Читать книгу Overall Equipment Effectiveness - Robert Hansen C. - Страница 10
ОглавлениеMany manufacturing organizations have trouble accurately measuring the financial benefits of proposed improvement projects. Important projects are often overlooked or not properly prioritized relative to average projects. As a result, a manufacturing area can get mired in driving false improvement projects, never making significant gains in reliability and productivity. Furthermore, financial accounting methods used by factories vary widely leading to more confusion in identifying meaningful projects.
Financial statements are the scorecards used to communicate and benchmark a company’s business. Understanding the link between OEE and financial statements is of paramount importance in ranking reliability and improvement projects.
It is beyond the scope of this book to cover financial accounting methods. However, the book uses a generic simplified approach that can fit most specific cases and give you a relevant first look at financial benefits. In the end, you should enlist the assistance of your company’s financial analyst to help you measure your OEE improvement projects using a method similar to the one presented here. With your analyst’s help, you will know the financial impact of the best projects and can convey the information to your company’s decision-makers in terms they understand and fully support.
Remember that OEE strategy is to be applied to your factory’s bottlenecks as well as other key areas that are either high cost or critical to the factory operation. By focusing on bottlenecks at key stages in the factory, OEE is a true measure of factory output. After identifying constraints, the next step is to exploit the resource. Your test question should be, “If good improvement occurred in this area, will a major impact occur with the factory’s overall improvement goals?” If your answer is yes, then you are working on the right areas. In turn, if you are successful, you will significantly impact the bottom line.
To help understand how OEE impacts manufacturing’s contribution to the bottom line (operating income before interest expenses and taxes), we can look at a hypothetical factory in three different situations. In section 3.1, a base case establishes the current situation. Section 3.2 considers the same factory with an improved OEE, making the same amount of product. Section 3.3 then looks at the same factory with an improved OEE, but selling all it can produce. This analysis will demonstrate how even small increases in OEE can leverage into big increases in the bottom line. In sections 3.4 – 3.6, we will use a similar approach to review the impact on another key financial parameter, Return On Assets (ROA) sometimes called the ‘productivity ratio’.
3.1 Case A: Base Case
Cost is often the major focus of manufacturing operations, and bottom line income is the key measure for factories. You would generally think that these two factors would always be in line with each other. However, I have seen situations in which factory costs were reduced, yet operating income went down. Cost optimization of a local area was detrimental to the factory as a whole. Maintenance budgets were slashed and mechanic overtime was eliminated. When problems occurred on off shifts, equipment stayed down until regular day shift. Maintenance labor expense was reduced but the factory bottleneck was starved as a result.
In most cases, however, factory effectiveness correlates very well with operating income. The following example fits the majority of cases where the financial aspects of a company are major critical goals.
Suppose a factory called R.C. Hansen’s Factory receives raw materials and semi-assembled parts, then provides finished products that are sold for a net price of $100 each. Assume the factory process is a set of transformation steps in series, similar to a long assembly line with minimum or zero buffers between steps.
Assumptions: During the year, the factory manufactured full out, operating around the clock, 320 of 365 days (7680 scheduled hrs). The other 45 days were used for experiments, shutdowns and planned maintenance days, holidays, training days and headroom allowance. Headroom allowance is where an annual production plan purposely reserves manufacturing capacity for risk management (uncertainty). This can be in the form of unanticipated new orders (positive) or a buffer to accommodate unusual OEE losses (negative). Note that headroom is part of excluded time and addressed when computing Total Effective Equipment Performance (TEEP). It is addressed in OEE only when it is “cashed” and re-identified as planned production time.
For the base case, the factory manufactured 1 million units. The product mix and output schedules were relatively constant over the year. The units were sold for a net price of $100 each. Therefore, Net Sales = 1,000,000 × $100 = $100 million
Before jumping into the financial breakout, we will use the following definitions for this chapter.
“Direct material includes all of the important materials or component parts that physically comprise the product. Examples are sheets of steel, electric motors, and microprocessors. Incidental material items-such as glue and fasteners-are considered indirect materials and are included in factory overhead. Supply items-such as cleaning supplies and lubricants-are considered factory supplies and are included in factory overhead.
Direct labor includes the salary and wage costs of factory employees who work directly on the product. Machine operators, assemblers, and painters are examples of such workers. The salary and wage costs of factory employees who work indirectly on the product-such as supervisors, inspector, material handlers and maintenance workers are considered indirect labor costs and are included in factory overhead.
Factory overhead, sometimes called manufacturing overhead or factory burden, includes all other manufacturing cost not included in direct materials or direct labor. Examples of items included in factory overhead are indirect material, factory supplies used, indirect labor, factory payroll taxes and fringe benefits, factory utilities (natural gas and electricity), factory building and equipment costs (insurance, property taxes, repairs and maintenance and depreciation), and other factory costs.” 1
With these definitions, assume the following breakout of the financial figures: Categories and proportions are similar to page 928 of the reference book. (figures in millions)
Of the categories above, Direct Materials and Direct Labor vary almost directly with the amount of product made for sale. If productivity improvements are made but the same amount of product is produced, the Direct Labor expense will vary with the productivity improvement. Usually these expenses vary with scheduled operating days and are the direct labor number of the financial breakout. However, with fewer operating days, Sunday overtime is often the first reduction target that could increase the rate of expense reduction for direct labor.
In our case, the small amount of process materials and utility costs collected in the Factory Overhead will be considered negligible when examining costs that vary with sales. If these expenses are significant in your situation, you will need to proportion the variable amount in these categories similar to the approach used in sections 3.2 and 3.3.
Suppose that using the methodology outlined in chapter 2 to study the factory bottleneck, we find a combined OEE of 60 percent, (assume Availability = 65 percent, Speed Factor = 0.97, and Quality Factor = 0.95. OEE = 65 percent × 0.97 × 0.95 = 60.0 percent).
Now you can examine the value of improved OEE for your factory, answering the question “What could have been?” You should begin to see the linkage between changes in OEE and operating income before interest and taxes at your factory.
Given this approach, let us now compute the combined Ideal Speed Rate for the base case. The Ideal Speed Rate (R) for our current factory process is obviously influenced by the specific product and process mix. We will assume the same mix for the two scenarios that follow.
From section 2.5, OEE computation method #3, we know that
We also know OEE (60 percent or 0.6), Number of Good Units Made (1,000,000), and Scheduled Time (7680 hr). Therefore, we can solve for Ideal Speed Rate, R. Substituting terms, and we have
This single measure for Ideal Speed Rate combines the various ideal rates for the product mix. Recall that the product mix and schedules have been assumed to be relatively constant for the past 12 months and for the near future. Any difference in the financials will be the direct result due to OEE improvement.
Various “what if” scenarios could be reviewed. Two important financial investigations would be “What if we had achieved higher OEE and 1. Produced the same volume as the Base Case or 2. Operated the same number of days and sold everything we could make?”
Recognize that we operated full out in the Base Case and marketing and sales are constrained until the factory increases annual capacity. Without specific cause and effect projects, demonstrating higher capacity may take several years. With well documented, targeted OEE projects and using this metric real-time (at least daily), you can statistically demonstrate higher capability in a short time frame.
When increased sales are uncertain, I have often had long discussions with financial analysts over what value to assign improvement projects for developing higher capability that only provides for potential future sales. Future sales are uncertain and may not develop for a year or more. Without the capability, increased sales would require future (significant) investment. With the capability, sales can be generated with minor expense increases, which will be demonstrated with question 2.
The answer is left up to you, but it must be somewhere in between question 1 and question 2. Depending on time, if only one third of the sales develop, it may double the business case addressed in question 1.
3.2 Case B: Same Output, Improved OEE
Case B provides the same scenario as Case A, except OEE improves from 60 percent to 66 percent (OEE = 66 percent: Availability 71.6 percent × Speed Factor 0.97 × Quality Factor 0.95 = 66.0 percent). For this example, we have made gains only in availability perhaps resulting from reliability projects or faster changeovers. A discussion on project value for improved availability, is presented in section 5.1 and encourages the elimination of unplanned downtime. Equipment reliability projects such as Reliability Centered Maintenance2 or condition monitoring practices focused on bottlenecks may contribute to increased availability. They can be reviewed and presented using these examples.
In Cases B and C, the increase in OEE of 6 percentage points represents a 10 percent improvement:
As in the base case, the factory makes 1 million units and sells them for a net price of $100 each. Given OEE (66 percent), Ideal Rate R (217 units/hr), and Number of Good Units Made (1,000,000), we can solve for Scheduled Time, the number of hours needed to produce the units with the new OEE level. Adapting the formula from section 3.1,
The impact of improving OEE has been to reduce Scheduled Time from 7680 hours to 6982 hours, a savings of 698 hours.
Suppose 96 of the 698 hours are used for training and the rest (602 hrs) are truly saved in reduced scheduled time. Using this reduction in scheduled hours, the reduction in Direct Labor expense for case B is computed.
$22.1 million is a savings of $1.9 million from the original cost of $24 million seen in section 3.1. This calculation of savings may actually be conservative. In practice, the reduced hours would probably be overtime hours. Then an even bigger expense reduction for Direct Labor would occur.
Furthermore, the education and experience gained with focused OEE projects should yield continuing benefits. Following the adage “Give a man a fish and he can eat for a day. Teach a man to fish and he can feed his family for a lifetime”. In this case, with four 24-hour days provided for training, every crew should have four full shifts of focused training about OEE improvement projects. Such training would leverage to additional projects.
The financial parameters for Case B, now reflecting fewer factory hours, follow: (figures in Millions)
Notice that Operating Income has increased from $9 million in Case A to $10.9 million in Case B. Thus, a 10 percent improvement from OEE has led to a 21 percent improvement in operating income.
Providing business case information in this format should help decision-makers in your company be more aware of the benefits as the information is linked directly to the key parameters for which they are responsible. Being sensitive to the interests of your audience will help you develop more win-win situations.
Realize too that eliminating root cause limiters, thereby improving OEE, will provide benefits year after year. You should also try to determine the strategy that set up the problem in the first place, then take steps so that the conditions of problem will not occur again. This avoids future losses.
In addition, information should be communicated quickly to all similar areas to hasten corrective actions throughout the company. In this way, improved OEE is engaged and sustained at higher levels for your factory and your sister factories.
3.3 Case C: Full Factory, Improved OEE
Case C provides the same improvement scenario as Case B, (OEE = 66 percent: Availability 71.6 percent × Speed Factor 0.97 × Quality Factor 0.95 = 66.0 percent). However, we can now sell everything we can make. Therefore, the objective is to leverage maximum output. As with Case B, OEE was improved by 6 percentage points, or 10 percent, over the original OEE in Case A. The question here focuses on the gain in Operating Income from a full factory with higher OEE.
Assume that the factory schedule is the same as in Case A (7680 scheduled hours). However, we will still provide 96 hours of planned training hours (part of excluded time in this scenario) to focus on aggressively improving OEE.
Given OEE, Scheduled Time, and Ideal Rate R (217 units/hr), we can solve for the Number of Good Units Made.
Number of Good Units Made = Scheduled Time × R × OEE = 217 units/hr × 7680 hr × 0.66 =1.10 million units
Both OEE and factory output have improved 10 percent. They directly correlate.
If we continue to sell the 1.10 million units for a net price of $100 each, then Net Sales will equal $110 million. The annual cost of materials increases by 10 percent from $25 million to $27.5 million, reflecting the increase in units. Factory Overhead expenses remain at $18 million for the year. The cost of Direct Labor for the year will be increased to account for the same level of planned training as in case B.
As a conservative approach, Selling Expense is increased by the same 10 percent as OEE and quantity produced. ($16 million + 10 percent = $17.6 million). This estimate is conservative because many portions of Selling Expenses (e.g. the cost of advertising -- we already can sell everything we make) would not necessarily increase just because more units were made and sold. Therefore, the actual increase in Selling Expenses may be lower, and in turn, the bottom line even greater.
The financial parameters for Case C follow:(figures in millions)
Notice that for Case C, even though OEE increased 10 percent, Operating Income increased 62.2 percent, from $9 million to $14.6 million.
Part of the hidden factory is leveraged to the full benefit of the company. As a result, the long-term health of the business is stronger. For factories that find their products becoming commodity products with small profit margins, having effective factories is of paramount importance.
The overall power of an aggressive OEE strategy lies not only in the impressive improvement shown in this example, but also in the compounding of these results as the education tools of OEE drive continuous improvement year after year.
One sector of companies is in the enviable arena of new growth looking for more capacity. This exciting environment may drive quick decisions to add capacity via more capital. A better strategy would be to develop the discipline to drive higher OEE with the existing factory. Not only is capital for additional capacity avoided, but also the full rewards of an effective factory will exist throughout the life of the factory.
3.4 Case D: OEE Impact on Return On Assets (ROA)
At the 1999 Society of Maintenance Reliability Professionals Conference, one of the speakers, Carol Vesier, Ph.D., President of RonaMax, LLC, focused on marketing reliability improvements with her clients by first presenting the need to improve the company financial picture. This approach spoke to the heart of what matters to corporate managers; it also showed that significant opportunity often exists if factories produce at the high end of industry benchmarks.
She discussed using the 4 P’s of marketing (publicity, product, place and price) as they apply to in-house marketing of reliability. The product is actually how (not what) reliability or productivity improvement gains achieve corporate financial goals. Publicity is the education that reliability improvement is far more valuable than just reducing maintenance costs. Placement is “picking where (and to whom) you decide to market reliability ....not all work processes will benefit equally”2. Often the support for reliability comes from beyond the factory gates primarily because the existing organization has a local paradigm and believes they are currently doing absolutely everything possible. “Price refers to shifting the focus from maintenance budgets to business benefits”3.
This way upper management starts with a future financial picture, then works backward to find the pieces that provides the foundation for the results. Ms. Vesier indicated that, inevitably, managers find that higher equipment reliability and performance of existing systems can deliver the results they are challenged to produce. The managers become champions of a targeted OEE strategy and fully support a top down driven initiative.
Ms. Vesier discussed a client who determined that developing the capability of its existing factories was ten times more financially effective than constructing new plants for capacity. She indicated this client could spend $10 for education, reliability improvements, and root cause problem elimination as equivalent to spending $100 in capital for new capacity. Does this seem realistic?
If this were true, and because the vast majority of factories are not operating at world-class levels, why wouldn’t companies leap at the strategy of improving reliability and productivity. Perhaps this book will provide the education necessary for positive reliability publicity.
To examine this claim, and to begin developing the product for this type of marketing approach, an understanding of the fundamental concepts of manufacturing accounting and the parameters of the Return on Assets (ROA) equation must be developed.
Using the ROA approach is very appropriate with the company decision-makers because it is the best way to demonstrate the effectiveness of manufacturing operations using the language of corporate financial managers.
“The return on the total assets available to a firm is probably one of the most useful measures of the firm’s profitability and efficiency. Return on assets, sometimes called the productivity ratio, is calculated by dividing the year’s operating income (income before deducting interest expense and income tax expense) by the average total assets employed during the year.
Because the return for a year is earned on assets employed throughout the year and assets may vary during that time, we compute the return on the average amount of assets. We obtain the approximate average by summing the beginning and ending asset total and dividing by two.
If the percentage is a true index of productivity and accomplishment, it should not be influenced by the manner in which the company is financed. Therefore, we use income before interest charges as a measure of the dollar return in the numerator.... The return on assets is useful for comparing similar companies operating in the same industry. It also aids management in gauging the effectiveness of asset utilization...”3
As indicated above, ROA is determined before interest and tax expense, and this approach will be used for the following analysis. In some references, “income” is defined as before interest, but after tax expense. (If this definition were applied, a tax constant is applied and the rate of improvement is the same ratio.)
The definitions for Direct Material, Direct Labor and Factory Overhead are provided in section 3.1.
As before, to examine the specific impact of improved OEE, the effects of inventory levels will be removed from the typical accounting spreadsheets by assuming the beginning and ending levels are constant for finished goods, work in progress and raw materials. Inventory value is a portion of factory assets, which becomes a part of Average Total Assets.
R.C. Hansen’s Factory will have Operating Income (Earnings Before Interest and Taxes) as developed in the earlier sections of this chapter, and use the results as input for the ROA equation. As before, the operating income categories and values are similar to an example provided in the accounting reference. If your factory figures are proportioned differently, you will need to modify the values to develop your own base case.
Again, in R.C. Hansen’s Factory base case, the parameters were 1,000,000 units sold at a net price of $100 each for a net sales of $100 million. The Factory worked full out at 320 scheduled days or 7680 hours.
Assume the same breakout of the financial figures as provided in section 3.1. (figures in Millions) The definitions of categories was discussed on page 49.
If the values in the various categories vary with your situation, you should modify the example using your figures to develop your specific business case analysis.
The operating income is the numerator for the ROA equation. The denominator is the average of end of fiscal year report of total assets for the two most recent years. This may be directly available from your financial analyst. In the case of R.C. Hansen’s Factory, assume base case ROA is 10 percent, therefore, Total Average Assets for the analysis time period is $9 million divided by 10 percent or $90 million.
Assume Total Average Assets consists of the following breakdown.
Total Asset proportions for a number of manufacturing industries have a 50 – 50 percent split between these two categories. If your company has a different proportion you can modify the formula as necessary. The proportion impacts the denominator of the ROA equation.
This is the base case value to compare the “what if” values against. The review comparison would be, “What would ROA have been if OEE were improved?”. Compare this to the need to add capital projects to provide more product.
As before, assume we have used the simple methodology to determine the combined Factory OEE of 60 percent. As in section 3.1, the ideal rate (R = 217 units per hour) can be determined by knowing the scheduled hours (7680 hours), OEE (.60) and the amount of good product produced (1,000,000 units).
All of the above parameters form case D, the base case ROA. The relevant business cases will be the examination of what ROA would have been with an improved OEE for two different scenarios. These would be 1. ROA if the same amount of product is produced and 2. ROA if the factory can sell everything it can make in 320 scheduled days.
3.5 Case E: Higher OEE with the Same Sales, ROA
This section will examine the base case scenario with OEE improved from 60 to 66 percent, a 10 percent increase, and we schedule to make and sell the same amount of product. With sales being constant, the variable part of the Total Average Assets stays the same ($45 million) as does the amount of Direct Materials used.
The marketing product is the improvement benefit in ROA. Requiring fewer hours to produce the units for sale and computing the financial impact resulting from this decrease generates this improvement.
With OEE at 0.66, the output rate is 66 percent of the ideal rate which is 217.0 × 0.66 = 143.2 units/hr. and the required scheduled time is one million divided by 143.2 or 6982 hours. The reduction of scheduled hours is 7680 − 6982 hours = 698 hours.
As before, assume 96 of the 698 hours are used for targeted OEE education and projects. The rest (602 hrs) are truly saved in reduced scheduled time and the Direct Labor expense for case D is computed.
A greater expense reduction in Direct Labor may occur if the reduced hours are overtime hours.
The financial parameters for Case D, now reflecting fewer factory hours and constant Total Average Assets follow: (figures in Millions)
Total Average Assets remain the same as in the base case ($90 million) because the amount of units and sales were the same as the base case. Therefore, ROA for case E is:
Thus, a 10 percent improvement in OEE has led to a 21 percent improvement in ROA:
This rate of improvement in ROA becomes the marketing product part of the business case in selling an aggressive OEE strategy. Remember that we allocated hours for initial training and projects within the financial plan.
3.6 Case F: Higher OEE, Selling Everything Produced, ROA.
Case F builds on the previous scenario with OEE improved from 60 to 66 percent (a 10 percent increase). It also assumes that we schedule to make and sell everything we can for the same production schedule of 7680 hours (320 days).
From Case E, we know that the output rate with 66 percent OEE is 143.2 units/hr. At that rate and 7680 hours, the Total Number of Good Units Made is 143.2 units/hr × 7680 hours or 1.10 million units. At a net price of $100 per unit, Total Sales are now $110 million.
The marketing product in case F is the improvement benefit in ROA resulting from increased sales.
With increased sales, a number of parameters will vary in both Operating Income and in Total Average Assets. Also, as in case E, we will provide for 96 Direct Labor hours for planned OEE education and targeted improvement projects.
The Direct Materials expense and the Selling Expenses will be increased by 10 percent.
The above scenario duplicates the situation for Operating Income as shown in section 3.3. Review pages 54-56 to understand the background reasons of the values and to see how relevant this approach is to your own situation.
As in section 3.3 the financial breakout for Operating Income is: (figures in Millions)
In case D, the proportion of Total Average Assets was assumed to be $45 million variable with sales and $45 million fixed. Therefore Total Average Assets will increase as follows:
Total Average Assets case E = $45 M × 1.10 + $45 M= $94.5 M
Now we can look at ROA in case F with these new numbers:
Thus, ROA has increased from 10 percent in Case D to 15.4 percent in Case F. In short, an increase of 10 percent in OEE has resulted in a 54 percent increase in ROA:
This increase becomes our reliability marketing product for case F, and it includes 96 hours of planned OEE education in addition to production scheduled time.
Earlier in this chapter, the claim was made that a company “could spend $10 for education, reliability improvements, and root cause problem elimination as equivalent to spending $100 in capital for new capacity”. As we have seen, a 10 percent increase in OEE provides 10 percent more capacity with the same equipment resources. In a very rough manner, we can compare this with the capital required for 10 percent capacity increase.
For the aggressive OEE strategy, assume 4 days of focused training, education, and project work per person, along with an equivalent amount of reliability project dollars, are needed to improve OEE from 60 to 66 percent. This amount represents about 1 week of everyone’s time, which is about 2 percent of the expense for Direct Labor plus an equal dollar amount (an additional 2 percent) for project materials and changes. Thus, the investment expense for an improved OEE approach would be:
4 percent (approximately) × $24 million, or $960,000.
For the capital project approach, assume that capital expense for new capacity is a direct ratio of Total Average Assets. Therefore, a 10 percent increase in capacity would cost 10 percent of $90 million, or $9 million. This amount is approximately ten times the expense of aggressively driving OEE to higher levels, consistent with the 10:1 ratio discussed at the Society of Maintenance Reliability Professionals Conference (see section 3.4).
In addition to this ratio, a difference in tax rates applies in favor of expense dollars for OEE education versus capital dollars for new factory hardware.
These benefits are for cases in which OEE improves from 60 percent to 66 percent, and were applicable to Ms. Vesier’s client. But what if we start with an OEE of 70 percent and improve to 77 percent (a similar 10 percent increase in OEE)? Using the same numbers and model provides the same ROA percent increase results. The difference, however, between starting at a low or a high OEE is that the opportunities to reduce major losses are more prevalent with low OEE. Breakthrough performance often occurs quickly.
Another important benefit of aggressively driving OEE over capacity projects is the timing of the benefits. If a decision is given today to start either program, timed to the point of achieving 10 percent more product capacity, the results might be as follows:
