In today's globalized industrial landscape, it is as important as ever for warehouses to be as efficient as possible. However, there are several factors that warehouses struggle with in terms of maximizing their efficiency. In many ways, blockchain technology can help, particularly with boosting inventory accuracy, increasing supply chain visibility, and making manufacturers more responsive to changing consumer demands.
The following are the main obstacles to warehouse efficiency:
● Inventory accuracy below 95%
● Order picking inefficiencies
● Slow to respond to changes in demand
Inventory Accuracy Below 95%
Inventory should always be stored in the correct and properly identified slots. If information is recorded via a manual tracking system, it should always be verified for accuracy, which requires more work and is a drain on employee productivity. The lack of visibility into supply chain and inventory data increases the risk of running out of stock at precisely the wrong time, thereby harming customer satisfaction. On the flip side, a warehouse could carry too much stock that it depreciates in value while incurring unnecessary storage costs.
If inventory data in a warehouse is recorded using pen and paper or manual data entry, inventory data is unfortunately prone to errors, sometimes having only 60% accuracy. Rudimentary digitization techniques can raise this accuracy to 95%, but that's often not enough. Inventory data accuracy of 100% is important for certain industries, such as aerospace manufacturing. Even in industries like apparel, customers expect to receive exactly the product they ordered. An error rate of 5% is far too high when accounting for returns and customer complaints. Mobile bar-code scanners combined with blockchain technology can raise this data accuracy to nearly 100%.
Order Picking Inefficiencies
Not only does inventory data need to be accurate, but the location of inventory items also need to be correct in order to maximize order picking efficiency. If the location of an item isn't adequately known, or it takes a long time to pull that information up, pickers take a much longer time searching for that item in the warehouse than they should. This causes shipping delays and subsequent bottlenecks in labor allocation and dock-door scheduling.
If warehouses still rely on manual or paper-based processes, unnecessary time is being spent with putaway and picking. To speed up order picking, warehouses should avoid having its workers manually enter SKU numbers, and instead have them use a handheld barcode scanner to get the most up-to-date reading of inventory. Additionally, system-directed putaway and picking, with the use of blockchain as a data repository solution, can help automate picking routes in the warehouse, reducing wear and tear on equipment and increasing workforce productivity.
Changes in Demand from Globalization and Seasonality
Globalization's effect on manufacturing is stiffer competition from all over the world. Although products from certain countries don't have the same workmanship as other places, companies are always in search for lower production costs. However, the race to the bottom with lower production costs may hurt their businesses in the long term, because consumers are increasingly demanding higher quality and better workmanship. Manufacturers and suppliers would do better to practice effective warehouse management that saves both time and money using automated processes and increased accuracy in order picking and deliveries. Those that find ways to both cut costs and increase accuracy while keeping customers happy would stay in business far longer than those who don't.
Sometimes changes in seasonality or the economy play a role in increasing volatility in consumer demand. A global financial crisis, for example, results in decreased consumer confidence, therefore increasing the amount of inventory sitting in warehouses, losing value by the month or year. Seasonality sometimes requires changes in numbers of workers in multiple locations, improvements in processes, and full visibility into the entire supply chain system in order to accurately forecast consumer demand.