{"id":2045,"date":"2014-01-28T18:51:34","date_gmt":"2014-01-28T18:51:34","guid":{"rendered":"http:\/\/sim.sbio.vt.edu\/?p=2045"},"modified":"2014-01-28T18:51:34","modified_gmt":"2014-01-28T18:51:34","slug":"demand-management","status":"publish","type":"post","link":"https:\/\/sim.sbio.vt.edu\/?p=2045","title":{"rendered":"Pricing optimization and demand management in the U.S. Hardwood Industry"},"content":{"rendered":"

Edgar Arias, PhD Candidate at VT<\/em>. Email earias@vt.edu<\/p>\n

As previous research shows, pricing is a key factor in the performance of a firm in international markets.\u00a0 Nowadays, it is a common practice for companies to determine their prices based on cost plus a profit markup and\/or market prices (Dolan, 2008<\/a>).\u00a0 The hardwood lumber industry usually works in a similar way.\u00a0 The problem with these approaches is that they are usually based on arbitrary decisions and do not account for the risk of either setting prices to high \u2013and therefore losing demand, or too low and leaving money on the table.\u00a0 The goal of the next phase of my doctoral study is to test a pricing methodology based on the principles of Revenue Management, that have proved to be successful in increasing profits in other industries, and that may also be valuable in the hardwood lumber industry.\u00a0 The focus of this phase will be centered in exporting firms because they have to deal with more challenges and complexities at the time of determining prices than those which focus on the domestic market.<\/p>\n

Revenue Management (RM) is the discipline within Scientific Management that deals with pricing questions such as: \u201chow much to ask<\/i>?\u201d, \u201cwhen to drop the price (if at all)<\/i>\u201d, \u201cwhat the asking price should be?<\/i>\u201d, \u201cwhich offer to accept?<\/i>\u201d among others, towards maximizing profitability.\u00a0 In other words, RM is concerned with demand-management decisions (Talluri & Van Ryzin, 2005<\/a>).\u00a0 In fact, RM is also known as demand management, yield management, pricing and revenue optimization, etc.<\/p>\n

Above questions are rather old concerns in business, as old as the notion of free market itself.\u00a0 But what is innovative about the RM approach is the application of principles and techniques original in Operations Research to find the right price for \u201cevery product, to every customer segment and through every channel\u201d (Phillips, 2005<\/a>). \u00a0It is based on the fact that markets are not perfect, and in those imperfections lies the opportunity to improve prices beyond what the market dictates, in such a way that profits are also improved (Ross, 2008<\/a>).<\/p>\n

Considering that RM literature in the forest products literature is practically inexistent, we will start by studying a fundamental element of the pricing optimization process: the Price-response (P-R<\/i>) function.\u00a0 The P-R<\/i> function (also known as curve) establishes how the demand of a product varies as a result of a change in price.\u00a0 This function (Figure 1) is seller specific \u2013companies supplying the same product to the same market will show distinct curves, and has the properties of being: non-negative, continuous, differentiable and downward sloping.\u00a0 Common Price response functions are: linear, logit, S-shaped, among others (Phillips, 2005<\/a>).<\/p>\n

\"sim<\/a><\/p>\n

Determining the P-R<\/i> function is necessary to address the basic price optimization problem which consist on maximizing the total contribution m<\/i><\/span>.\u00a0 Each customer order sold at a price p<\/i> and with a cost c<\/i>, has a unit margin equal to p-c<\/i> (Phillips, 2005<\/a>).\u00a0 Therefore we define total contribution as following:<\/p>\n

m(p)=(p-c)d(p)<\/p>\n

In general the total contribution function (Figure 2) is concave, with an apex located at the point where the first derivate equals zero.\u00a0 In other words, the point is where total contribution is maximized and the price is optimal (p*<\/i>).<\/p>\n

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This simple model grows in complexity as we incorporate elements such as supply constraints and price differentiation by regions.\u00a0 In this study, we will analyze historic sales data in order to determine optimum pricing values for each product in each geographic region, which will serve the sales and marketing groups in negotiating with customers abroad in a lumber supply-constrained scenario.\u00a0 In the process, we will map the path that follows from base prices, through invoice prices to pocket prices (Marn & Rosiello, 2008<\/a>) \u2013what companies actually charge \u2013and determine why companies sometimes make less money per order than the market.\u00a0 In other words, we will look for potential leakages in revenue and opportunities to fix them.<\/p>\n

References<\/h2>\n