Application of Revenue Management in the Electricity Industry Elena Ng - 221027 Faisal Khan-220939 G2 – BBA2
Revenue Management 2010-1
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Contents
1. Introduction................................................................................................................. Error! Bookmark not defined. 1.1 Overview of themes ................................................................................................................................................ 5 1.2 Objectives ............................................................................................................................................................... 1 2. Revenue Management ................................................................................................................................................... 1 2.1 Effect of adopting RM techniques .......................................................................................................................... 2 2.2 Pre-requisites for RM .............................................................................................................................................. 2 2.2.1 Demand ............................................................................................................................................................ 2 2.2.1.1 Market Segmentation................................................................................................................................2 2.2.1.2 Forecasting................................................................................................................................................3 2.2.1.3 Flunctuation..............................................................................................................................................3 2.2.2 Supply .............................................................................................................................................................. 3 2.2.2.1 Fixed Capacity..........................................................................................................................................3 2.2.2.2 Perishability..............................................................................................................................................3 2.2.2.3 Reservation...............................................................................................................................................3 2.2.2.4 Overbooking.............................................................................................................................................4 2.2.2.5 Low marginal sales costs..........................................................................................................................4 2.2.2.6 High marginal production costs................................................................................................................4 2.2.3 Link with electricity utility industry ................................................................................................................ 4 3. Nature and scope of the industry................................................................................................................................... 5 4. A Conceptual Framework for RM ................................................................................................................................ 6 4.1 Strategic Levers .................................................................................................................................................... 11 4.2 Multi-dimensional Nature of Demand .................................................................................................................. 12 4.3 The Pricing and Revenue Optimisation (PRO) Cube ............................................................................................. 8 4.4 Conceptualisation model for Electricity.................................................................................................................. 9 4.4.1 Three Dimensions to Revenue Optimisation ................................................................................................... 9 4.4.1.1 Conceptual view of RM model dimensions...............................................................................10 4.4.2 Conceptual operation of the model.................................................................................................................10 4.4.2.1 Capacity Management................................................................................................................10 4.4.2.1.1 Forecasting.................................................................................................................11 4.4.2.1.2 Distribution Channels................................................................................................11
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4.4.2.2 Customer Segmentation.............................................................................................................11 4.2.2.2.1 Price Sensitive ...........................................................................................................11 4.2.2.2.2 Industries....................................................................................................................11
4.4.2.3 Time.............................................................................................................................12 4.4.2.3.1 Season........................................................................................................................12 4.4.2.3.2 Weather.....................................................................................................................12 5. Revenue Measurements .............................................................................................................................................. 16 6. Analysis....................................................................................................................................................................... 17 6.1 Internal Factors ..................................................................................................................................................... 17 6.2 External Factors .................................................................................................................................................... 18 7. Conclusion .................................................................................................................................................................. 18 8. References..................................................................................................................................................................15
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Application of Revenue Management in the Electricity Industry 1. Introduction
With the de-regulation of the electricity markets since 1990s, there have been considerable changes in the structure of the industry. Moving to a de-centralised free-market system will help make companies more efficient with freedom of entry and thus more competition (Cross, 2000, p. 35). As witnessed in other industries, de-regulation resulted in drastic alteration in the economic models. Companies became more aggressive in terms of market growth and generating revenues. Adopting revenue management, which has been successfully implemented in various deregulated industries, is one possible approach (Talluri et al., 2004, p. 551). 1.1 Overview of themes
Firms practising Revenue Management have moved from the conventional paradigm that determines cost as a primary driver of profitability to revenue because of the concept that driving down cost has a limit whereas there is no limit as to how much a firm can grow in revenues (Cross, 2000, p. 36). The electricity retail market has characteristics of industries that currently apply revenue management. The application of revenue management entails some pre-requisites or certain characteristics. These prerequisites will be evaluated and their relevance to the electricity industry will be correlated. Based on that a theoretical model would be formulated and its feasibility analysed. 1.2 Objectives
The objective of this study is to determine the applicability of revenue management in the electricity industry by preparation of a conceptual model based on the formerly determined pre-requisites. Furthermore, the feasibility of the model will be examined whilst also including the opportunities and limitations in relation to the operation of revenue management supported by views from different authors on the subject. Foremost, the paper will undertake the task to define revenue management and its effect in other industries; and such in constructed in the following sections.
2. Revenue Management
Ingold et al. (2000, p. 3) identify the inception of revenue management (RM henceforth) with the deregulation of the airline industry in the 1980s. RM (also referred to as yield management) could be defined in many terms as witnessed by the explanation of various authors but the one that is in compliance with the scope of this study has been colluded by Siguaw et al. (2003, p. 539) who state that ‘RM is the method that involves the application of information systems and pricing tactics to ensure the allocation of the right capacity to the right customer at the right place and the right time.’
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Thus RM requires a comprehensive system which requires compiling information that is usually historical in nature, accurate forecasting, and categorising customers based on price sensitivities and determining when to price differently based on the demand. This system was adopted in the airline industry and its effect will be addressed below. 2.1 Effect of adopting RM techniques
Airline industry was the first to adopt RM upon deregulation, which led to loosened control over prices (Talluri & Van Ryzin, 2005, p. 7). This led to the emergence of variable pricing to undercut the competitors and determine the minimum fare that should be available for a specific flight (Kimes & Chase, 1998, p. 158).With incrementing revenues due to a surge in price-sensitive customers choosing low-cost carriers, many airlines adopted the RM model (Talluri & Van Ryzin, 2005, p. 7-10). Envied by the profits enjoyed by airlines adopting RM, many other industries made a concentrated effort to adopt RM according to their requirements. However, for RM to be effectively applied, certain criteria were set and these shall be explored subsequently. 2.2 Pre-requisites for RM
The implementation of revenue management strategies in diverse commercial sectors is subject to various combinations of duration control and variable pricing that is present in each industry. Also, revenue management is more effective on businesses that have the following characteristics: perishable inventory, relatively fixed capacity, segmented customer markets, flexible cost and pricing structure, certainty of demand and readily available data. (Siguaw et al., 2003, p. 542) The pre-requisites compatible with electricity market are as follows: 2.2.1 Demand
Economists define demand as ‘the amount of a good or service that a purchaser is willing and able to buy for any given price at any given time’ (Tranter et al., 2009, p. 9). 2.2.1.1 Market Segmentation
According to the report The Basics of Yield Management (2005, p. 10), one of the foremost step in RM is to define the various segments of the service market, so as to price differently based on practice called price discrimination. The objective being to determine and categorise price sensitive and non-price sensitive customers. Furthermore, in some cases price-sensitive customers may still pay higher prices during high demand time periods and thus the second classification can be termed as time sensitive and non-time sensitive (Barth, 2002, p. 138). A constructed example could be that customers who are usually price sensitive may still decide to use electricity on a hot sunny day during the peak hours, thus paying premium prices.; whereas, industries which are traditionally non-price sensitive may be able to shift production to off peak hours for a discounted rate. �
2.2.1.2 Forecasting
Forecasting is necessary in revenue management systems in order to predict demand, price sensitivity, and possible cancellations in order for the system to perform well. For instance, forecasts can be about the demand of energy on a specific day or time in the future (Talluri et al., 2004, p. 407). Forecasting assists in minimising uncertainty, making decisions about pricing and scheduling (The Basics of Revenue Management, 2005, p. 12). Kimes (1989, p. 18) states that forecasting often relies on historical data, which in the case of electricity industry, is readily available, quite detailed and accurate (Talluri et al., 2004, p. 552-555). 2.2.1.3 Fluctuation
The demand patterns could be identified as being cyclical in nature (such as time-of-day/week/season) or based on trends (growth in demand due to economic growth) (The Basics of Revenue Management, 2005, p. 11). RM system is used in stabilizing the fluctuations in demand by raising prices during periods of peak demand and reducing the prices during periods of low demand (Kimes, 2007, p. 17; The Basics of Revenue Management, 2005, p. 11). 2.2.2 Supply
Economists define supply as ‘the amount of a good or service that a seller is willing and able to sell for any given price at any given time’ (Tranter et al., 2009, p. 9). 2.2.2.1 Fixed Capacity
In the electricity industry, there is high variability in the demand of energy that changes by time, day, temperature and season. However, the generation and transmission capacity is quite inflexible (Talluri & Van Ryzin, 2004, p. 552). 2.2.2.2 Perishability
Bitran & Caldentey (2003, p. 4) state that perishability of a product or a service should be treated as a timedependent quantity. This indicates that the value of the product becomes worthless and can no longer be sold on after a certain amount of time, and therefore cannot be held as inventory for future use; i.e. if an electric generator generates 300 Kilowatt (KW) of electricity and only 200 KW is transmitted to demand centre, then 100 KW represents a lost opportunity to generate revenue as it cannot be stored for future sale. 2.2.2.3 Reservation
Talluri & Van Ryzin (2004, p. 554) proposes that transmission or supply of electricity can be reserved in certain places. In the case of electricity, firms or household buys options (similar to reservation without purchase), also known as pre-emption (Talluri & Van Ryzin, 2005, p. 583).
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2.2.2.4 Overbooking
According to Talluri & Van Ryzin (2004, p. 129), overbooking is used to maximise capacity utilisation in the reservation system taking into consideration cancellations. It serves as a protection for companies wishing to utilise their available capacity to its potential (Ingold et al., 2000, pp 10). However, Rehkopf (2006, p. 52 as cited in Blumenthal et al., 2009, p. 24-25) argues that overbooking may not be applicable in manufacturing industry as a customer’s order of a good may be considered as purchase rather than mere reservation. 2.2.2.5 Low marginal sales costs
As observed in a study by Kimes (1989, p. 17), this theory can be termed as with additional increase in revenue derived from a sale of a unit, the cost incurred to earn the revenue diminishes up to the limit of available resources or units. In other words, once an amount of energy is being delivered to a household, the cost incurred to supply extra energy reduces. As long as the transmission and generation capacity is not fully used, it does not make much difference to deliver extra energy demanded from the customer. 2.2.2.6 High marginal production costs
Conversely, Kimes’s (1989, p. 17) study indicate that, upon reaching the maximum capacity, the cost incurred to produced an additional unit is exponential to the cost of establishing an additional production unit and often not feasible in the short run. In a simpler term, if the electricity generation capacity is fully used and customers demand extra energy, this extra energy cannot be easily added onto the transmission capacity because of the high fixed cost involved in establishing additional generators. 2.2.3 Link with electricity utility industry
Below is a table adopted from The Basics of Revenue Management (2005, p. 8-9) showing how electricity utility industry meets the criteria for application of RM. Revenue Management Criterion
Market Segmentation
Electric Power Utilities
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Pricing peak vs non-peak hours/seasons
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Residential vs Commercial vs Industrial customers
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Central plant
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Distributed generation
Unit of fixed capacity
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•
Lost energy
•
Pricing based on demand & supply
•
Load and distribution management
•
Energy storage (more like quota)
Forecasting cycles:
•
Yes
Seasonal
•
Yes
Day-of-week
•
Yes
Time-of-day
•
Yes
Other
•
Balancing of demand and supply
Unit of perishable inventory
Low marginal costs for incremental sales
Booking taken in advance
Demand
Having determined that the characteristics of electricity industry are in accordance with the criteria for RM, the next section will explore the market structure and scope of the industry and attempt to create a conceptual framework for academic purpose.
3. Nature and scope of the industry Until the de-regulation of the electricity industry, the market was often dominated by state-owned
monopolies that were engaged in all the functions such as generation, transport and retail and the prices were also standard (Domanico, 2007, p. 2). Due to the economic importance of electricity for overall development, security of supply and complexity of commodity, the state justified its intervention, which led to lack of economic incentives for efficiency, subsidies by the state and led to a state of lack of innovation and completive environment (Domanico, 2007, p. 2). This vertical structure (monopoly) often lead to underinvestment, inefficiency, standard prices and no freedom of choice for consumers to choose from electricity supplier (Zubavičiūtê, 2010, p. 6). The demand for free movement of goods, services, capital and labour resulted in deregulation of many industries, thus affecting electricity sector as well (Zubavičiūtê, 2010, p. 5). The deregulation focussed on separating the generation and transmission (distribution) functions, which lead to competitive market for �
generators, distributors of electricity and a diverse retail market (Talluri & Van Ryzin, 2004, p. 552). These structural breakdowns lead to efficiency in generation of electricity because of perceived competition, investment in transmission of infrastructure to gain market share and more revenues, and competitive prices by retailers (Zubavičiūtê, 2010, p. 8-9).
Below is an illustration of the market structure of the industry in a competitive environment:
*IPP stands for Independent power producer *Source: (Hunt & Shuttleworth as cited in Talluri, 2004, p. 553) The market structure, scope along with the characteristics of the industry mentioned afore, gives it a base to create a conceptual framework for application of RM in the industry and such shall be attempted subsequently.
4. A Conceptual Framework for RM The groundwork laid afore pertaining to the characteristics of RM and the electricity industry will help to
define a clear scope for implementation of RM tactics. This section will focus on existing RM models before pursuing to formulate one that would seek to correspond to the electricity industry. Two models studied are presented below: ��
4.1 Strategic Levers
RM requires managing four Cs which are calendar (reservation in advance), clock (time), capacity (inventory), cost (price) to manage a fifth C, customer demand, in such a way as to maximise profit (Kimes & Chase, 1998, p. 156). A successful RM strategy according to Kimes & Chase (1998, p. 157) depends on effective control of customer demand which can be accomplished through pricing and duration of customer use. Variable pricing could be adopted to control demand while duration control represents controlling demand over the long run through various techniques such as reducing uncertainty of arrival and duration amongst others (Kimes & Chase, 1998, p. 157). A model adopted by Kimes & Chase (1998, p. 157) is presented below followed by a brief explanation:
The authors classify industries into different quadrants based on their combination of pricing and duration control. Quadrant 1 – Industry uses fixed price for a predictable duration Quadrant 2 – Industries traditionally associated with RM, use variable pricing and predictable duration Quadrant 3 – Fixed price with unpredictable duration
Quadrant 4 – variable pricing with unpredictable duration The purpose of this model is to identify in which quadrant do the industries lie and what needs to be done to move to Quadrant 2 as successful RM applications are found in this quadrant. (Kimes & Chase, 1998, p. 157). ��
4.2 Multi-dimensional Nature of Demand
Talluri & Van Ryzin (2005, p. 11) mentions that demand has multiple dimensions of which the three most important ones are (i) product, (ii) customers and (iii) time. The dimensions indicate a customer’s valuation for a particular product at a particular point in time. Talluri & Van Ryzin (2005, p. 11), however do identify other dimensions such as location or channel, but believe that the dimensions provided is more cordial to the scope of study and suffice to illustrate the idea. The model is illustrated below:
The concept aims to exploit the heterogeneity in consumer’s perception of a product at a given point in time. This leads to many possible practices like price discrimination, Expected Marginal Revenue, auctions and many others. In this model, any one or two dimensions can be fixed and the firm then focuses on optimising the other one(s). For example, some of the RM problems look at dynamically pricing a single product to heterogeneous customers over time: they fix the product dimension and optimise over the customer and time dimensions (Talluri & Van Ryzin, 2005, p. 12). 4.3 The Pricing and Revenue Optimisation (PRO) Cube
Differingly, the goal of RM according to Phillips (2005, p. 26) is to provide the right price for every (i) product, (ii) customer segment and (iii) through every channel. The model is replicated below (Phillips, 2005, p. 27):
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As seen in the figure, each element within the cube represents a combinatio of product, channel and customer segment (Phillips, 2005, p. 26). The theory is similar to the model fabricated by Talluri & Chase, as can be observed above. Taking these two models, and into consideration the characteristics of the electricity industry, a model best suit ble for the electricity industry is presented i the following section. 4.4 Conceptualisation mode for Electricity
After extensive research to retrieve a RM model based on the electricity util ty industry, three general models were adopted and referring o those models, a modest attempt is made to formulate a theoretical model of RM conducive to the peculiar characteristics of the electricity industry. 4.4.1Three Dimensions to R venue Optimisation
In the electricity industry, the produ t supplied is ubiquitously singular and henc the product dimension is not incorporated, while the channel imension has been modified to include a br ader scope, i.e., Capacity. The model is presented below, follo ed by justification of the conceptual model.
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Prices should be well defined so as to that the goal of RM can be achieved that is ‘to allocate the right capacity to the right customer at the right place and time’ (Siguaw et al., 2003, p. 539). However, the dimension of place (location) has been delineated, but the essence of the definition is not diminished. To elaborate further on the dimensions, a chart is presented to as to ease the understanding of the reader pertaining to what constitutes the three dimensions. 4.4.1.1 Conceptual view of RM model dimensions ����������� �������� ������������ ��������
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4.4.2 Conceptual operation of the model
The three dimensions are inter-related and each affects the other or has an influence in determining the overall price. The operational aspect of the model is hypothetically examined and the three dimensions are elaborated below: 4.4.2.1 Capacity Management
Capacity Management (CM hereafter) requires maintaining an intricate balance between ‘supply vs demand vs resources vs costs’ (Grummit, 2009, p. 5). Tranter et al. (2009, p. 140) state that CM also known as Inventory Management is the process of organizing the amount of units, services and products available to sell through various distribution channels. In other words, ‘Capacity Management is the ability to balance demand from customers and the capability of the service delivery systems to satisfy the demand.’ This emphasizes on two objectives; forecasting demand and handling capacity to meet the forecasted demand by using various distribution channel (Armistead & Clark, 1994, p. 6). These two objectives are provided below: ��
4.4.2.1.1 Forecasting
Forecasting according to Huyton & Thomas (2000, p. 260) is the key to effective RM and helps to reduce uncertainty (Forecasting, 2010 as cited in Rodriguez & Steinort, 2010, p. 12). One of the approaches for scheduling of load production is use of historical data (Aghazadeh, 2007, pp 33-34; Shahidehpour et al., 2002, p. 111-113). Electricity industry has a background for utilisation of scientific software for demand management and sophisticated trading technologies, which makes access to historical data readily available and are often quite detailed and accurate (Talluri & Van Ryzin, 2004, pp. 551-555). 4.4.2.1.2 Distribution Channels
For achieving an optimal business mix of customers for RM, it is imperative to allocate the appropriate amount of inventory in the suitable channel (Tranter et al., 2009, p. 213). Buhalis & Laws (2004) mention that an effective distribution channel influences the decision making process of the consumers. In conjunction, Rodriguez & Steinort (2010, p. 4) consider the supplier’s perspective by stating that a distribution channel is the path to entice the consumers with innovative product, personalise service, and promotions. In the electricity industry, de-regulation has resulted in a competitive market for generators, distributors and retailers of electricity (Talluri & Van Ryzin, 2004, pp. 552). The competition has resulted in open access for transmission and generation and distribution of electricity (Jamison, 2000, p. 64). This led to the emergence of purchasing agencies, wholesale distributors, retailers, aggregators (customers as a buying group) and even brokers (Shahidehpour et al., 2002, p.6-8; Talluri & Van Ryzin, 2004, pp. 552-553). 4.4.2.2 Customer Segmentation
There are many ways to segment the customers. Some are provided below: 4.2.2.2.1 Price Sensitive
Consumer demand is often price sensitive, meaning increasing or decreasing prices will influence how much a customer will buy the product. For example, if the price of electricity increases, schools that need to use energy during the day will still pay for the energy fee. In other words, schools can be considered non-price sensitive customers. However, there are also customers who are very price sensitive. For instance, household may refrain from utilising too much energy if the price of electricity rises (Marion, 2006, p. 1). Thus, the objective of RM is to determine those customers that are not price sensitive and charge them a higher price level (The Basics of Revenue Management, 2005, p. 10). 4.2.2.2.2 Industries
Different industries have different levels of price elasticity. It rather depends on urgent, non-discretionary or non-urgent, interruptible nature of service (The Basics of Revenue Management, 2005, p. 10). The table below highlights how different industries have different peak periods: ��
Characteristics
Residential
Commercial
Peak Usage
Evenings, weekends
Weekdays – office hours
Sensitivity
Limited
Limited
Industrial
Nights, mornings, afternoons Yes
Since different industries have different peak periods and have varying degree of sensitivity, this could be helpful for the suppliers or generators of electricity to determine the prices for each industry based on these characteristics. 4.4.2.3 Time
Phillip (2005, p. 33) mentions that much of the growing interest in RM can be attributed to velocity of pricing decisions. In the past, prices were constant over a short duration, but now in some industries it is constantly fluctuating on a daily or even hourly basis. Phillip (2005, p. 33) also emphasises that companies that change prices rapidly in response to changing conditions gain an advantage. Such vagary in prices can be seen in the electricity industry for the following reasons: 4.4.2.3.1 Season
Seasonal effect like number of daylight hours affects load pattern (Shahidehpour et al., 2002, p. 22). In addition, certain seasons are traditionally associated with high usage, and thus, this can help the electricity companies to forecast on the load patterns based on this. 4.4.2.3.2 Weather
Shahidehpour et al. (2002, p. 22) consider temperature to be the most influential factor in load forecasting. The temperature change could influence the energy usage. Other factors that can affect the energy usage are humidity, wind and any abrupt or un-occasional weather conditions (Shahidehpour et al. 2002, p. 22). Now that the model has been paraphrased, its feasibility and factors affecting applicability of RM in general shall be propsed at a later stage. The next step is to identify the performance or revenue measurements of the industry:
5. Revenue Measurements Kimes & Thompson (2004, p. 3) present Revenue per available time-based inventory unit (RevPATI) as the
most commonly used performance measurement in RM. The time-based inventory varies amongst the industries, from room-night in hotels, seat-mile in airlines to seat-hour in restaurants (Kimes & Thompson 2004, p. 3).
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RevPATI is calculated by ‘multiplying the utilisation percentage by the average rate paid per customer (Kimes & Thompson 2004, p. 3). In the case of electricity industry, the inventory or the product could be classified as being the ‘supply of electricity’ and hence can be termed as output sales. The time factor of the inventory unit can be complex but the assumption is made to consider ‘hour’ as a time factor as electricity consumption can change by the hour or time of the day. A example is fabricated for ease of understanding. Suppose that an electricity generator plants generates on a given day 1000 KW but has an output sales percentage of 90% and the price of 1 KW is set at 2$ on average, then in any given day, there are 24 hours. Thus, the problem could be calculated as follows: 1000 KW x90% = 900KW x 2$ = 1800 / 24H = 75$ Kimes & Thompson (2004, p. 4) illustrate that RevPATI can be increase by either achieving higher capacity utilisation or by raising the average rate. Having examined the essential characteristics of RM in relation to the electricity industry, the paper will propose some factors that are affecting the implementation of RM in the industry.
6. Analysis The application of RM demands certain pre-requisites, which become the core competencies to evaluate
against. The study conducted in the paper found that the electricity closely adheres to the criteria set for application of RM. However, RM has not been effectively utilised in the industry to such an extent as would warrant a research or case study (Talluri & Van Ryzin, 2004, pp. 552). This penultimate section will evaluate as to which factors are restricting the implementation of RM in the industry. Factors could be internal as much as external and such will be sorted accordingly. 6.1 Internal Factors •
Upon de-regulation, the prices were not set by the state and hence the were no distinct rates. Electricity rates are to some extent ambiguous. Kimes (1989, p. 19) encourages the industry to set distinct rates to get more benefits out of RM systems
•
Traditionally forecasting was focused on load, but with de-regulation has moved to price, which can pose a large error because price volatility (Shahidehpour et al., 2002, p. 57)
•
Reservation system is distinctively different when compared to industries traditionally applying RM. This inhibits the decision makers or the RM system to implement overbooking or price discrimination. This affects the duration control lever.
•
Applying differential pricing based on time may require technological investment to tract time-of usage, requires constant monitoring and accurate billing (Talluri & Van Ryzin, 2004, pp. 554-555).
•
To put RM into practice would require determining value of futures and long term contract for generators, evaluate complex contract conditions (such as pre-emption) and handle new forecasting ��
requirements such as weather, economic condition, price sensitivity, and market-price predictions (Talluri & Van Ryzin, 2004, pp. 554-555). 6.2 External Factors •
Although the electricity market has been liberalised, the industry still has to adapt to the competitive free market in an extensive scale and as a result still suffers from price inflexibility.
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Generally, price discrimination is applied to identify price sensitive and non-price sensitive consumers (Withiam, n.d.) so as to charge premiums or reduced rates. However, in the case of electricity, there is no perceived benefits for paying premium as the product by itself is intangible and perhaps undistinguishable, although debatable.
•
Dynamic pricing may not adhere well with households and may only be restricted to larger industrial clients (Talluri & Van Ryzin, 2004, pp. 554-555) .
The concluding section will channelize the studies conducted hitherto and present some feasibility prospects for the application of RM. 7. Conclusion
The Strategic Levers Model by Kimes & Chase (1998, p. 157) presented previously indicates that the industries that have successfully applied RM lay in Quadrant 2. Industries present in Quadrant 2 tend to use variable pricing and a specific or predictable duration (Kimes & Chase, 1998, p. 157). The electricity industry looks posed to be categorised in Quadrant 2 and hence there is huge potential for application of RM tactics. Electricity industry has traditionally applied forecasting measures and the approach in practice currently is quite scientific and hence accurate. The challenge that lies ahead is utilising these data along with historical prices and demand, to formulate a diverse set of rates. It is possible to apply the principle of price discrimination, as the consumers can be easily segmented and their characteristics are often attributable to their respective segments.
To summarise, the electricity industry has tremendous potential to successfully apply RM albeit some limitations or complications. However, the inertia of the industry to transform itself since de-regulation has to some extent resulted in the limited implementation of RM tactics in the Industry.
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8. References
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