Okay, let's break down the Average Warehouse Handling Time KPI in the Logistics & Transportation industry.

Average Warehouse Handling Time KPI

Data Requirements

To accurately calculate Average Warehouse Handling Time, you need a variety of data points. Here's a breakdown:

Specific Fields and Metrics:

• Order ID/Reference Number:

  A unique identifier for each order processed through the warehouse.

• Inbound Time Stamp:

  The exact date and time when goods arrive at the warehouse. This could be the time of unloading from a truck or receipt at a receiving dock.

• Outbound Time Stamp:

  The exact date and time when goods leave the warehouse. This could be the time of loading onto a truck or dispatch from a shipping dock.

• Handling Time (per order):

  The difference between the Outbound Time Stamp and the Inbound Time Stamp for each order. This is calculated as:

  .

• Order Type:

  Categorization of the order (e.g., inbound, outbound, cross-docking, returns). This allows for analysis of handling time by type.

• Product Type/SKU:

  The specific product being handled. This can help identify products that take longer to process.

• Warehouse Location:

  If you have multiple warehouses, this field is crucial for comparing performance across locations.

• Handling Team/Resource:

  The team or individual responsible for handling the order. This can help identify bottlenecks or high-performing teams.

• Quantity of Items:

  The number of items in the order. This can help understand if larger orders take longer to process.

• Unit of Measure:

  The unit of measure for the quantity (e.g., pieces, pallets, boxes).

Data Sources:

• Warehouse Management System (WMS):

  This is the primary source for most of the required data, including timestamps, order details, and product information.

• Transportation Management System (TMS):

  Can provide information on arrival and departure times, especially if integrated with the WMS.

• Enterprise Resource Planning (ERP) System:

  May contain order details and product information.

• Manual Logs/Spreadsheets:

  In some cases, especially for smaller operations, data might be manually recorded. This should be avoided for accuracy and scalability.

• Real-Time Location Systems (RTLS):

  Can provide precise location and time data for goods within the warehouse.

Calculation Methodology

Here's a step-by-step guide on how to calculate Average Warehouse Handling Time:

1. Data Collection:

   Gather all the necessary data points from the sources mentioned above. Ensure data accuracy and consistency.

2. Calculate Handling Time per Order:

   For each order, subtract the Inbound Time Stamp from the Outbound Time Stamp. This will give you the handling time for that specific order. For example:

   • Order A: Outbound Time: 10:00 AM, Inbound Time: 8:00 AM. Handling Time = 2 hours.

   • Order B: Outbound Time: 3:00 PM, Inbound Time: 1:00 PM. Handling Time = 2 hours.

   • Order C: Outbound Time: 11:00 AM, Inbound Time: 9:30 AM. Handling Time = 1.5 hours.

3. Sum of Handling Times:

   Add up the handling times for all orders within the period you are analyzing (e.g., daily, weekly, monthly). In our example: 2 + 2 + 1.5 = 5.5 hours.

4. Count of Orders:

   Determine the total number of orders processed within the same period. In our example: 3 orders.

5. Calculate Average Handling Time:

   Divide the total sum of handling times by the total number of orders. Formula:
   

   In our example: 5.5 hours / 3 orders = 1.83 hours per order.

6. Time Unit:

   The result will be in the same time unit as your timestamps (e.g., hours, minutes).

Application of Analytics Model

An AI-powered analytics platform like 'Analytics Model' can significantly streamline the calculation and analysis of Average Warehouse Handling Time. Here's how:

• Real-Time Querying:

  Users can use free-text queries to extract the necessary data from various sources. For example, a user could ask: "Show me the average handling time for all outbound orders in warehouse A for the last week." The platform would automatically translate this into a database query and retrieve the relevant data.

• Automated Calculation:

  The platform can automatically calculate the handling time for each order and then compute the average, eliminating the need for manual calculations.

• Automated Insights:

  The AI can identify trends and patterns in the data. For example, it might highlight that certain product types or handling teams have significantly longer handling times. It can also detect anomalies, such as unusually long handling times for specific orders, triggering alerts.

• Visualization Capabilities:

  The platform can present the data in various formats, such as charts, graphs, and dashboards. This allows users to easily visualize trends, compare performance across different warehouses or teams, and identify areas for improvement. For example, a line graph could show the trend of average handling time over time, while a bar chart could compare handling times across different warehouses.

• Customizable Dashboards:

  Users can create custom dashboards to track the KPIs that are most relevant to their operations.

• Predictive Analytics:

  The platform can use historical data to predict future handling times, allowing for proactive resource planning and optimization.

Business Value

The Average Warehouse Handling Time KPI is crucial for several reasons:

• Efficiency Improvement:

  By tracking this KPI, businesses can identify bottlenecks and inefficiencies in their warehouse operations. This allows them to implement process improvements, optimize workflows, and reduce handling times.

• Cost Reduction:

  Faster handling times translate to lower labor costs, reduced storage costs, and improved resource utilization.

• Improved Customer Satisfaction:

  Faster order processing leads to quicker delivery times, which enhances customer satisfaction and loyalty.

• Better Resource Planning:

  Understanding average handling times allows for more accurate resource planning, ensuring that the right number of staff and equipment are available when needed.

• Performance Benchmarking:

  This KPI can be used to benchmark performance against industry standards and identify areas where the company can gain a competitive advantage.

• Data-Driven Decision Making:

  The KPI provides valuable data for making informed decisions about warehouse layout, staffing, and technology investments.

• Reduced Errors:

  Faster handling times can also lead to fewer errors in order processing, improving overall operational efficiency.

In summary, the Average Warehouse Handling Time KPI is a critical metric for logistics and transportation companies. By leveraging data, analytics, and AI-powered platforms, businesses can optimize their warehouse operations, reduce costs, and improve customer satisfaction.