With increased adoption of IoT and automation it is imperative for larger organizations to streamline repetitive assembly line activities with minimal monitoring.
In a warehouse, IoT can be used to maintain quality across the supply chains and also keep a check on the conditions of the items in storage . IoT devices can help to monitor, detect, report the status, receive information and take action based on the information they collate.
To scale up, organizations have to manage increasing demand and hence it is imperative to adopt new technology. Leveraging IoT, cloud-based management systems and intelligent technologies is need of hour.
The Process Diagram:
Applications of IoT in Warehouse management:
Challenges and KPI metrics :
For bigger warehouses and plants, where materials are received in bulk, it is a Herculean task to validate each receipt against orders.
Amongst the critical KPIs in a warehouse are the metrics that measure performance of receiving goods. All warehouse operations typically start with receiving, and inefficiencies at this stage will trickle through all the subsequent processes.
Critical KPI metrics that adhere to the receiving processes are:
- Cost of Receiving Per Receiving Line: The warehouse incurs significant expenses on each receiving line. Handling costs are included as well.
- Receiving Productivity: In terms of effort needed per warehouse per clerk per hour.
- Receiving Accuracy: The percentage accuracy of the receipts, i.e., the proportionate correctly received orders.
- Dock Door Utilization: Percentage of how the total dock doors are utilized in percentage.
- Receiving Cycle Time: Processing time of each receipt.
Each receipt has to go through virtually three steps:
SCAN: Many warehouses still unload truckloads of materials, packed in pallets, and place them in the warehouse doing a recordkeeping manually.
To better streamline this process we can use a GS1 datamatrix label on each of the pallet. This format of label will be common across the globe and can be used in any scanner to decipher the material, batch, quantity etc. by simply scanning the code.
For many pallets which miss a datamatrix label on it ,an inhouse app running on a handheld device can be used to print and stick the labels on the pallets instantly.
These codes stuck on pallets can then be received in warehouses by passing through a dock door equipped with a scanner and lights indicating success and failure:
Validation: This step consist of scanning and validating the contents in the pallets vis-à-vis the purchase order data. On scanning the pallets, it validates the data against the order and creates the required goods receipt documents automatically by doing the required validations against backend tables.
If the validations are successful, then a green visual indicator is displayed indicating SUCCESS whereas a red indicator flags an anomaly against the order.
This whole process can be done using an app on the mobile device/scanners which interfaces with the visual indicators
In case of a red indicator, a manual intervention will be needed to check the cause and rectification actions taken accordingly.
Receive: Once the pallets are scanned and validated, the same app can be used to validate placement of the goods in the storage bins. If it’s a green then the fork lift operator places the goods in the entered bin and thus put away of received materials is completed. The same process can also be automated with the use of robots instead of fork lift operators.
Conclusion: Implementing such solutions for large scale manufacturing clients will help to reduce the effort and increase accuracy in case of mass receipts by improving the KPIs as mentioned above.
This is a low cost mechanism increase efficiency for all organizations with large scale warehouse operations.