Management Accounting: Cost & Inventory Optimization

Explore the three inventory types—raw materials, work in progress, finished goods—and stores and spares, with examples and distinctions in liquidity, dependent versus independent demand, and cyclic inventory.

Explore cyclic inventory with a sawtooth pattern, where a 10,000 unit order is consumed at 500 units per day over 20 days, yielding an average inventory of 5,000 units.

Assess carrying costs and safety-related costs, plus restocking costs, compute average inventory, and analyze the trade-off between inventory levels and ordering to minimize total cost.

Calculate total cost for various restocking quantities by summing carrying cost (q/2 × 0.75) and reordering cost (D/q × 50), showing a minimum near 2500 units.

Plot the total carrying cost and the total reordering cost against inventory levels to identify the cost-minimizing quantity. The curves intersect at 2500 units, defining the economic order quantity.

Minimize total inventory costs by applying the economic order quantity model to determine q*, using demand, carrying cost, and reordering cost in a square-root formula.

Explore calculating carrying cost, restocking cost, and the economic order quantity; derive an EOQ of about 89.4 items and balance total carrying and ordering costs to optimize inventory.

Learn how safety stock and reorder point extend the economic order quantity model to prevent stockouts, accommodate lead time, and reflect factors like seasonal variations and supplier reliability.

Apply EOQ to set 166-printer order, 13 per year, averaging 83 units (plus 15 safety stock to 98); copper reorder point is 400 tons with a 2-week lead time.

Explore ABC inventory analysis, a Pareto-based method that categorizes items by annual dollar usage into A, B, and C groups to optimize inventory and minimize costs.

Apply ABC inventory analysis by computing projected annual and dollar usage for 20 items, sort by dollar usage from largest to smallest, and calculate cumulative dollar usage.

Compute the cumulative percentage of total dollar usage, interpret the results, and classify 20 inventory items into A, B, and C groups using a cumulative percentage plot.

Apply the economic order quantity model to determine q*, minimize total inventory costs, and compute the reorder point with safety stock. Explore material requirements planning and just-in-time inventory.

Explore how the economic order quantity adapts to production and gradual receipt, with the production lot size model and the one minus d over p adjustment.

Apply the economic order quantity and production lot size models to determine optimal order quantities, total inventory cost, ordering frequency, and production-based inventory dynamics for carpet stock.

Use Excel to compute total cost, carrying cost, and reorder cost across restocking quantities. The economic order quantity is 2000 units, balancing carrying and ordering costs.

Identify the optimal order quantity in the production lot size model by balancing carrying cost and reordering cost, using demand rate, production rate, and the factor (1 - d/p).

Compute the economic order quantity (EOQ) using the basic model to find q* and total inventory cost, then apply the production lot size model for run length and max inventory.

Explore the three assumptions behind order quantity in EOQ, and how quantity discounts and other costs influence the optimal order size.

Explore the quantity discount model through a practical problem. Examine how a component supplier can optimize order quantities under tiered discounts while considering carrying costs and ordering costs.

Explore economic order quantity (EOQ) by balancing carrying costs and ordering costs using Excel, determine Q* (200 boxes), and compare total costs under a discount pricing schedule.

Analyze the EOQ cost model under quantity discounts, compute demand multiplied by unit price for each discount bracket, and compare total costs to identify the optimal order quantity.

Calculate the economic order quantity for a 2500 annual demand and assess quantity discounts, showing 200 boxes is optimal under basic EOQ, while 400 boxes minimizes total cost with discounts.

Analyze economic order quantity for a two-wheeler component maker, compare quantity discounts, and determine the most cost-effective order size using carrying and ordering costs.

Learn how to summarize interval data using a histogram and frequency distribution, defining classes, boundaries, width, and midpoints with absentee data as an example.

This lecture shows how to construct a frequency distribution by selecting the number of classes and class width based on observations, then defining boundaries and intervals to build the table.

Determine class counts for a frequency distribution using table guidelines, the 1 + 3.3 log(n) formula, or a cube-root method, with 100 exam scores.

Display data as a histogram in Excel using Analysis ToolPak, with input data and bin numbers; determine class counts with min/max, count, and 1+3.3 log10(n) or cube root of 2×n.

Learn how the count IFS function applies multiple criteria across ranges to compute frequency distributions, then build histograms with Excel, including exclusive vs inclusive classes and relative frequencies.

Explore how to construct and format an Excel histogram for absence data, including class boundaries, bin ranges, data labels, and converting frequencies to relative frequencies and percentages.

Explore constructing and interpreting a frequency distribution using Excel histograms, including setting bin labels, adjusting class boundaries, class width, midpoints, and exclusive versus inclusive classes.

Explore how quantity discounts reshape the total cost and EOQ, and show that 90 units is the optimal order size under D2 and D3 pricing.

Determine the economic order quantity by balancing carrying and ordering costs across quantities, and analyze quantity discounts to identify the optimal order size near 72–73 televisions.

Explore quantity discount pricing and the carrying and ordering costs to compute the economic order quantity and total cost under a tiered price schedule.

Compute the economic order quantity from demand, ordering cost, and carrying cost, and compare total costs across price breaks to find 90-unit orders as the lowest.

Assess the economic order quantity for 200 televisions by comparing total costs for 73 and 90 units, with $2,500 ordering, $190 carrying cost, and $900 price, finding 90 units optimal.

Explore the cost-volume relationship in management accounting, identifying fixed, variable, and mixed costs, and how volume changes alter per-unit and total costs.

Use excel to calculate variable, fixed, and mixed costs, and illustrate how total cost and cost volume diagrams change with volume using monitor production as an example.

Split fixed costs into fixed and variable components using the least squares line and cost-volume diagram, and describe total cost with two components: fixed per period and variable per unit.

Explore how cost behavior with volume is modeled along a straight line, estimating total fixed cost and unit variable cost using judgment, high-low, scatter diagrams, and linear regression.

Learn to plot cost against volume using a scatter diagram, label x as volume and y as cost, and apply least squares to assess the linear relationship.

Identify the strength and direction of the linear relationship between cost and volume by using a trendline on a scatter diagram, yielding y = 0.6x + 880.

Explain covariance as a measure of linear relationship, showing how sign and magnitude reflect direction and strength, and introduce the correlation coefficient with -1 to 1 bounds.

Compute covariance and the correlation coefficient in Excel from two data ranges (volume and cost), yielding a 0.7402 correlation that indicates a strong positive linear relationship, with sample covariance 21,000.

Explain covariance and correlation between volume and cost, highlighting a strong positive linear relationship with covariance 21,000 and correlation 0.7402, and show their calculation in Excel using covariance.s and correl.

Describe drawbacks of the correlation coefficient and introduce the coefficient of determination, or R square, as explained variation, then apply the least squares method to derive the regression line.

Compute covariance and correlation in Excel using cor, covariance.s, and covariance.p; derive the least squares line and interpret r-squared 0.5478 as 54.78% explained variation.

Apply the least squares method to cost vs volume data, yielding ŷ = 880 + 0.6x; interpret slope 0.6 as marginal cost and 880 as fixed cost, with R^2 0.5478.

Learn to build correlation and variance–covariance matrices in Excel with the Analysis Toolpak, compute correlation and covariance, and apply least squares to estimate fixed and variable costs.

Calculate the least-squares line for the relationship between the number of tools (x) and daily electricity cost (y) by computing sums, means, and a scatter diagram.

Compute covariance and correlation in Excel using covariance.s and the correlation kernel with A2:A11 and B2:B11, and interpret 0.8711 as a positive link between tools and electricity cost.

Compute the coefficient of correlation and the coefficient of determination with the least squares line, noting a strong positive relationship and 75.88% explained variation, including slope and intercept values.

Use least squares to link number of tools with electricity cost, with R-squared 0.7588 and unexplained variation 24.12%, yielding the line y = 9.59 + 2.25x.

Assess the coefficient of determination for advertising cost and sales, interpret explained versus unexplained variation, examine correlation, not causation, and use the least squares line to estimate sales.