Chapter 22 Mini Case: Cash Conversion, Inventory, and Receivables Management

Review the mini case in Chapter 22 of your text. Complete all requirements of the case.Bracelet Blanks, Inc. (BB) generated $43,803,000 in sales (all on credit) during 2010. The cost of goods sold was 57% of that total. Accounts receivable totaled $3,240,222, inventory totaled $842,020, and accounts payable totaled $1,826,070.

Read and address the following points:

• Calculate BB’s current cash conversion cycle.
• BB currently uses 3,000 ingots of aluminum each year to manufacture bracelet blanks. The order cost (including shipping) is $5,000 per order, and carrying costs are $75 per unit per year. Determine the economic order quantity, the amount of safety stock, and the reorder point for aluminum ingots assuming there is a 1-week lead time and the firm would like a safety stock of 3%.
• In an attempt to boost sales, BB is considering relaxing its credit standards by extending more credit to small firms. BB charges $1.50 per unit. Variable costs are $0.5126 per unit and fixed costs are $10,000,000 per year. The relaxation of credit standards is expected to result in a 3.8% increase in sales (the firm has sufficient excess capacity to handle the increase) as well as an increase of three days in the average collection period. They also expect bad debts to rise from the current level of 0% to 0.5% of sales. Assuming that BB requires a 13% return on investments of this type, should the firm relax its credit standards? Explain.
• Additionally, BB offers its credit customers terms of net 30. However, it is considering changing the terms to 20/10 net 30 in an attempt to reduce the amount of time it takes to collect its accounts receivable. The firm believes this change alone would decrease the average collection period by five days. BB also expects that 63% of its customers will elect to pay within the discount period and that the increased attractiveness of the terms will increase sales by 1% a year. It is not expected that bad debts will change from the current level of 0% as a result of this change in terms. BB’s opportunity cost of funds invested in accounts receivable is 10%. Should the firm offer the cash discount? Explain. Evaluate this scenario separately from the one described in part d.

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Part Two – Data Characteristics

Read Lecture One on descriptive data and review the Employee Data . Be sure to familiarize yourself with the different variables shown on the Data tab. In this course, we will be using the Employee Data and statistical tools to answer a single research question: In our BUS308 company, are the males and females paid equally for equal work?

Lecture One discusses different ways data values can be classified. In our data set for the equal pay for equal work assignment, students in the past have correctly identify the variable gender (coded M and F for male and female respectively) as nominal level data, but they often see gender1 (coded 0 and 1 for male and female respectively) as interval or ratio level data. Why? What could cause this wrong classification? What data do you use in your personal or professional lives that might suffer from not being correctly labeled/understood? (This should be started on Day 1.)

Part Three –Descriptive Statistics

Read Lecture Two on describing data sets and view The Role of Data & Analytics Today video. Lecture Two discusses several different ways of summarizing a data set–central location, variability, etc. Often, business reports provide a mean or average value for some measure (such as average number of defects per production run). Why is the average alone not enough information to make informed judgements about the result? What other descriptive statistic should be included? Why? Can you illustrate this with an example from your personal or professional lives? (This should be started on Day 3.) 

Part Four – Probability

Read Lecture Three on probability. Lecture Three introduces the idea of probability—a measure of how likely it is to get a particular outcome. Looking at outcomes as resulting from probabilities (somewhat random outcomes/selections) rather than fixed constants often changes the way we see things. How does considering the salary outcomes in our sample the result of a probabilistic sample rather than a completely accurate and precise reflection of the population change how we interpret the sample statistic outcomes? What results in your personal or professional lives could be viewed this way? What differences would this cause? Why? 

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