Hampshire Company: Quantitative Analysis Case Study

Hampshire Company: Quantitative Analysis Case Study

Antonio Testa – ACC550

Southern New Hampshire University

TABLE OF CONTENTS

Hampshire Company: Quantitative Analysis Case Study1

Abstract4

Keywords4

Introduction5

Cost-Volume-Profit Analysis5

Break-Even Analysis6

Figure 17

Implications of CVP Analysis7

Inventory Management8

Cost Allocation Method8

Table 18

Just-In-Time Inventory System9

Pros and Cons to JIT9

Recommendations for Inventory Management10

Benchmarking11

Internal Benchmarking12

Table 212

External Benchmarking13

Alternative Costing Methods13

Activity-Based-Costing (ABC)13

Criteria in Determining Adoption of ABC14

Traditional vs Alternative ABC Models15

Figure 216

    Recommendations16

Works Cited18

Abstract

This case study explores the internal processes of Hampshire Company: a manufacturer of umbrellas using fiscal year 2014 data. Data and observations collected throughout the case study will provide information to make recommendations to the board of directors who are considering expansion. Using quantitative analysis, managers used analytical tools including cost-volume-profit to forecast economic changes involving; revenue, costs, profits and break-even markers. Managers also reviewed current inventory management processes using variable and absorption costing methods, just-in-time inventory system, and benchmarking tools in attempt to lower operating costs and attain a better understanding of its external environment. The study also examined the current traditional costing method used by Hampshire as well as an alternative model (activity-based-costing) to determine if expansion does occur, which system would be more beneficial for the company.

        Keywords. Cost-volume profit, break even, inventory management, just-in-time, benchmarking, variable and absorption costing, activity-based costing

Introduction

        The Hampshire Company, a supplier of umbrellas for commercial use, reported sales of $750,000 during fiscal year 2014 (Testa, 2017a). These umbrellas are very popular based on their selling price as well as the durability and comfort of use. Based on this popularity, the company’s president, Brian McCloskey is currently reviewing two offers from touring companies to produce umbrellas for their companies. These new sales would lead to an expansion of current production of stick umbrellas, as well as developing a collapsible model. As CFO of Hampshire Company, my team conducted a quantitative analysis of the current internal processes based on current economic forecasts to recommend procedures to improve internal processes, increases in profits, and determine the impact of expansion.

Cost-Volume-Profit Analysis

Cost-Volume-Profit (CVP) is an analytical tool used by managers to forecast economic changes involving; revenue, costs, profits, including break-even analysis. This information (e.g. break-even point) provides managers the point to which their operational expenses are covered by sales (Busan & Dina, 2009, pp. 103-104). When there are changes in the amount of production and sales within the company, CVP “assumes that the number of units sold is equal to the number of units produced” (Anderson & Leese, 2016, p. 88). These assumptions simplify the variables that lead to determining the short-term economic model of the company and include (Said, 2016, pp. 3-4):

• Total fixed costs remain constant
• Selling prices are constant
• Revenues and costs are being compared over a unit-volume base

Fiscal Year 2014

The company had sales of $750,000 based on producing 60,000 umbrellas and selling each at $12.50. The operating costs for the year were $655,525 which included our variable costs (our cost to make each umbrella) and the fixed costs (manufacturing and administrative). This brought us a net income before taxes of $94, 475 for the 2014 fiscal year. CVP analysis also allows managers to calculate the contribution margin. In 2014, the contribution margin (CM) was found to be $6.50 per unit (umbrella) sold which resulted in $390,000 or 52% of the total sales for 2014. Managers may also use CVP to make assumptions on growth; for 2015 we anticipate a 20% increase in sales. The analysis shows; increasing the units we produce to 72,000, while keeping price and variable costs per unit the same, looking at the operating leverage based on 2014 results, and assuming the percentage of increased sales, will results higher profits without increasing our fixed costs (Testa, 2017a).

Break-Even Analysis

Break-even point in an operation occurs when “sales equal total costs and profit equals zero” (Said, 2016, p. 6). The CVP analysis showed that our company’s break-even point in 2014 was 45,465 units. This is found by taking our fixed costs of $295,525 and dividing it by the CM, the resulting break-even-point in sales would be $568,317. This shows the company had a margin of safety units of 14,535 which resulted in a margin of safety in dollars of $181,683 (Testa, 2017a). Based on the CVP analysis, the company is operating above the break-even point as indicated by the total sales of 60,000 umbrellas (Fig. 1).

[pic 1]

Figure 1.Break-even-point analysis. This figure illustrates the break-even-point for FY 2014

Implications of CVP Analysis

There are three implications I have found using CVP analysis when it comes to planning for our company’s future. The first, CVP analysis; “expands the use of information provided by breakeven analysis” (Indra, 2011, para 2).  The analysis will allow us to decide how many units we need to manufacture to cover the entire amount of operating expenses without recording a loss (Busan & Dina, 2019, p. 104). Second, CVP analysis allows the company to determine “the quantity of units which must be produced and sold to generate a desired dollar-amount of operating income” (Anderson & Leese, 2016, p. 98). Finally, CVP analysis in the short-term provides only approximate results that are dependent on certain assumptions. When using CVP analysis, our managers must be aware of these assumptions and consider the result may not yield a feasible solution (Anderson & Leese, 2016, p. 98).