Stackelberg Leader Follower Models For Strategic Decision Making Engineering Essay

Stackelberg Leader Follower Models For Strategic Decision Making Engineering Essay This paper reviews some Stackelberg Leader-Follower models used for strategic decision making. The simple Stackelberg duopoly is looked at first, and a generalisation of the Stackelberg duopoly problem is given. By studying the models by Murphy et al. (1983) and Smeers and Wolf (1997), the paper reviews Stackelberg model from its classical form to the recent stochastic versions. The paper looks at the mathematical formulation of both a nonlinear mathematical programming model and a nonlinear stochastic programming model. Towards the end of this paper, a simple numeric example is given and practical applications of Stackelberg Leader-Follower models are discussed. Chapter 1: Introduction In economics, an oligopoly is considered to be the most interesting and complex market structure (amongst other structures like monopolies and perfect competition). Most industries in the UK and world- from retailing to fast food, mobile phone networks to professional services- are oligopolistic. Given the current financial climate, it is imperative for firms to be sure that they make decisions accurately, maximising not only their profit, but also their chances of remaining competitive. Many mathematicians and economists have attempted to model the decision making process and profit maximizing strategies of oligopolistic firms. For example, A. A. Cournot was one of the first mathematicians to model the behaviours of monopolies and duopolies in 1838. In Cournots model both firms choose their output simultaneously assuming that the other firm does not alter its output (Gibbons, 1992). Later, in 1934, H. V. Stackelberg proposed a different model where one of the duopoly firms makes its output decision first and the other firm observes this decision and sets its output level (Stackelberg, 1934). The classical Stackelberg model has been extended to model a variety of strategic decision making. For example, Murphy et al. (1983) model the output decision making process in an oligopoly. Later works by Smeers and Wolf (1997) extend this model to include a stochastic element. More interestingly, in a model by He et al. (2009), the Stackelberg theory is used to model the interaction between a manufacturer and a retailer when making decisions about cooperative advertising policies and wholesale prices. The objective of this paper is to review the Stackelberg models from its classic form to the more recent stochastic versions. In chapter 2, the simple Stackelberg duopoly is reviewed and a generalisation of the Stackelberg duopoly problem is given. In chapter 3, more complicated and recent models are reviewed. The mathematical formulation of Murphy et al.s (1983) and Smeers and Wolfs (1997) model is given. At the end of chapter 3, a numerical example is applied to Smeers and Wolfs (1997) model. In chapter 4, practical applications of Stackelberg leader-follower models are discussed. Chapter 4 also looks at the drawbacks of and possible extensions to Stackelberg models. Appendix 1 explains the Oligopoly market structure and economics involved in profit maximisation. Chapter 2: Classical Stackelberg Leader-Follower Model 2.1 Duopoly Behaviour Stackelberg (1934) discussed price formation under oligopoly by looking at the special case of a duopoly. He argued that firms in a duopoly can behave either as dependent on or independent of the rival firms behaviour: Referring to the two firms as firm 1 and firm 2, respectively, firm 1s behaviour can be generalised as follows: Firm 1 views the behaviour of firm 2 as being independent of firm 1s behaviour. Firm 1 would regard firm 2s supply as a given variable and adapts itself to this supply. Thus, the behaviour of firm 1 is dependent on that of firm 2 (Stackelberg, 1934). Firm 1 can view the behaviour of firm 2 as being dependent on firm 1s behaviour. Thus, firm 2 always adapts itself to the formers behaviour (firm 2 views firm 1s behaviour as a given situation) (Stackelberg, 1934). However, according to Stackelberg (1934), there is a difference in the firms actual positions; each of the firms could adapt to either of these two positions, making price formation imperfect. Stackelberg (1934) describes three cases that arise from this situation: Bowler (1924) first described a situation when both firms in the duopoly strive for market dominance. According to Bowler (1924), for this to happen the first firm supplies the quantity it would if it dominated the market with the second firm as a follower. This supply is referred to as the independent supply. By supplying this output level the first firm tries to convince the second firm to view its behaviour as a given variable. However, the second firm also supplies the independent supply since it is also striving for market dominance. This duopoly is referred to as the Bowler duopoly with total supply of the duopoly equalling the sum of two independent supply. According to Stackelberg (1934), the price formation under the Bowler duopoly is unstable because neither of the firms tries to maximise profit under the given circumstance. The second case described by Stackelberg (1934) is a situation where both firms favour being dependent on the other firms behaviour. The first firm would have to match (in a profit maximising manner) its output level to the each output in the second firms feasible set of output. The second firm does the same and both firms are thus followers. This is a Cournot duopoly, first described by A. A Cournot in 1838. According to Stackelberg 1934, the price formation here is unstable because neither of the firms tries to achieve the largest profit under the given circumstance. The third case is a situation where one firm strives for independence and the other favour being dependent. In this case both firms are better off doing what the other firm would like. Both firms adapt their behaviour to maximising profit under the given circumstance. This situation is referred to as the asymmetric duopoly or more commonly as the Stackelberg duopoly. The price formation is more stable in this case because, according to Stackelberg (1934), no one has an interest in modifying the actual price formation. The Stackelberg model is based on the third case of a Stackelberg duopoly. 2.2 The Model In the Stackelberg duopoly the leader (Stackelberg firm) moves first and the follower moves second. As opposed to other models like the Bertrand model and Cournot model where firms make decisions about price or output simultaneously, firms in the Stackelberg duopoly make decisions sequentially. The Stackelberg equilibrium is determined using backwards induction (first determine the follower firms best response to an arbitrary output level by the Stackelberg firm). According to Gibbons (1992), information is an important element of the model. The information in question is the Stackelberg firms level of output (or price, Dastidar (2004) looks at Stackelberg equilibrium in price). The follower firm would know this output once the Stackelberg firm moves first and, as importantly, the Stackelberg firm knows that the follower firm will know the output level and respond to it accordingly. Inspired by the work of Gibbons (1992), Murphy et al. (1983) and Dastidar (2004), a general solution to the Stackelberg game (duopoly) is derived in the parts that follow. 2.2.1 Price function, cost functions, and profit functions Suppose that two firms in a duopoly supply a homogeneous product. Denote the demand function of this market as, where is the total level of output supplied by the duopoly (is the Stackelberg firms output level and is the follower firms output level). The price function can be re-written as. Denote the cost functions (Appendix 1) as for the Stackelberg firm, and for the follower firm. The profit function of the Stackelberg firm is given by: Similarly, the profit function of the follower firm is given by: 2.2.2 Backward induction to derive the best response functions and Stackelberg equilibrium According to Gibbons (1992), the best response for the follower will be one that maximises its profit given the output decision of the Stackelberg firm. The followers profit maximisation problem can be written as: This can be solved by differentiating the objective function and equating the differential to zero (as seen in Appendix 1). Using chain rule to differentiate equation [2] and setting the differential to zero, the following result is obtained: Note that this is a partial differentiation of the profit function since the function depends on the demand function which depends on two variables. Equation [4] gives the followers best response function. For a given the best response quantity satisfies equation [4]. As a result, the Stackelberg firms profit maximisation problem becomes: By differentiating the objective function in equation [5] and equating the differential to zero, the following result which maximises the Stackelberg firms profit is obtained: By solving equation [6] with the follower firms best response profit maximising output, is obtained by the Stackelberg firm given the followers best response. Gibbons (1992) describes as the Stackelberg equilibrium (or the Nash equilibrium of the Stackelberg game). 2.2.3 Example Gibbons (1992) considers a simple duopoly selling homogeneous products. He assumes that both firms are identical and the marginal cost of production is constant at. He also assumes that the market faces a linear downward sloping demand curve. The profit function of the firms is given by: where, with representing the Stackelberg firm and representing the follower firm. Using backward induction, the follower firms best response function is calculated: Solving equation [8]: The Stackelberg firm anticipates that its output will be met by the followers response. Thus the Stackelberg firm maximises profit by setting output to: Solving equation [10]: Substituting this in equation [9]: Equations [11] and [12] give the Stackelberg equilibrium. The total output in this Stackelberg duopoly is. Note: Gibbons (1992) worked out the total output in a Cournot duopoly to be (using this example) which is less than the output in the Stackelberg duopoly; the market price is higher in the Cournot duopoly and lower in the Stackelberg duopoly. Each firm in the Cournot duopoly produces; the follower is worse off in the Stackelberg model than in the Cournot model because it would supply a lower quantity at a lower market price. Clear, there exists a first mover advantage in this case. In general, according to Dastidar (2004), first advantage is possible if firms are identical and if the demand is concave and costs are convex. Gal-or (1985) showed that first mover advantage exists if the firms are identical and have identical downward sloping best response functions. Chapter 3: Recent Stackelberg Leader-Follower Models The classical Stackelberg model has been an inspiration for many economists and mathematicians. Murphy et al. (1983) extend the Stackelberg model to an oligopoly. Later, Smeers and Wolf (1997) extended Murphy et al.s model to a stochastic version where demand is unknown when the Stackelberg firm makes its decision. In a more recent report by DeMiguel and Xu (2009) the Stackelberg problem is extended to an oligopoly with multi-leaders. In this section the models proposed by Murphy et al. (1983) and Smeers and Wolf (1997) are reviewed. 3.1 A Nonlinear Mathematical Programming Version The model proposed by Murphy et al (1983), is a nonlinear mathematical programming version of the Stackelberg model. In their model, they consider the supply side of an oligopoly that supplies homogeneous product. The model is designed to model output decisions in a non-cooperative oligopoly. There are followers in this market who are referred to as Cournot firms (note that from now onwards the follower firms are referred to as Cournot firm as opposed to just follower firms) and leader who is referred to as the Stackelberg firm (as before). The Stackelberg firm considers the reaction of the Cournot firms in its output decision and sets its output level in a profit maximising manner. The Cournot firms, on the other hand, observe the Stackelberg firms decision and maximise their individual profits by setting output under the Cournot assumption of zero conjectural variations (Carlton and Perloff, 2005, define conjectural variation are expectations made by firms in an oligopolistic market about reactions of the other firm). It is assumed that all the firms have complete knowledge about the other firms. 3.1.1 Notations and assumptions For each Cournot firm, let represent the output level. For the Stackelberg firm, let represent the output level (note that is used here instead of, as seen earlier, to distinguish the Stackelberg firm from the Cournot firms). is the total cost function of level of output by Cournot firms and is the total cost function of level of output by the Stackelberg firm. Let represents the inverse market demand curve (that is, is the price at which consumers are willing and able to purchase units of output). In addition to the Cournot assumption and assumption of complete knowledge, Murphy et al. (1983) make the following assumption: and are both convex and twice differentiable. is a strictly decreasing function and twice differentiable which satisfies the following inequality, There exists a quantity (the maximum level of output any firm is willing to supply) such that, For referencing, these set of assumption will be referred to as Assumption A. Assumption 2 implies that the industrys marginal revenue (Appendix 1) decreases as industry supply increases. A proof of this statement can be found in the report by Murphy et al. (1983). Assumption 3 implies that at output levels the marginal cost is greater than the price. 3.1.2 Stackelberg-Nash-Cournot (SCN) equilibrium The Stackelberg-Nash-Cournot (SCN) equilibrium is derived at in a similar way to the Stackelberg equilibrium seen in chapter 2. Using backward induction, Murphy et al. (1983) first maximise the Cournot firms profit under the assumption of zero conjectural variation and for a given. For each Cournot firm let the set of output levels be such that, for a given and assuming are fixed, solves the following Cournot problem: According to Murphy et al. (1983), the objective function in equation [15] is a strictly convex profit function over the closed, convex and compact interval. This implies that a unique optimum exists. The functions can be referred to as the joint reaction functions of the Cournot firms. Murphy et al. (1983) define the aggregate reaction curve as: The Stackelberg problem can be written as: If solves, then the set of output levels is the SNC equilibrium with To get this equilibrium, the output levels need to be determined. Murphy et al. (1983) use the Equilibrating program (a family of mathematical programs designed to reconcile the supply-side and demand-side of a market to equilibrium) to determine: Let the Lagrange multiplier associated with the maximisation problem [19] be. Murphy et al.s (1983) approach here is to determine for which the optimal. The following result, obtained from Murphy et al. (1983), defines the optimal solution to problem [19]: Theorem 1: For a fixed, consider Problem suppose that satisfy Assumption A. Denote by the unique optimal solution to and let be the corresponding optimal Lagrange Multiplier associated with problem [19]. (In case since alternative optimal multipliers associated with problem [19] exist, let be the minimum non-negative optimal Lagrange Multiplier.) Then, is a continuous function of for. is a continuous, strictly decreasing function of. Moreover, there exist output levels and such that and . A set of output levels optimal to Problem, where, satisfy the Cournot Problem [15] if and only if, whence, for. (This theorem is taken from Murphy et al. (1983) with a few alterations to the notation) The proof of this result can be found in the report by Murphy et al. (1983). This theorem provides an efficient way of finding for each fixed. For example, one can simple conduct a univariate bisection search to find the unique root of. 3.1.3 Properties of and Murphy et al. (1983) describes the aggregate Cournot reaction curve as follows: is a continuous, strictly decreasing function of. If the right hand derivative of with respect to is denoted as (the rate of increase of with an increase in ), then for each : The proof to these two properties can be found in the report by Murphy et al. (1983). Murphy et al. (1983) state that if solves the Stackelberg problem [17], then the profit made by the Stackelberg firm is greater than or equal to the profit it would have made as a Cournot firm. Suppose that is a Nash-Cournot equilibrium for the firm oligopoly. is the output the Stackelberg firm would supply if it was a Cournot firm. solves: But since solves the Stackelberg problem [17], the following must hold: In fact, is the lower bound of. The proof to this can be found in Murphy et al. (1983) From assumption 3 in Assumption A, it is clear that. Thus, it is clear that is an upper bound. However, according to Murphy et al. (1983) another upper bound exists. In a paper by Sherali et al. (1980) on the Interaction between Oligopolistic firms and Competitive Fringe (a price taking firm in an oligopoly that competes with dominant firms) a different follower-follower model is discussed. In this model, the competitive fringe is content at equilibrium to have adjusted its output to the level for which marginal cost equals price. Murphy et al. (1983) summarise this model as follows: For fixed and suppose is a set of output levels such that for each firm solves: and For the Stackelberg firm, let satisfy: In addition to Assumption A, if is strictly convex, then a unique solution exists and satisfies conditions [23] and [24]. The Equilibrating Program with a fringe becomes: Theorem 1 holds for with and which implies that. In fact, if is strictly convex, is the upper bound of. Collectively, is bounded as follows: 3.1.4 Existence and uniqueness of the Stackelberg-Nash-Cournot equilibrium Murphy et al. (1983) prove the existence and uniqueness of the Stackelberg-Nash-Cournot (SCN) equilibrium. Their approach to the proof is summarised below: Existence For the SNC equilibrium to exist, and for should satisfy Assumption A. Since is bounded and is continuous (as is continuous), the Stackelberg problem [17] involves the maximisation of a continuous objective function over the compact set. This implies that an optimal solution exists. From Theorem 1 it is seen that a unique set of output levels, which simultaneously solves the Cournot problem [15], exists. As a result the SNC equilibrium exists. Uniqueness If is convex, then the equilibrium is unique. Since is convex, the objective function of the Stackelberg problem [17] becomes strictly concave on. This has been proven by Murphy et al. (1983) and the proof can be found in their report. This implies the equilibrium is unique. 3.1.5 Algorithm to solve the Stackelberg problem Murphy et al. (1983) provide an algorithm in their report to solve the Stackelberg problem. This algorithm is summarised as follows: To start with the Stackelberg firm needs the following information about the market and the Cournot firms: Cost functions of the Cournot firms, satisfying Assumption A. The upper bound as per Assumption A. The inverse demand function for the industry, which also satisfies Assumption A. With this information, the Stackelberg firm need to determine the lower bound and split the interval into grid points with, where and (from [26]). A piecewise linear approximation of is made as follows: Here, is an approximation to and from equation [20] it follows that: Note that at each grid point the approximation agrees with. The Stackelberg problem [17], thus, becomes: can be re-written as: Where and Thus problem [30] becomes: The objective function is strictly concave and solvable. Let be the objective function of the Stackelberg problem [17] and the objective function of the piecewise Stackelberg problem [32], then: Suppose is the optimum level of output. First, suppose that is an endpoint of the interval, then. Now suppose that, that is, . Then needs to be evaluated in order to determine. Theorem 1 can be used here. Recall that is a continuous, decreasing function of. To find the point where (part iii of Theorem 1), the following method is suggested by Murphy et al. (1983): Figure : Method for determining Source: Smeers and Wolf (1997) (alterations made to the notation) First determine using the bounds. Next, determine using the bounds. Then determine using the bounds.Next, determine using the bounds and so on. If then evaluate using the bounds. Having evaluated for some grid points, the game can either be terminated with the best of these grid points as an optimal solution or the grid can be redefined at an appropriate region to improve accuracy. Murphy et al. (1983) go on to determine the maximum error from the estimated optimal Stackelberg solution. This is summarised below: Let be the derivative of with respect to , then: Let be the marginal profit made by the Stackelberg firm for supplying units of output, Let be the actual optimal objective function value in the interval with the estimate being . Then the error of this estimate is defined as: satisfies the following: This concludes the review of Murphy et al.s (1983) nonlinear mathematical programing model of the Stackelberg problem in an oligopoly. 3.2 A Stochastic Version Smeers and Wolf (1997) provide an extension to the nonlinear mathematical programming version of the Stackelberg model by Murphy et al. (1983) discussed in subsection 3.1. In the same way as Murphy et al.s (1983) model, the Stackelberg game in this version is played in two stages. In the first stage, the Stackelberg firm makes a decision about its output level. In the second stage, the Cournot firms, having observed the Stackelberg firms decision, react according to the Cournot assumption of zero conjectural variation. However, Smeers and Wolf (1997) add the element of uncertainty to this process. When the Stackelberg firm makes its decision the market demand is uncertain, but demand is known when the Cournot firms make their decision. This makes the Smeers and Wolfs (1997) version of the Stackelberg model stochastic. Smeers and Wolf (1997) assume that this uncertainty can be modelled my demand scenarios. 3.2.1 Notations and Assumption For the costs functions, the same notations are used. is the total cost function of level of output by Cournot firms and is the total cost function of level of output by the Stackelberg firm. The demand function is changed slightly to take into account the uncertainty. is a set of demand scenarios with corresponding probabilities of occurrence As such, is the price at which customers are willing and able to purchase units of output in demand scenario . has a probability of occurrence. The same Assumption set A discussed in subsection 3.1.1 apply here with alterations made to conditions [13] and [14]. Assumption set A can be re-written as: and are both convex and twice differentiable, as before. is a strictly decreasing function and twice differentiable which satisfies the following inequality, There exists a quantity (the maximum level of output any firm is willing to supply in each demand scenario) such that, For referencing, these set of assumption will be referred to as Assumption B. 3.2.2 Stochastic Stackelberg-Nash-Cournot (SSNC) equilibrium Smeers and Wolf (1997) use the same approach seen before to derive the SSNC equilibrium. The Cournot problem [15]can be re-written as follows: For each Cournot firm and each demand scenario, let the set of output levels be such that, for a given and assuming are fixed, solves the following Cournot problem: Note that is the output level of Cournot firm when the demand scenario is . For each, according to Murphy et al. (1983), the objective function in equation [40] is a strictly convex profit function over the closed, convex and compact interval. The functions can be referred to as the joint reaction functions of the Cournot firms for a demand scenario. The aggregate reaction curve becomes: The Stackelberg problem with demand uncertainty can be written as: Note the Stackelberg problem defined problem [42] differs from that defined in [17]. This is because of the element of uncertainty. The Cournot problem [40] is similar to the Cournot problem [15] because the demand is known when the Cournot firms make their decision. In the Stackelberg problem [42] note the element. This is the estimated mean price, that is, the Stackelberg firm considers the reaction of the Cournot firm under each demand scenario and works out the market price in each scenario, and it then multiplies it by the probability of each scenario. The summation of this represents the estimated mean price. If solves the stochastic, then the set of output levels is the SSNC equilibrium for demand scenario. To get this equilibrium, the output levels need to be determined. Smeers and Wolf (1997) use the same approach as Murphy et al. (1983) in doing so. The Equilibrating program is the same as that in [19], with changes made to the Cournot output and demand function: For each demand scenario , Theorem 1 lays out a foundation on how to solve the Equilibrating program in problem [19] and can also be used to solve [44]. Smeers and Wolf (1997) Summarise Theorem 1 as follows: Theorem 2: For each fixed, An optimal solution for the problem satisfies the Cournot problem [40] if and only if the Lagrange multiplier,, associated with the Equilibrating program [44], is equal to zero. This multiplier is a continuous, strictly decreasing function of . Moreover, there exists and such that: (This theorem is taken from Smeers and Wolf (1997), with a few alteration to the notations) The properties of are the same as those discussed in subsection 3.1.3. The existence and uniqueness of the SSNC equilibrium is shown in the same ways as the SNC equilibrium of Murphy et al.s (1983) model discussed in subsection 3.1.4. 3.2.3 Algorithm to solve the Stackelberg problem The Stackelberg problem here is solved in the same way Murphy et al. (1983) proposed (discussed in subsection 3.1.5). In their report, Smeers and Wolf (1997) do not specify the upper and lower bound of, thus, it is assumed that is bounded by.The interval can be split into grid points with, where and . The piecewise linear approximation of in [27] can be re-written as follows: Here, has the same properties as [29]. The Stackelberg problem [42], thus, becomes: Hereafter, the algorithm summarised in subsection 3.1.5 can be used to solve this problem. 3.3 Numerical Example In Murphy et al.s (1983) report a simple example of the Stackelberg model is given. They consider the case of a linear demand curve and quadratic cost functions: It is assumed that the Stackelberg firm and Cournot firms are identical. The Cournot problem [15] becomes as follow, with as the optimal solution: Solving this problem yields: Note the upper bound of is found by setting. The working to get equation [51] is shown in Appendix 2. The aggregate reaction curve can be written as: Using this information, this example is now extended to Smeers and Wolfs (1997) model with numerical values. Note that the functions listed in equations [49], [50], [51] and [52] satisfy Assumptions A B and other properties discussed in previous sections. Suppose and. And suppose demand is unknown when the Stackelberg firm makes its decision. The cost functions of the firms will be as follows: Figure : Different Demand Scenarios The tables below describe the possible demand scenarios, probability of each scenario occurring, the joint reaction curve and aggregate reaction curve for, and: Scenario, Demand, Probability, = Demand falls, = Demand remains unchanged, = Demand Increases, Scenario, Joint reaction curve, Aggregate reaction curve, Using this information, the Stackelberg problem [42] can be solved. First, the estimated price element can be calculated as follows: Substituting this result back into the Stackelberg problem [42] gives: This problem can easily be solved by differentiating the objective function and finding the value of for which the differential is equal to zero. The working to obtain the following optimal solution is shown in Appendix 2. Using this result, the following result is obtained for each demand scenario: Figure : Optimal Output, Price and Profit 1 260.870 98.02 652.96 147.04 2 260.870 134.39 798.42 201.58 3 260.870 170.75 943.87 256.13 Stackelberg firm Profit, Cournot firm Profit, Industry Profit, 1 21,243.87 12,010.81 69,387.12 2 35,573.12 22,574.95 125,872.92 3 49,802.37 36,444.87 195,581.87 The tables in figure 3 state the SSNC equilibriums for each scenario, and the profits made by each firm in this oligopoly and the total industry profit in each scenario. Note that since is strictly convex, the equilibrium obtained for each scenario is unique. Also note that in all three scenarios, the Stackelberg Output and profit is greater than that of the Cournot firms, illustrating the first mover advantage. Chapter 4: Discussion In this section, the practical applications, drawbacks and possible extensions to Stackelberg models are discussed. 4.1 Practical Applications of Stackelberg models Stackelberg models are widely used by firms to aid decision making. Some examples include: Manufacturer-Retailer Supply Chain He et al. (2009) present a stochastic Stackelberg problem to model the interaction between a manufacturer and a retailer. The manufacturer would announce its cooperative advertising policy (percentage of retailers advertising expenses it will cover-participation rate) and the wholesale price. The retailer, in response, chooses its optimal advertising and pricing policies. When the retailers advertising and pricing is an importan

Mass Customization and Global Logistics :: Economics Business Custom Essays

"Mass Customisation is - the customisation and personalisation of products and services - for individual customers at a mass production price. So, what does mass customisation mean for manufacturers and retailers? Simply this, that there’s money to be made and customer satisfaction to be achieved by allowing the buyer to customise his or her own purchases by choosing the size, colour and style from a predetermined, often extensive, list of ‘options’. The ultimate goal is to manufacture on a mass scale, retain or improve the margins associated with mass production, and supply a final product that meets each buyer’s individual desires. Apparel captures a major share of mass-customised products, but the concept stretches far beyond a single industry — to furniture, automobiles, eyeglasses, aeronautics, Barbie dolls, computers and so on. For the manufacturer, mass customisation offers an advantage because it differentiates his product from that of his competitors. It enables him to charge a premium for allowing his customer the ‘privilege’ to specify the final details of his purchase. Moreover, it allows the consumer to ‘buy in’ emotionally to the purchase, thereby reducing the risk that he will return the product he’s purchased — forcing the manufacturer to restock or mark down, or even worse scrap, the item. Success in mass customisation is achieved by producing items quickly; therefore it is critical for the manufacturer to find ways to reduce product development cycles whenever possible. In 2002. Fortune Magazine, and many other worldwide publications, proclaim: "You will have it your way". Mass customisation has come full circle. Allow us to provide our definition of mass customisation. It is the opportunity created by taking an otherwise standard product or service and modifying it to meet the unique requirements and choices of a single individual. Mass customisation provides uniqueness and freedom of choice; perfect fit with multiple options; fair, competitive cost; single-piece production; timeliness; quick-to-market; and, very importantly, the consumer is involved throughout the process. A compelling aspect of mass customisation is that it meets diverse objectives. The customer desires uniqueness; freedom of choice; perfect fit or form; fair, competitive cost. The manufactures want to differentiate from their competitors, to improve profit margins and to reduce risk and returns. Retailers want to sell products at higher profit margins, to provide product variety and choice for the customer and to minimise the inventory risk. Today’s customer for mass customisation tends to address the desires of more affluent people, those, for example, who can afford a custom-built yacht, expensive vehicle or a special item of clothing, but that situation is changing fast. Tomorrow’s opportunity for mass customisation will, in my view, be quite literally everybody for everyone; whether rich or poor, the desire for choice and

Hipaa Essay

HIPAA Abstract The Health Insurance Portability and Accountability Act, or better known as (HIPAA) began in 1996 as an Act to help individuals keep their health insurance as they moved from one job to another. As the future brought new advancements HIPAA evolved to include much more than portability. HIPAA now includes many complex rules to protect patient privacy along with the use of information technology that transfers medical records. HIPAA Nearly a decade ago, lawmakers tried to combine the older age ethical tradition of patient privacy with newer age health technology advances, in hopes of saving more lives and reducing such high medical costs. Congress’ intention of the HIPAA Privacy Act was to bring the healthcare industry into the 20th century, while saving U.S citizens billions of dollars. As health care technologies advance so does the rules, rights, and regulations of HIPAA. It’s important to know the â€Å"in’s† and â€Å"outs† of HIPAA and these new advancement’s. Having a guest speaker for HIPAA helped me learn and realize these new advancement’s, a long with what HIPAA really stands for, the rights of patients, and what a breach is and how to prevent it. In the words of the guest speaker, HIPAA equals privacy. Each letter in HIPAA stands for and explains exactly what the Act is. The letter â€Å"H† in HIPAA stands for health, the health of the patient. â€Å"I† in HIPAA stands for insurance, the availability of health plans for the patient. The â€Å"P† in HIPAA stands for portability, it’s portable. â€Å"A† is for accountability, they are accountable for here actions. And finally the last â€Å"A† in HIPAA stands for act, the action of carrying something out. All of these letter s may make up HIPAA but it’s important to know what they actually mean and stand for. After learning the patient rights from the guest speaker I think it makes up the most important part of HIPAA. Knowing your rights under HIPAA can save you from trouble in the future. The first right of HIPAA is The Right to Access, how you the patient can access their health information and obtain copies of their health information. The second patient right is The Right to  Restrictions which gives you the right to restrict certain disclosures of your health information. Another important patient right to HIPAA is The Right to Amendment, it gives you the right to request on amendment to your health information. The next right is The Right to Accounting of Disclosures, this right makes sure your request on accounting of disclosure made on your health information is met. The next patient right is The Right to Complain of Privacy Rights Violation, which I think is the most important. It gives you the right to complain if you feel that your health information has been used or disclosed inappropriately. The last patient right the speaker talked about was how the patients’ health information us used and disclosed. Which allows many ways on how your health information is used or disclosed in regards to treatment, payment, and health care operations. Also patient rights of authorization to release medical or he alth information and the right to revoke authorizations. As you can see there are many rights that the patient has. These rights ensure that patients get the right care in regards to health and how medical records are stored. Even though these rights protect patients there are still major problems that can happen. One of the major problems with HIPAA is a breach. A breach is the unauthorized access use, or disclosure of protected health information that compromises the privacy of such information. According to the HIPAA guest speaker, penalties for a breach can equal up to 1.5millon a year. For individuals found guilty of breach, penalties can be up to $100,000 per year, per violation and or up to ten years in prison. You may be wondering how they decide if there is a breach. Some exceptions to breach that the guest speaker informed us of are unintentional access or use of health information. Only if that information accessed was made in a good faith within employment and the inform ation was not further accessed or used, it is not considered a breach. Another exception of a breach situation is child abuse. Law Enforcement must collect medical evidence to investigate and prosecute a possible child abuse case. Along with Law Enforcement Officials, Social Services also have HIPAA exceptions so they can serve victims of abuse, neglect, and domestic violence. A breach in HIPAA can be very serious, so it’s important to practice good prevention precautions. Some of the guest speaker’s tips on preventing a breach were locking files to secure important papers. Also securing areas that have any health  information, so only the people who are authorized have access to them. Not only do health care workers take precautions to avoid a breach, but so does HIPAA. HIPAA officials do random checks on health care patients in different facilities to ensure that only the authorized workers had access to their medical records. One of HIPAA’s main goals are to protect the patient’s privacy. Taking these precautions as a health care worker can prevent any complications regarding HIPAA and most importantly patient privacy. Having a guest speaker come into class helped me understand more concepts of HIPAA I didn’t understand. She taught me what HIPAA is really about, patient rights, and how important it is to prevent a br each. Learning more about HIPAA will help me in my future career. HIPAA will directly affect my future, as I am currently going for a medical assistant degree. But HIPAA doesn’t just affect people going into the medical field it affects the patients. Therefore it is important for everyone to learn and understand the importance of HIPAA. References Law and Ethics (For the Health Professions) 6e (2013). HIPAA. Pages From74 – To 76 http://www.uthscsa.edu/hipaa/patientrights.asp http://www.ndaa.org/ncpca_update_v16_no4.html http://hipaacow.org/ http://www.hhs.gov/ocr/privacy/index.html http://www.hipaa.com/

Students At The University Of Wisconsin Madison - 856 Words

Students often do not take advantage of the opportunities available to them in the wider community because of a wide variety of engagement and extra-curricular opportunities offered on college campuses. Students at the University of Wisconsin-Madison can take advantage of the campus’ urban location to explore and build connections with the local community, in addition to furthering skills gained from the classroom. At college, students are often surrounded by others who are similar to them – mostly between ages of 18 and 22, pursuing a college education and beyond. The homogeneity within the campus bubble can make students forget what the real world is like. â€Å"You’re really missing something if you just come here for school and you don’t step off of campus and explore living in a brand new place on your own,† Ashley Viager, Assistant Director of Chadbourne Residential College, said. It is also important to understand that community extends beyond campus to the entire city, Stephanie Harrill, Badger Volunteers director, said. This is especially beneficial during a time of growth and development at a higher education institution. This kind of awareness allows for students to not only make the best out of their college experience but also contribute and help their communities to better the environment around them. Community engagement also gives students the opportunity to apply theoretical concepts into the real world to consolidate knowledge in a way that classroomShow MoreRelatedWhat Is Wisconsin Essay938 Words   |  4 PagesWisconsin University is located in Madison, Wisconsin. Wisconsin University, or UW, is located in Milwaukee’s upper east side, close to Lake Michigan. Many streets in Madison, including Hamilton Street, Washington Avenue, Franklin Street, and Paterson Street, are named after many of the signers of the Constitution. 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