DATABERG
Data sits in the foundation of all warehousing processes: from receiving inventory goods to shipping an order. And with the implementation of new smart tools, such as AI, IOT, Advanced analytics, and Blockchain, the volume of the data generated on a daily basis is increasing continuously. That is why adopting certain data metrics and KPIs is key for ensuring successful financial and operational warehousing performance in the long-run.
In this article, you will find the most important data-based metrics, which every warehousing company should adopt in order to achieve a higher level of control over its processes, assets, workforce, productivity, etc. Those are derived from the 2015 Warehousing Education and Research Council (WERC) DC Measures Report and have been adjusted to suit data-driven companies in the sector. Let’s dive in.
On-time shipment
This key performance indicator shows the ability of the company to ship orders in (short) time. According to a 2017 research, this metric has a direct connection to the level of customer satisfaction; because nowadays, clients demand to have everything on hand as quickly as possible, every time.
The ratio used for the calculation of these metrics is units shipped on time/ total units shipped for a specific time period.
Taking this formula into account, data integration helps to accurately track the performance in terms of OTS in real-time. This is essential because, in the warehousing sector, time is a key asset that should be effectively optimized. Companies simply cannot “afford the luxury” to have access to old data, as this can cause errors, misleading trends, and wrongful insights, which can lead to wrong decision-making and trammel the smooth flow of operations.
When having real-time data analytics on hand, warehousing businesses can derive valuable insights about the level of their performance and make the necessary changes to enhance the percentage of the on-time shipped goods. A good reference point, in this case, is OTS to be 99.87% or higher, as this is the average percentage of the on-time shipped orders of leading companies in the warehousing industry. Such data-based KPI will show that the organization is able to compete with the leaders in the sector, which offers many opportunities for growth and development.
Internal order cycle time
This second data metric is closely related to OTS. It is the elapsed time from receiving customer order to shipping it. The goal is to use as little time as possible, taking into account the Internal order cycle time of warehousing leader organizations: 3.8 hours.
Here, again, time is crucial for high-quality customer CX. Yet, how can data analytics help this KPI to be improved?
Data integration can provide business insights, which to recognize outdated planning processes, wrong allocation of machine capital and other tangible assets, unoptimized flow of activities and tasks, and gaps in order-preparation strategy. With appropriate data available, we are able to track the location of specific goods and batches of products. And when knowing where to find a certain product, the IOCT is diminished.
But this is not enough. Advanced analytics provide companies with the opportunity to optimize the workflow in order to save time and resources. In this case, not only real-time data is key, but also Dark Data. Such a tool unveils trapped data from machines, sensors, and devices, and can be extremely valuable for decision-making in regards to successful and time-wise operational performance.
Order picking accuracy
This indicator measures the percentage of the right, accurate orders, taking into account occurred errors throughout the process, including mistaken goods, wrong product consolidation, etc.
The formula for OPA is the number of accurate orders picked/ total number of orders picked. Efficient, warehousing business entities achieve 99.84% OPA.
This metric is crucial because correct orders equal to happy customers. And when we satisfy our clients the chance for them to recommend our company to their partners and acquaintances increases tremendously.
Using RIFD tags enable real-time tracking of goods, not only within the warehouse but throughout the whole supply chain. Such a real-time identification allows quick location classification and at the same time, diminishes the risk of picking the wrong goods or baches from the inventory. Integrating RIFD with advanced data analytics helps the organization to instantly identify any order mistakes, and intervene in the process to prevent wrong order shipping.
Back-orders time
BOT show the ratio of orders that are held or delayed, due to lack of available stock. The formula for this metric is number of undeliverable orders/ number of total orders. A reference point, in this case, is 0.05%: the average percentage of BOT that warehousing leaders generate.
This indicator is essential to take into account in order to ensure smooth workflow and warehouse motion. For this reason, having the right data on hand is crucial. The integration of high-quality data facilitates warehouse business entities to implement a data-based decision-making process for inventory planning. Using real-time data analytics enables constant control and monitoring of the available goods which are in stock.
This way, the management will have a clear overview of all kinds of inventories, including transit, cycle, safety, obsolete, decoupling, and others. Such practice allows the instant recognition of risks in regards to stock availability and enables the management to intervene if any problem occurs with the inventory levels.
Conclusion
The combination of all the presented data-based metrics is key to achieving perfect orders: orders containing the right products, of the right quality and in the right amounts, which are delivered to the right person, at the right time and location. So perfect orders result in higher satisfaction levels, better productivity and financial performance, and successful business growth.
