most
• Technicians starting each week from a ranked list

Situation

By the time we were brought in, retailer deductions at a consumer goods company were arriving faster than finance could research them. Most were accepted once they had aged out, with no record of why each one was taken or who should act on it.

Task

We were asked to turn deductions from a write-off into a managed process: find the cause of each, recover what could be recovered and stop the causes from recurring.

Action

• Agreed a short list of deduction causes with finance, sales and logistics, and coded every deduction by cause, customer and owner
• Brought deductions, invoices and retailer remittance data into a Power BI semantic model, so each deduction can be traced to the original invoice
• Built a Pareto of causes with the dollars behind each, so effort goes first to the few causes that carry most of the value
• Routed each deduction to sales, logistics or pricing through Power Automate flows, with a service level for researching and resolving it
• Gave each owner a work list ordered by value and due date, so disputes are raised while proof of delivery and pricing records are still at hand
• Tracked recovery and prevention every month, by cause and customer, so the effect of each fix at the source is visible

Result

• The few causes behind most of the deductions fixed at the source
• Recoveries pursued while the evidence is fresh
• Sales, logistics and finance working from the same list

Situation

At a consumer goods company, rebate agreements with customers were applied inconsistently. Accruals were booked in one system and settlements in another, by different teams. Accruals were corrected at year end, and disputes with customers over what was owed lasted months.

Task

Our task was to manage every rebate by the terms of its agreement, from accrual through settlement, and to show where money was leaking and why.

Action

• Captured each customer’s contract terms, with tiers, periods and eligible volumes, in a Power Apps form, so every agreement sits in one structured place
• Brought sales, invoices and settlement records into a Microsoft Fabric lakehouse, matched to the agreement that governs each customer and product
• Calculated accruals from sales as they happen, applying the tier and period each agreement specifies, rather than adjusting them at year end
• Reconciled every settlement against the accrual and the contract, with the difference explained by customer: wrong tier, ineligible volume or wrong period
• Ranked leakage by customer and cause in a Power BI report, so finance and sales see first where the most money is lost
• Set a review cadence with sales to act on the largest leaks and to work customer disputes from the same model

Result

• Year-end surprises replaced by monthly visibility
• Disputes resolved with the agreement and the data side by side
• Leakage visible by customer, and acted on

Situation

Before the engagement, the revenue cycle team at a healthcare network handled claim denials in order of arrival. Small claims consumed the team’s time while appeal deadlines passed on claims that could still have been recovered, and each missed deadline meant money already earned was lost.

Task

We were asked to make sure recoverable claims were appealed before their deadlines, and to stop preventable denials at the source.

Action

• Brought denial, claim and payment data into a Power BI semantic model and grouped every denial by payer, reason code and dollar value
• Built a work queue that ranks denials by value and appeal deadline, so claims with the most to recover and the least time left come first
• Gave every denial an owner and a status, so each one is accounted for and managers can see at a glance what is stuck
• Flagged claims nearing their appeal deadline, so no recoverable claim lapses without someone deciding whether to appeal
• Reported root causes to the registration, coding and clinical teams, so each team sees the denials its own work can prevent
• Tracked recovery and prevention by payer every week, so leaders see both the money recovered and whether fixes upstream are working

Result

• Appeals filed before deadlines on the claims that matter
• Denial causes fed back to the teams that can prevent them
• Recovery visible by payer every week

Situation

As days sales outstanding drifted up across its regions, a distributor-led consumer business found its collections effort aimed at size rather than risk. Teams called the largest balances first, while an open dispute could hold up payment of an entire account. Each region set its own priorities.

Task

Our task was to bring cash in sooner: work the riskiest accounts first, keep disputes from blocking payment, and protect important distributor relationships.

Action

• Combined receivables ageing and dispute status by account and region in one Power BI semantic model, the shared base for every collections team
• Built a collections priority score that weighs risk, ageing and value together, so the accounts most likely to pay late rise to the top
• Turned the score into a ranked call list for each region, moving reliable payers with large balances further down the queue
• Linked dispute resolution to the account, so collectors could see which disputes were holding up payment and drive each one to closure
• Delivered a weekly DSO and cash forecast by region, giving finance and regional leaders an early view of the cash still to come in
• Set up access by role, so each regional team sees its own accounts while working to the same scoring rules as every other region

Result

• Cash collected earlier from the accounts most likely to slip
• Disputes cleared before they block payment of the whole account
• Regional teams working the same priorities, under the same rules

Situation

A consumer goods company was serving its smallest accounts on the same delivery terms as its largest. Over the years, delivery frequency and order minimums had been set by history rather than by what each customer cost to serve. Sales and logistics had no shared basis for changing either.

Task

We were asked to measure what each customer actually costs to serve and redesign delivery frequency, minimums and channel around it, tier by tier.

Action

• Measured cost to serve customer by customer, covering picking, delivery, returns and service time, in a Power BI semantic model built on order and logistics data
• Weighed each customer’s cost to serve against what the customer is worth, making the cost of serving the long tail visible
• Defined customer tiers, each with its own service rules for delivery frequency and order minimums, in place of terms inherited from history
• Modelled scenarios for changes in frequency and channel, showing for each tier where a change paid for itself before anyone committed to it
• Walked sales and logistics through the tiers and scenarios together, so both teams could agree on the service each tier receives
• Set a quarterly review of tiers with sales, so customers move between tiers as their orders and cost to serve change

Result

• Service rules matched to what each tier is worth
• Delivery cost per unit down on the long tail
• Sales and logistics agreeing on service by tier, reviewed every quarter

Situation

A medical technology company was dispatching field service calls from spreadsheets, and its technicians received work orders by phone and email. Every day, dispatchers assigned calls without seeing parts availability or travel time, and service levels set out in customer contracts were missed.

Task

Our brief was to put work orders, parts and travel on one dispatch screen and give technicians one place to receive and record their work.

Action

• Configured Dynamics 365 Field Service for work orders, service contracts and parts, so every call is logged against its account and contract
• Brought parts availability into the dispatch view, so each call is matched with the parts it needs before a technician sets out
• Set up a schedule board showing travel time, technician skills and the contracted service level, so dispatchers assign each call with the full picture
• Delivered a mobile app for technicians with offline capture, so work orders, notes and parts used are recorded on site, even without a signal
• Replaced phone and email hand-offs: work orders now reach technicians in the app, and completed work flows straight back to dispatch
• Built Power BI reporting on service levels and first-time fix, by account and contract, from the same Field Service data

Result

• First-time fix up as parts and skills are matched at dispatch
• Service levels tracked per account and contract
• Field data captured once, on the phone

Situation

During outages, a utility’s operations, customer service and communications teams each worked from different numbers across its regions. The restoration report was only put together once an event was over, and crew utilization was reviewed weeks later, long after crews could have been moved.

Task

Our task was to give every team working an outage one live picture of the event by region, and a reliable record to review it afterwards.

Action

• Combined outage, crew and restoration data by region into one Power BI semantic model, refreshed in near real time while an event is under way
• Agreed on one set of definitions for outages and restoration times with operations, customer service and communications, so every team quotes the same figures
• Measured restoration time against standards by region, so incident command could see early where restoration was falling behind
• Tracked crew utilization by shift as the event unfolded, rather than weeks later, showing where crews could be redeployed
• Built a briefing page for incident command and communications that sets out outages, crews and restoration by region at a glance
• Reconstructed the event timeline from the data for the post-event review, so the discussion starts from the recorded sequence of events

Result

• One picture of the event for operations and communications
• Crews redeployed while the event is still running
• Post-event reviews built from the recorded timeline

Situation

A consumer goods company reconciled trade spend accruals to customer settlements only once a year. Through the year, monthly margin rested on unreconciled accruals, and the year-end adjustment was large enough to change the margin already reported. Sales and finance each kept their own view of trade spend.

Task

We were asked to reconcile trade accruals to settlements every month and take the surprise out of year end.

Action

• Captured every trade event and customer agreement with its accrual rules, so each accrual can be traced to the event that created it
• Brought agreements, accruals and settlements together in a Microsoft Fabric lakehouse, matched by customer and event, as the single source for the reconciliation
• Reconciled accruals to settlements by customer and event every month, flagging the gaps that need an explanation before the books close
• Gave sales finance ownership of variance explanations, recorded next to each gap so the reasons are kept for year end
• Made the trade reconciliation a gated step in the close calendar: the month does not close until the reconciliation is complete and explained
• Published one view of trade spend by customer and event in Power BI, used by sales and finance alike

Result

• Margin reported monthly on the same basis as year end
• Year-end adjustments reduced to small, explained items
• Sales and finance agreeing on one trade number

Situation

Each month, a manufacturer reported standard cost variances as a single total for each of its plants. The month-end pack showed how much, not why: causes were investigated by hand after it was published, and the same variances kept coming back.

Task

Our task was to explain each variance down to its driver before the pack went out, and to put that explanation in the plant controllers’ hands.

Action

• Brought standard costs, actual costs and production volumes into one Power BI semantic model, with a single agreed calculation for each type of variance
• Decomposed every variance into price, usage, volume and mix by plant and product family, so each amount in the pack comes with its cause
• Tracked driver trends by plant across months, with alerts that flag an unusual month to the plant controller before the pack is published
• Captured each plant controller’s commentary through a form in the report, stored next to the variance it explains, so the reasoning travels with the numbers
• Redesigned the monthly pack to open on the three largest drivers, with detail by plant and product family a click away
• Flagged variances that recur from one month to the next, so plants can see which causes are still open and follow them through

Result

• Causes explained in the pack on the day it is published
• Plant controllers explaining their own numbers
• Fewer recurring variances as the causes are resolved

Situation

At a financial institution, figures already presented to the board kept changing between meetings. Restatements and late postings silently rewrote historical numbers, and directors asked why last quarter’s figures had moved. Finance had no simple way to show where a published number came from.

Task

We were asked to make every board figure reproducible: locked once published, traceable to the ledger, and changed only through a visible, controlled restatement.

Action

• Locked month-end snapshots in the semantic model, stored in a Microsoft Fabric lakehouse, so late postings no longer rewrite figures already published
• Compared the live ledger with the locked snapshots after each close, so a late posting to a closed month is surfaced and reviewed, not absorbed silently
• Set up a controlled restatement process: a change to a closed month is approved, recorded with its reason and shown as a restatement
• Traced every published figure back to the ledger, so any number in the board pack can be followed to the entries behind it
• Generated the board pack directly from the locked snapshots, in both languages, so every version shows the same figures
• Built an audit view of who changed what, and when, across snapshots and restatements, open to finance and auditors alike

Result

• Numbers quoted to the board stay the same a year later
• Restatements visible and explained rather than silent
• Audit questions answered from the system in minutes

Situation

A consumer goods company was settling its distributors’ claims for promotions and incentives from spreadsheets, each one checked manually. Validation took weeks and distributors chased the company for payment, while the same claim was sometimes paid twice.

Task

Our task was to automate claim validation, so the right claims are paid on time and the rest are flagged for review before payment.

Action

• Set up claim intake through a portal that accepts a claim only with the required evidence, replacing the spreadsheets distributors used to send
• Captured the terms of each incentive agreement, such as eligible products, rates and periods, as rules the validation can apply
• Automated validation with Power Automate flows that check each claim against sell-out data and the incentive agreement, so a claim that matches both moves straight to payment
• Added duplicate and tolerance checks, routing any claim that fails them to an exception queue for review before payment
• Generated the payment file from validated claims, so finance pays only the amounts the checks have cleared
• Delivered a status view for distributors showing where each claim stands, from receipt to payment, so they no longer need to chase

Result

• Valid claims paid within the agreed days
• Duplicates and out-of-policy claims stopped before payment
• Distributors checking claim status themselves instead of chasing payment