Научная статья на тему 'Pricing in liner shipping industry: a Review and assessment'

Pricing in liner shipping industry: a Review and assessment Текст научной статьи по специальности «Экономика и экономические науки»

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Аннотация научной статьи по экономике и экономическим наукам, автор научной работы — Ceren Akman Biyik

Pricing decisions are both strategically and tactically important, particularly in oligopolistic markets like liner shipping industry. Predicting any rate increases in near future before making pricing decisions are important and should be done very carefully. Therefore the purpose of the study is to explore the pricing decisions of liner shipping companies, propose a decision support system based on business forecasting methods for real time pricing decisions.

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Текст научной работы на тему «Pricing in liner shipping industry: a Review and assessment»

Section 7. Pricing.

DOI: http://dx.doi.org/10.20534/EJEMS-17-2-64-69

Ceren Akman Biyik, Dokuz Eylül University, Turkey PhD candidate in Business Administration E-mail: ceren.akman@deu.edu.tr

Pricing in Liner Shipping Industry: A Review and Assessment

Abstract: Pricing decisions are both strategically and tactically important, particularly in oligopolistic markets like liner shipping industry. Predicting any rate increases in near future before making pricing decisions are important and should be done very carefully. Therefore the purpose of the study is to explore the pricing decisions of liner shipping companies, propose a decision support system based on business forecasting methods for real time pricing decisions.

Keywords: Pricing, liner shipping, forecasting, decision support system.

Introduction field of marketing and business, the aim of the study is to

The need for transportation stem from the need fill the gap in the literature and to propose a model for of moving goods from where they are supplied to the pricing decisions by building a decision support system where they are demanded. Hence, transportation of goods via sea way is as old as the international trade. Sea transportation was the most common used mode of transportation since the beginning of Silk Road then after the industrial revolution of 18th century formed the liner shipping. However, sea transportation has shown the most significant increase after the containerization process in the mid-1960s that triggered globalization.

Liner shipping industry (LSI) can be accepted as a service industry and as an oligopolistic market. As many industries, LSI is vulnerable to economic crisis. After experiencing many world crises, the industry formed its own rights and rules. There were price wars between the carriers in the industry which was changed by forming alliances to stabilize the profits and to stand against the crisis. While everything was seemed afloat, there comes the biggest crisis in container shipping history; 2008 mortgage crisis. This financial crisis was not only effected the global demand but also caught the carriers with excess capacity. It became obvious that alliances itself was not enough to solve pricing problem in LSI, when the market experiences spot based rates.

Pricing is one of the most important element of services marketing mix, however there are few studies addressing pricing in both marketing and services marketing literature. Since there are lack of interest on pricing in the

(DSS) based on forecasting methods to predict prices in LSI. The study designed as follows; review of the literature pricing and LSI, then explain forecasting and DSS to understand the design of the proposed model to illustrate the DSS based on business forecasting methods.

Price and Pricing

Price can be defined as the amount of money that should be sacrificed to acquire something desired. Conventionally in narrow terms, price is the formal ratio involves the quantity of money (or goods and services received by the seller) that needed to get a given quantity of goods or services (received by the buyer). In its broader form, price defined in terms of sales transactions that are negotiated. Economic theory assumed that price has an influence on buyer choice since it is perceived as purchase cost indicator [1; 35; 36; 5].

In terms of marketing, price is the most important element of the marketing mix, the mnemonic 4Ps coined by McCarthy in 1960, and also the element of service marketing mix which was broadened by Booms and Bitner in 1981 as 7Ps [5; 54; 44; 16]. Price is set to offer a real value to the customers. The duty of any business is to deliver value at a profit. The value creation and delivery can be divided into three phases; choosing the value, providing the value, and communicating the value. After choosing the value, marketing must determine specific

product, prices and distribution for providing the value; before communicating the value [28, 63-74]. Therefore companies must set relevant pricing strategies in order to maximize their profits while creating value for their customers.

Many terms replaced the term price overtime. For instance, rate, fees, premiums, rent, fares, tolls, tuition, service charges, interest, subscriptions, tariffs and duties (in international marketing) which of all are pricing decisions and should be considered like setting the price of a product in service that is purchased. When compared to product pricing, it can be said that services industries take a different approach to pricing than manufacturers [36, 5; 30, 168]. Generally in LSI freight rate purport price, therefore in this study, the term freight rate will be used as a substitute for the price to explain the value paid for the shipment.

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Price has a unique function in the marketing mix. As a strategic variable and a short term decision of a firm, price can be changed in a second since it is the most flexible element of the marketing mix and it can change very quickly with no investment needed. Considering the marketing mix as a whole, price is the only component that can be changed easily without a cost. Therefore, price is the most important element to manage in the marketing mix, since it is very flexible and dynamic [11; 7; 45; 15, 229; 27, 345; 3].

Price is the marketing strategy variable alone generating directly income while all other variables generate costs [36, 8]. Therefore most ofthe academics highlighted price as the only element in marketing mix that generates revenue [41; 46; 36], whereas the other variables (product, promotion, place or distribution) are the cost components [20; 15]. Kohli and Suri (2011) suggested that pricing should be aligned with the marketing strategy and pricing should be the key component not just a tactic while determining the marketing strategy. By this way companies can get the maximum leverage. They emphasized that pricing and marketing strategy should offer synergy [26]. Surely, these advantages have some consequences. Since price and pricing strategies are easy to change, any failures in pricing and/or intention to undercut the market price may lead to price wars.

In the field of marketing, economics, and strategic management, many scholars neglected price and pricing strategies. Though it is a strategic tool, there is lack of studies in the literature. Despite the fact that price is one of the most important components of the marketing mix, price theory and pricing literature did not win enough recognition in business practice [49; 48; 39; 2],

and little interest shown in the academic field of both marketing, business and economics [47; 34; 12; 21; 6]. According to Indounas (2006) the lack of interest on pricing is due to its complexity and being as one of the most complex decisions that a company faces, for Shapiro (1968) it's due to fuzzy business thinking [23; 47].

Price perceived as a strategic weapon and therefore it is dangerous to manage the changes in price and pricing policies because it may cause serious damages in the way it is used. Many of the academics and professionals avoid using, studying price, pricing as a competitive tool. Mostly economists have suggested several theories to the non-price factors of competitive strategy [53]. Maybe it is therefore there are lack of studies based on pricing in marketing strategy and pricing in competitive strategy.

Pricing is the biggest marketing headache of managers [13; 27, 345]. Therefore in giving pricing decisions, managers feel the most pressure and they can be least certain about their decision outcomes. Classical economic model of price determination relied on the assumption that the firm maximizes short-run profits [8] however, the conventional wisdom is that the price is dictated by the market and there is no control over the price by decision-makers. On the other hand, getting closer to the "right" price may have a great impact on better pricing which mean a lot for the company [13].

Liner Shipping Industry

Sea transportation is accepted as the economic life-blood of many countries in the movements of the goods from the place of production to the place of consumption. Sea transportation as a low cost mode of transportation is essential to the economic activities development and for trade to grow [31]. Freight transportation demand called as derived (secondary) demand by the economists, and therefore this makes sea transportation also a derived demand. This is due to the fact that freight transportation demand is based on the customer demand for the product to be moved [9, 24]. One of the important contributions of industrial revolution was the use of steam power and in sea transportation it enables the ship navigate on regular basis which was named as liner services. Thereupon, increase in international trade triggered some innovations in LSI.

The containerization period, unitization ofthe cargo in mid-1960 s, can be accepted as a milestone and breakthrough event in liner shipping history. Especially the way containers changed the LSI is worth noting. With the deregulation process, the transformation of the LSI has been started and continued with liberalization and globalization.

After the deregulation period industry witnessed increasing competition between 1978 and 1996 [9, 102]. Technological advances in transportation, shipping and communication triggered globalization which made it easy to buy product or services worldwide [28, 54].

Basically, the market rate in liner shipping is determined by supply and demand. Supply represents the vessel capacity offered by shipping companies, whereas the demand refers to the customer demand for sea transportation. Contestable markets theory is relevant to LSI in order to explain the market structure and the price setting [10]. On the other hand, considering container LSI as an oligopoly [51] - none of the

sellers controlling the market - firms immediately act according to price changes therefore pricing strategies are crucially important.

Shipping cycles are affected from the business cycles in the world economy, and therefore global crisis has an impact on the LSI [50; 17]. It is important to take into account past events in the world economy, trade, and shipping as a whole while making important decisions such as pricing since LSI has been affected from business cycles. As the industry is vulnerable to economic crisis also, business forecasting methods can be a useful to decrease the uncertainty and give insights about the past behavior of the data in crisis period.

Figure 1. Shipping cycles 1869-2009

Stopford (2009) points out that, rationale behind forecasting is not to predict the unpredictable or to make precise predictions, but to help reducing the uncertainty by narrowing the odds. It is important to know and interpret the past better than the others to succeed in the business [50, 701]. Notwithstanding that, there are few studies that use forecasting methods in LSI [32; 14; 40; 38; 4].

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The Structure of Proposed Pricing DSS

Forecasts are very important for economic and business decisions. Makridakis et al (2009) shed a light on how hard to make better forecasts by exemplifying financial crisis of 2008. Also they mentioned that the conventional way of future oriented decision-making starts with forecasting then continues with planning. Conversely, in order to handle uncertainty there is a need to develop alternatives among cases, scenarios. The key idea they asserted is "not to believe any predictions about the future, but to develop plans that will be sensitive to surprises" [33].

Forecasting is to gather and analyze the right information about the present [50, 701]. Currently it is not easy to reach the right information and real-time data for forecasting, but it is seriously important. Due to increasing uncertainty in the market, it is getting harder to foresee what is coming, which makes business forecasting methods useful to predict the future events with taking into account past events.

State-of-the-art computer hardware technology has made modeling feasible by artificial neural network (ANN) [24]. ANNs have many commercial applications which includes forecasting [25; 18, 499]. ANNs are mathematical models inspired by the performance capability of the human brain. Human beings have learning ability and intuitively they find solutions to the problems [37,106]. The brain is capable of organizing structural constituents called neurons, as to perform certain computations (e. g., pattern recognition, perception, and

motor control). In performing these tasks, the brain is many times faster than a highly sophisticated digital computer that is available today [19, 30].

There are similarities between ANN techniques and conventional forecasting methods. Both try to search variables that predict the dependent variable successfully. There is a theoreticel advantage ofANN, being a forecasting tool; to specify relationships. By using the provided examples, the method learn the relationships. No assumptions needed about the population distributions and, differing from the conventional forecasting methods, ANNs can perform with incomplete data. Also ANNs are very valuable in case there is a high correlation between inputs, or any missing value, or highly nonlinear systems are available [18, 499-500].

DSSs are the synonyms of analytics, business intelligence (BI), decision-making BI systems and knowledge management. Decision support is a broader concept that requires computerized systems and other tools to assist decision-making of an individual, a group, or an organization [29, 320; 42]. Typically, the reason to build a DSS is either to support the solution of a certain problem or for evaluation of an opportunity [52, 88].

Managers are in need of computerized information systems in supporting their decision-making process. The aim of the developers of computerized decision support is to improve the effectiveness and efficiency of human decision-making by information technology (IT) solutions. The regular tasks of managers are to download and analyze sales data, prepare reports, and also to analyze and evaluate forecasting results. In performing these tasks, DSS may help managers in allocation of resources, comparing budget to actual results, operating results, projecting revenues, and in the evaluation of the scenarios [42].

As one of the DSS model, data-driven DSS build in a way to organize and summarize data in different extents to retrieve fast and for ad hoc analysis. The main objective of these systems is to assist managers in transforming data into information and knowledge and by the help of these, activities such as real-time operations, management control, and strategic planning can be supported [43].

Some of the major reasons that the companies should benefit from computerized decision support are; (1) unstable and rapid changing economy, (2) increased competition; especially in global scale, (3) need for accurate information, (4) need for timely information, (5) need for higher decision quality, (6) need for special analyis of profitability and efficiency, (7) reduce costs [52, 18].

Pricing is a managerial decision and under managerial economics, economic tools and techniques applied to business in order to make administrative decisions. In order to identify pricing strategies, managerial economics can be useful to match short-run objective of the business in a quick and effective way. It provides a link between traditional economics with decision sciences in order to advance vital tools for managerial decision making [22, 3]. Considering this fact, for better decision making forecasting can be a useful technique to promote a DSS for the liner shipping companies.

After reviews of pricing and liner shipping industry literature in terms of forecasting, as it is seen there are few studies addressing forecasting of the prices however there is no study regarding pricing decisions and how to formulate, model the pricing decision process. Therefore, the study is an attempt to propose a forecasting technique based DSS to enable the user (decision-maker) to give fast and better decisions.




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Figure 2 depicts the forecasting techniques based DSS. The flow of the system starts with the user interface to the database, then continues with data flow to the forecasting techniques like ANN and time-series business forecasting models (TSBFM) like exponential smoothing, Holt-Winter's, ARIMA etc. After the calculations in the relevant forecasting models, the results data (from best performing models) are transferred to the database and received by the user for decision making.


Pricing is one of the most important managerial decision that deserves a multidisciplinary approach. Price and pricing cannot be evaluated in the context of economics,

marketing and management only. There is a need to integrate and implement decision sciences to pricing decisions. Especially, considering short-term managerial decisions like pricing, it is important to develop scenarios by utilizing from the forecasts in order to stay competitive in the market.

As explained above, in LSI the effects of 2008 crisis still continues. It is important to be cautious in such situations and should set strategies by predicting how future crisis may have an effect to LSI. Maybe there cannot be a precise method to forecast the rates. However it is important to use a tool to see beyond the fog and then make plans about how to react in the coming situations by utilizing from forecasting techniques based DSSs.


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