Updated: Jul 22, 2019
Every established company has a certain amount of complexity in its product portfolio. Adding new products, variants and features is very seductive because it helps grow the business. That growth usually comes with a cost – creating complexity. Product proliferation inevitably leads to internal complexity in the form of excessive overhead, too many vendors and a myriad of components, resulting in:
Tailored parts increase every day and interfere with standard products.
Numerous subtle variations in similar designs, with minimum component standardization and reuse, creating slow and obsolete inventories.
Out-of-control variety impacts all business functions and customers, compromising cost, quality and delivery.
Very few companies understand true product profitability before they decide to add a new feature or new design to satisfy a customer.
Every new part or variant added to the portfolio increases the cost of complexity and must eventually be offset by price increases or cost reductions. It gets to a point where the incremental cost of adding a new product variant is way higher than the incremental margin gain that can be attained.
To make the problem worse, most companies do not measure cost of complexity and so do not know the real impact that proliferation has on the bottom line.
In our experience, the main reasons companies do not have proper metrics or processes to manage complexity are:
They do not have a reliable and practical way to measure the cost of complexity and understand true product profitability and
They do not segregate their portfolios into groups of products and customers according to economic value, in ways that they can apply different strategies to recover the cost of complexity.
The key management challenge is to balance scalability and variety (customization), given that scale is rapidly compromised when complexity is mismanaged.
The use of data analytics to understand true product profitability, coupled with a complexity management process, which can differentiate between the vital few and the trivial many, can lead to a successful strategy to keep complexity costs under control.
How to understand and optimize your offering to improve true product profitability?
1. Use data to understand true product profitability and to streamline your offering (cut the “long tail”):
Most companies are sitting on a wealth of data that they can use to understand complexity, especially data at the intersection of customers and products, such as sales and purchase transactions. The reason why most companies do not use data to track complexity is that they don’t have a workable framework to analyze and from which to derive actionable insight.
The best way to handle large amounts of product and customer data is to resort to the empirical and age-proven Pareto Principle, aka the 80/20 Rule.
Using the Pareto Distribution to classify and overlay products and customers based on profit and cost data is a simpler way to segregate the portfolio. And it’s no surprise when, time after time, the analytics keeps pointing to the fact that only a few customers and products account for the majority of margin dollars and growth.
By using Pareto to distribute overhead and support costs amongst all products, in proportion to individual economic contribution and complexity level (measured in terms of revenue, number of transactions or parts-count), we can arrive at true product profitability. This is the equivalent of having an individual P&L for each product.
When we look at cumulative profits in a Pareto Chart (using gross or contribution margins), we don’t observe the loss of profitability that comes from complexity. Only when we overlay the true profitability curve on the same chart do we see the impact on the bottom line caused by the “long tail of complexity”.
Managers need to use true profitability as a guide to eliminate low-profit contributors and rationalize the portfolio. They also need to apply different marketing strategies for different groups of customers and products, based on their strategic and economic value, since not all “freeloading products” can be phased-out or replaced in the short-term, especially those that serve strategic customers.
Every portfolio will contain a number of products that fall below a true profitability threshold. As companies prune the “long tail” they also need to be able to heal true profitability of remaining products, by either recovering the cost of complexity by better pricing or by reducing the cost of the product. There are several options to repair true profitability, depending on where the product is in the economic value matrix created by the Pareto analytics:
Reduce cost of goods sold (COGS) by reducing material cost. Companies should pursue a 10 to 15% reduction in material cost for products sold below the true profitability threshold via direct material optimization.
Rationalize the offering of non-strategic products. Replacing products sold to non-strategic customers with those sold to core customers is another option.
Build marketing strategies around true profit contributors. By understanding the entire product P&L, sales organizations can focus sales efforts on true profit contributors.
Develop pricing mechanisms for different product and customer groupings. Non-strategic products sold to non-core customers should reflect the full cost of complexity.
Stop creating more low-profit contributors. The best way to sustain true profitability is to keep freeloaders from entering the portfolio.
To reduce internal complexity companies, you need to focus on two sets of actions: streamlining the portfolio (cutting the long tail) and simplifying the product line. However, reengineering a product line is not a trivial or an overnight exercise. Product line simplification needs to start with pragmatic goals, for example increasing reuse and standardization of components and consolidating the number of suppliers for certain product groups in the portfolio. The use of data analytics in conjunction with the 80/20 rule can be instrumental in determining simplification priorities. Linking bill of materials (BOM) and finished SKUs data for example, can determine component overlaps and standardization opportunities.
Typical techniques used to reduce internal complexity are:
Reengineering the product to reduce cost. Based on value engineering and similarity index teams develop ideas to reduce cost without compromising quality.
Standardization and modularization. Teams set goals for complexity levels desired within a segment of the portfolio.
Design and develop for true product profitability. When companies account for true product profitability early on, they have a better chance of creating sustainable value – products designed not only with sales specifications in mind, but also with the ability to be manufactured in existing high-volume production lines.
Supplier consolidation, category management and outsourcing. Reducing the number of suppliers through greater collaboration with suppliers and more outsourcing of less strategic products and components are great ways to reduce complexity.
Finally, to sustain true profitability, companies need a stronger complexity governance process composed of four elements:
Clear roles and responsibilities. Assign a complexity manager to coordinate the entire process. This individual is the connection point between the external and the internal complexity arenas.
Decision-making forums. The organization needs effective cross-functional decision-making forums to expedite decisions at the right levels of the organization: R&D, sales, sourcing, operations and finance.
Analytical tools and KPIs. Visual representations of product portfolios using data analytics with simulation tools allow for timely, accurate decisions. Monitoring true product profitability performance is the most effective way to measure progress.
The right behavior. New ways of working must be integrated into the complexity management process. They need to be present in the work routines of sales, sourcing and R&D to induce the desired behavior.
Living with complexity demands a clear understanding of true product profitability. It also requires that mechanisms are in place to control future complexity and sustain profitability.