Every brand is afraid of having its shopping cart and inventory shelves empty. Most of the stockouts occurred because of an inappropriate inventory replenishment process. Let us read more about it.
Challenges That Occur Due To An Unpredictable Inventory
Think about how it can take a toll on the customer’s goodwill due to unpredictable and unplanned inventory management that leads to backorders or cancelled orders. Amidst all of this, if the brand loses a client, it can’t get any worse. Such a thing may include lost sales because of delayed over fulfilment and decreased plan orders from clients. It may also have high shipping prices because of a sudden requirement for stock replenishment. At this point, IoT solutions can turn out to be adequate resources.
What Role Does IoT And Analytics Play In Solving This Issue?
Both Internet Of Things and Big Data have curated an excellent road for businesses to fall out of the traditional demand forecast schedules by determining historical information and offering recommendations. It has become easier to connect the links between warehouse locations, POS, and data generated at the suppliers.
With the internet of things and big data, it is clear that now everyone is swiftly shifting towards a centralized monitoring approach. Such a thing retrieves data from the point of application and offers it on a decision-making platter. When we speak of intelligence, we indicate the difference between system inventory management and physical inventory. Apart from this, sensor data for tracking item parameters help come in handy to analyze the threshold for reorder points of items. With such data in its order, cloud-enabled analytics should be the next in line.
One of the best examples would be to consider AWS IoT analytics. It will help accomplish an intelligent replenishment system for inventory. Such a thing can store timestamps automatically from the items in stock and make it convenient to determine the time series. One can also focus on the improvisation of performance analysis of inventory levels. This is done using the machine learning potentiality of the same. One can also easily set up the business logic-based configurations in the data processing pipelines connected to the integrated AWS IoT core.
For example, one can easily set up a visual sensor that can scan the QR code on a specific item. It has an expiry date, and one provides notifications to the sales stakeholders for order replenishment. Now, you must focus on the massive amount of unstructured data from the IoT-ized items that must be processed. AIA can come in handy to cleanse data and encourage ad-hoc querying. This helps offer post-analytic inferences and improve inventory management recommendations. So, it becomes a cakewalk for the inventory manager to receive benefits from it in no time.
The Bottom Line
Earlier, businesses were able to determine POS data and IT system inventory data that helped them to create a balance of inventory stockout issues. Now, we are highly equipped to add prescriptive analytics, smart tracking technologies, and sensors. Although it is difficult to turn into an entirely stockout-proof solution, IoT encourages inventory replenishment to help cut down on worries
