Data can solve stock availability issues

Posted in Inventory and warehousing.

Efficient inventory management is crucial. Research suggests that a majority of buyers will look to other businesses if the business they first went to is out of stock. Stock availability issues have the potential to deter potential customers and lose customer loyalty. Not only does a business lose out on sales, but customers also walk away feeling dissatisfied.

On the flip side, maintaining high levels of each and every SKU is just not practical. Space costs money, the value of unsold goods deteriorates over time – perishables especially – and excess obsolete inventory takes up space where more useful goods could have been stored. However, stock availability issues can creep in when stock does run out.

What causes stock availability issues?

Stock shrinkage can be caused by theft or damage of goods. Goods can be taken or damaged in transit. In both cases, it would be expected that customers don’t get their orders delivered and businesses lose stock without any gain. Liaising with dissatisfied customers is a requirement and alternative solutions should be put forth. Another shipment can be sent, or a customer can cancel their order, either way, stock is lost, and nothing is gained from the loss.

Lack of foresight can also lead to stock availability issues. Failure to predict customer demand can lead to stock quickly diminishing. Without a quick restock (which itself takes time), customers will be met with “out of stock” notifications or put in orders that they will only later find out could not be fulfilled.

Poor supply chain planning or being blindsided by supplier issues can also lead to stock unavailability. There will always be unpredictable variables that can threaten sufficient inventory storage. When manufacturers have problems, that affects downstream suppliers. Fluctuations in demand means predictions can be off. However, businesses can prepare for unexpected circumstances.

How data can be used to solve these issues

Preparations are best made when there is data to support what kind of preparations need to be made. With the Internet of Things, almost everything is connected digitally, and data can be extracted from them. Real-time data is a useful tool for making decisions on the fly. It can be used to forecast future demand and stock levels. Historic data allows businesses to look at past events to find patterns to predict what may happen next. Together, these can be used to mitigate or avoid any problems surrounding stock availability issues.

Orders and inventory can now be tracked in real-time from anywhere. Having a stronger handle of what is happening to the stock and deliveries allows businesses to keep a tight hold on theft. With the ability to track the movement of inventory as well as the person responsible for moving that item, warehouse staff are held more accountable, thereby reducing the risk of theft. Granted, theft might not be fully eradicated, but lost inventory can now more easily be recovered.

Having analytical software to extract meaning from bigger datasets allows businesses to make wider, more veracious predictions regarding the required stock levels. Demand forecasting software means businesses can predict how much inventory they need, based on historical demand, seasonality and sales forecasts. Forecasting tools help businesses improve decision-making and reduce the likelihood of stockouts or overstocking.

Inventory management software can easily display inventory levels in real-time. Multi-channel sales can lead to inconsistencies showing up as the channels don’t typically talk to each other. Inventory management software can unify inventory data such that it is reflected across all channels, ironing out inconsistencies.

Even such, it is important to make sure that data stored on the system is consistent with reality. Conducting regular cycle counts and audits should make sure that discrepancies are found and corrected.