Most of us are familiar with benchmarks. You know, when we compare our performance metrics with those of the industry or with those of “top performers,” whatever that means.
Printing Industries of America (PIA) conducts an annual ratio study that allows respondents to compare their own performance with “profit leaders” on metrics such as paper costs as a percentage of sales, or sales per employee. In-plant pundits advise us to benchmark as well.
In-plant managers from time to time gather pricing data on different types of printing in an attempt to benchmark their prices against other in-plants. Typically, respondents are asked to price an array of different types of jobs. I guess the idea is that one can somehow draw a conclusion about his/her pricing strategy by comparing the prices charged by his/her in-plant to those charged by others.
This can be a slippery slope.
Most metrics developed to measure performance in the private sector are of limited value in evaluating the performance of an in-plant, especially one located in the public sector—state and local government, including public education—and non-profit organizations. Remember that the revenue stream for an in-plant is generated internally, meaning when a support service “sells” printing to another unit of the organization, it is moving funds from one budget line to another. If the seller (in-plant) sets prices to generate “profit,” the result is an unapproved budget adjustment from the buyer to the seller.
To put it another way, the seller can affect the buyer’s budget by altering the amount charged for printing, and the seller can build a cash reserve by “overcharging” the customer. In essence, the seller is earning revenue that was not approved in a budgeting process, and a lot of organizations frown on that practice. Budgets are critical management tools, and the ability to effectively change management decisions by altering pricing can lead to major heartburn.
Another problem lies in the organization’s interpretation of the term “self-supporting.” The assumption seems to be that if an in-plant is self supporting, it recovers all of its costs.
Not so fast!
Organizations use a variety of operational and pricing models.
- Some set prices to recover all costs, but the definition of “all costs” varies from one organization to the next.
- Some set prices to recover a portion of the cost. For example, some fund payroll centrally, but require the in-plant to recover all remaining operating costs.
- Some require the in-plant to recover labor costs, but not benefits, which are funded centrally.
- Some require the in-plant to recover direct labor manufacturing costs, but fund salaries for administrative support centrally.
- Work study and intern programs often provide “free” labor to the in-plant, but the activities performed generate revenue.
- Some require the in-plant to fund capital acquisition through a central capital budgeting process; others allow the in-plant to fund capital equipment from revenue, often earned by raising prices to internal customers. Depending on whether the in-plant is located in the public or private sector, it may or may not charge depreciation. Those that charge for depreciation are at a disadvantage because they have to charge more to recover it.
- Some organizations allow the in-plant to lease equipment and build recovery of the lease payments into its operating budget.
- Some organizations overlook operating budget shortfalls (losses)—as long as they aren’t “excessive”—on the theory that the “loss” amounts to compensation for soft costs like brand management and helping customers prepare files for print.
- Some in-plants pay rental fees for space; others do not.
- Some in-plants pay for utilities; others do not.
- Some in-plants pay an Information Technology and/or network fee; others do not.
- Some pay a “tax” back to the parent organization and/or a high-level administrator; others do not.
- Some pay fees for organizational overhead—HR and accounting are good examples; others do not.
The list goes on and on.
The result is that if you try to use ratios and/or benchmarks based on sales data, there is no way to control for the variables because you never know which models were used, how prices were determined, which costs were recovered, and whether the data from other organizations used the same pricing and cost models.
We run into this frequently in our consulting work:Chambers Management Group
: “Are you self supporting?”In-plant Manager
: “How much rent did you pay last year?”Manager
: “We don’t pay rent.”
See what I mean? We all have different interpretations of what we mean by “self-supporting.”
To demonstrate the problem, we looked at a pricing survey recently conducted by a national print organization for which respondents were asked to price a variety of types of jobs. In one category, the prices for a booklet ranged from $625 to nearly $4,000. That’s right; the high bid was 625 percent higher than the low bid.
What does that mean, if anything? Is the in-plant with the lower price that much more efficient? Or, does the spread represent two of the many, many combinations of variables at play when in-plants price their work? We contend that the explanation for the differences in prices is explained by the way each shop determines its cost structure.
We led a workshop recently in which two in-plant managers were discussing this very subject. Both had similar equipment, both were from similarly sized schools, and both had about the same number of staff. But one school paid $75,000 annually for space, $25,000 for utilities and about $150,000 as a tax to central administration, while the other did not. If both are operating at or near capacity and labor costs are more or less the same, the shop paying the extra fees will have a higher operating base and will have to charge more to recover it, right?
So what could possibly be gained by comparing ratios based on revenue between these two shops? The first shop is $250,000 in the hole before it earns a dime, and the only way it can recover its costs, which are inflated, is to charge more. Clearly, comparing the prices charged by these two shops, or trying to calculate productivity-to-sales or similar revenue-based ratios, has very little managerial value.
If you’re looking for a benchmark to evaluate your performance, we suggest that you continuously benchmark your pricing against the prices charged by available commercial alternatives on a job-by-job basis. You are not competing with other in-plants, but you are competing with the print shops that operate in your area, and the selling price of your product is an important decision criteria.
Commercial printers know they cannot compete solely on price, and anyone who has purchased printing for any period will attest to the fact that the range from the low to high price on a print bid usually runs in the neighborhood of 50 to 100 percent. If the in-plant’s price is consistently lower than the low commercial price, your shop is doing great! If its price hits the middle of the range, you’re probably doing all right. If it’s higher, you have a problem.