Cloud-Native solution for Real-Time inventory in QSR For Operational/Cost Efficiency and To Reduce Wastage
Inventory Management is a persistent struggle for many QSRs. In recent times, we have heard about a lot of occurrences where QSRs have run out of items based on demand slip-ups. This has cost them reputation, revenue and customer loyalty when needed the most. But to be honest, it is difficult to precisely forecast how much inventory is required to meet demand without purchasing too much, which could waste both money and food. Thus, the value of real-time inventory management and how best to restock on demand. Cloud-based systems can help QSRs soften these issues and avoid the fury of starving customers.
It’s imperative how real-time inventory management can drive significant value for your business. It will make it more efficient, more productive, and better able to respond to changing market conditions. In doing so, you will create the foundation for new growth.
The most essential aim to espouse real-time inventory management for a QSR is to create a real-time view of what’s happening to your inventory. This is particularly critical during peak periods, when having an optimal level of inventory is decisive.
Employing real-time inventory management solution will save up your crew to focus on what especially matters, rather than spending their relevant time by keying in data.
Growing QSR businesses need to ensure that their supply chain expands respectively. That’s where easier scalability is published as the result of embracing real-time inventory management capabilities. While there is early investment in achieving a new, real-time inventory management system, QSRs should keep in mind the gains and the implied cost savings. Effect from fewer stock-outs, shortages and canceled orders can pay itself in notably less time than ever imagined feasible.
But that’s not only it, by numbers QSRs can optimize their operational efficiency by up to 25 percent using a Cloud-based real-time inventory system along with features like:
1. Dynamic Menu updates on all digital channels.
2. Notify the back-office systems or connected devices like crew iPads etc. to notify low item count.
3. Order the items automatically when a low threshold is reached (with manual approvals when needed).
4. Notify Delivery or 3rd parties on outages, keep the B2B systems up to date.
QSRs can also contribute towards the environment using these systems by reducing their food wastage by accurate tracking of ingredients used in the food items. With the power of Cloud computing, QSRs can now be ahead of the curve to make shortage slip-ups obsolete and be super-efficient/clever in their quest for inventory management.
Challenges in making a QSR Inventory Real-time
Data Collection
To know or to make any decision around inventory items, QSRs need to know the data points. This includes data from every system and touchpoint which can impact the supply chain. This is a huge challenge for many QSRs as this information can be found in disparate systems. Getting all the data in one place is essential to success, and without proper updates, the information can become inaccurate and inefficient.
Accuracy
It is indispensable to know where items are at any time in the supply chain. It is also important for you to be able to warn customers of any deliveries that may be late. QSRs face huge customer frustration by not being upfront as they are not able to determine the accurate state.
Forecasting
QSRs struggle to keep up with the demand. It is a constant scuffle for many to accurately forecast how much inventory is necessary to meet demand without purchasing too much, which could waste both money and food. Shortages are common in the restaurant industry, but not forecasting the scale of it could be annihilating for the business.
Reliability/Scalability
IT solutions exist to help with inventory management, but constantly struggle with the reliability and scalability of the data. On-prem solutions don’t scale with the needs, and often fail to capture the data reliably to share for inventory management. Poor data quality affects many aspects of the business, not just one.
Monitoring
Most of the inventory management solutions suffer from zero to low monitoring. It’s hard to figure out for the crew on why the problem occurred and how can they fix it. Lack of proactive monitoring is a real challenge to the upkeep of the systems which can help bring efficiency.
Our Framework to Solve the Challenges
Cloud-Native
Our solution framework brings Cloud-native to the forefront which has become the new norm to help businesses operate:
1. Faster with greater flexibility.
2. Offers elasticity, resilience, high availability and responsiveness.
3. Cost-effective as you can pay per use.
4. Supports fastest idea-to-delivery time for Pilot Use cases.
Streaming/Real-time Data — Smart Kitchens/Cloud POS/Ingredient Mgmt.
Our framework enables streaming/real-time data ingestion from multiple sources in disparate formats and storing the information in a centralized storage. Data from streaming IoT devices, Cloud-based POS, ingredient management systems, shelf sensors, cameras, etc. are easy to collect and process in the Cloud. Streaming data also includes operations data originating from systems capturing operations events data sources, such as records systems.
Scalable/Reliable Data Storage
All the data including raw copies of source system data and transformed data needs to go into a single store for forecasting and visualization. In the Cloud world, it’s called the Data Lake. The Data Lake on the Cloud automatically crawls data sources, identifies data formats, and then suggests schemas and transformations, saving time and complexity. Inventory change capture or sensors data from systems are collected in real time based on the capability of the system. The data is stored for historical purposes as well, which helps ensure accuracy in forecasting. No data is turned away and all data types are supported.
Real-time Data Analytics and Visualization
Streaming analytics helps businesses to understand their need in real time and adjust their actions to better serve the needs of beneficiaries. Cloud streaming solutions provide capabilities to analyze streaming data, gain actionable insights and respond to Analytics Framework for maintenance events in real time. There are managed systems that scale automatically to match the volume and throughput, and taper down according to your incoming data, making sure that elasticity doesn’t come at a cost.
Once the data is processed, it can then be used for visualization to gain deeper insights into patterns and trends. BI tools or Cloud-powered visualization service makes it easy to deliver insights into maintenance trends.
Forecasting
Giving customers a notification beforehand to show you are proactive and that you’re able to manage the situation ahead of time, instead of waiting for a complaint, is the name of the game. Cloud-based ML capabilities can act on real-time data and historical data to quickly come up with a model to forecast inventory needs, and can also accurately predict when the surge or downturn can happen based on the past events. A wide range of solutions exist in ML/AI space which can handle auto modeling to custom deep learning. Businesses can get started at a very low cost and see the results in the pilot approach instead of a big bang, making them more agile.
Forecasting inventory needs can also mitigate the industry’s constant scuffle with food waste. Most QSRs can reduce waste through our solution’s precise stocking capabilities and can increase order efficiency. Many that utilize these tools are already seeing them pay off, and even small, typically slow technology adopters are becoming adept in their use.
Monitoring
Cloud-native solutions come within built support for proactive monitoring. This solution can set your goals for operational analytics which can help collect and analyze data to flag missed sales opportunities due to low inventory. Streaming telemetry can help with failure notification and automated remediation to improve the availability and reliability of the solution.
Reference Architecture and Best Practices
Cloud-native architectures can be built with different Cloud providers. AWS, GCP and Azure are leaders in this. We have captured the reference architecture for AWS and GCP below to help with the understanding:
With AWS Cloud
1. Real-time data will be captured from IoT Sensors/Cloud POS/Ingredient mgmt. systems/Record systems. AWS IoT Greengrass can broker the conversation with IoT sensors via the IoT rule to convert it to Kinesis streams which can then be massaged with Kinesis firehose.
Record systems or inventory change capture applications can stream the data to Kinesis using the SDK.
2. Real-time data is processed by applying windows and identifying data types to be fed to the inventory database. Managed Aurora relational DB is the perfect choice for it, as it handles the failover and provides scalability with read replicas.
3. EKS can host the applications which can read the inventory data, can act as an integration hub to feed the information back to back-office systems, Delivery partners, and logging information about low stocks. It can also frame the order on low items and order it too after manual approval.
4. Data is also passed on to the Data Lake in Amazon S3 and then processed further using ETL processes.
5. Amazon Sagemaker can be used to feed off the data from the Data Lake to build models for forecasting.
a. Inhouse Spark jobs can be put on AWS EMR to keep the value of existing jobs.
b. Sagemaker Autopilot can help kickstart forecasting at low TCO.
6. Appsync can help build forecasting apps to hand it to the crew members which gives them real-time insights and alerts.
7. Data is also shared with analytics engines like Amazon Redshift/Athena to do the BI. Dashboards can help with visibility to management for the success.
8. AWS CloudWatch helps with proactive monitoring with alerts, uptime checks and thresholds. This is keeping the system healthy and help automated remediations of the problems.
9. AWS IAM provides the security and access management of the data for compliance purposes.
10. Terraform and Jenkins are used to automate the deployments and management of the infrastructure.
With Google Cloud Platform
1. Real-time data will be captured from IoT Sensors/Cloud POS/Ingredient mgmt. systems/ Record systems. Cloud IoT core can broker the conversation with IoT sensors via the gateway to convert it to pub/sub streaming data which can then be massaged with Cloud dataflow.
Record systems or inventory change capture applications can stream the data to cloud pub-sub using the SDK.
2. Real-time data is processed by applying windows and identifying data types to be fed to the inventory database. Managed Cloud Spanner/SQL is the perfect choice for it, as it handles the failover and provides scalability with read replicas.
3. GKE can host the applications which can read the inventory data, can act as an integration hub to feed the information back to back-office systems, Delivery partners, and logging information about low stocks. It can also frame the order on low items and order it too after manual approval.
4. Data is also passed on to Data Lake in Cloud storage or Cloud Bigtable for time series analysis and then processed further using ETL processes.
5. BigQuery ML/AI Platform can be used to feed off the data from the Data Lake to build models for accurate forecasting.
a. BigQuery ML supports SQL queries-based model on its storage or federated storage.
b. Auto ML can use the structured data for forecasting model without Data Science knowledge.
6. Firebase can help build forecasting apps to hand it to the crew members which gives them real-time insights and alerts.
7. Data is also shared with analytics engines like BigQuery to do the BI. Dashboards can help with visibility to management for the success.
8. Cloud Monitoring/Logging helps with proactive monitoring with alerts, uptime checks and thresholds. This is keeping the system healthy and help automated remediations of the problems.
9. Cloud IAM provides the security and access management of the data for compliance purposes.
10. Terraform and Jenkins are used to automate the deployments and management of the infrastructure.
Conclusion:
In the world of growing competition, innovation and modernization of the QSR systems, real-time tracking with Cloud-based inventory offers restaurants several advantages in operational efficiency. Such features prevent QSRs from having to pay staff to conduct time-consuming and tedious manual counts after hours, ultimately saving labor costs as well as being precise with their product usage. Our framework does just that with the best from Cloud-native technologies and it can work on any public/private Cloud. We believe this will be a true north star for QSRs to achieve operational efficiency and reduce wastage.
Authors
Amit Sharma — Director, Engineering
Ravi Evani — Vice President, Engineering
