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## Costing and Budgeting within management accounting

As part of business studies, we must need to have knowledge on this subject. Today business environment are very dynamic, competitive and even complex and in order to survive and run a business successfully in this climate. we need to have sufficient experience and knowledge in term of management accounting which are related to business. And as part of it we need to develop focusing and need to go through all of the following.

## 1.1.2. The need for, and operation of, different costing methods:

Basically two main costing methods are unavailable under the operation of different costing methods:
Marginal Cost:
An accounting system in which is variable cost units and fixed cost are into also bed into cost units but whiten out in the profit and loss account of the period to which they volute
Industrial Costing:
Accounting system in which cost units are coasted into one main product there are deferent types of methods below
Process costing
Bath process costing
Service costing
Job costing
Contract costing
Different costing method
CC comment Corspotroration Batch process cost this industry is into manufacturing of batches of products.
Bengal oil refries:
Panther Pickles
Batch prepossess Marfa costing industry
3.
Selling price per unit= £210
Variable cost:
Labour (14Ã-6.8)=95.2
Royalties=9
Materials(20Ã-3.2)=64
A) Total variable cost per unit=168.2
Fixed cost=72000
Say number X unit need to produce to earn the organization a profit of £44000.
210X -168.2X-72000=44000
=) 41.8X=116000
=)X=2775.1196
Xâ‰ˆ2775 UNIT.
B)
210Ã-41200-(168.2Ã-41200+72000)
=8652000-7001840
=1650160 [P2]

## 1.1.3 Calculate costs using appropriate techniques: Work in progress valuation:

Cost per toy is 1.66
Valuation in progress is 5300

## 1.1.4 Collect, analyse and present data using appropriate techniques:

Analysis of data is a process of inspecting, cleaning, transforming, and modelling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.
Data mining is a particular data analysis technique that focuses on modelling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis, and confirmatory data analysis. EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.
Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The term data analysis is sometimes used as a synonym for data modelling, which is unrelated to the subject of this article.

## The process of data analysis

Data analysis is a process, within which several phases can be distinguished:
Data cleaning
Initial data analysis (assessment of data quality)
Main data analysis (answer the original research question)
Final data analysis (necessary additional analyses and report)

## Data cleaning

Data cleaning is an important procedure during which the data are inspected, and erroneous data are -if necessary, preferable, and possible- corrected. Data cleaning can be done during the stage of data entry. If this is done, it is important that no subjective decisions are made. The guiding principle provided by Adèr (ref) is: during subsequent manipulations of the data, information should always be cumulatively retrievable. In other words, it should always be possible to undo any data set alterations. Therefore, it is important not to throw information away at any stage in the data cleaning phase. All information should be saved (i.e., when altering variables, both the original values and the new values should be kept, either in a duplicate dataset or under a different variable name), and all alterations to the data set should carefully and clearly documented, for instance in a syntax or a log.[2]

## Initial data analysis

The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that are aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:[3]

## Quality of data

The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analyses: frequency counts, descriptive statistics (mean, standard deviation, and median), normality (skewness, kurtosis, frequency histograms, and normal probability (plots), associations (correlations, scatter plots).
Other initial data quality checks are:
Checks on data cleaning: have decisions influenced the distribution of the variables? The distribution of the variables before data cleaning is compared to the distribution of the variables after data cleaning to see whether data cleaning has had unwanted effects on the data.
Analysis of missing observations: are there many missing values, and are the values missing at random? The missing observations in the data are analyzed to see whether more than 25% of the values are missing, whether they are missing at random (MAR), and whether some form of imputation (statistics) is needed.
Analysis of extreme observations: outlying observations in the data are analyzed to see if they seem to disturb the distribution.
Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.
The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.[4] by Philip kotler

## Quality of measurements

The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature.
There are two ways to assess measurement quality:
Confirmatory factor analysis
Analysis of homogeneity (internal consistency), which gives an indication of the reliability of a measurement instrument, i.e., whether all items fit into a one-dimensional scale. During this analysis, one inspects the variances of the items and the scales, the Cornbrash’s Î± of the scales, and the change in the Cornbrash’s alpha when an item would be deleted from a scale.

## Initial transformations

After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase.
Possible transformations of variables are
Square root transformation (if the distribution differs moderately from normal)
Log-transformation (if the distribution differs substantially from normal)
Inverse transformation (if the distribution differs severely from normal)
Make categorical (ordinal / dichotomous) (if the distribution differs severely from normal, and no transformations help)

## Did the implementation of the study fulfil the intentions of the research design?

One should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups.
If the study did not need and/or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in sample.
Other possible data distortions that should be checked are:
dropout (this should be identified during the initial data analysis phase)
Item nonresponse (whether this is random or not should be assessed during the initial data analysis phase)
Treatment quality (using manipulation checks).[8]

## Characteristics of data sample

In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis phase.
The characteristics of the data sample can be assessed by looking at:
Basic statistics of important variables
Scatter plots
Correlations
Cross-tabulations[9]

## Final stage of the initial data analysis

During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.
Also, the original plan for the main data analyses can and should be specified in more detail and/or rewritten.
In order to do this, several decisions about the main data analyses can and should be made:
In the case of non-normal: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method?
In the case of missing data: should one neglect or impute the missing data; which imputation technique should be used?
In the case of outliers: should one use robust analysis techniques?
In case items do not fit the scale: should one adapt the measurement instrument by omitting items, or rather ensure comparability with other (uses of the) measurement instrument(s)?
In the case of (too) small subgroups: should one drop the hypothesis about inter-group differences, or use small sample techniques, like exact tests or bootstrapping?
In case the randomization procedure seems to be defective: can and should one calculate propensity scores and include them as covariates in the main analyses?[10]

## Analyses

Several analyses can be used during the initial data analysis phase:[11]
Univar ate statistics
Bivariate associations (correlations)
Graphical techniques (scatter plots)
It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:[12]
Nominal and ordinal variables
Frequency counts (numbers and percentages)
Associations
circumambulations (cross tabulations)
hierarchical log linear analysis (restricted to a maximum of 8 variables)
log linear analysis (to identify relevant/important variables and possible confounders)
Exact tests or bootstrapping (in case subgroups are small)
Computation of new variables
Continuous variables
Distribution
Statistics (M, SD, variance, skewness, kurtosis)
Stem-and-leaf displays
Box plots
From the above information we can see that maximum number of customer using current account and minimum number of customer using high interested deposit account. [P4]

1.

## 1.2.2 Calculate and evaluate indicators of productivity, efficiency and effectiveness:

2.
In terms of effectiveness we can say the company being effective in every week as the actual unit is bigger than budgeted. [P5, P6]
3.

## 1.2.3 The principles of quality and value, and identify potential improvements:

“A quality management principle is a comprehensive and fundamental rule / belief, for leading and operating an organisation, aimed at continually improving performance over the long term by focusing on customers while addressing the needs of all other stake holders”.
The eight principles are-
1. Customer-Focused Organisation
3. Involvement of People
4. Process Approach
5. System Approach to Management
6. Continual Improvement
7. Factual Approach to Decision Making and
8. Mutually Beneficial Supplier Relationships.
Potential improvement: Guildhall toys plc increase their sale by selling more unit of toys with a good quality. Minimizing the work hour of labours can decrease the cost and at the same time increase the profit. If Guildhall Toys plc can control their cost of production in a proper and systematic way they can make more profit.
The success of a biennial budget cycle would depend on whether lawmakers were able to separate budget and no budget issues in the way that proponents envision. Various practical hurdles could make separating the two types of issues difficult.
Biennial budgeting could make two major improvements to the budget process. First, it might give lawmakers and agency officials time to evaluate federal programs more effectively and help them carry out the requirements of the Government Performance and Results Act of 1993 (GPRA). Second, it could help ease the annual logjam of budget legislation that has contributed to recent difficulties in the annual appropriation process.
A biennial budget cycle would not come without costs. Members would need to weigh the potential gains from more time for oversight and a more efficient appropriation process against the potential drawbacks of weakened Congressional control of the budget, less accountable federal agencies, and a budget process that might be less responsive to changing conditions. [P7]

## 1.3.1 The purpose and nature of the budgeting process:

Business budgeting is a basic and essential process that allows businesses to attain many goals in one course of action. There are several goals that many businesses seek to achieve (or should be trying to work toward) when they create and implement a budget. These goals include control and evaluation, planning, communication, and motivation.

## Control and Evaluation

Control and evaluation is the most important purpose of budgeting. Budgeting gives a chance to a company to have a certain degree of control over costs, such as avoiding many types of expenses to take place if they were not budgeted for, or assigning responsibility for these expenses. A budget also gives a company a benchmark by which to evaluate business units, departments, and even individual managers.

## Planning

Planning is another primary purpose of budgeting. Budgeting allows a business to take stock of revenue and expenses from the previous period, and judge where the business will be in future periods. It also helps the business to add and remove products and services from its plan for the future period. The budgeting process of large organization may be completed by individual business units and compiled to form a master budget for the organization. Which will help the management to see the company’s actual position and according to that a company can plan better.

## Communication and Motivation

Another very important purpose of budgeting is communication and motivation. It allows management to communicate goals and to promote goal congruence so resources can be coordinated and focused in key areas. By involving employee in budgeting a company also can motivate its employee. While top-down budgeting does not accomplish this goal very effectively, participative budgeting can be motivating. When an employee is involved in creating his or her department’s budget, that person will be more likely to strive to achieve that budget. In budgeting process we need to collect some information as well. They are-
Need to invest some time to create a realistic budget.
Collect historical information on sales and costs from last year if they are available only as a guide.
Create realistic budgets by using historical information, business plan and any changes in operations or priorities to budget for overheads and other fixed costs.
It’s best to ask staff with financial responsibilities to provide with estimates of figures for your budget. [P8]

## 1.3.2 Appropriate budgeting methods and its needs:

Net budget: this budget is than the Gross Budget. It is the budget that spends the property tax. It does not include non-property tax revenue.
Labour budget: this is a Schedule for expected labour cost. Expected labour cost is dependent upon expected production volume (production budget)
Overhead budget: this budget shows the expected cost of all production costs other than direct materials and direct labour
Control budget:. he exercise of control in the organization with the help of budgets is known as budgetary control or control budget.

## Budget overhead interest rate for each month

Month1=12500/5100
Etch month per units =2.45
Month2=12500/5250
Etch month per units = 2.58
Month 3= 12500/5100
Etch month per units = 2.29 [P9, P10]

## 1.4.3 Report findings to management in accordance with identified responsibility centres:

Managerial accounting, or management accounting, is a set of practices and techniques aimed at providing managers with financial information to help them make decisions and maintain effective control over corporate resources. For example, managerial accounting answers such questions as: The practical role of managerial accounting is to increase knowledge within an organization and therefore reduce the risk associated with making decisions. Accountants prepare reports on the cost of producing goods, expenditures related to employee training programs, and the cost of marketing programs, among other activities. These reports are used by managers to measure the difference, or “variance,” between what they planned and what they actually accomplished, or to compare performance to other benchmarks.
For example, an assembly line supervisor might be interested in finding out how efficient his/her line is in comparison to those of fellow supervisors, or compared to productivity in a previous
Time period. An accounting report showing inventory waste, average hourly labour costs, and overall per-unit costs, among other statistics, might help the supervisor and superiors to identify and correct inefficiencies. A detailed report might evaluate the assembly line data and estimate trends and the long-term effects of those trends on the overall profitability of the organization. [P14]

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