Budget Analysis
Weekly Topics

Topics & Notes

Reading/Assignments

WEEK 1 

Who makes a budget?

A budget is a plan for spending.  By starting with your projected income, you can put together a spending plan.  It will be for whatever period you determine. Many of these future expenses will be fixed expenses such as mortgage payments, insurance, certain utilities, consumer debt payments, taxes, etc.  The budget plan is the most important tool in managing your company’s finances. 

Anyone who needs to manage money should make a budget.  Budgets are managed at many levels within the company.  The budget document that a company develops should be agreed upon by all those in your company that are impacted by the budget and those who are involved with income generation and expenditures.

There are several good reasons to create a budget and to make it a good one. The reasons are tied to the people who will read and use the budget. Each reader will look at the budget in a different way and do something different with it. If you know your readers, you can make a budget that will impress everyone—and, more important, show how your group is contributing to the organization and therefore approve the funds you need to proceed. If you know how the budget will be used, you will know how to write it in an easy-to-use way. More important, it will help you succeed and show that you are a good manager and that your team is doing a good job. So, let’s take a look at
your audiences and what they will do with your budget.

Who reads a budget? 

Anyone on the management team may need to read the budget.  Lenders will be interested in your budget.  If you are a public company investors may review your budget(s).

You and your team are your first, and most important, audience for your work plans and your budget. When you read the budget, you want it to make sense. This means that you understand
it, of course, but it means more than that. The budget should be believable and workable and it should work the way your team works and be appropriate to your situation. Your boss is your second audience. 
Of course, you want the budget to be correct, clear, and complete for him or her.  If your boss checks your work closely, you don’t want any errors to show up. If your boss doesn’t check it closely, you certainly don’t want the budget to go further upstairs with mistakes in it. Your boss will also check the totals of the budget against available funds. In some companies and in many government agencies, the boss will also check the budget against rules and limitations. Some organizations require that top managers approve the line-item budget. Your boss will also seek or approve funds for the budget. In a company, you may do work for another department, and then bill that department for the work you do. Or the cost may be billed to a client, but your boss will need to make sure that you are planning to spend the right amount of money for that client.
 

Six Steps to Creating a Budget

Step 1: Setting of Objectives

Step 2: Analyzing available resources

Step 3: Negotiating to estimate budget components

Step 4: Coordinating and reviewing components

Step 5: Obtaining final approval

Step 6: Distributing the approved budget

We've made a really good budget.  What makes it good?

• It's written clearly, so that anyone can understand it.
• It is based on good information from our customers and our own experience.
• We started with last year's actual expenses, but we also did some planning for the coming year.
• We researched the most important items and made some good management choices, such as buying the old copier.

In preparing our budget, we've set up our team for a year of success.

• Read Chapter 1 & 2
• Key Terms List 1
• Key Terms List 2
• Assignment Week 1
  1. Why is it important to have a budget?" and "What will a budget accomplish for your company?"
  2. Chapter 1 looks at the different types of budgets.  Why do we need all of these different types of budgets?  Why cant one type suffice all situations? Would a company need to do every type of budget listed?
  3. In chapter 2, the text talks about the need of strategic planning and control.  Compare and contrast the content from Chapter 2 with the Four Basic management functions that you learned in MGT 301, Plan, Organize, Lead and Control.

WEEK 2 

• Time Periods - Budget time periods may vary, depending upon the size and scope of your company, but a 12 month period is the minimum time frame for projecting a budget.
• Budget & Vision - A company’s vision has to be funded by the budget(s) if you ever hope to achieve your goals. If you fail to find your goals you are sure not to fulfill your vision. Companies need find the funds to meet their budgeted needs. These funds can come from retained earnings, stock issues, bonds or several types of loans.
  
  A budget is a financial plan to control future operations and results. It is expressed in numbers, such as dollars, units, pounds, hours, manpower, and so on. It is needed to operate effectively and efficiently. Budgeting, when used effectively, is a technique resulting in systematic, productive management. Budgeting facilitates control and communication and also provides motivation to employees.
• Forecasting Income - Forecasting Income - begins with a review of the revenue during past periods, can be usually from 5 to years if the information is available. If not available, then you will use what you have available. A decision to be made is will you use a simple average formula, or will you use a weighted average formula, with most of the weight being given to the periods closer to the time frame you are dealing with.
  
  Other factors to take into account:
  
  1. Seasonality of the business
  2. What are the percentage increases in sales in the business over the past periods based on yearly increases and also you need to look at monthly and quarterly increases as well.
  3. Are the out of the ordinary contracts/bids/sales that might come you way in the future that will impact the forcast?
• Key Accounting Concepts - Click Here for Important Accounting Terms
• Break Even Formula
  • TR (Total Revenue) = TC (Total Cost)
  • U (SP) = FC + U (VC) where
  • U = Volume in Units Sold
  • SP = Selling Price
  • FC = Fixed Costs
  • VC = Variable Costs

• Read Chapter 3 & 4
• Assignment Week 2
  1. Does one need to be an accounting major to do a budget? Why or Why not?
  2. Are their ethical implications when doing a break even analysis? In other words, can one "massage" the numbers to make it come out they way one wishes it to come out
  3. Problems - A company produces widgets for $25 a case. Fixed costs are $80,000 and variable costs are $15 per case.
  1. What profit or loss would result if 4,000 cases are sold?
  2. What profit or loss would result if 10,000 cases are sold?
  3. What is the break - even volume in cases?

WEEK 3 

• Define the Project- Measure Twice, Cut Once by Geoff Hart

  Hart, G.J. 2000. "Measure twice, cut once." Reprinted courtesy of Computerworld Canada, Nov. 17/2000:21. We've all heard the carpenter's adage "measure twice, cut once": It means that acting without planning can be expensive, and because of the potential cost of poorly thought-out actions, we should not only plan, but plan twice. The person who builds a boat in a basement and discovers they can't get it out the basement door isn't an urban legend; it happened to someone in my neighborhood when I was a teen. Then there were some friends who, supervising the construction of their new home, arrived one day to find the builders scratching their heads in consternation; the single-piece bath and shower unit had just arrived, the day after the bathroom tiles had finished drying and the door had been hung.

  Budgeting for projects is different than budgeting for recurring events or items. With projects, the budget items are never exactly the same. The end goal, costs and/or resources will need to be re-evaluated each time a project budget is prepared.

  There are the steps to create a project budget:

  1. Define the project
  2. Create the work plan
  3. Calculate the cost

  Project management skills are valuable in many areas of life and can help you achieve your goals faster, easier and/or with less waste.

  Define the Project

  A project plan is a written overview of definition of what the project is, who is involved, what resources are available, when specific deliverables are due and who will do them, etc. It is a means to get all parties informed, in agreement and working towards one goal. The book provides an example you can use to develop a Quick Project Overview (QPO). There are 5 steps they detail. Please list them in a document and upload the document to the assignment's drop box.

• Create the Work Plan - The work plan provides a platform for the budget and schedule. The work plan tells what is being done so that the cost and length of time needed can be determined. The work plan is essential for an accurate budget and schedule. The official project management term for a work plan is a Work Breakdown Structure (WBS). It is a detailed “to do” list for everyone on the project.

  It’s not always easy to create a good plan. The book has a tip that is a good way to start writing one. It is simply that. Just start writing. You can revise or correct once you have gotten something written. Revisions are what change things from so-so to great.

  The steps to creating a work plan are:
  1.Make an incomplete list of tasks. Just start writing things down.
  2.Complete the list through visualization, plus asking the right questions and using experts
  3.Group the tasks that go together. Each group of tasks should deliver one result. Ex: Tasks - Hold toothbrush. Open toothpaste tube. Squirt toothpaste on toothbrush. Close toothpaste tube. Result – prepare toothbrush to brush teeth.
  4.Out the groups in the order necessary to achieve the desired output. Ex: You need to prepare your toothbrush before you brush your teeth.
  5.Organize the list into a detailed WBS
  6.Check the list and ensure key questions are answered
  7.Proofread and format the completed list
  

• Calculate the Cost - The cost of the work to be done plus the cost of whatever we need to buy to do it together are the estimated cost of the project. Pages 90 and 91 in the book provide an example of calculating costs.
• Track the Project - Once you have a project budget and plan, you need to track it to be the work gets done, within budget and on time. Depending on the formality and complexity of the plan, it can be as simple as putting a check next to the task or as formal as publishing communications for all members with status checks, progress reports and deliverable reports.

• Read Chapter 5 & 6
• Assignment Week 3
  1. The title of chapter 5 says Profit Planning: Targeting and Reaching ACHIEVABLE Goals. What are some of the pitfalls when setting goals. Are their ethical implications when one sets goals for individuals, or for departments? How does one go about setting reachable goals?
  2. Do you agree or disagree with the following, "If I set goals too low, I will set the bar so that everyone can EASILY attain the goals and not push themselves?" Do you think most people work towards a goal, and do not work to get past the goal?
  3. Review the schedules in Chapter 6, do you see a "flow" in the order that these schedules are presented? Do you see a methodology presented here? What do you see as the strengths of this method? Do you see any weaknesses?

WEEK 4 

• Intro to Regression Analysis
• Intro to Regression Analysis using Excel
• Use a Partner for Proofreading -

  Main Entry: double-check

  Pronunciation: "d&-b&l-'chek, 'd&-b&l-"
  Function: verb
  transitive senses : to subject to a double check

  an article double-checked for accuracy

  intransitive senses : to make a double check

  From the Merriam-Webster Dictionary.

  
  
  When you submit a budget, it is not always a dynamic, flexible document. It is often considered to be ‘in stone’ and not changeable. So, it you are going to be held to it, you better be sure it says what you really intend for it to say. Check it twice!

  There are various ways listed to check it twice

  • Have a partner proofread it
  • Read it out loud….read it backwards
  • Implement a policy whereby EVERYTHING gets checked twice
  • KNOW your spreadsheet application
  • Use spreadsheet cross checking
  • Document version control
  • Verify your budget assumptions
  • Final proof reading steps

  Have a partner check it

  No matter how precise you think you may be, you can still make errors. Errors in math, spelling, formatting, alignment etc….any (or all!) are possible. When we know what we meant to say, we often read it that way…even if that is not what we actually wrote. So have someone else look at it. Fresh eyes are a good way to ensure you wrote what you intended. If you can get them to read it out loud, even better. If you have nobody to read it and you are in a hurry (be even more cautious when you are hurried!!) you can read it out loud yourself. If you read it backwards, you may also catch errors. Start at the last word of the last sentence and read back to the start.

  Please let me know if you have any questions.

   

• Avoid Spreadsheet Errors -

  Spreadsheets are a wonderful tool in budgeting. They auto calculate, they make things easier to manage, they spell check etc. However, spreadsheets make errors. Many people do not know that Excel is often off because it rounds at a certain point. The book has an example on page 99. Look at the example. I had i situation where  the spreadsheet I created was off by a dollar and I spent hours trying to figure it out. That was a waste of time, and there was a bit of egg on my face when I found out why it was happening. I considered myself a formula and spreadsheet guru, and normally I am, but as they say EVERYONE makes mistakes.  Morale of this story, be careful with all data input.

  Automatic Cross-Checking in Spreadsheets & Document Version Control
   

  Some ways to avoid spreadsheet mistakes:

  Cross check your columns. Use checksum. Spreadsheet programs provide a number of ways that you can ensure accuracy. Use them! Use document version control. I know people that simply save a new file with each version, and I have done so myself, without indicating which is the latest and greatest. This results in sending wrong versions or having to cull through various files to figure out which one you want to send. It you use a system whereby you indicate in the naming convention what version it is, you can save yourself time, possible embarrassment if you send the wrong one etc.

  Verifying Budget Assumptions
   

  Implement a policy in which everything that leaves your department is checked twice

  I once managed a department that keyed very critical and high profile information. An error was immediately escalated to the senior executive when it occurred. To avoid getting bad exposure, we implemented a policy that required all entries to be double checked within 24 hours so that we knew it was accurate. The accuracy of the data was so critical; it was worth the “double work.” It is important to note that everyone makes mistakes when you implement this policy however. Let people know why you are implementing the policy (not to punish offenders but rather the information they handle is so important that it is necessary to double check.) If people feel that they will be reprimanded for mistakes, they will not only make more mistakes, they will also experience lower morale, job stress and all that micromanagement brings with it.

  The Final Proofreading Steps
   

  So you're confident that it is done and done right?

  Do a final check of your pages, format alignment, headers and footer are accurate etc. Then, let it sit a while. Use fresh eyes one more time to avoid the little silly mistakes (like the time I worked for a company that had 17,000 employees. When they changed our FMLA forms and process they forgot to remove the name of the company that we got the program from and it appeared every time someone opened the application!)

• Read Chapter 7, pay careful attention to page 99-regression analysis.
• Assignment Week 4
  1. What are the benefits of using Regression Analysis in cost estimating? Can you see any downsides to using this technique?
  2. How do Fixed, Variable, and SemiVariable costs differ? How can one keep these costs straight as to which category they will belong to?
  3. ABC Drink Company has sales in 2009 of $586,000 and its Cost of Sales were $268,900. Its cost of sales averaged 47.5% over the previous 2 years. The company projects sales in 2010 of $821,200. They are about to open their first resturant in Louisville. They project fixed costs for the new unit to be $101,000, the average menu item will be $2.75 each, with a variable cost of each item to be $1.10. Do you have enough information to determine a break even cost? If so, what are the number of units sold for the resturant to break even?

WEEK 6 

• Variance Analysis - Variance analysis is usually associated with explaining the difference (or variance) between actual costs and the standard costs allowed for the good output. For example, the difference in materials costs can be divided into a materials price variance and a materials usage variance. The difference between the actual direct labor costs and the standard direct labor costs can be divided into a rate variance and an efficiency variance. The difference in manufacturing overhead can be divided into spending, efficiency, and volume variances. Mix and yield variances can also be calculated.

  Variance analysis helps management to understand the present costs and then to control future costs.

  Variance analysis is also used to explain the difference between the actual sales dollars and the budgeted sales dollars. Examples include sales price variance, sales quantity (or volume) variance, and sales mix variance. A difference in the relative proportion of sales can account for some of the difference in a company’s profits.
  

• Favorable Variance - Always a positive indicator? - In a standard costing system, some favorable variances are not indicators of efficiency in operations. For example, the materials price variance, the labor rate variance, the manufacturing overhead spending and budget variances, and the production volume variance are generally not related to the efficiency of the operations.

  On the other hand, the materials usage variance, the labor efficiency variance, and the variable manufacturing efficiency variance are indicators of operating efficiency. However, it is possible that some of these variances could result from standards that were not realistic. For example, if it realistically takes 2.4 hours to produce a unit of output, but the standard is set for 2.5 hours, there should be a favorable variance of 0.1 hour. This 0.1 hour variance results from the unrealistic standard, rather than operational efficiency.
  

• Labor Variance - This variance tells us how efficient the direct labor was in making the actual output that was produced by the direct labor.

  The direct labor efficiency variance compares the standard hours that it should have taken to make the actual output Vs. the actual hours it took and multiplies the difference in hours by the standard cost per direct labor hour.

  Here’s an example with amounts. Let’s assume the standard for direct labor is 3 hours per unit of output and the standard cost for an hour of direct labor is $10. Let’s say the output for the period is 6,000 units and the actual direct labor hours were 18,400 hours and the labor earned $10.30 per hour. The standard direct labor cost for the actual output should have been 18,000 hours (6,000 units of output times 3 standard hours) at $10 per hour for a total of $180,000. The actual direct labor cost was $189,520 (18,400 hours at $10.30 per hour). This means a TOTAL (efficiency and rate) variance of $9,520. Some of that variance is due to the rate being $0.30 too much and some of that variance is due to the direct labor using too many hours—not being efficient.

  The direct labor efficiency variance focuses on the direct labor hours: 6,000 units of output should have taken 3 hours each for a total of 18,000 direct labor hours. The actual direct labor hours were 18,400 hours. This means there was an unfavorable direct labor efficiency variance of 400 hours times the standard rate of $10 for a total of $4,000.

  The direct labor rate variance is the $0.30 unfavorable variance in the hourly rate ($10.30 actual rate Vs. $10.00 standard rate) times the 18,400 actual hours for an unfavorable direct labor rate variance of $5,520.

  The combination of the unfavorable direct labor efficiency variance of $4,000 + the unfavorable direct labor rate variance of $5,520 is the total unfavorable direct labor variance of $9,520.
  

• The POWER of Variance Analysis -

   A very useful concept was drilled into me early in my career: An important aspect of success is to understand costs

  A very dramatic story was told to me wherein it was explained that Andrew Carnegie built the successful U.S. Steel Company not by building the best steel, but by carefully understanding his cost per pound. At the time, this was a very novel concept. Whereas his competitors guessed as to what they could charge, Carnegie knew exactly how low he could go and still make money.

   

  This knowledge meant that he could under-bid competitors and decide when to "walk away from the table" (meaning he knew when to exit the bidding process because he couldn't make a profit). Let's discuss the power of variance analysis in modern business at a high level.

   

  What is a variance?

  Let's start with the basic concept of a variance. It is simply the difference between what you expected and what you really received. If you expected something to cost $1 and it, in fact, cost $1.25, then you have a variance of $0.25 more than expected. This, of course, means that you spent $0.25 more than what you planned.

   

  Materiality

  When you are calculating your variances, take materiality into consideration. If you have a variance of $0.25, that isn't a big deal if the quantity produced is very small. However, as the production run increases, then that variance can add up quickly. Most projects generate tons of variances every day. To avoid a tidal wave of numbers that are inconsequential, instead focus on the large variances. For example, it is far more important to find out why there is a $10,000 cost variance than to spend two days determining why an expense report was $75 over budget.

   

  Types of Variances

  As mentioned, there are many different types of variance analysis. Three high-level views that I tend to focus on are:

   

  Estimate to Planned. This is the difference between what we quoted and how we actually planned to do the work. I look at what has changed and why. It may be that there are new processes, vendors, materials, technology, laws, etc. If the variances are significant, I search for alternatives before work commences whenever possible. If alternatives are not possible, then I learn from the situation and communicate what not to do for subsequent work. You may wonder why there is a difference between planned and estimated. This is due to situations where projects are quoted based on best guesses and black magic. In other words, the quote is created without formal detailed planning often by a group who will not actually do the work. As you can imagine, it is advantageous to keep the quoting and planning teams in synch over time.

   

  Planned to Actual. This variance looks at the difference between how work is planned and how it actually is executed. By comparing planned to actual, we can see how the work changed once in progress. There may be changes brought on by the project team, by the customer, by vendors or by a change in the environment, such as new regulations. Regardless, the changes need to be analyzed so issues can be identified and mitigation strategies can be developed to protect future work.

   

  Estimate to Actual. Here, we compare what we quoted to what we actually did. This is a crucial comparison. If jobs are estimated in a manner that operations cannot support, then there are substantial risks including profit losses and even project failures. Again, by analyzing the numbers, we can determine what changed, why and then take corrective action. For subsequent work, we may need to change vendors, processes, materials, contractual stipulations, etc.

   

  Alternatively, unplanned customer change requests may be fully billable, in which case we need to identify those changes as such and invoice accordingly. It is very important to point out that some of the most financially successful groups I have worked with are very adept at writing detailed quotes, assembling solid plans and then capturing customer change requests and billing for them.

   

  Why do we do this?

  In short, we want to do variance analysis in order to learn. One of the easiest and most objective ways to see that things need to change is to watch the financials and ask questions. Don't get me wrong: You cannot and should not base important decisions solely on financial data.

   

  You must use the data as a basis to understand areas for further analysis. For example, if a bandsaw is a bottleneck, then go to the department and ask why. The reasons for the variance may range from the normal operator being out sick, to a worn blade, to there not being enough crewing and a great deal of overtime being incurred. Use the numbers to highlight areas to investigate, but do not make decisions without first investigating further.

   

  Power of trends

  Point in time variances, meaning singular occurrences, can help some. To make real gains, look at trends over time. If our earlier variance of $0.25 is judged as a one-time event, is that good or bad?

   

  We cannot tell with just one value, so let's look at the trend over time. If we see that the negative variance over time was $0.01, $0.05, $0.10, $0.12 and $0.25, then we can see that there apparently is a steady trend of increasing costs and, if large enough to be material, should be investigated. Yes, this can take a lot of time if done manually. However, spreadsheets and computer systems can be used to generate real-time variance reports that are incredibly useful with little to no work to actually run the report.

   

  Getting help

  This article is very high-level. Variance analysis and cost accounting in general are very interesting fields with a great deal of specialized knowledge. For example, we discussed three high-level comparisons in this article. A person versed in cost accounting can drive down into the variances to identify quantity, cost and time variances. If you really want to make gains by using variance analysis, at least get trained--or someone else trained--in the skills necessary. To put some power here, bring in a formally trained and experienced cost accountant or Certified Management Accountant (CMA) to either own the ongoing process or at least set up the process for your organization's subsequent use.

   

  Summary

  By using variance analysis to identify areas of concern, management has another tool to monitor project and organizational health. People reviewing the variances should focus on the important exceptions so management can become aware of changes in the organization, the environment and so on. Without this information, management risks blindly proceeding down a path that cannot be judged as good or bad.

• Read Chapter 8
• Assignment Week 6
  1. In your own words, what is variance analysis? What are the pros and cons of this technique? How can it be useful in the budgetary process?
  2. If you were asked by one of the culinary instructors to go into their class and give a talk on variance analysis in the preparation of food in resturants, what would you tell them>
  3. Problems - Review the sample problems/situations in the chapter. Make up your own problem, based on one of the examples, then show your solution to your problem.

WEEK 7 

Three Methods Of Sales Forecasting

Sales forecasting is especially difficult when you don’t have any previous sales history to guide you, as is the case when you’re working on preparing cash flow projections (see next link - Cash Flow Projections) as part of writing a business plan. Here, Terry Elliott provides a detailed explanation of how to do sales forecasting.

There are all sorts of ways to estimate sales revenues for the purposes of sales forecasting.

One point to remember when sales forecasting is that if you plan to work with a bank for financing, you will want to do multiple estimates so as to have more confidence in the sales forecast. How do you do this?

Sales Forecasting Method #1

For your type of business, what is the average sales volume per square foot for similar stores in similar locations and similar size? This isn't the final answer for adequate sales forecasting, since a new business won't hit that target for perhaps a year. But this approach is far more scientific than a general 2 percent figure based on household incomes.

Sales Forecasting Method #2

For your specific location, how many households needing your goods live within say, one mile? How much will they spend on these items annually, and what percentage of their spending will you get, compared to competitors? Do the same for within five miles (with lower sales forecast figures). (Use distances that make sense for your location.)

Sales Forecasting Method #3

If you offer say, three types of goods plus two types of extra cost services, estimate sales revenues for each of the five product/service lines. Make an estimate of where you think you'll be in six months (such as "we should be selling five of these items a day, plus three of these, plus two of these.") and calculate the gross sales per day. Then multiply by 30 for the month.

Now scale proportionately from month one to month six; that is, build up from no sales (or few sales) to your six month sales level. Now carry it out from months six through 12 for a complete annual sales forecast.

Don’t Just Do One Sales Forecast

Instead of forecasting annual sales as a single figure, use one or two of the sales forecasting methods above and generate three figures: pessimistic, optimistic, and realistic. Then put the figures in by month, as depending on your business, there could be HUGE variations by month. (Some retail firms do 50 percent of their gross sales around Christmas, from the end of October to the end of December, for example, yet barely get by June through August.)

Include Expenses in Your Sales Forecasting

Now put in your expenses by month, including big purchases by season (or however you buy materials/goods). Remember, you may buy materials or inventory in say, July, for Christmas, yet not get all of your receipts until 45 days after Christmas. There can be big cash flow implications. Also, will you be buying vehicles? Capital equipment? Make sure to show depreciation expense.

In your expenses, put in an allowance for bad debts. Figure how much of your sales are by cash, how much by credit card, how much by your extending credit. Deduct say four percent or more for credit card expense for that portion sold by credit card. For payroll expenses, put in estimated tax withholding payments quarterly that must be paid to the government.

If you're going to a bank for financing, be able to answer questions such as, have you made an allowance for a reserve cash account, for your slow months, but also in case you have to quickly replace a vehicle or equipment? You say you'll charge x dollars for your product, but what happens when your competition cuts the price by 33 percent and still makes a profit?

 

How specifically will you grow your business-- selling more to existing customers, selling existing products to new customers, selling new products to existing customers, and selling new products in order to attract new customers? They're going to want to see if you've got a real plan.

 

Remember that it is acceptable (and realistic) to have a negative cash flow projection for the early months of your cash flow projection period.

Sales Forecasting Summary

I guess you can see that instead of estimating one big sales figure for the year when sales forecasting, a more realistic monthly schedule of income and expenses gives you far far more information on which to base decisions. That's what "keeping the books" is designed to do: give YOU information you can make good decisions on.

So in effect, you prepare three cash flow projections, where you vary the percentage of sales or other figures to arrive at three different scenarios: pessimistic, optimistic, and realistic. The pessimistic view should be the "worst case" situation; plan to have enough capital and patience to get through that scenario. If it turns out that the actual results are better than that - great!

The Cash Flow Projection

The Cash Flow Projection shows how cash is expected to flow in and out of your business. For you, it's an important tool for cash flow management, letting you know when your expenditures are too high or when you might want to arrange short term investments to deal with a cash flow surplus. As part of your business plan, a Cash Flow Projection will give you a much better idea of how much capital investment your business idea needs.

For a bank loans officer, the Cash Flow Projection offers evidence that your business is a good credit risk and that there will be enough cash on hand to make your business a good candidate for a line of credit or short term loan.

Do not confuse a Cash Flow Projection with a Cash Flow Statement. The Cash Flow Statement shows how cash has flowed in and out of your business. In other words, it describes the cash flow that has occurred in the past. The Cash Flow Projection shows the cash that is anticipated to be generated or expended over a chosen period of time in the future.

While both types of Cash Flow reports are important business decision-making tools for businesses, we're only concerned with the Cash Flow Projection in the business plan. You will want to show Cash Flow Projections for each month over a one year period as part of the Financial Plan portion of your business plan.

There are three parts to the Cash Flow Projection. The first part details your Cash Revenues. Enter your estimated sales figures for each month. Remember that these are Cash Revenues; you will only enter the sales that are collectible in cash during the specific month you are dealing with.

The second part is your Cash Disbursements. Take the various expense categories from your ledger and list the cash expenditures you actually expect to pay that month for each month.

The third part of the Cash Flow Projection is the Reconciliation of Cash Revenues to Cash Disbursements. As the word "reconciliation" suggests, this section starts with an opening balance which is the carryover from the previous month's operations. The current month's Revenues are added to this balance; the current month's Disbursements are subtracted, and the adjusted cash flow balance is carried over to the next month.

Here is a template for a Cash Flow Projection that you can use for your business plan (or later on when your business is up and running):

CASH FLOW PROJECTIONS

(Add a row of monthly headings to cover one year period)

CASH REVENUES
Revenue from Product Sales
Revenue from Service Sales
TOTAL CASH REVENUES

CASH DISBURSEMENTS
Cash Payments to Trade Suppliers
Management Draws
Salaries and Wages
Promotion Expense Paid
Professional Fees Paid
Rent/Mortgage Payments
Insurance Paid
Telecommunications Payments
Utilities Payments
TOTAL CASH DISBURSEMENTS

RECONCILIATION OF CASH FLOW
OPENING CASH BALANCE
ADD: TOTAL CASH REVENUES
DEDUCT: TOTAL CASH DISBURSEMENTS
CLOSING CASH BALANCE

Remember, the Closing Cash Balance is carried over to the next month. Once again, to use this template for your own business, you will need to delete and add the appropriate Revenue and Disbursement categories that apply to your own business.

The main danger when putting together a Cash Flow Projection is being over optimistic about your projected sales.




Resist the urge! Don't trim the marketing budget.

That's the advice of strategists specializing in small- to mid-sized companies as we head into an increasingly tight economy and competition for business gets tougher. While it's temping to view marketing dollars as discretionary -  they don't go toward payroll, overhead or production - experts say now is the time smaller companies need promotional efforts most to stand out from the crowd and solidify their brands in the mind of customers who themselves are feeling the pinch of shrinking budgets.

Already there are clear signs of a knee-jerk reaction. According trade publication Advertising Age, in October, the most recent month for which data is available, U.S. ad spending fell 2.5 percent from the year-earlier period. Instead of hacking, it's better to first ensure that your existing budget - whether spent on traditional outlays like print and television advertising, public relations and special events or newer forms of digital media and so-called guerilla marketing - is deployed as efficiently as possible.

Here's some practical advice from a few experts: Clearly link marketing strategies to outcomes, says Sally Hodge, president of Hodge Schindler Integrated Communications in Chicago, whose accounts include an airport shuttle service, the American Association of Endodontists, a design school and a financial consulting firm. "What's critical is not just using the right strategies for your business, but to make sure you have the metrics in place so you know these are the right strategies," she says. Measuring promotional efforts can be as simple as asking at the point of sale how customers heard about your product or service, to tracking whether your Web traffic increases after a PR effort lands your business prominent coverage in a print article or a newscast.

Spend smarter. If you are ultimately forced to cut, having clear metrics in place will let you know where you're getting the most bang for your buck, Hodge says. And regardless of which promotional channels you use, get the best value for your marketing dollars. Some examples: Hodge currently likes radio spots for some good bargains, but says you must understand your business and customers well enough to determine if rates and returns measure up. Permission-based Internet marketing strategies, such as e-postcards - easy to design and execute - are an effective alternative to direct mail; they save on traditional printing and postage, and can link the recipient to a special Web page about your product or offer.

Focus on existing customers, says Gary Slack, chairman and CEO of Slack Barshinger, a marketing consulting firm with offices in Chicago and San Francisco. "Find ways that you can increase revenue with people who are already doing business with you," says Slack, whose clients span large public companies to smaller concerns, like a virtual events host and an online distributor of food equipment and replacement parts. "When times get tough, too often companies take their eye off their existing customer base and start focusing on attracting new customers, which is usually more expensive, entails acquisition costs and has long sales or buy cycles," he says.

In order to get a larger share of the existing pie, small companies should consider ways to deepen their business relationships, such as investing in original research on behalf of a customer to help them better understand a business issue for which they can provide a solution. Slack also recommends scheduling regular meetings to understand customers' concerns, and then responding with improvements to a product line or service to make it more valuable. Whenever possible, find out where customers are getting new information on their industry and what is influencing their purchasing decisions. "There are some companies that look at downturns as a real opportunity to get an advantage over weaker companies in their sector," says Slack.

Enlist customer feedback, says Karen Woon, San Francisco-based director of marketing for Prophet, a brand consultancy that has offices worldwide. "This is a great time to tap into consumers' insights and understand what really matters to them," says Woon. "Think of it as an opportunity of sorts because many of your competitors will scale back." Give consumers a forum to provide insight on new products and services, she says, such as an area on your Web site where they can provide feedback. For its part, Prophet conducts an annual survey with both current and prospective clients dedicated to an area of content the firm is interested in learning more about. Most recently Prophet queried on the topic of innovation. "It gives us an opportunity to learn what's on people's minds," she says. Woon says tight budgets are a good time for the "test and learn" approach, where new marketing initiatives can be rolled out on a small scale and expanded only when they prove to be effective.

"It makes a lot of sense to try a lot of initiatives on a small scale and see if they work," she says. Consider non-traditional methods, says Joel Warady, who runs a small marketing consulting firm in Evanston, Illinois. His clients, primarily consumer businesses with annual revenues of $50 million or less, have become adept at creating product buzz without the use of traditional marketing methods such as print advertising. "The companies that are succeeding are the ones that are smart with their marketing dollars," says Warady. "We focus a lot of our time and efforts on line." One such client is Peak Enterprises, a Sarasota, Florida-based maker of an oral hygiene product aimed at Gen-Xers called the Tung Brush. The tongue cleaner has been used by contestants on talent show "American Idol" after efforts to place the product directly into the hands of the show's make-up artist. And the Tung Brush put in an appearance on the content-sharing Web site YouTube when it was incorporated into a young man's humorous quest to mark visits to100 monuments across the United States by licking them. The product is now carried by Wal-Mart, among other retail chains. Says Warady: "Those are the creative ideas that small companies should take advantage of."

• Read Chapters 9 & 10
• Assignment Week 7
  1. Do sales forecasts impact manufacturing budgets?  How or Why not?  If a company has sales forecasts of 1000 units, how many should be made?  WHy?  Should potential stockouts be considered?
  2. What are the impacts on the company for producing too much inventory?
  3. In the midst of the downturn with our economy, would you recommend a reduction in the markting budget? State your case.

WEEK 8 

Authorizing and Checking Expenses

A budget is a written plan for how you will spend money. Throughout the year you will need to be sure you are sticking to the plan, that the plan you projected was appropriate and if it was not appropriate, you will have to adjust the plan. Tracking your budget involves: Authorizing and tracking expenses Closing a budget period Comparing estimated vs. actual budget Over and under spending Adjusting the budget Reviewing financial statements   Authorizing and Tracking: When your budget gets approved and set up by accounting, you have money allocated to you. That means you have unspent money that has a ‘name’ on it – it is supposed to be spent somewhere specific. You need to track it to be sure that it is spent where it is supposed to be spent. Spending in business is often called purchasing. When you are talking about budgets and purchasing, tracking is also discussed. Purchase Orders (PO’s) used to be required for most large business purchases. A PO is an approval for the purchase by the person who is responsible for the budget. Purchases under a specific amount (often $100) were made using petty cash. Today however, credit or debit cards make purchasing much easier. It does still require tracking however. Tracking is a 4 step monitoring of transactions. Those transactions are: Decision to buy Receiving the item Approving payment Paying the bill   If a decision is made to make a purchase, but the purchase is not made yet, but the money is set aside, it is called accruing the money. Accrual accounting is tracking expenses that are committed but not spent. Cash accounting is when you track money that has been spent. Accrual accounting is often a better method for sticking to a budget as it is taking into consideration that money has been allocated already.

• Read this article - CLICK HERE TO GO TO ARTICLE, Notice this article was written in 1998. Notice the similarities between then and now.

  A budget is a financial plan to control future operations and results. It is expressed in numbers, such as dollars, units, pounds, hours, manpower, and so on. It is needed to operate effectively and efficiently. Budgeting, when used effectively, is a technique resulting in systematic, productive management. Budgeting facilitates control and communication and also provides motivation to employees.

• G & A Budgets - Another non-production budget that is integral to the success of the company is the general and administrative budget. This contains the costs of the corporate management staff, plus all accounting, finance, and human resource personnel. Since this is a cost center, the general inclination is to reduce these costs to the bare minimum. However, in order to do so, there must be significant investment in technology to achieve reductions in the manual labor required to process transactions; thus, there must be some provision in the capital budget area for this.

  There is a feedback loop between the staff and direct labor budgets and the general and administrative budget, because the human resources department must staff itself based on the amount of hiring or layoffs that are anticipated elsewhere in the company. Similarly, a major change in the revenue volume will alter the budget for the accounting department, since many of the activities in this area are driven by the volume of sales transactions. Furthermore, a major increase in the capital budget, especially for items requiring prolonged construction activities, will require an investment in additional cost accounting personnel, who will track these expenditures. Thus the general and administrative budget generally requires a number of iterations in response to the changes in many other parts of the budgets.

  At this point, I do hope that you are beginning to see a linkage between these different types of budgets, and how changes in one will effect another.

• Capital Investment Budget -

  Funds used by a company to acquire or upgrade physical assets such as property, industrial buildings or equipment. This type of outlay is made by companies to maintain or increase the scope of their operations. These expenditures can include everything from repairing a roof to building a brand new factory.

  The amount of capital expenditures a company is likely to have depends on the industry it occupies. Some of the most capital intensive industries include oil, telecom and utilities.

  In terms of accounting, an expense is considered to be a capital expenditure when the asset is a newly purchased capital asset or an investment that improves the useful life of an existing capital asset. If an expense is a capital expenditure, it needs to be capitalized; this requires the company to spread the cost of the expenditure over the useful life of the asset. If, however, the expense is one that maintains the asset at its current condition, the cost is deducted fully in the year of the expense.

• Read Chapters 11, 12 and 13
• Assignment Week 8
  1. From the article on R&D budgeting, what similarities did you notice from 1998 and today? Differences?
  2. You are the manager in this situation:
     Your work for a large corporation with a freeze on hiring full time staff. Your department has seen headcount reduced. You, however, still have many major projects still left to be done, but not the workers to get it done. You do have in your general budget room to hire temp workers. After calculating the cost of hiring temps to get it all done, you realize that it is more thank the cost of a full time person.

WEEK 9 

General Tips on Budgeting and Forecasting

 

• Look at what you've done and make sure it makes sense.
• Remember that the revenues and expenses in your budget need to match.
• Preparing a good budget or forecast is a matter of being systematic, careful, logical, and thorough.
  For example, work on one account at a time, or one type of expense or revenue across all accounts.
• Document assumptions, methodology, and special entries carefully and thoroughly.
  For example, when budgeting in Pillar, you can enter Notes for individual line items or in the Memo module.
• Check the bottom-line for each entity (e.g. department, group of accounts, account...).
• Compare the budget or forecast to actual data (e.g. last year) or to the current year budget.
• There is actually a lot of consistency from year to year. The core components of the budget tend to be stable.
• As you make changes, keep track of the bottom-line.
• Budget at the appropriate level of detail.
  • A good rule of thumb is to budget at the level that you want to be able to monitor actual revenues and expenditures vs. the budget.
  • It does not mean that each and every account is budgeted at the same level of detail.
  • Where appropriate, make use of summary GL codes to group revenues or expenditures together for budgeting purposes.
  • Where appropriate, make use of pools to group accounts together for budgeting purposes.
• Determine what expenditures, revenues, and transfers are on-going vs. one-time; pay attention to whether on-going expenditures are being funded by one-time revenues; and communicate with your Budget Officer as different units have different policies regarding on-going vs. one-time.
• Be aware that on-going and base are not necessarily the same, either for income or expenditures.
• Make sure that the legal restrictions of various funds are being met .
• Print a summary and ask yourself, "What will people who do not know all the details think when they look at this; will it look right to them?"
• Trust your judgment: If something does not look quite right to you, it almost certainly is not.
• Monitoring your actuals against the budget on a regular basis makes it much easier to forecast the year-end balance and budget the following years.
• Try budgeting monthly; salaries are already budgeted this way.
• Check one more time!

QUANTITATIVE FORECASTING METHODS

Forecasting can be broadly considered as a method or a technique for estimating many future aspects of a business or other operation. There are numerous techniques that can be used to accomplish the goal of forecasting. For example, a retailing firm that has been in business for 25 years can forecast its volume of sales in the coming year based on its experience over the 25-year period—such a forecasting technique bases the future forecast on the past data.

While the term "forecasting" may appear to be rather technical, planning for the future is a critical aspect of managing any organization—business, nonprofit, or other. In fact, the long-term success of any organization is closely tied to how well the management of the organization is able to foresee its future and to develop appropriate strategies to deal with likely future scenarios. Intuition, good judgment, and an awareness of how well the economy is doing may give the manager of a business firm a rough idea (or "feeling") of what is likely to happen in the future. Nevertheless, it is not easy to convert a feeling about the future into a precise and useful number, such as next year's sales volume or the raw material cost per unit of output. Forecasting methods can help estimate many such future aspects of a business operation.

Suppose that a forecast expert has been asked to provide estimates of the sales volume for a particular product for the next four quarters. One can easily see that a number of other decisions will be affected by the forecasts or estimates of sales volumes provided by the forecaster. Clearly, production schedules, raw material purchasing plans, policies regarding inventories, and sales quotas will be affected by such forecasts. As a result, poor forecasts or estimates may lead to poor planning and thus result in increased costs to the business.

How should one go about preparing the quarterly sales volume forecasts? One will certainly want to review the actual sales data for the product in question for past periods. Suppose that the forecaster has access to actual sales data for each quarter over the 25year period the firm has been in business. Using these historical data, the forecaster can identify the general level of sales. He or she can also determine whether there is a pattern or trend, such as an increase or decrease in sales volume over time. A further review of the data may reveal some type of seasonal pattern, such as peak sales occurring before a holiday. Thus by reviewing historical data over time, the forecaster can often develop a good understanding of the previous pattern of sales. Understanding such a pattern can often lead to better forecasts of future sales of the product. In addition, if the forecaster is able to identify the factors that influence sales, historical data on these factors (or variables) can also be used to generate forecasts of future sales volumes.

FORECASTING METHODS

All forecasting methods can be divided into two broad categories: qualitative and quantitative. Many forecasting techniques use past or historical data in the form of time series. A time series is simply a set of observations measured at successive points in time or over successive periods of time. Forecasts essentially provide future values of the time series on a specific variable such as sales volume. Division of forecasting methods into qualitative and quantitative categories is based on the availability of historical time series data.

QUALITATIVE FORECASTING METHODS

Qualitative forecasting techniques generally employ the judgment of experts in the appropriate field to generate forecasts. A key advantage of these procedures is that they can be applied in situations where historical data are simply not available. Moreover, even when historical data are available, significant changes in environmental conditions affecting the relevant time series may make the use of past data irrelevant and questionable in forecasting future values of the time series. Consider, for example, that historical data on gasoline sales are available. If the government then implemented a gasoline rationing program, changing the way gasoline is sold, one would have to question the validity of a gasoline sales forecast based on the past data. Qualitative forecasting methods offer a way to generate forecasts in such cases. Three important qualitative forecasting methods are: the Delphi technique, scenario writing, and the subject approach.

DELPHI TECHNIQUE.

In the Delphi technique, an attempt is made to develop forecasts through "group consensus." Usually, a panel of experts is asked to respond to a series of questionnaires. The experts, physically separated from and unknown to each other, are asked to respond to an initial questionnaire (a set of questions). Then, a second questionnaire is prepared incorporating information and opinions of the whole group. Each expert is asked to reconsider and to revise his or her initial response to the questions. This process is continued until some degree of consensus among experts is reached. It should be noted that the objective of the Delphi technique is not to produce a single answer at the end. Instead, it attempts to produce a relatively narrow spread of opinions—the range in which opinions of the majority of experts lie.

SCENARIO WRITING.

Under this approach, the forecaster starts with different sets of assumptions. For each set of assumptions, a likely scenario of the business outcome is charted out. Thus, the forecaster would be able to generate many different future scenarios (corresponding to the different sets of assumptions). The decision maker or businessperson is presented with the different scenarios, and has to decide which scenario is most likely to prevail.

SUBJECTIVE APPROACH.

The subjective approach allows individuals participating in the forecasting decision to arrive at a forecast based on their subjective feelings and ideas. This approach is based on the premise that a human mind can arrive at a decision based on factors that are often very difficult to quantify. "Brainstorming sessions" are frequently used as a way to develop new ideas or to solve complex problems. In loosely organized sessions, participants feel free from peer pressure and, more importantly, can express their views and ideas without fear of criticism. Many corporations in the United States have started to increasingly use the subjective approach.

QUANTITATIVE FORECASTING
METHODS

Quantitative forecasting methods are used when historical data on variables of interest are available—these methods are based on an analysis of historical data concerning the time series of the specific variable of interest and possibly other related time series. There are two major categories of quantitative forecasting methods. The first type uses the past trend of a particular variable to base the future forecast of the variable. As this category of forecasting methods simply uses time series on past data of the variable that is being forecasted, these techniques are called time series methods.

The second category of quantitative forecasting techniques also uses historical data. But in forecasting future values of a variable, the forecaster examines the cause-and-effect relationships of the variable with other relevant variables such as the level of consumer confidence, changes in consumers' disposable incomes, the interest rate at which consumers can finance their spending through borrowing, and the state of the economy represented by such variables as the unemployment rate. Thus, this category of forecasting techniques uses past time series on many relevant variables to produce the forecast for the variable of interest. Forecasting techniques falling under this category are called causal methods, as the basis of such forecasting is the cause-and-effect relationship between the variable forecasted and other time series selected to help in generating the forecasts.

TIME SERIES METHODS OF FORECASTING.

Before discussing time series methods, it is helpful to understand the behavior of time series in general terms. Time series are comprised of four separate components: trend component, cyclical component, seasonal component, and irregular component. These four components are viewed as providing specific values for the time series when combined.

In a time series, measurements are taken at successive points or over successive periods. The measurements may be taken every hour, day, week, month, or year, or at any other regular (or irregular) interval. While most time series data generally display some random fluctuations, the time series may still show gradual shifts to relatively higher or lower values over an extended period. The gradual shifting of the time series is often referred to by professional forecasters as the trend in the time series. A trend emerges due to one or more long-term factors, such as changes in population size, changes in the demographic characteristics of population, and changes in tastes and preferences of consumers. For example, manufacturers of automobiles in the United States may see that there are substantial variations in automobile sales from one month to the next. But, in reviewing auto sales over the past 15 to 20 years, the automobile manufacturers may discover a gradual increase in annual sales volume. In this case, the trend for auto sales is increasing over time. In another example, the trend may be decreasing over time. Professional forecasters often describe an increasing trend by an upward sloping straight line and a decreasing trend by a downward sloping straight line. Using a straight line to represent a trend, however, is a mere simplification—in many situations, nonlinear trends may more accurately represent the true trend in the time series.

Although a time series may often exhibit a trend over a long period, it may also display alternating sequences of points that lie above and below the trend line. Any recurring sequence of points above and below the trend line that last more than a year is considered to constitute the cyclical component of the time series—that is, these observations in the time series deviate from the trend due to cyclical fluctuations (fluctuations that repeat at intervals of more than one year). The time series of the aggregate output in the economy (called the real gross domestic product) provides a good example of a time series that displays cyclical behavior. While the trend line for gross domestic product (GDP) is upward sloping, the output growth displays a cyclical behavior around the trend line. This cyclical behavior of GDP has been dubbed business cycles by economists.

The seasonal component is similar to the cyclical component in that they both refer to some regular fluctuations in a time series. There is one key difference, however. While cyclical components of a time series are identified by analyzing multiyear movements in historical data, seasonal components capture the regular pattern of variability in the time series within one-year periods. Many economic variables display seasonal patterns. For example, manufacturers of swimming pools experience low sales in fall and winter months, but they witness peak sales of swimming pools during spring and summer months. Manufacturers of snow removal equipment, on the other hand, experience the exactly opposite yearly sales pattern. The component of the time series that captures the variability in the data due to seasonal fluctuations is called the seasonal component.

The irregular component of the time series represents the residual left in an observation of the time series once the effects due to trend, cyclical, and seasonal components are extracted. Trend, cyclical, and seasonal components are considered to account for systematic variations in the time series. 'h e irregular component thus accounts for the random variability in the time series. The random variations in the time series are, in turn, caused by short-term, unanticipated and nonrecurring factors that affect the time series. The irregular component of the time series, by nature, cannot be predicted in advance.

TIME SERIES FORECASTING USING SMOOTHING METHODS.

Smoothing methods are appropriate when a time series displays no significant effects of trend, cyclical, or seasonal components (often called a stable time series). In such a case, the goal is to smooth out the irregular component of the time series by using an averaging process. Once the time series is smoothed, it is used to generate forecasts.

The moving averages method is probably the most widely used smoothing technique. In order to smooth the time series, this method uses the average of a number of adjoining data points or periods. This averaging process uses overlapping observations to generate averages. Suppose a forecaster wants to generate three-period moving averages. The forecaster would take the first three observations of the time series and calculate the average. Then, the forecaster would drop the first observation and calculate the average of the next three observations. This process would continue until three-period averages are calculated based on the data available from the entire time series. The term "moving" refers to the way averages are calculated—the forecaster moves up or down the time series to pick observations to calculate an average of a fixed number of observations. In the three-period example, the moving averages method would use the average of the most recent three observations of data in the time series as the forecast for the next period. This forecasted value for the next period, in conjunction with the last two observations of the historical time series, would yield an average that can be used as the forecast for the second period in the future.

The calculation of a three-period moving average can be illustrated as follows. Suppose a forecaster wants to forecast the sales volume for American-made automobiles in the United States for the next year. The sales of American-made cars in the United States during the previous three years were: 1.3 million, 900,000, and 1.1 million (the most recent observation is reported first). The three-period moving average in this case is 1.1 million cars (that is: [(1.3 + 0.90 + 1.1)/3 = 1.1]). Based on the three-period moving averages, the forecast may predict that 1.1 million American-made cars are most likely to be sold in the United States the next year.

In calculating moving averages to generate forecasts, the forecaster may experiment with different-length moving averages. The forecaster will choose the length that yields the highest accuracy for the forecasts generated.

" It is important that forecasts generated not be too far from the actual future outcomes. In order to examine the accuracy of forecasts generated, forecasters generally devise a measure of the forecasting error (that is, the difference between the forecasted value for a period and the associated actual value of the variable of interest). Suppose retail sales volume for American-made automobiles in the United States is forecast to be 1.1 million cars for a given year, but only I million cars are actually sold that year. The forecast error in this case is equal 100,000 cars. In other words, the forecaster overestimated the sales volume for the year by 100,000. Of course, forecast errors will sometimes be positive, and at other times be negative. Thus, taking a simple average of forecast errors over time will not capture the true magnitude of forecast errors; large positive errors may simply cancel out large negative errors, giving a misleading impression about the accuracy of forecasts generated. As a result, forecasters commonly use the mean squares error to measure the forecast error. The mean squares error, or the MSE, is the average of the sum of squared forecasting errors. This measure, by taking the squares of forecasting errors, eliminates the chance of negative and positive errors canceling out.

In selecting the length of the moving averages, a forecaster can employ the MSE measure to determine the number of values to be included in calculating the moving averages. The forecaster experiments with different lengths to generate moving averages and then calculates forecast errors (and the associated mean squares errors) for each length used in calculating moving averages. Then, the forecaster can pick the length that minimizes the mean squared error of forecasts generated.

Weighted moving averages are a variant of moving averages. In the moving averages method, each observation of data receives the same weight. In the weighted moving averages method, different weights are assigned to the observations on data that are used in calculating the moving averages. Suppose, once again, that a forecaster wants to generate three-period moving averages. Under the weighted moving averages method, the three data points would receive different weights before the average is calculated. Generally, the most recent observation receives the maximum weight, with the weight assigned decreasing for older data values.

The calculation of a three-period weighted moving average can be illustrated as follows. Suppose, once again, that a forecaster wants to forecast the sales volume for American-made automobiles in the United States for the next year. The sales of American-made cars for the United States during the previous three years were: 1.3 million, 900,000, and 1.1 million (the most recent observation is reported first). One estimate of the weighted three-period moving average in this example can be equal to 1.133 million cars (that is, [ 1(3/6) x (1.3) + (2/6) x (0.90) + (1/6) x (1.1)}/ 3 = 1.133 ]). Based on the three-period weighted moving averages, the forecast may predict that 1.133 million American-made cars are most likely to be sold in the United States in the next year. The accuracy of weighted moving averages forecasts are determined in a manner similar to that for simple moving averages.

Exponential smoothing is somewhat more difficult mathematically. In essence, however, exponential smoothing also uses the weighted average concept—in the form of the weighted average of all past observations, as contained in the relevant time series—to generate forecasts for the next period. The term "exponential smoothing" comes from the fact that this method employs a weighting scheme for the historical values of data that is exponential in nature. In ordinary terms, an exponential weighting scheme assigns the maximum weight to the most recent observation and the weights decline in a systematic manner as older and older observations are included. The accuracies of forecasts using exponential smoothing are determined in a manner similar to that for the moving averages method.

TIME SERIES FORECASTING USING TREND PROJECTION.

This method uses the underlying long-term trend of a time series of data to forecast its future values. Suppose a forecaster has data on sales of American-made automobiles in the United States for the last 25 years. The time series data on U.S. auto sales can be plotted and examined visually. Most likely, the auto sales time series would display a gradual growth in the sales volume, despite the "up" and "down" movements from year to year. The trend may be linear (approximated by a straight line) or nonlinear (approximated by a curve or a nonlinear line). Most often, forecasters assume a linear trend—of course, if a linear trend is assumed when, in fact, a nonlinear trend is present, this misrepresentation can lead to grossly inaccurate forecasts. Assume that the time series on American-made auto sales is actually linear and thus it can be represented by a straight line. Mathematical techniques are used to find the straight line that most accurately represents the time series on auto sales. This line relates sales to different points over time. If we further assume that the past trend will continue in the future, future values of the time series (forecasts) can be inferred from the straight line based on the past data. One should remember that the forecasts based on this method should also be judged on the basis of a measure of forecast errors. One can continue to assume that the forecaster uses the mean squares error discussed earlier.

TIME SERIES FORECASTING USING TREND AND SEASONAL COMPONENTS.

This method is a variant of the trend projection method, making use of the seasonal component of a time series in addition to the trend component. This method removes the seasonal effect or the seasonal component from the time series. This step is often referred to as de-seasonalizing the time series.

Once a time series has been de-seasonalized it will have only a trend component. The trend projection method can then be employed to identify a straight line trend that represents the time series data well. Then, using this trend line, forecasts for future periods are generated. The final step under this method is to reincorporate the seasonal component of the time series (using what is known as the seasonal index) to adjust the forecasts based on trend alone. In this manner, the forecasts generated are composed of both the trend and seasonal components. One will normally expect these forecasts to be more accurate than those that are based purely on the trend projection.

CAUSAL METHOD OF FORECASTING.

As mentioned earlier, causal methods use the cause-and-effect relationship between the variable whose future values are being forecasted and other related variables or factors. The widely known causal method is called regression analysis, a statistical technique used to develop a mathematical model showing how a set of variables are related. This mathematical relationship can be used to generate forecasts. In the terminology used in regression analysis contexts, the variable that is being forecasted is called the dependent or response variable. The variable or variables that help in forecasting the values of the dependent variable are called the independent or predictor variables. Regression analysis that employs one dependent variable and one independent variable and approximates the relationship between these two variables by a straight line is called a simple linear regression. Regression analysis that uses two or more independent variables to forecast values of the dependent variable is called a multiple regression analysis. Below, the forecasting technique using regression analysis for the simple linear regression case is briefly introduced.

Suppose a forecaster has data on sales of American-made automobiles in the United States for the last 25 years. The forecaster has also identified that the sale of automobiles is related to individuals' real disposable income (roughly speaking, income after income taxes are paid, adjusted for the inflation rate). The forecaster also has available the time series (for the last 25 years) on the real disposable income. The time series data on U.S. auto sales can be plotted against the time series data on real disposable income, so it can be examined visually. Most likely, the auto i sales time series would display a gradual growth in sales volume as real disposable income increases, despite the occasional lack of consistency—that is, at times, auto sales may fall even when real disposable income rises. The relationship between the two variables (auto sales as the dependent variable and real disposable income as the independent variable) may be linear (approximated by a straight line) or nonlinear (approximated by a curve or a nonlinear line). Assume that the relationship between the time series on sales of American-made automobiles and real disposable income of consumers is actually linear and can thus be represented by a straight line.

A fairly rigorous mathematical technique is used to find the straight line that most accurately represents the relationship between the time series on auto sales and disposable income. The intuition behind the mathematical technique employed in arriving at the appropriate straight line is as follows. Imagine that the relationship between the two time series has been plotted on paper. The plot will consist of a scatter (or cloud) of points. Each point in the plot represents a pair of observations on auto sales and disposable income (that is, auto sales corresponding to the given level of the real disposable income in any year). The scatter of points (similar to the time series method discussed above) may have an upward or a downward drift. That is, the relationship between auto sales and real disposable income may be approximated by an upward or downward sloping straight line. In all likelihood, the regression analysis in the present example will yield an upward sloping straight line—as disposable income increases so does the volume of automobile sales.

Arriving at the most accurate straight line is the key. Presumably, one can draw many straight lines through the scatter of points in the plot. Not all of them, however, will equally represent the relationship—some will be closer to most points, and others will be way off from most points in the scatter. Regression analysis then employs a mathematical technique. Different straight lines are drawn through the data. Deviations of the actual values of the data points in the plot from the corresponding values indicated by the straight line chosen in any instance are examined. The sum of the squares of these deviations captures the essence of how close a straight line is to the data points. The line with the minimum sum of squared deviations (called the "least squares" regression line) is considered the line of the best fit.

Having identified the regression line, and assuming that the relationship based on the past data will continue, future values of the dependent variable (forecasts) can be inferred from the straight line based on the past data. If the forecaster has an idea of what the real disposable income may be in the coming year, a forecast for future auto sales can be generated. One should remember that forecasts based on this method should also be judged on the basis of a measure of forecast errors. One can continue to assume that the forecaster uses the mean squares error discussed earlier. In addition to using forecast errors, regression analysis uses additional ways of analyzing the effectiveness of the estimated regression line in forecasting.

How to Forecast using Regression Analysis

 

Introduction

Regression is the study of relationships among variables, a principal purpose of which is to predict, or estimate the value of one variable from known or assumed values of other variables related to it.

Variables of Interest: To make predictions or estimates we must identify the effective predictors of the variable of interest: which variables are important indicators and can be measured at the least cost, which carry only a little information, and which are redundant.

Predicting the Future: Predicting a change over time or extrapolating from present conditions to future conditions is not the function of regression analysis. To make estimates of the future, use time series analysis.

Experiment: Begin with a hypothesis about how several variables might be related to another variable and the form of the relationship.

Types of Analysis

Simple Linear Regression: A regression using only one predictor is called a simple regression.

Multiple Regression: Where there are two or more predictors, multiple regression analysis is employed.

Data: Since it is usually unrealistic to obtain information on an entire population, a sample which is a subset of the population is usually selected. The sample may be either randomly selected for a researcher may chose the x-values based on the capability of the equipment utilized in the experiment or the experiment design. Where the x-values are preselected, usually only limited inferences can be drawn depending upon the particular values chosen. When both x and y are randomly drawn, inferences can generally be drawn over the range of values in the sample.

Scatter Diagram: A graphical representation of the pairs of data called a scatter diagram can be drawn to gain an overall view of the problem. Is there an apparent relationship? Direct? Inverse? If the points lie within a band described by parallel lines we can say there is a linear relationship between the pair of x and y values. If the rate of change is generally not constant, then the relationship is curvilinear.

The Model: If we have determined there is a linear relationship between t and y we want a linear equation stating y as a function of x in the form Y = a + bt + e where a is the intercept, b is the slope and e is the error term accounting for variables that affect y but are not included as predictors, and/or otherwise unpredictable and uncontrollable factors.

Least Squares Method: To predict the mean y-value for a given t-value, we need a line which passes through the mean value of both t and y and which minimizes the sum of the distance between each of the points and the predictive line. Such an approach should result in a line which we can call a "best fit" to the sample data. The least squares method achieves this result by calculating the minimum average squared deviations between the sample y points and the estimated line. A procedure is sued for finding the values of a and b which reduces to the solution of simultaneous linear equations. Shortcut formulas have been developed as an alternative to the solution of simultaneous equations.

Solution Methods: Techniques of Matrix Algebra can be manually employed to solve simultaneous linear equations. When performing manual computations, this technique is especially useful when there are more than two equations in two unknowns.
Several well-known computer packages are widely available and can be utilized to relieve the user of the computational problem, all of which can be used to solve both linear and polynomial equations: the BMD packages (Biomedical Computer Programs) from UCLA; SPSS (Statistical Package for the Social Sciences) developed by the University of Chicago; and SAS (Statistical Analysis System). Another package that is also available is IMSL, the International Mathematical and Statistical Libraries, which contains a great variety of standard mathematical and statistical calculations. All of these software packages use matrix algebra to solve simultaneous equations.

Use and Interpretation of the Regression Equation: The equation developed can be used to predict an average value over the range of the sample data. The forecast is good for short to medium ranges.

Measuring Error in Estimations: The scatter or variability about the mean value can be measured by calculating the variance, the average squared deviation of the values around the mean. The standard error of estimate is derived from this value by taking the square root. This value is interpreted as the average amount that actual values differ from the estimated mean.

Confidence Intervals: Interval estimates can be calculated to obtain a measure of the confidence we have in our estimates that a relationship exists. These calculations are made using t-distribution tables. From these calculations we can derive confidence bands, a pair of non-parallel lines narrowest at the mean values which express our confidence in varying degrees of the band of values surrounding the regression equation.

Assessment: How confident can we be that a relationship actually exists? The strength of that relationship can be assessed by statistical tests of that hypothesis such as the null hypothesis which are established using t-distribution, R-squared, and F-distribution tables. These calculations give rise to the standard error of the regression coefficient, an estimate of the amount that the regression coefficient b will vary from sample to sample of the same size from the same population. An Analysis of Variance (ANOVA) table can be generated which summarizes the different components of variation.

When you want to compare models of different size (different numbers of independent variables and/or different sample sizes) you must use the Adjusted R-Squared, because the usual R-Squared tends to grow with the number of independent variables.
The Standard Error of Estimate (i.e. square root of error mean square) is a good indicator of the "quality" of a prediction model since it "adjusts" the Error Sum of Squares (EMS) for the number of predictors in the model as follow:

EMS = Error Sum of Squares/(N - Number of Linearly Independent Predictors)

If one keeps adding useless predictors to a model, the EMS will become less and less stable. R-squared is also influenced by the range of your dependent value so if two models have the same residual mean square but one model has a much narrower range of values for the dependent variable that model will have a higher R-squared. This explains the fact that both models will do as well for prediction purposes.

A considerable portion of the output of the computer programs previously mentioned are devoted to a description of the tests of significance of the regression.

• Read Chapters 14, 15, and 16.  This is a section that is math based.  I know that math is not most students favorite subject, but it is a valuable tool in budgeting.  Any questions, please let me know, sooner than later.
• Budgeting and Forecasting-Read this
  1. Assignment 1 - How can the forecasting methods listed in Chapter 14 help in the budget process?  Choose one of the methods, and specifically state how this method can benefit someone in forecasting for budgetary purposes.
  2. Assignment 2 - Joan's Real Estate Company has sold the following number of houses for the past 12 months.

  Jan 52

  Feb 81

  Mar 47

  Apr 65

  May 50

  Jun 73

  Jul 45

  Aug 60

  Sep 50

  Oct 79

  Nov 45

  Dec 62

  Calculate (a)3 month average and (b) 5 month average. Based on your 3 and 5 month averages, what would you forcast sales to be for the following January? WHY?

  3. Assignment 3 - How can regression analysis be a valuable tool for forecasting?  When could you use this tool?  Are their times this tool would not be beneficial?

W.J. Patterson Ph.D. Candidate- Organization & Management   Copyright © 2009. All rights reserved.   Last Updated: .