Overview

Telle Tires & Auto Centers operates a multi-location tire and auto service network, moving high volumes of tires, wheels, and auto parts through receiving, warehousing, and point-of-sale every day. Each location handles its own intake, storage, and shipment cycles, all feeding into Protractor, the company's dealer management and ERP system of record.

As the network scaled, inventory tracking stayed tied to manual data entry — reading DOT and sidewall codes by eye, keying invoices by hand, and reconciling stock counts location by location. Tire retail runs on thin margins and fast turnover, so any gap between what the shelf holds and what the system shows turns directly into lost sales, emergency restocking, or a customer walking out. Telle Tires engaged Scanflow to close that gap and give every location a shared, real-time view of inventory.

Business Challenge

As Telle Tires' network grew, manual tire data capture and invoice entry left the business without a single reliable picture of stock across its locations:

1. 01Manual Tire Data Capture Slowing Receiving — DOT codes, sidewall specs, brand, and size were read and keyed by hand at receiving, introducing transcription errors at the exact point where inventory records begin.
2. 02Duplicate Invoice Entry Delaying Stock Visibility — Invoice line items were entered manually into Protractor, duplicating effort already captured on the supplier's paperwork and delaying when new stock became visible to staff.
3. 03Fragmented Records Across Locations — Inventory records were fragmented across locations, with no shared, real-time view of what was actually on shelf at any given site.
4. 04Cycle Counts Running Behind Physical Stock — Stock updates lagged physical movement, so cycle counts and shipment processing ran behind the true state of the warehouse.
5. 05Mismatches Surfacing as Stock-Outs, Not Caught Early — Discrepancies surfaced as stock-outs and emergency restocking rather than being caught during routine counts, adding operational risk and cost.

The Scanflow Solution

Scanflow deployed a mobile-first tire identification and inventory system directly into Telle Tires' receiving and warehouse workflow, combining sidewall/barcode scanning, document parsing, and cycle-count automation with a direct Protractor sync — connecting capture, verification, and the ERP in one continuous flow instead of a chain of manual handoffs.

• Automated Barcode & Sidewall Recognition Front-line staff scan tire barcodes and sidewall markings — DOT codes, size, brand — directly from the tire using the Scanflow mobile app, replacing manual transcription with a single camera pass.
• AI-Based Invoice & Document Parsing Incoming supplier invoices are parsed automatically on capture, populating line items in Protractor without re-keying and cutting the lag between goods arriving and inventory reflecting it.
• Automated Cycle Counts Scheduled and ad hoc counts run through the same mobile flow, replacing clipboard-and-spreadsheet counts with a scan-based process staff can complete in a fraction of the time.
• Seamless Protractor ERP Sync Every scan writes directly into Protractor, so receiving, counts, and stock levels stay synchronized across the dealer management system without a separate reconciliation step.
• Centralized Multi-Location Dashboard A shared dashboard gives Telle Tires visibility into stock levels, scan activity, and count status across every location, replacing site-by-site spreadsheets with one consistent view.

Onboarding Timeline

Scanflow followed its standard rapid-deployment framework, bringing Telle Tires to full production in approximately four weeks:

• Week 1 — Solution configuration and Protractor integration setup.
• Week 2 — Mobile app rollout and training for receiving and warehouse teams at pilot locations.
• Week 3 — Cycle-count workflow validation and accuracy checks across pilot sites.
• Week 4 — Full network rollout and dashboard go-live across all locations.

Business Impact

By replacing manual tire documentation and invoice entry with scan-based capture and a direct Protractor sync, Telle Tires established a standardized, real-time inventory process across every location.

Key Business Outcomes

• 98–99% inventory accuracy across all locations — removing manual transcription as a source of error at receiving and during counts.
• 3x faster shipment and receiving processing — cutting the time from goods arriving at the dock to their being reflected in Protractor.
• Marked reduction in stock-outs and emergency restocking — catching discrepancies during routine scans instead of after they become a shortage.
• Real-time, audit-ready records — every scan logged instantly, giving Telle Tires a traceable, exportable record of inventory movement.
• A workflow that scales with the network — the same scan-verify-sync workflow runs at every site, so adding locations doesn't add a reconciliation problem.

Compliance & Traceability Improvements

• Chain-of-custody style record for every unit — every scanned tire or shipment carries a timestamped, auditable trail from receiving through to stock update.
• Faster audit and reporting readiness — stock reports and count histories are available on demand instead of being reconstructed from spreadsheets.
• Better visibility for procurement — a shared, real-time view of stock supports more accurate reorder and replenishment decisions across the network.

Telle Tires gained robust digital control and complete asset traceability, transforming warehouse operations across the network with Scanflow.

The Scanflow deployment was built to extend to new Telle Tires locations without adding manual workload — each new site follows the same scan-verify-sync workflow and reports into the same centralized dashboard. As the network grows, inventory accuracy and audit readiness scale with it rather than becoming harder to maintain.

Overview

Médecins Sans Frontières (MSF) moves medicine boxes through a supply chain where every box carries a 12-digit product code — but that code isn't printed in a fixed, predictable format. It sits embedded within each box's printed description, which varies from manufacturer to manufacturer and product to product. Reading it manually meant interpreting each box individually, and capturing batch number, lot number, and expiry date added a second manual step prone to its own errors.

MSF engaged Scanflow to build a client-specific OCR SDK that could read each box's description, extract the correct 12-digit code regardless of formatting, and capture batch, lot, and expiry data — all mapped and stored automatically.

Business Challenge

1. 01No Fixed Format for the 12-Digit Code — Each medicine box's 12-digit product code is embedded within its printed description rather than printed in a consistent, standalone location — meaning format and position vary by manufacturer and product line.
2. 02Manual Reading Couldn't Scale Consistently — Identifying the correct code required a person to read and interpret each box's description individually, a process too slow and inconsistent to sustain at the volume MSF's supply chain moves through.
3. 03Batch, Lot, and Expiry Capture Added Manual Risk — Recording expiry date, batch number, and lot number by hand introduced the same transcription risk as manual code reading, on data that matters directly for compliance and traceability.
4. 04No Standard Template Could Cover Every Box — Because format varies box to box, a single fixed-position scanning template couldn't reliably work across MSF's full range of medicine packaging.

The Scanflow Solution

Scanflow built a client-specific OCR SDK for MSF that reads each box's printed description, extracts its 12-digit product code regardless of format or position, and captures batch, lot, and expiry data alongside it — mapping every field directly into MSF's database.

• Description-Based OCR Reading The SDK reads the full printed description on each medicine box, rather than relying on a fixed code location or format.
• Intelligent 12-Digit Code Extraction The system analyzes the OCR'd description text to identify and extract the correct 12-digit code, adapting to formatting differences from box to box.
• Batch, Lot & Expiry Capture A batch-scanning step captures expiry date, batch number, and lot number for the same box, alongside the extracted product code.
• Automatic Database Mapping Every extracted field — code, batch, lot, and expiry — is mapped and written directly to MSF's database, with no manual entry step.

Onboarding Timeline

Placeholder — confirm actual onboarding phases and dates with the MSF project team before publishing. Suggested structure:

• Week 1 — description samples collected and SDK calibrated to MSF's box formats
• Week 2 — pilot testing against known codes and batch records
• Week 3 — accuracy validation and refinement
• Week 4 — full rollout and database integration go-live

Business Impact

By replacing manual code reading and manual batch/lot/expiry logging with a single OCR-driven scan, MSF gets a consistent, database-backed record for every medicine box — without depending on a fixed label format.

Key Outcomes

• Eliminates manual code reading — removing the need to interpret each box's description by hand.
• Adapts to format variability — extracts the correct 12-digit code regardless of how or where it's printed within the description.
• Reduces batch data transcription risk — expiry date, batch number, and lot number captured directly from the scan.
• Database-ready from the point of scan — every field mapped and stored automatically, with no manual entry step.

Traceability & Compliance

• Every box carries a code, batch, lot, and expiry record captured at the point of scan
• Consistent capture supports audit and regulatory reporting needs across MSF's supply chain

Built for MSF's Specific Format Challenge

The SDK was purpose-built around MSF's actual packaging variability rather than a generic, one-size-fits-all label template — designed to extend to new box formats as MSF's supply chain evolves.

Note: No quantified metrics (accuracy rate, error-reduction percentage, volume of boxes processed) have been confirmed for this deployment. Add specific figures here once available from MSF or the project team — do not publish estimated numbers in their place.

MSF strengthens medicine box traceability with Scanflow, helping ensure every box's identity and batch record is captured accurately, automatically, and ready for audit.

Scanflow is an AI-enabled visual intelligence platform from OptiSol Business Solutions, purpose-built for asset identification, inventory automation, and visual inspection across tire, ITAD, solar, VIN, and quality-control workflows.

Overview

Bonfiglioli is a family-run Italian manufacturer of gearmotors, drive systems, planetary gearboxes, and inverters, serving industrial automation, mobile machinery, and renewable energy customers around the world. Every motor that leaves the line carries a unique serial number, and that identity has to be established, verified against a complete build, and matched to a pass or fail result before the unit can move downstream.

At the point of assembly, three things have to happen for every single unit: its serial number has to be captured, its components have to be checked for correct orientation, and the build has to be confirmed complete. Missing even one part, or getting an orientation wrong, is the kind of defect that is easy for a rushed manual check to miss and costly to catch later. Bonfiglioli engaged Scanflow to move that verification onto an automated, real-time inspection station tied directly to its quality database.

Business Challenge

Verifying every motor's identity and build completeness by hand left gaps at exactly the point where Bonfiglioli needed certainty:

1. 01Manual Serial Number Capture at End of Line Operators read and logged each motor's unique serial number by hand or with a handheld scanner, a process that slowed throughput and left room for transcription errors at the point where a unit's identity is first recorded.
2. 02Inconsistent Orientation Checks Confirming that components were assembled in the correct orientation depended on an operator's eye, run after run a subtle misalignment was easy to miss under production pace.
3. 03Missing Parts Escaping Detection A missing component is one of the easiest defects to overlook visually at speed, and any build that passed with a part missing carried that gap downstream, undetected until it surfaced later.
4. 04Inspection Results Disconnected from Records Pass/fail outcomes were not consistently tied to a motor's serial number in a queryable database, weakening traceability for any downstream quality review, audit, or recall.

The Scanflow Solution

Scanflow deployed an AI-powered inspection station on Bonfiglioli's assembly line that captures a motor's serial number, verifies orientation, checks for missing parts, and renders a pass/fail decision automatically, at the point of assembly, before the result is written directly to the quality database.

• Automated Serial Number Recognition A camera reads each motor's unique serial number as it reaches the station, establishing the unit's identity before any other check runs.
• Orientation Verification Scanflow checks that components are assembled in the correct orientation, flagging any misalignment automatically rather than relying on a visual pass by an operator.
• Missing-Part Detection The vision model checks the build against its expected components, flagging any motor that is missing a part before it can proceed.
• Automated Pass/Fail Gate If any check fails a missing part or an orientation error the unit is flagged as a fail and held at the station. If every check clears, the motor passes automatically.
• Direct Database Logging Passing results, together with the serial number and inspection data, are written straight to the database, creating a serial-linked quality record for every unit with no manual re-entry.

Onboarding Timeline

Scanflow followed its standard rapid-deployment framework to bring the inspection station to production in approximately four to six weeks:

• Solution configuration mapping the serial number location, orientation checkpoints, and expected part list for the target motor line.
• Camera setup installing and calibrating the camera at the inspection station.
• AI model deployment tuning serial recognition, orientation, and missing-part detection models to Bonfiglioli's motor variants.
• Database integration connecting the pass/fail gate and inspection results to the quality database.
• Validation and performance testing verifying detection accuracy and pass/fail logic under live assembly conditions.
• Production deployment full go-live on the pilot line, with a path to extend to additional lines and motor variants.

Business Impact

By replacing manual serial capture and visual checks with an automated, database-connected inspection station, Bonfiglioli established a consistent, traceable verification step for every motor that reaches the end of the line.

Key Business Outcomes

• Missing-part checks run on every unit no motor with an incomplete build clears the station undetected.
• Orientation errors caught at the source misalignment is caught at the point of assembly instead of surfacing further downstream.
• Database-backed quality records for every unit every passing unit carries a serial-linked record written automatically, with no manual data entry.
• Failing units flagged and stopped in real time incomplete or misassembled units are held immediately rather than moving further down the line.
• Consistent inspection criteria across shifts pass/fail decisions run on the same automated checks every time, removing operator-to-operator variation.

Traceability Improvements

• A permanent, queryable link between unit and result every motor's inspection outcome is tied to its unique serial number in the database.
• Faster response to quality reviews and audits quality records can be pulled up by serial number for any downstream review or customer query.
• Uniform records across the entire line the same verification sequence applies to every unit, independent of shift or operator.

Building a Scalable Foundation

The Scanflow deployment was designed to extend to additional lines and motor variants without adding manual inspection steps — each new line runs the same serial-scan, orientation-check, and missing-part detection sequence, feeding the same database. As Bonfiglioli's production scales, every motor keeps a verified build and a traceable, serial-linked record from the point of assembly onward.

Bonfiglioli established a consistent, traceable verification step for every motor that reaches the end of the line, with serial-linked quality records written automatically to the database.

Overview

Daimler's commercial vehicle plant builds multiple brands on the same production line, sharing components, stations, and, in some cases, similar-looking parts across brands. A hornpad carrying the wrong brand logo is exactly the kind of mix-up that can happen during manual handling on a shared line, and one that isn't always obvious without checking it against the build record.

Daimler engaged Scanflow to put a mobile scanning solution directly in the hands of line staff: scan the truck's VIN, scan the hornpad and its logo, and check automatically whether the two match what MES expects for that specific vehicle.

Business Challenge

Building multiple brands on one shared line created a specific risk: a brand-critical part fitted to the wrong vehicle, without an easy way to catch it in the moment:

1. 01Shared Lines Increase Brand Mix-Up Risk Building multiple brands on the same line and stations means components that differ only by logo or badge, like a hornpad, can be misrouted to the wrong vehicle during manual handling.
2. 02Logo Mismatches Are Easy to Miss Visually A hornpad carrying the wrong brand logo doesn't necessarily look wrong at a glance, especially at normal line pace, making it a defect that a visual-only check can miss.
3. 03No Direct Check Against MES at the Point of Assembly Confirming that a truck's VIN and its hornpad matched the correct brand meant cross-referencing build records separately, rather than checking it in real time as the part was fitted.
4. 04Fixed-Station Inspection Doesn't Fit Every Checkpoint Not every point where a hornpad could be mismatched had a fixed inspection station, leaving some checks dependent on manual attentiveness alone.

The Scanflow Solution

Scanflow deployed a mobile scanning app that captures a truck's VIN and its hornpad logo together, checking the pairing against MES in real time to confirm the brand is correct for that specific vehicle.

• Mobile VIN Capture Line staff scan the truck's VIN directly from a mobile device, without needing a fixed station.
• Hornpad & Logo Capture The same mobile scan captures the hornpad and its brand logo.
• Automatic MES Cross-Check Scanflow checks the scanned VIN and hornpad logo against MES to confirm the brand matches what's expected for that specific vehicle.
• Real-Time Mismatch Flagging If the hornpad logo doesn't match the brand expected for that VIN, the mismatch is flagged immediately, before the vehicle moves further down the line.
• Mobile-First Coverage Because the check runs on a mobile app rather than a fixed station, it can be applied at any point on the line where a hornpad is fitted or verified.

Onboarding Timeline

Scanflow followed its standard rapid-deployment framework to bring the mobile verification workflow to production in approximately four to six weeks:

• Solution configuration mapping VIN-to-brand rules in MES and the hornpad and logo variants across brands.
• Mobile app rollout deploying the mobile scanning app and training line staff on its use.
• AI model deployment tuning logo recognition across the brand variants on the line.
• MES integration connecting the mobile app's VIN and logo captures to MES for real-time cross-checking.
• Validation and performance testing verifying match accuracy and mismatch flagging under live line conditions.
• Production deployment full go-live on the pilot checkpoint, with a path to extend to additional checkpoints and parts.

Business Impact

By checking VIN and hornpad logo together against MES on a mobile app, Daimler put a direct, real-time brand verification step in place on a shared multi-brand line.

Key Business Outcomes

• Hornpad brand mismatches are caught by checking directly against MES not by relying on a visual-only check.
• VIN and hornpad logo are verified together at the point where the part is fitted or checked.
• The mobile-based approach extends coverage to checkpoints that don't have a fixed inspection station.
• Mismatches are flagged in real time before a truck with the wrong brand hornpad moves further down the line.

Brand Assurance Improvements

• Every VIN-to-hornpad check is verified against the same MES brand record removing dependence on manual cross-referencing.
• A mobile-first approach so verification isn't limited to fixed stations.
• Consistent checks support brand assurance across a shared, multi-brand production line.

Building a Scalable Foundation

The Scanflow deployment was designed to extend to other brand-specific components beyond the hornpad, and to additional checkpoints along Daimler's shared multi-brand line, using the same mobile capture and MES cross-check approach.

Daimler strengthened brand assurance on a shared multi-brand line with Scanflow, catching hornpad-to-VIN mismatches in real time, wherever line staff need to check.