ACC 325 Exam 3 Study Guide
value engineering
a systematic evaluation of all aspects of the value chain, with the objective of reducing costs and achieving a quality level that satisfies customers
inventoriable costs
all costs of a product that are considered as assets in the balance sheet when they are incurred and that become cost of goods sold only when the product is sold
absorption costing
all variable and fixed manufacturing costs are included as inventoriable costs; "absorbs" all costs
variable costing
all variable manufacturing costs (direct and indirect) are included as inventoriable costs
theoretical and practical capacity
supply side; capacity levels in terms of what a plant can supply
target rate of return on investment
target annual operating income / invested capital
target pricing or target costing
target price (per unit) - target operating income (per unit); price taking
market-based pricing approach
target pricing or targeting costing method
decision nodes
indicated by circles in a decision tree; label is where the split was made
master budget capacity utilization
level of capacity utilization that managers expect for the current budget period, which is typically one year
normal capacity utlization
level of capacity utilization that satisfies average consumer demand over a period of time
gini impurity score
measures the purity of data
decision tree
modeling technique for segmenting the target variable into different regions based on a set of rules; more flexible
logistic regression
modeling technique that estimates the relationship between independent feature variables
past costs
never relevant
second step of data science framework
obtain and explore data
relevant
occur in the future, differ among alternative courses of action
third step of data science framework
prepare data
throughput costing
"super-variable costing"; an extreme form of variable costing in which only direct materials are included as inventoriable costs
predictive modeling
a data science technique used to make predictions based on past or current data
bottleneck
a phenomenon where the performance or capacity of an entire system is limited by a single or limited number of components or resources
high gini impurity score
bad
fourth step of data science framework
build a model
components of data science
computer science and data skills, substantive expertise and management accounting knowledge, as well as math and statistics knowledge
cost-based pricing approach
cost-plus pricing method
normal capacity and master budget capacity utilization
demand side; capacity levels in terms of demand for the output of the plant
seventh step of data science framework
deploy the model
terminal nodes
ending circles in a decision tree; where you get the number of one result and one of the other
fifth step of data science framework
evaluate the model
relevant costs
expected future costs
relevant revenues
expected future revenues
confusion matrix
false positives and negatives
first step of data science framework
gain a business understanding of the problem
low gini impurity score
good
theoretical capacity
producing at full efficiency all the time
practical capacity
reduces theoretical capacity by considering unavoidable operating interruptions
cost-plus pricing method
sales price + markup; price setting
data science framework
seven steps
incremental cost
the additional total cost incurred for an activity
incremental revenue
the additional total revenue from an activity
differential cost
the difference in cost between two alternatives
differential revenue
the difference in total revenue between two alternatives
theory of constraints (TOC)
throughput margin, investments, operating costs
capacity levels
used as denominators to compute the budgeted fixed manufacturing cost rate
data science
using data analytics to draw conclusions from data
sixth step of data science framework
visualize and communicate insights
