The maritime shipping industry growth and development is an attribution of the continuous changes of the geopolitical scenarios of the world, global trade significant expansion, and technological changes and developments. The global maritime industry comprises of a variety of independent agents such as commodity producers, shipping companies, terminal operations, freight brokers, ports, and the port authorities (Caschili & Medda, 2012). With the outgrown world population, with their specific expectations and needs transportation of the various goods is the only way to meet their requirements. More so, dispersion of the production places and the geographical extension the global transportation means have gained more popularity in the last few decades. According to Caliskan and Ozturkoglue (2016), the shipping industry transports approximately 85% of the total volume of goods from international trading activities making it a main player in shaping the world economy. This project analyzes the proper inventory planning and management as a reflection of the temporary gap between supply and demand incorporating its relationship with demand forecasting, including the importance of the whole procedure in container shipping. The project also discusses demand forecasting in investment decisions and capacity calculations. More so, it discusses factors likely to affect the demand forecasting and then summarizes the importance of using the maritime transport system.
Background Information
The freight transportation system in the globe comprises roads, railways, airfreight, inland waterways coastal, and the ocean routes. The international maritime transport system mainly complements other modes, thus, playing an important role in the socio-economic development of any state. According to Mensah, Anim, Obeng, and Peprah (2016), the maritime industry is broad incorporating all entities involved with processes of construction, designing, operating, acquiring, supplying, maintaining as well as repairing the waterway vessels. More so, it incorporates the operations and management of the shipping lines, shipyards, custom brokerages services as well as the freight forwarding and shipping services. Taking advantage of the opportunities available in the broad maritime industry requires the proper planning in the capacity of the immediate service provision and evaluation of the investments necessary in the scaling-up processes to ensure the future demands are met (Mensah et al., 2016). The various decisional processes in the maritime industry including inventory planning and management, capacity planning, supply chain planning production, and product development, require forecasting. The maritime industry experiences uncertainties in demand for various services, which then poses various significant problems in the sector, thus the need for forecasting the port usage.
Delegate your assignment to our experts and they will do the rest.
The intercontinental shipping industry has developed over the years due to the mutual relationships existing between the independent rational agents acting in the various activities. The intercontinental shipping freight in 1980, accounted for around 23% of the total volume, the transport demand rising to the range of 77-90% in 2008 (Caschili & Medda, 2012). Despite the various downside risks and the heightened uncertainties in the international maritime industry, there is an expected annual growth rate for the period 2019-2024 of around 3.5% (UN, 2019). The Twenty-foot Equivalent Units (TEUs) has also been on the rise since 1990, in which the total volume carried was 28.7 million, which has increased to 148.9million in the year 2008 (Caschili & Medda, 2012). More so, the vessel capacities have increased since 1996 to date, in which only 1 % of the 1996 vessels were larger than5000 TEUs, while in 2006, 30% of the vessels had their capacity increased. The exhibited increase in the ferried volumes is driven by the growth in the gas and dry bulk containerized cargoes (UN, 2019). Therefore, the technical improvements, as well as the revolution in containerization's design, speed and size of the vessels, and the port operations automation are responsible for the exhibited success in the maritime shipping industry.
Inventory planning
Inventory planning and managing activities are concerned with reducing the level of stock while the costs remain as low as possible. According to Caliskan and Ozturkoglu (2016), the maritime managers with a lot of significant data and information are capable of constantly updating the market demands, which helps in the forecasting procedures. Forecasting on the market situations helps in systems coordination of inventory management. Inventory planning in the maritime industry influences the levels of the customer services, which are measured in terms of the order fulfillment rates, lead time, order time, line fill rate, and the case fill rate (Caliskan & Ozturkoglu, 2016). The inventory planning happens to be a new service in the ports in which they manage their customers’ inventories, thus the fulfillment of their respective orders. According to Caliskan and Ozturkoglu (2016), the inventory planning and management in the maritime industries are done in companies that are vertically integrated with which a single company happens to control the various inventories together with the ship’s fleet moving the goods. Through the application of information technologies in the ports, various inventory activities are performed such as the review processes of cycle counts, vendor managed inventories, and serial, lot number controls whenever the goods are being transported from the ports.
In the maritime industries, fleet ships are responsible for transporting multiple bulk products from the various production ports to the customers. Edirisinghe, Jin, and Wijeratne (2016) affirm that containers and container ships are supplementary, such that the transportation of cargo by container shipping lines is impossible without containers. Therefore maintaining a container inventory planning is necessary for the maritime sectors. A container refers to a transport equipment article, which has a permanent and strong character to allow repeated use (Edirisinghe, Jin & Wijeratne, 2016). The empty containers effective and efficient management and repositioning is a critical issue in the liner shipping industries. The container shipping firms mostly move the empty containers from ports with a surplus to areas of deficits. Edirisinghe, Jin, and Wajeratne (2016) confirm that the repositioning of empty containers is very costly, which accounted for more than 20 billion dollars globally in 2002. More so, transportation of empty containers tends to have a negative environmental effect as the increased movement rates increase emissions, congestion as well as fuel consumption in which the shipping industry suffers the pressures of carbon emissions (Schaltegger & Burrit, 2018). Therefore, a mitigation solution to the negative effects of relocating the extra ships is necessary as it is beneficial to consignees, primary shippers, shipping lines, and the world at the macro-level (Shaheen, Totte & Stocker, 2018). An efficient container interchange initiative would help solve an imbalance on the container inventory while still avoiding excess emissions to the environment. Therefore, an appropriate demand forecasting can inform the container inventory planning, thus avoiding the costs associated with the movement as well as expenses resulting from the late transportation of the containers.
Demand forecasting in the port usage
Demand forecasting mainly undertakes predictive analytics to help with the understanding of the consumer’s demand for the products and services. With the increased variability and uncertainties in the maritime industry services, forecasting is necessary for various decisional processes including, inventory planning, capacity planning, production, product development, and supply chain planning (Mensah et al., 2016). The management of uncertain demands poses significant problems in the maritime industry hence the need to forecast. The demand forecasting is a necessary move in the sector because of the downside risks such as protectionism and trade tensions that are contributing to the slow growth in the maritime industry (UN, 2019). Brexit in which the UK and Northern Ireland left the European Union, geopolitical turmoil, China's economic transitions, and the disruptions in the supply of certain products such as oil explain the slower pace demand for maritime industry service in 2018 (Hellenistic Shipping News, 2019). There is a projected growth in the period 2019-2024 of around 3.5%, which is particularly driven by the expansion in the gas and dry bulk containerized cargoes (UN, 2019). Nonetheless, the existing heightened tensions between China and America elaborates on the uncertainties in the future maritime industry. More so, developments in various market areas that were suffering setbacks including, iron-ore trade disruptions in Australia that resulted from cyclone veronica and the Vale dam effects in Brazil, prove the risks in the maritime industry hence the need for demand forecasting.
Forecasting generally encompasses the aspect of assessing an event status before its occurrence. According to Mensah et al. (2016), demand forecasting involves predicting, estimating, or projecting the demand expected in certain services or products for a future specified time. Forecasting the port usage incorporates the timing predictions and the future requirement amount of services. Forecasting the product demand in the immediate future as well as in the longer periods plays a crucial role in the success of any industrial activities. Mensah et al. (2016) states the uses of forecast in any organization as helping managers in the planning process of their system and planning its application. To properly prepare for the future levels of production, forecasting well on the future demands is necessary. According to Mensah et al. (2016), many industries fail in their planning as they make to stock, in which they deploy finished goods inventories into the various locational fields instead of making to order. For example, an order cycle in any industry is likely to take months or weeks to go through the various procedures of sub-assemblers, part suppliers, the manufacturers, and to the eventual shipment of the good to the final customer. Thus, the product demand forecast properly informs the business decisions by reducing the uncertainty aspects that are an unpleasant fact in the environment in which companies operate.
Demand Forecasting Approaches
The main techniques in forecasting include; the extrapolation and the explanatory methods, with each incorporating the qualitative and quantitative categories data analysis (Mensah et al., 2016). The quantitative method mainly employs the mathematical approaches to forecast, termed as objective means. On the other hand, the qualitative methods depend on intuitions, opinions, emotions, and judgment hence termed as the subjective approaches (Mensah et al., 2016). In the forecasting process of the port usage, one can use the exponential smoothing, the widely applied extrapolation method. The exponential smoothing method is a sensible technique of forecasting as it mainly applies recent data while smoothing the fluctuations in moving the average (Mensah et al., 2016). The exponential smoothing is the most applicable in business due to the automatic and frequent forecast of many items in the businesses. In the exponential smoothing, the forecast errors are better in the weighted and simple methods of forecasting.
The formula for the exponential smoothing
F t+1 = a Y t + (1-a) F t
F t+1 = Refers to the forecast of the time series in t+1 period
Y t = the real value of time series in t period
F T = Time series forecast for t period
A= Refer to the smoothing constant
In the weighted moving averages approach, every data value is assigned a different weight, in which the most recent values are used in the computation of the weighted average as the forecast (Mensah et al., 2016). In the weighted moving average technique, the recent observations tend to receive the most weight, which then decreases for the older observations. The forecast errors in the weighted technique are lower than the simple forecast, although larger than other approaches.
Weighted moving averages formula
F t+1 = Sum (the weight in I period) (Real value in I period)/E (Weights)
Forecasting port usage and the importance of Information technology
In the process of forecasting port usage, the information technology supports the communication activities and all the processes involved. IT avails programs in the forecasting processes, which are efficient and fast in the processing of data. Most of the IT programs operate seamlessly, with more use of the dialogue boxes and menus while supporting balance sheets, spreadsheets, and databases. According to Mensa et al. (2016), various IT programs avail the automatic forecast-method types, which are very effective in the forecasting time series of large batches. Some of the methods include general statistical packages, spreadsheets, and enterprise resource planning. Therefore, information technology is significant as a way of automating the forecasting processes while ensuring the sophistication levels are high.
Factors contributing to inaccuracy in Port Usage Forecasting
Forecasts do not meet the set criteria or attain the accuracies they are needed to meet, which results in more expenses as people try to eradicate the problems. Jarret (2015) highlights unsuitable software as one of the reasons for the inaccuracies in the forecasting processes. The lack of the necessary abilities and possession of mathematical errors in software makes it unsuitable for forecasting demand. More so, inexperienced and untrained forecasters using perfect software tend to misuse it thus, giving wrong data.
Fiddling or over-adjustment are behaviors by unskilled forecasters in the forecasting process that affects accuracy (Jarret, 2015). In most cases, the untrained forecasters' constant adjustments of the forecast whenever there is new information. Therefore, fiddling does not improve the accuracy of the data hence becoming a wasted effort.
Process contamination causes inaccuracies, in which the forecasting participants’ biases and personal agendas affect the forecasting process. Jarret (2015) describes forecasting as a scientific exercise that is dispassionate that seeks the ‘best guess’ on what is likely to transpire in the future. In this case, the forecasters work against the scientific aspect of the forecasting process by presenting biased information that is twisted representing the data that management and leadership want to happen against the right information depicted by the market place.
Data analysis using Maersk Line Port Data
In the Maersk line, both the qualitative and quantitative methods are employed in the forecasting process of their shipping demand, with the quantitative as the most appropriate technique, as it curbs the problem of excess capacity. Maersk reviewed their forecasting methods due to the fit-to purpose, the market dynamics, and sources of data (Mensah et al., 2016).
Choosing the best method of forecasting
In Maersk, demand's time-series data for 22 months, starting from January 2010 to October 2011 is gathered (Mensah et al., 2016). Information is categorized into the import and export services as different factors influence importing and exporting services. The data gathered is presented in a time series plot in which the pattern exhibited can either be a cyclical, seasonal, trend and horizontal (Jarret, 2015). The pattern, in the Maersk, is identified at a month's interval. The data trend for 22 months shows the random fluctuations, which are then, assessed paying keen attention to the deviations that are taken into account after the forecasting processes.
The time-series image below portrays Maersk's actual demand for export services for January 2010 -October 2011 (Mensah et al., 2016).
The figure shows a horizontal trend, which means that the data during the time under study was fluctuating around a constant mean, depicting a relatively stable demand. The stability on export demand tends to decrease from January 2011 upwards in which it stabilizes later in April to August of the same year. Therefore, it is clear from the figure that the demand for export in Maersk is relatively stable with a few random variations.
The moving average, as the technique of forecasting in Maersk, tries to average the data recorded within the period selected to allow the computation for the demand in the next period (Jarret, 2015). The moving average, it averages the past actual demand for the previous six months, which is then applied in the prediction of the seventh month.
Formula
Moving Average= Sum (data values of the most recent months)/n
Employing the 6-months data for computation of the next period using the excel QM in moving average data to forecast the demand for Maersk export services.
The figure below shows the prediction of Maersk’s export demand for the next period starting after the end of the six months (Mensah et al., 2016).
The prediction method is very accurate at the beginning and the end, where errors are minimal, contrasting the middle where demand variations are very high. Analyzing the trend line shows that there is generally a decrease in demand with time. The figure shows an irregular demand pattern, which can be minimized by the use of a short period, which tends to be more accurate. According to Jarret (2015), using a small period, the forecasted demand tends to be near the actual demand than when using a large moving average.
The image below illustrates the moving average technique using the three-month period, which appears to be more accurate, compared to using the 6, months hence, the most appropriate in forecasting the export demand in Maersk line port (Mensah et al., 2016).
Demand Forecasting Importance and Capacity Planning
Capacity planning refers to the process organizations adopt to determine the capacity of production necessary in meeting the changing demands for their respective services and goods (Mensah et al., 2016). In contrast, demand refers to the actual quantities that the consumers or customers order to meet their needs and wants. In the capacity planning processes, an organization’s management heavily relies on the previous years' forecasted values to avoid halting their service production processes while remaining highly responsible (Jarret, 2015). Therefore, the inaccurate demand forecasting is capable of directly affecting the planning of capacity, which influences negatively responsiveness, operational success as well as efficiency in the port and any other business organization.
Inaccurate demand forecasting correlates highly to the use of extra resources as a way of catering for the excess demands (Edirisinghe, Jin & Wijeratne, 2016). For example, companies are likely to load consumer products in a container that is forty-foot bigger than the normal capacity of the twenty-foot in order if the actual demand exceeds the forecasts. More so, hiring extra part-time workers, the channelings of the excess demand quantities to third parties for transportation purposes are consequences of inaccurate forecasting. Therefore, the demand forecasts are necessary inputs in the ports as they influence various business decisions on quantity in matching the demands expected within the port and in the whole supply chain.
Advantages of maritime transport
The international maritime transport system mostly complements other transportation modes with the intercontinental containerized cargoes. The system acts as a connection between railways, roads, and inland waterways through the coastal and the ocean routes (Naletina & Perkov, 2017). The maritime industry is the best choice in the transportation of commercial products due to its cost-effectiveness and its environmentally friendly nature compared to other transportation modes such as trucks and rails. On the fuel efficacy aspect, the waterborne barge is 370% fuel-efficient compared to tracks and 39% efficient than the railway transport system (Naletina & Perkov, 2017). Maritime transport tends to have lower emission rates when compared to trucks and railways. For example, analyzing a ton of greenhouse gases released in a ton of miles traveled, water transportation is 370% less damaging to the atmosphere compared to the trucks and 39% less dangerous compared to the rails. More so, the maritime industry is continually adopting new strategies such as the application of the high-pressure containers to help reduce the environmental effects of fuel-burning by using natural gases.
References
Caliskan, A., & Ozturkoglu, Y. (2018). Maritime logistics. In Intelligent Transportation and Planning: Breakthroughs in Research and Practice (pp. 822-845). IGI Global. https://www.researchgate.net/publication/308467624_Maritime_logistics
Caschili, S., & Medda, F. R. (2012). A review of the maritime container shipping industry as a complex adaptive system. Interdisciplinary Description of Complex Systems: INDECS , 10 (1), 1-15.
Edirisinghe, L., Jin, Z., & Wijeratne, A. W. (2016, December). Container inventory management: Factors influencing container interchange. In the University of Sri Jayewardenepura, Sri Lanka, 13th International Conference on Business Management (ICBM) .
Hellenic Shipping News (2019). Brexit and Ports: A potential Game Changer. Worldwide Online Daily Newspaper on Hellenic and International Shipping. https://www.hellenicshippingnews.com/brexit-and-ports-a-potential-game-changer/
Jarrett, J. E. (2015). Throughput Port Demand Forecasting. Int. J. Econ. Manag. Sci. , 4 (9). https://www.researchgate.net/publication/286524617_Throughput_Port_Demand_Forecasting
Mensah, J., Anim, S. K. Obeng, F., & Peprah, J. A. (2016). Demand Forecasting In the Maritime Industry, a Case of Maerskline Ghana. Archives of Business Research , 4 (1).
Naletina, D., & Perkov, E. (2017). The economic importance of maritime shipping referencing Croatia. Economic and Social Development: Book of Proceedings , 248. https://www.researchgate.net/publication/324389577_
Schaltegger, S., & Burritt, R. (2018). Business cases and corporate engagement with sustainability: Differentiating ethical motivations. Journal of Business Ethics , 147 (2), 241-259. https://www.researchgate.net/publication/283572251_
Shaheen, S., Totte, H., & Stocker, A. (2018). Future of mobility white paper. California Transportation Plan for 2050. UC Berkeley UCONNECT. https://escholarship.org/content/qt68g2h1qv/qt68g2h1qv.pdf
United Nations (UN). (2019). Review of Maritime Transport. United Nations Conference on Trade and Development (UNCTAD). https://unctad.org/en/PublicationsLibrary/rmt2019_en.pdf
