Today, even with more data digitized and workflows digitalized, complete confidence in the numbers reflected in inventory management systems is lacking. Most organizations are not yet reporting 100% inventory accuracy or the ability to sense demand. As such, inventory planning has not yet been perfected.
Why?
Data fragmentation remains an issue for most organizations. Information systems are siloed both within and outside the four walls even though operational functions and supply chain organizations have become more codependent than ever.
I foresee that rapidly changing, though, thanks to the growing availability, affordability and adaptability of cloud-based software platforms. Plus, we’re learning more every day about the role of hardware, software, and people in inventory management and developing new technology capabilities as a result. Consider how far we’ve come.
From Automating Data Capture to Automating Data Analysis
Though the barcode had been around for decades, it became a game-changer about 10 years ago as e-commerce sales topped $1 trillion for the first time. The ease of click-to-buy models complicated inventory management and fulfillment workflows. The supply chain was no longer linear.
Thus, the business case for barcode-based track and trace solutions grew quickly. With a single scan, multiple data fields could be automatically and accurately funneled into back-end systems, compiled into functional data sets, then distributed for further analysis by inventory and operations managers, buyers, and planners. Eventually, we found a way for barcode scanners to read QR codes, further expanding inventory monitoring capabilities. Workers could instantly report the status of every item they handled, as well as inventory sitting on the shelf or stockpiled at the receiving dock. The labor and operational costs of “inventory management” dropped, even as technology spend climbed.
Then as radio frequency identification (RFID) technology matured, it proved that data capture – and track and trace – could be further automated. Thousands of tags could be read each second by fixed readers strategically placed throughout facilities or handheld readers operated by workers, and data could be fed in bulk into inventory management systems with increased accuracy.
This influx of data was a surprising workforce multiplier. As requisitions for skilled data scientists increased, so did the realization that we must automate analytics if we want to be able to sense, analyze and act on both supply and demand trends in real-time.
Assigning Value – and Actions – to Inventory Data
Real-time inventory status is key to making the right labor, procurement, merchandising, pricing, and promotion decisions, which barcode, QR code and RFID systems technically provide. However, hardware components don’t analyze or action captured data. That’s where software comes in – and independent software vendors (ISV).
Ever since cloud-based software-as-a-service (SaaS) platforms became available at scale, we’ve seen a quantum leap in inventory management capabilities. Structured and unstructured data generated by Internet of Things (IoT) components can now flow freely through a data pipeline or directly to a data lake. As a result, application programming interfaces (API) and machine learning algorithms can be leveraged more extensively to access and mine data in the context of a specific operation or function in a low-cost manner.
Everyone from procurement planners to loss prevention experts can plug into the same information systems via APIs and extract actionable insights most relevant to their roles. Workflow apps can also be built quite easily to help drive the best next actions from operations managers, associates, and delivery drivers. A prescriptive analytics platform, for example, can be taught to detect certain patterns in the data and “prescribe” tasks to employees when inventory-related issues or opportunities arise.
Similarly, an intelligent demand sensing platform can aggregate inventory data from multiple business systems and analyze it alongside contextual third-party data – weather, traffic, holidays, and other demand-influencing events. It can then prescribe specific procurement, merchandising, pricing or promotion actions that can right-size supply against demand.
The Biggest Lesson Learned: Opening Ecosystems Leads to New Solutions
In short, this software-led shift from “systems of record” to “systems of intelligence” and, ultimately, “systems of engagement” has been key to progressively improving inventory availability and performance in the last decade. SaaS solutions have even automated decision-making to a certain extent, taking the last bit of manual work – and risk – out of the inventory planning and management equation.
However, we must do more to ensure all stakeholders have full transparency into inventory status from the first mile through the last – or from the stockroom to the store floor. We must tear down solution development silos. Technology vendors and ISVs must commit to building and utilizing open platforms when designing inventory-related solutions. And we must ensure these solutions openly share, actively analyze and intelligently action data so all supply chain entities can effectively predict, sense, and shape inventory demand.
To learn more about how software can be developed, adapted, or applied to improve inventory management, click here.
