Chapter 16.pdf

# Thus a savings of 630 490 5 140 could be expected as

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policy, we found the bad debt expense to be \$630. Thus, a savings of \$630 – \$490 5 \$140 could be expected as a result of the new credit policy. Given the total accounts receivable balance of \$3000, this savings represents a 4.7% reduction in bad debt expense. After consid- ering the costs involved, management can evaluate the economics of adopting the new credit policy. If the cost, including discounts, is less than 4.7% of the accounts receivable balance, we would expect the new policy to lead to increased pro fi ts for Heidman’s Department Store. Problem 13, which provides a variation of Heidman’s Department Store problem, will give you practice in analyzing Markov processes with absorbing states. 16.2 Accounts Receivable Analysis 23610_ch16_ptg01_Web.indd 15 01/10/14 6:20 PM

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16-16 Chapter 16 Markov Processes SUMMARY In this chapter we presented Markov process models as well as examples of their applica- tion. We saw that a Markov analysis could provide helpful decision-making information about a situation that involves a sequence of repeated trials with a fi nite number of possible states on each trial. A primary objective is obtaining information about the probability of each state after a large number of transitions or time periods. A market share application showed the computational procedure for determining the steady-state probabilities that could be interpreted as market shares for two competing supermarkets. In an accounts receivable application, we introduced the notion of absorb- ing states; for the two absorbing states, referred to as the paid and bad debt categories, we showed how to determine the percentage of an accounts receivable balance that would be absorbed in each of these states. Markov process models have also been used to model decision problems in sports. The Management Science in Action, Markov Process Models and Fantasy Sports, describes the use of a Markov process to model a fantasy football draft to maximize a fantasy team owner’s chances of winning their fantasy league. MANAGEMENT SCIENCE IN ACTION MARKOV PROCESS MODELS AND FANTASY SPORTS* Fantasy sports are a billion-dollar-plus industry with an estimated 30+ million people playing fantasy sports in the United States alone. In fantasy sports, a fantasy team owner picks players for their teams to compete against other fantasy teams. A fantasy team’s score is determined by the statistics accumu- lated by the real players in their actual games. For in- stance, in fantasy football, a fantasy team owner earns points for the yards gained, touchdowns scored, etc. by the players in their fantasy team’s starting lineup. The largest fantasy sport in the United States is fantasy football where fantasy team owners select players for their teams from the National Football League (NFL). Most fantasy football leagues select their teams through a fantasy draft. A fantasy draft is similar to a professional sports draft in that each team takes a turn to select a player for their fantasy team. All current players in the NFL are eligible to be drafted, and each fantasy team must fi ll certain roster spots for their team (e.g., a fantasy team may
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• Spring '18
• Markov process, Markov chain, Andrey Markov, Markov decision process

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