Inventory management can seem like an entirely operations-focused pursuit. There often is engineering value, though, when looking deeper at the layers of inventory management systems.
From identification to action, there are many potential points in an inventory flow.
An inventory system, then, consists of more than just a data repository. It is an end-to-end process where physical-world inventory items generate information-useful events, and this data takes shape and meaning for human operators and computer programs alike.
In this article, you will find a basic overview of inventory data-processing pipelines and a few thoughts about automation and artificial intelligence in inventory systems.
A Simple View of Inventory Data Flow
Here is a basic, generalized view of an inventory architecture:
Physical Inventory → Identification → Data Collection → Connectivity → Processing → Analysis → Decision → Action
These stages represent a general set of concerns that inventory data flows through.
If inventory data is missing, malformed, or misinterpreted at any point, later steps in the process can have wrong or unusable information.
Let's briefly examine each of these stages.
1. Identification
An inventory system must identify an item before it can collect and process data about that item.
There are a variety of methods for identification, including:
- Barcodes
- QR codes
- RFID tags
- BLE identifiers
- Asset IDs
Each type of identification has advantages and drawbacks. RFID, for instance, can be read without line of sight, but BLE-based identification might give richer positional data than QR codes or RFID.
The right type of identification depends on the needs of the particular environment and the information the inventory system requires about the item.
2. Collecting Inventory Events
Before identifying an inventory item, the system needs to define some sort of event about that item.
These events may take many forms, including:
- Item received
- Item moved
- Item picked
- Item shipped
- Item returned
- Item counted
- Item location changed
All of these are examples of inventory events. One simplified view of an inventory event might be:
- Item ID
- Location ID
- Event Type
- Timestamp
- Quantity
There are often many other fields in an inventory event, depending on the needs of the particular system. For instance, inventory events may be augmented with metadata about the user, device, source system, or transaction involved in the event. The general idea, however, is that inventory events should represent actual inventory activity rather than focusing on just overall inventory amounts or other aggregated data.
3. Connecting the Data
Now that identification and event data have been established, systems need to exchange that data between processes or even organizations.
A connected system might appear as:
Barcode/RFID/BLE → Scanner or Gateway → Network → Inventory Platform → Application
Depending on the architecture, data may flow through APIs, gateways, databases, message queues, or cloud services. Overall, some sort of end-to-end information pathway should be established from the physical inventory environment to the particular software managing that environment's inventory state.
4. Processing Inventory Data
Inventory data is rarely, if ever, in a directly usable form when it is first captured.
A simplified version of inventory data processing might look like this:
Receive Event
↓
Validate Data
↓
Identify Item
↓
Check Current State
↓
Update Location
↓
Update Inventory State
↓
Store Event
Data validation is important, since erroneous or malformed data can result in unexpected results downstream in the pipeline. If the particular inventory system deals with high volumes of data, architects should also think about ordering, idempotency, error handling, and monitoring as well.
5. From Data to Inventory Intelligence
Inventory events are useful, but there is an inventory data processing layer needed before this information can drive decisions in the business. Here is one possible view of that layer:
Tracking → Visibility → Analysis → Decision Support
Tracking provides the raw information that the system has seen. Visibility is an expanded view of some or all of that information presented in a more easily understandable format. Analysis takes tracking data and applies analysis to it, extracting value, inventory patterns, or problems. Finally, decision support uses this information to draw some conclusions or recommendations.
It is worth emphasizing that more raw inventory data is not always better. The inventory data must be high quality and properly interpreted in order to draw meaningful conclusions from it.
Where AI Fits
There are a number of places where artificial intelligence fits naturally in an inventory context.
Depending on the particular system and data available, AI may be used to help analyze demand, make inventory suggestions, detect anomalies or events, or identify patterns in inventory data.
For instance, AI algorithms may process historical demand and inventory data to draw insights about future inventory planning.
At the same time, AI systems are only as good as the data going into them. If data collection is erroneous, malformed, or unreliable, conclusions drawn from that data may also be invalid.
Another important consideration is inventory event reliability and accuracy. Inventory data is, by nature, information about events in the physical world.
If inventory item locations, for instance, are frequently incorrect or out of date, event data may reflect that incorrect information. Even more concerning, event data may not be properly associated with the correct physical items. As a result, AI conclusions about inventory trends, forecasts, or suggestions may reflect incorrect conclusions.
That is why accurate inventory event collection, reliable data processing pipelines, and correct interpretation of data are all essential before applying AI to an inventory system.
Inventory Event Automation
An interesting application of inventory events is around inventory-event-driven automation. That is, inventory events may be used to drive simple or complex logic in an automated fashion.
One simplified example might be:
IF stock level < threshold
↓
Generate replenishment alert
While another example might be:
IF current location != expected location
↓
Flag inventory discrepancy
The details of how that logic is implemented may vary from case to case, but the general idea is that inventory events can provide triggers for automated logic processes to occur, rather than requiring constant human attention or involvement.
Taking Inventory Architecture to Inventory Systems
Overall, there is a simple-but-flexible inventory-systems architecture that may support a wide range of different use cases and environments. That architecture might look like this:
Physical Inventory
↓
Identification
(Barcode / RFID / BLE)
↓
Data Collection
↓
Connectivity
↓
Data Processing
↓
Inventory Platform
↓
Analytics / AI
↓
Decision
↓
Action
Not all systems require all components in this architecture. A smaller inventory operation may only require barcode scanning on a basic inventory database. A more involved architecture with more complex physical inventory items and events may require RFID, location tracking, API integrations, cloud infrastructure, analytics, and automation.
The architecture chosen, then, should be based on the particular needs of the environment.
Engineering Inventory System Risks and Concerns
There are also some engineering-related pitfalls associated with inventory systems.
These are some common concerns:
*Data Accuracy
*
Poorly formatted or incorrect data has the ability to derail the entire inventory system. Validation is therefore an absolutely essential concern in inventory system design.
Integration
Inventory systems often need to integrate with other business systems. APIs and generally flexible data interfaces will allow inventory platforms to exchange information with other systems with greater ease.
Scalability
As a system scales, inventory-event volume grows as well. Systems architecture should be built to handle greater and greater data volumes without becoming unstable or difficult to manage.
Reliability
Inventory systems often underpin time-critical operations. Designers and engineers should consider reliability, resiliency, and monitoring so that events and data are consistently processed and maintained.
Security
Inventory systems often contain valuable information. This information should be protected by appropriate authorization, authentication, and access controls to prevent unauthorized access.
Beginning Inventory Projects the Right Way
Starting an inventory project can be daunting. It is easy to fixate on the technology that will power the system. RFID tags, automation, and AI vision systems are all tremendously powerful technologies. They can help tremendously in the right setting and the right context.
Technology, though, is only a means to an end. Starting with some inventory process questions will allow architects to examine the problem space before choosing a solution.
Here are some sample questions:
What inventory events need to be captured?
Where does the data come from?
How often does it change?
Who needs this information?
What decisions need this information to support?
What processes should take place if an inventory event occurs?
What happens if an inventory event goes wrong or has bad data?
These questions will allow designers to look at the inventory context before making technology decisions. Once these questions have been asked, the appropriate technology can be chosen based on those answers.
Inventory management as a whole is addressed more thoroughly at theinventorymaster.com.
Final Thoughts
Inventory management systems often consist of more than just data repositories. Inventory data pipelines are generally pipelines of physical-world objects and their behaviors.
Identify → Capture → Connect → Process → Analyze → Decide → Act
Technologies will change, but reliable inventory data is always necessary to drive decisions in operations.
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