I adapted an activity-based costing approach (ABC) to model "average" list dynamics at a fictional press to explore rough answers. After looking at them and living with them in the realm of small numbers (a.k.a., today's sales figures), and as applications of new capabilities advance into the new century, next questions may also include: "What comes next?" I.e., post content-monetization alone; what new products and/or services can be added to a firm's offerings to cover overhead?
ABC is used in high-overhead or high-fixed-cost industries to examine performance across product lines to inform R&D, pricing, and production. I added per-product and total contribution and sales figures to model front-list contribution and Return On Assets (ROA) for the year.
Method: For illustration purposes, I used four "product lines" as examples of titles with different "financial footprints" in a press: 1) Average, 2) Complex, 3) Distribution, and 4) FTPB (first-time paperback). Average is a basic monograph or edited volume; Complex might be a coffee table book or a photography or textbook; Distribution is an inventory-only title, for sale in territory; and, FTPB is a paperback edition for a title that came out in hardback in a prior year. Co-pubs could be added, but four types are enough for illustration purposes. Electronic editions are accounted for by lowering the average per-unit variable costs and adjusting average sales and revenue figures for the line. What we're after here is changes in developmental overhead to publish the list.
Each product line spends more or less time on different desks in a press (on average); so, each will consume more or less overhead at different stages. Which are more profitable; which less; and when?
When scholarly presses sold thousands and thousands of copies of any given text, almost any set of choices was financially viable. These days, a press needs to be mindful at a more granular level. So, after modeling ROUGH list dynamics, I take a look ROA and break-even for sales of 200 copies to 1,500 per product type. Additional charts are on the attached.
Numbers for a real-world press will have slightly different break-even points for each product line, but the relative stature of these categories will be consistent; e.g., a chart of data from our mock press shows that Distributions have higher ROA than original titles, all sales being equal. It also reflects the greater ROA of FTPBs, relative to other products; both Distributions and FTPBs are "in the money" at lower sales volume than other titles. These trends suggest answers to our initial questions.
To estimate average ROA per product line, the sheet linked to above apportioned overhead as a measure of fixed assets consumed by each product, and so shows average overhead per project and per product line, or just how "expensive" one project is relative to another.
This can be an important consideration as a press reaches capacity; with resources stretched thin, adding an "expensive" project that will consume many internal resources could overtax capacity and that could result in delays in times to market across all projects. When a press is at or near capacity, it might be time to add a few "inexpensive" projects in order to add revenue without driving [as many] costs. It is also time to consider which projects tax resources specifically in pipeline departments (EDP) where delays can have the greatest enterprise-wide effects.
However, ROA and margins alone don't tell the whole story. A firm needs volume to cover costs. It also needs original publications, before it can offer FTPBs.
On a tool like the attached, a press can see that projects can have a higher contribution margin but be less profitable than other projects with lower margin (they can deliver lower sales volume, less revenue, and/or have lower return on the assets they consume).
Publishers can also note that Distribution titles have small and fixed contribution margins (fluctuation of contribution margin is largely a result of including a fixed cost like copy-editing in COGS), and yet Distributions deliver very high ROA and have a low break even. These figures in aggregate contribute to the ROA for the front list.
A firm needs to look at all the above when considering profitably of a portfolio of product lines: margin, sales volume, project volume, project compliment (how many of each kind of project), and of course overhead.
The sheet linked to herein, includes measures of per title and total contribution and overhead, and so shows several of these "moving parts" of profitability at a glance for planning purposes. With real-world data and structural enhancements to such a sheet, similar ABC approaches can help model impact of changing "product mix" and sales targets to achieve optimal gains for stakeholders.
That said; at a glance, the current mock-up does provide some insight to questions above and shows that various product lines at a given press can have their own metrics and margins contribute their own value to front-list success. Lines can be sub-divided for greater analytic clarity, and new lines and services can be added to deliver new Rs for the Is.
Caveats:
1) Changing numbers of titles published at the fictional press in the mockup would impact total overhead, and visa versa; so these numbers should not be altered in the attached independently. Sales figures, on the other hand, can be; variable costs are set at an average rate and so will adjust with changes in sales. The attached includes sales-figure data tables and charts like the above.
2) The attached has only four, gross categories (Average, Complex, Distribution, and FTPB); product lines and metrics can and should be added/subtracted to reflect a current portfolio at a press.
3) The attached has grossly simplified numbers, as it is for illustration purposes only; more detail would be added in any real-world analysis.
4) One very important thing that this sheet does not model is the impact on the pipeline or on time to market for adding or subtracting certain titles; as suggested above, adding/subtracting some products has more impact on average time to market than it will for others. E.g., Average and Complex titles drive workload in EDP. Distributions and FTPBS do not. So, as the EDP pipeline fills, opportunity costs rise for each Average and Complex title added; delays increase and sales revenue lost would be accounted for as added costs. These costs could include delays in time to market for all front-list titles. Additional Distributions and FTPBs, on the other hand, largely "skip" EDP and could have other resources such as marketing "spread thin" to cover their launch/soft launch. Another tool would be needed to model relative "next title" impact across product lines at or near capacity.
5) Most importantly: best practices for activity-based costing is to utilize a cost driver to allocate overhead in each department. The most common cost-driver is line-worker hours. Herein, one product was used as a baseline "single-unit" of work. Experts familiar with a process can do an excellent job of "ball parking" relative workload for major product lines, compared to a baseline product; however, data reveal more granular detail, and this detail can be surprising. The adapted model attached will reveal trends in list dynamics. It is also advisable to collect data on hours worked on various products (on average) and re-run the analysis to confirm and explicate trends.
Disclaimer:
This sheet, linked to herein, merely models relative product-profitability dynamics for a MOCK company; this in order to demonstrate a managerial accounting approach to overhead allocation by product line for strategic purposes. A bona-fide tool for an actual firm can be built for any company; however, this sheet is for entertainment and illustration purposes only. Please contact me if you have quesitons on the limitations of this sheet.
