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Modernizing The Worlds Largest QSR Supply Chain

Predictive Forecasting

ERP Software Integration

Cross-Party Accessibility


Computer Vision

Case-level Visibility

Real-time Data

Smart Inventory Management

Cold-Chain Compliance

Digital Ledgers

Problem Statement
"How might we build a scalable, self-reliant, and sustainable digital supply chain using next-gen technology?"

Main Use Cases We Researched

Linear Traceability

Product traceability through technologies like blockchain could allow a global QSR supply chain to track product supply in real-time, improving food safety, transparency, and operational efficiency.

Cold Chain Compliance

The use of RFID tags and temperature sensors could allow a global QSR supply chain to monitor and verify cold chain compliance in real-time, preventing temperature excursions during transport and storage.

Predictive Forecasting

Applying robust machine learning algorithms to sales, inventory, and shipping data could enable more accurate demand forecasting in a global QSR supply chain, allowing for optimized production planning.

Inventory Management

The use of RFID tags on products could allow a global QSR supply chain to automatically scan and track inventory as it is delivered and stocked, enabling real-time visibility and optimization of inventory levels across the supply chain.

Streamlined Ordering

Automated inventory monitoring and reordering systems could allow a global QSR supply chain to trigger orders based on real-time usage data, ensuring consistent stock levels and minimizing disruptions from inventory shortages.

Risk Identification

Real-time monitoring and predictive analytics could enable a global QSR supply chain to identify and respond proactively to risks like weather events, equipment failures, or other disruptions, minimizing downtime and service impacts.

Technology Exploration & Key Findings

We tested ultra-durable RFID tags and sensors embedded in specialized corrugated packaging to withstand extreme temperatures, humidity, and other environmental factors across the global QSR supply chain. We also continuously optimized the placement and positioning of RFID sensors throughout key points in the supply chain such as on loading bay dock doors and production lines. The pilot validated the ability to continuously monitor conditions and product locations in transit down to the item-level via RFID, despite harsh supply chain environments from factory to restaurant.

To create a modern digital supply chain, we relied heavily on advanced data architecture, one that could provide a framework for implementing AI and machine learning algorithms, but also be able to stream and process the enormous amounts of data being produced by this system when at scale. The primary functions of AI and machine learning algorithms were to ingest this real-time data including sales, inventory, promotions, and other factors to create predictive demand models. The pilot demonstrated the ability to generate highly accurate demand forecasts and proactively identify potential stockouts and disruptions in near real-time across the global QSR supply chain.

We tested blockchain efficacy and feasibility during the pilot to evaluate the use of distributed ledger technology for end-to-end traceability across the supply chain network. The pilot validated the ability to create transparent, immutable digital records for ingredients and products, enhancing visibility and food safety while reducing the risk of fraud.

We tested various forms of sensors that utilized pattern recognition software and computer vision algorithms during the pilot. We researched using deep learning algorithms to analyze images captured during the food preparation process, with the goal of automating quality control and inspection. The pilot showed an accuracy rate of over 90% in detecting food defects, demonstrating the feasibility and value of deploying computer vision for process optimization across the global QSR supply chain.

We supplemented the research with RFID tags to include IoT devices and smart sensors, which created a multi-dimensional data source for supply chain use cases. By deploying smart sensors and RFID tags across multiple sites to capture real-time temperature, location, and other data about ingredients and prepared items. The pilot demonstrated the ability to prevent temperature excursions, verify compliance, and enable item-level traceability across the supply chain using connected IoT devices.

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