Container Demand Forecasting: Overcoming Bottlenecks in Large-Scale Logistics Planning

In the daily operation of the logistics industry, for large-scale international shipments, enterprises need to accurately calculate the number of containers required for each batch of products for container planning.

However, this task often requires reconciling shipment details with product cabinet count tables and doing a lot of manual calculations. As the number of orders and the amount of data increases, traditional manual processing methods expose problems such as inefficiency, error-proneness, and difficulty in responding to order changes, which has become a major bottleneck in logistics planning. How to improve accuracy and speed while reducing the burden of manpower has become a pain point that enterprises need to solve urgently.

Taking the scenario in the attached diagram as an example, in order to complete the shipment planning, the operator needs to refer to two key tables for comparison and calculation:

1. Shipment Schedule: Record the basic information of each shipment, including:
   • Shipment Date: Determine the time point of shipment.
   • Invoice Number: Marks the unique identification of each shipment.
   • Part Number: A unique code for each product that is used for subsequent comparisons.
   • Shipped Quantity: The total quantity of the batch of products.
   • Customer name, origin, destination: used for logistics routing and distribution planning.
   • Terms of Trade & Freight Forwarding: Impacting logistics costs and transportation options.
2. Product Cabinet Table: Provides the standard loading capacity for each product, including:
   • Part number: corresponds to the part number in the shipping details for comparison and searching.
   • Shipped quantity/container: indicates the maximum number of products that can be accommodated in each container (such as 900, 600, etc.). This is the key data for subsequent calculations.

In practice, the process of comparing and calculating these tables is usually done manually, and the overall process is cumbersome and challenging, including the following steps:

1. Compare the part number of the product one by one: the personnel need to extract the part number of each product from the shipment schedule, and then manually find the corresponding part number in the product cabinet number table to confirm the loading capacity of the product. If there are many job numbers, the comparison process is prone to finding errors or omissions.
2. Calculate the number of containers for each shipment: After confirming the loading capacity, the operator needs to manually calculate and calculate the total number of containers for each batch of products according to the formula “Shipped Quantity ÷Shipped Quantity/Container”. For example, if the quantity of a shipment is 70,200, if the capacity of each container is 900, it needs to be calculated: 70,200÷900=78 (rounded up). This requires manual processing of the base rule and logging the results into a new report.
3. A large number of repetitions: for multiple shipment records (may exceed 100 times), the operator needs to repeatedly carry out the above comparison and calculation actions, and the workload increases exponentially with the growth of the shipment scale.
4. Fill in the new report: Once you’ve completed your calculations, you need to enter the results into a new report one by one and check that each piece of data is correct.
5. Handling temporary changes: If there is a change in the quantity or part number in the shipping details, it needs to be re-compared and calculated, which means that the previous work may need to be completely overturned, adding additional time and labor costs.

Challenges and pain points of manual operation:

1. The workload is huge: The repetitive operations of multi-table comparison and pen-by-pen calculation are time-consuming and labor-intensive, and difficult to complete quickly.
2. Error-prone: The slightest negligence in manual search and calculation can lead to data errors, which in turn affect subsequent logistics planning.
3. Inefficiency: In the case of large order volumes, manual operation cannot meet the needs of fast shipments.
4. Inability to respond to changes in real time: Lack of flexibility in manual processes when order demand is adjusted or data is updated, which can lead to planning delays.

Such an operating model is not only a waste of human resources, but can also have a negative impact on logistics costs and service quality, so more efficient solutions are needed to replace it.

How does RPA solve the cumbersome problem of comparing the requirements of the cabinet？

To address these pain points, RPA (Robotic Process Automation) technology can automate the entire process, completely reduce human involvement, and improve accuracy and efficiency. Here are the steps to solve it:

1. Data capture: The RPA robot will first automatically extract the shipment details and product cabinet list from the system, and convert these data into a standardized format for processing to ensure that all data sources are consistent.
2. Compare part numbers: The robot uses the built-in lookup function (similar to Vlookup in Excel) to automatically match the corresponding loading capacity in the product case number table according to the part number in the shipment schedule, and record the results in the staging field.
3. Calculate the total number of containers: The robot calculates according to the formula “Shipped quantity ÷ number of containers per container”, and automatically processes the base system (e.g. rounding up to ensure that all products can be fully loaded), and records the results in a new report.
4. Generate a new report: After all calculations are completed, the RPA robot generates a complete report with shipment details and the total number of containers required, in a clear format and accurate data, which can be directly used for logistics planning and subsequent resource allocation.
5. Real-time update and notification: If there is any change in the shipment details or loading capacity, the robot will re-compare and calculate in real time, and automatically notify the relevant departments of the update results to ensure that all personnel have the latest information at the same time.

Through the above steps, RPA not only shortens the time of data processing, but also significantly reduces the risk of errors, making logistics planning more accurate and efficient.

The demand for the order leads to the benefits of RPA

After introducing RPA technology into the data processing process of the logistics industry, enterprises will gain the following key benefits:

1. Efficiency improvement

1. RPA can process data at extremely high speeds, dozens of times faster than manual operations, and reduce hours of work to minutes.
2. For high-volume order processing, RPA can run around the clock without interruption or breaking, greatly improving work efficiency.

2. Improved accuracy

1. Automatic data comparison and calculation avoids human negligence or errors, and ensures the accuracy of each data.
2. Ensure accurate calculation of the number of containers to avoid waste of logistics resources or additional costs due to calculation errors.

3. Cost savings

1. Reduce the need for manual operations, reduce labor costs, and free employees from repetitive tasks.
2. Reduce additional costs caused by misallocation of logistics resources, such as excess freight or delay charges due to misestimating the number of containers.

Strengthen resilience

1. Instant updates and notifications allow businesses to respond quickly to order changes, reduce adjustment time, and ensure logistics plans are not impacted.
2. In the face of peak periods or sudden large orders, the efficient processing capability of RPA can stably support business operations.

In a highly competitive and volatile logistics market, RPA technology has become a powerful tool to improve operational efficiency and accuracy. RPA can not only quickly process large-scale data, but also ensure the accuracy of results, helping enterprises reduce operating costs and improve logistics planning capabilities.

The introduction of RPA is not only a choice for technological upgrading, but also an important cornerstone for building intelligent logistics in the future. Companies should actively adopt such automation solutions to improve their market competitiveness and customer satisfaction.

KSCC is a management consulting company in Taiwan. Our services include corporate in-house training, consulting, and leadership management.For more information about our corporate services, please feel free to visit our website: https://kscthinktank.com.tw/custom-training/ 

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