Efficiency and cost are critical issues for the maritime shipping industry, which is undergoing rapid change. The Parcel Size Distribution function (PSD) is an essential Parcel Size Distribution (PSD) is a term used by logistics professionals to describe the statistics of various parcel sizes in a particular shipment or many different shipments over time. The contents of parcels are grouped into three categories according to size: small, medium and large. The frequency of each parcel grouped by size is measured, which can be used to analyse the distribution of parcels.
The Importance of Analyzing Parcel Sizes in Maritime Shipping
The reasons for using Parcel Size Distribution (PSD) analysis in marine shipping operations are numerous and wide-ranging. Let’s examine why PSD is necessary and how it can be leveraged to optimise shipping—well, everywhere.
1. Logistics Optimization:
By analysing the size of parcels, maritime logistics companies can fine-tune their storage and transportation strategies. This has several benefits:
Efficient Storage Design:
Knowing the distribution of parcel sizes helps logistics providers provide adequate storage space per parcel, preventing wastage of space within the warehouse. This is beneficial for enhancing inventory management as storage areas can be used effectively. For example, if we know that medium parcel sizes are the most common, we can create storage zones specifically for them, minimising handling time and facilitating the flow of goods.
Optimized Loading:
Parcel Size Distribution (PSD) analysis is a strategic tool for planning the loading of containers, trucks, and storage units. By understanding the normal sizes of parcels, the logistics team can optimize the use of space in shipping containers, thereby reducing the number of trips and transportation costs. Moreover, optimized loading of containers can ensure that goods won’t be damaged during the transportation process, because safe and balanced loads reduce the potential of accidents.
2. Cost Management:
In the maritime shipping industry, understanding PSD is instrumental in managing costs effectively:
Accurate Shipping Cost Forecasting:
They charge based on the parcel size and weight. This means that if a company knows the distribution of their PSD, they can forecast the shipping costs better. The company can use this forecasting to negotiate with the carriers about the rates, and this will reduce the overall transportation costs. Understanding the common parcel size also helps select the cheapest shipping options.
Reduced Packaging Costs:
Companies that know the sizes of parcels they have to deal with regularly can order bulk stocks of packaging materials, tailored to the most typical package sizes, avoiding package waste and reducing overall packaging costs.
3. Enhanced Operational Efficiency:
PSD analysis helps with streamlined operations, and this is important for competitiveness in shipping:
Streamlined Processes:
Standardised parcel sizes allow for improved handling and processing, from sorting to delivery. Automated systems can be calibrated to the most common parcel sizes, reducing bottlenecks and improving turnaround times.
Optimized Resource Allocation:
PSD analysis can help schedule labour and equipment requirements based on the size and quantity of parcels, ensuring that resources are more efficiently deployed. For example, based on distribution of the size of the shipments, one can work out the number of workers and equipment needed to process the shipments, and hence control the inventory and improve the productivity.
The Broader Impact on the Maritime Industry
In an industry where operational efficiency and cost control are crucial, PSD analysis is a useful tool for logistics providers to sharpen their competitive edge. Using the PSD, companies can reduce costs, improve service delivery to their customers, and cement their position in the ever-globalising shipping world.
Furthermore, as sustainability takes center stage in maritime shipping, the optimization of parcel sizes through PSD analysis can significantly contribute to ship sustainability. A more efficient loading process and reduced material waste not only improve operational efficiency but also align with the broader sustainability efforts of companies, aiming to reduce their carbon footprint.
The Parcel Size Distribution (PSD) function is one of the most underrated and underutilised tools in maritime shipping. Analysing the PSD function can enhance storage and transportation efficiency, slash costs, and improve operational performance. The result is better service, higher customer satisfaction, and a stronger market position.
Key Components
In marine shipping, Parcel Size Distribution (PSD) is a fundamental concept to facilitate logistics, minimise costs, and maximise operational efficiency. PSD analysis consists of several components that break down the parcel-size distribution of a shipment and are used to interpret the PSD. We use PSD to chart parcel size distributions and its components to dissect the cargo.
1. Size Categories:
The first of the seven principles of PSD is classifying parcels into different size categories. This allows for simplifying the analysis and planning process to employ more targeted strategies. Below are the most common size categories along with examples:
Small Parcels:
Examples: Jewelry, small electronics (like smartphones), books.
Dimensions: Generally, less than 12 inches on any side and lightweight.
Medium Parcels:
Examples: Clothing, shoes, mid-sized electronics (like tablets).
Dimensions: Typically, between 12 and 24 inches on any side and moderately weighted.
Large Parcels:
Examples: Home appliances (like microwaves), large electronics (like TVs).
Dimensions: Usually more than 24 inches on any side and heavier.
Extra-Large Parcels:
Examples: Furniture, ample equipment.
Dimensions: Significantly large, often exceeding standard pallet sizes.
Sorting parcels by size like this enables logistics companies to configure their storage and transport processes according to the requirements of each size category and become more efficient as a result.
2. Frequency Distribution:
For the PSD, frequency distribution refers to the occurrence of the same parcel size in a shipment. The number of parcels in each parcel size category can be illustrated with histograms or bar charts, e.g.:
Small Parcels: 50% of the total shipment.
Medium Parcels: 30% of the total shipment.
Large Parcels: 15% of the total shipment.
Extra-Large Parcels: 5% of the total shipment.
The frequency distribution is significant because it helps us determine the most popular parcel sizes that a company handles. Knowing this allows logistics providers:
Optimize storage space by allocating more room to frequently occurring parcel sizes.
Streamline sorting and processing operations by focusing resources on the most common sizes.
Better plan vehicles, ensuring that the most frequent parcel sizes are loaded to best fill the available volume.
3. Cumulative Distribution:
Cumulative distribution is a way of displaying the running total of parcels as you move from the smallest to the largest size category. Each value in the graph represents the percentage of parcels less than or within that size category. If your parcel distribution is:
Small Parcels: 50%
Medium Parcels: 30%
Large Parcels: 15%
Extra-Large Parcels: 5%
The cumulative distribution would be:
Up to Small Parcels: 50%
Up to Medium Parcels: 80% (50% small + 30% medium)
Up to Large Parcels: 95% (80% up to medium + 15% large)
Up to Extra-Large Parcels: 100% (95% up to large + 5% extra-large)
How Cumulative Distribution Helps:
Overall Visualisation: Cumulative distribution visually represents the percentage of total parcels below each size threshold and provides an at-a-glance overview of the landscape of parcel sizes.
Allocation of resources: If, for example, 80 percent of parcels are small or medium-sized, logistics providers can direct more resources (say labour or equipment) to handling that size.
Capacity Management: The cumulative distribution helps the warehouse plan space and vehicle load depending on the cumulative percentage of parcel size. For instance, as 90 percent of the parcels are less than 5kg, the storage and transport capacities will not be overwhelmed by a large volume of parcels more than 5kg.
The main components in Constructing Parcel Size Distribution(PSD) are grouping parcels into different groups by size (small, medium, large, extra-large), analysing the frequency of each size of parcel in a shipment, and calculating the cumulative distribution to achieve the parcel size distribution amongst the whole shipments.
Constructing parcel size distribution is essential in predicting storage requirements, improving transportation efficiency, controlling transportation costs, and maximising operational efficiency within the maritime shipping industry.
Data Collection and Potential Challenges in Parcel Size Distribution (PSD) Analysis
In the maritime shipping industry, accurate analysis of Parcel Size Distribution (PSD) is crucial for optimizing operations, managing costs, and ensuring customer satisfaction. However, effective PSD analysis relies heavily on precise data collection and overcoming several potential challenges. This blog post explores the importance of data collection in PSD analysis and highlights the potential challenges that companies may face.
Importance of Data Collection in Decision Making
1. Precision in Decision Making
Accuracy Matters: The foundation of any reliable PSD analysis is accurate data. Precision in data collection ensures that the resulting PSD insights are trustworthy. When decisions are made based on accurate data, companies are more likely to achieve operational efficiencies and cost savings. For example, knowing the exact proportion of different parcel sizes allows for more effective storage planning and better utilization of transportation resources.
Improved Forecasting: Precise data also plays a critical role in forecasting future trends. By understanding historical parcel size data, logistics providers can predict future shipping patterns and allocate resources more effectively. This foresight allows companies to prepare for peak seasons, adjust storage needs, and scale transportation capacities accordingly.
2. Optimized Operations
Enhanced Storage and Transportation: Accurate knowledge of parcel size distribution allows companies to design storage spaces and plan transportation routes that minimize wasted space and time. For example, understanding that a significant portion of parcels are medium-sized could lead to more efficient warehouse shelving layouts and optimized loading strategies for transport vehicles.
Cost Management: With precise PSD data, companies can forecast shipping costs more accurately and negotiate better rates with carriers. Businesses can present detailed and reliable parcel size data to carriers, helping secure more favorable shipping contracts based on the actual distribution of parcel sizes.
3. Boosting Customer Satisfaction
Improved Service Quality: Efficient operations, driven by accurate PSD analyses, result in faster and more reliable deliveries. This directly enhances customer satisfaction by ensuring that orders arrive on time and in good condition. In a competitive industry like maritime shipping, high levels of customer satisfaction are key to retaining business and building a strong reputation.
Data Collection and Potential Challenges
Data collected from the maritime shipping industry about Parcel Size Distribution (PSD) plays a vital role in determining the proper routing, cost, and timeframe. PSD analysis is a necessary tool for improving customer service and detailed planning.
Reliable data must be collected in order to conduct PSD analysis accurately. It is of utmost importance to take various factors into account and overcome numerous potential challenges that could arise. In this blog post, we will examine the role of data collection in PSD analysis and provide some examples of the possible challenges a company may face in its operations.
Importance of Data Collection in Decision Making
1. Precision in Decision Making:
Data is the Heart of the Matter: Accurate data is the starting point for any valuable PSD analysis. Precise data collection drives the trustworthiness of the eventual PSD analytics. With trustworthy data, companies are more likely to improve operating efficiencies and reduce costs. For instance, knowing the precise percentages of parcels of different sizes will likely lead to improved planning of available storage space and better utilisation of transportation resources.
Better predictions: Precise data also helps to predict future trends. Logistics providers can use historical data on parcel size to forecast future shipping trends and allocate resources accordingly. Delivery companies can plan for peak seasons, prepare sufficient storage space, and scale transport capacity accordingly.
2. Optimized Operations:
Improved storage and transportation: Precise knowledge about parcel-size distribution enables logistics companies to design storage rooms and plan transportation routes to minimise wasted space and time. For instance, if we know that many parcels are of medium size, we could design some storage room shelves or loading strategies for vehicles in a way that exploits this knowledge.
Cost Control: With precise PSD data, companies can better forecast shipping costs and negotiate rates with carriers. Businesses can show detailed and accurate parcel size data to carriers to ensure they win the best shipping contract based on the true distribution of parcel sizes.
3. Boosting Customer Satisfaction:
Higher service quality: efficient operations, as a result of accurate PSDs, means faster and more reliable deliveries, directly impacting customer satisfaction by ensuring that orders arrive at their destination on time and intact. As in most industries, high customer satisfaction is a competitive advantage in the maritime shipping industry and can be a significant factor in retaining business and preserving a reputable brand.
Potential Challenges in PSD Analysis:
While the challenges in PSD analysis are significant, overcoming them can lead to correct and effective analysis, thereby reaping the benefits of improved decision-making and operational efficiency.
1. Data Collection Methods:
Manual data entry: this is often the primary source of errors in PSD analysis. Errors during manual data entry will bias your results, leading to bad decisions (and potentially expensive ones!). Automating data collection, where possible, is an option. Alternatively, you can put strict quality controls in place.
Differing measurement standards: Departments or systems within an organisation may not measure the same parcels against the same standard, and this inconsistency can compromise the reliability of PSD analysis. All teams and systems should measure the same parcels against the same standard.
2. Data Management:
Processing large volumes of data: Given the massive volume of data maritime shipping generates daily, a robust and scalable data management system and tools must be implemented to ensure that data can be handled and processed promptly and with minimal errors. Ship owners and operators must be able to collect, process and analyse PSD data seamlessly to keep pace with the increasing volume and complexity of data. Just like companies process data produced by other sources of information, they should be able to process data from PSD.
Data Integration: Combining data from multiple sources (warehouses, transportation systems, customer service platforms, etc.) can be complicated. If the data collected from different sources is formatted differently or if different standards were used for reporting the data, integration is difficult. It is important to have a unified data-management system that can integrate data from various separate sources without compromising the integrity of the data.
3. Dynamic Nature of Shipments:
Changing Shipment Profiles: The size and volume of shipments can change over time due to market trends, seasonal factors or business growth. These changes can significantly impact the PSD, making maintaining a constant and accurate analysis challenging. Companies must regularly update their PSD models to reflect these changes and ensure the operational strategy remains relevant.
Continuous analysis: Due to the dynamic nature of PSD data, continuous analysis is required to maintain optimal operational efficiency and cost-effectiveness. A static analysis can age very quickly, leading to sub-optimal operation. Real-time data analytics and monitoring tools can help companies maintain responsiveness to changing parcel size distributions.
Ensuring proper data collection and management is vital for the success of PSD analysis in maritime shipping. Accurate data will lead to better decisions and better performance. Companies that do not adequately collect and manage their data can face challenges. These include data entry errors, non-standardised measurement systems and dynamic shipments. Using robust data management systems and continuous data analysis, logistics providers can leverage PSD insights to keep up with the dynamics of this evolving industry.
The Dynamic nature of Shipments and the need for Continuous PSD Analysis
The parcel shipment profile in maritime shipping is dynamic. It changes with the seasons, market trends, and company growth. A static analysis of Parcel Size Distribution (PSD) can quickly become obsolete. This post explores what drives change in shipment profiles and the significance of continuous PSD analysis to optimise storage, transportation, and cost efficiency.
1. Changing Shipment Profiles:
In terms of the profile of shipments in maritime industry, many factors impact its pattern:
Seasonal Variations:
Example: at Christmas, there are likely to be many more parcels of small or medium size (e.g. gifts and other small items, televisions, computers and other household electronic goods). At other times of the year, there might be more of a continuous distribution of parcel sizes, with fewer small parcels and more large parcels.
Impact: These seasonal changes require changes in storage and transport strategies to accommodate the influx of smaller parcels during peak periods.
Market Trends:
Example: Increased e-commerce due to online consumer demand for speed and convenience may increase small and medium parcels. Similarly, a trend in bulk buying, such as at sales events or for commercial needs, can increase large parcel volumes.
Aftermath: Market trends can immediately shift the bell-curved parcel-size distribution, forcing companies to update their logistics strategies regularly.
Business Growth:
Example: As a company grows its operations, its shipping profiles might change, for example, entering new markets or introducing new product lines that shift the weighting of parcel sizes, which means changing existing storage and transport plans.
Outcome: Since growth-oriented changes require the firm to adapt its logistics infrastructure along with increasing business requirements, logistics management must be flexible.
2. Impact on PSD and Logistics
The evolving PSD profile has a direct effect on shipment and, consequently, influences the logistics management in many areas:
Storage Design:
Adaptability: Storage solutions must adapt to fluctuations in parcel size distribution. A previously optimal storage layout might become a bottleneck if the kinds of parcels to be stored change significantly, leading to inefficient use of space and high handling times.
Transportation Planning:
Loading Efficiency: With the changing pattern of the parcel size distribution, we can load trucks or containers more or less efficiently. For example, a sudden increase in the amount of small parcels may leave gaps among the parcels and reduce the loading efficiency, thereby increasing the cost of transportation; on the other hand, if a large number of significant parcels arrive, we may need to adjust the type of vehicles or the route planning.
Cost Forecasting:
Dynamic Rates: Rates negotiated based on an old PSD may no longer be attractive if the PSD changes. Suddenly, shipping costs go up and profit margins shrink. Companies should continually revisit their PSD analysis to negotiate the best rates based on the most recent data.
3. The Need for Continuous Analysis
Because shipments are constantly changing, the continuous PSD analysis is needed. Here’s how it can be done:
Real-Time Data Collection:
Automation: Automated data collection processes (e.g., barcode scanners, and IoT devices) can be implemented to ensure that all information is captured in real time, providing an up-to-minute view of shipment profiles and reducing the risks of manual record-keeping errors.
Regular Updates:
Regular Analysis: Regularly refreshing the PSD analysis allows companies to respond promptly to changes in shipment profiles, adjust to a changing marketplace, avoid inefficiencies, and maintain the smooth flow of commerce.
Adaptive Strategies:
Flexibility: Because shipments are unpredictable, businesses’ storage allocations, transportation plans, and cost-management strategies must be flexible enough to change if a new PSD analysis indicates a different optimal way to run the logistics process.
Due to seasonal changes, market trends, or business growth, PSD of shipments is a constantly moving target that requires regular analysis. This enables companies to make more informed decisions, improve and optimize operations, and meet customer expectations.
There are challenges to this: human error, variable metrics, and processing a large volume of data are all part of the challenge.
Conclusion:
To summarise, parcel size distribution plays an important role on the operation of maritime shipping industries. By analysing the parcel size in different categories and calculating their distribution or curve, companies can improve the design of storage, the loading strategies, forecast shipping costs accurately, reduce packaging cost, etc. The cumulative distribution analysis makes managing resources and capacities easier and more efficient.
Since PSD calculations are quite sensitive to the date of sampling, effective PSD analysis requires close monitoring and regular updating of the data as shipments change due to seasonality, market trends, and business growth. Unless robust data management and real-time data collection tools are in place, it is difficult to address the issues arising from poor data entry and measurement.
To summarize the key points:
Logistics Optimization: PSD helps optimize storage and transportation, reducing space wastage and ensuring safer, more efficient loading.
Cost Control: Precise PSD analysis results in precise shipping cost prediction and reducing shipping costs.
Operational Efficiency: Streamlined processes and optimized resource allocation improve handling, productivity, and competitiveness.
Dynamic Shipment Profiles: Shipping profiles are not static, and can change over time, making extensive analysis necessary and creating an adaptive element to the planning of logistics.
Data Collection Challenges: Multiple systems generate immense amounts of data. How to extend these systems to ensure that the data are collected accurately and integrated across the various systems to enable data-driven PSD?
Using PSD insights, companies can increase efficiency, lower transport cost and strengthen their position in an increasingly competitive maritime shipping industry. If you are not already using PSD analysis to improve your logistics performance, then it’s time to be proactive to increase your company’s efficiency and profitability. How are you currently handling PSD in your shipping strategy, and what are the challenges you are facing? Do share with us your experience.
Source:
https://www.mt.com/hr/hr/home/applications/L1_AutoChem_Applications/L2_ParticleProcessing.html https://www.researchgate.net/figure/Parcel-size-distribution-of-a-cotton-and-b-corn-planted-parcels-for-six-test-sites_fig2_258900650 Picutre:
Comments