Managing a product portfolio is a tricky business at the best of times. There’s a constant tension between product development and its desire to create new things, operations and its focus on costs and complexities, and sales with its need to cater to customer needs in expanding the top line. Buoyed by the long economic expansion, many businesses have grown their product portfolios. Increasing profits gave companies comfortable investment cushions, and ongoing digitisation and automation fuelled flexibility and economies of scale.
Some companies have felt the challenges associated with an expanding portfolio for a while; for others, COVID 19 has become a breaking point. Many senior executives are now working to reduce their product portfolios whilst others are planning to reallocate their R&D budgets to new products.
The Volvo group offers a good example of the benefits of actively managing a portfolio. In 2011, Volvo decided to phase out its five and six cylinder engines and replace them with four and three cylinder engines based on a unified, modular engine architecture. The intent was to become a leader in lowering carbon emissions, but the change also helped streamline production by replacing eight separate engine architectures on three platforms.
This is a well trodden path and we have seen companies of late, reducing their product portfolio or SKU count by as much as 90% allowing them to reduce lead times and increase sales and profitability by high single or low double digit figures.
In the consumer packaged goods industry companies have found the focussing on revenue growth at the expense of profitability is seldomly a winning strategy. In situations where SKU counts are allowed to proliferate it was found that sales per SKU reduced by more than 30 percent and margins dropped by as much as 10 percent. By undertaking a simplification program that comprises portfolio optimisation, product design, and commercial network alignment product portfolios can be reduced by 25 percent while leading to an improvement in gross margin of as much as 3 percent.
By leveraging the insights gained from the crisis and adding the right tools and processes, companies can actively shape a simpler, more effective product portfolio that can both reduce the burden of risk management now and better serve customers once the crisis eases.
Distinguishing good complexity from bad
The goal of portfolio management is not simply to reduce complexity as much as possible. It is also about differentiating good complexity, which generates more customer value than it costs (because customers are actually willing to pay for the variance), from bad complexity, which adds complexity without contributing significant customer value.
Some variance and complexity is important because it helps to distribute risks and, when combined with a smart modularisation effort, can increase scale effects on a large number of components while also providing customers with choice. However, too much variance and complexity creates risks and raises overall costs, such as by generating additional compliance requirements that absorb more management attention than anticipated or by spurring further R&D efforts to maintain the products over their lifecycle.
How portfolios grow…and grow…and grow
Organisations don’t deliberately increase complexity. Instead, two different mechanisms automatically increase complexity and product variance in the pursuit of growth and additional customer value.
1) Incremental growth
The first kind of portfolio growth is incremental, taking place as companies develop and adjust existing product features. A company sees an opportunity to sell items or services that are fundamentally like what it’s already selling, but with small variations: an additional size for a mobile phone, or a newgeneration engine with lower fuel consumption. Managers might see an opening to capture small, additional markets that have specific requirements yet promise low incremental investment and cost, such as homeowners who lack the space for a conventional undercounter dishwasher but will buy a small countertop model. Underlying this growth is the common assumption that higher coverage of markets and consumer niches and therefore additional revenue will more than offset additional cost.
2) Disruptive growth
The second kind of portfolio growth is disruptive and happens when companies add entirely new technology to existing product lines,or set up completely new product lines. A company might replace internal combustion engines with electronic drivetrains, or meat in frozen meals with plant based substitutes. For the past several years, one of the most frequent sources of disruptive portfolio growth has been the addition of software to traditional hardware products. Underlying this trend is the assumption that manufacturers can dramatically increase customer value by embedding software.
Both mechanisms have dramatically increased portfolio growth. A globally growing economy, burgeoning revenues and profits, and speedy consumer development in emerging countries have created explosive complexity and variance in multiple industries. For example, premium automotive OEMs’ model offerings almost doubled between 2005 and 2020. In the industrial components sector, over the course of 15 years, the number of base models for a single product line grew by 20 times.
To counter spiralling complexity, companies can establish effective portfolio management and with it, an ongoing practice of pruning their portfolios.
Under innovation pressure
Both traditional and breakout industries face rising pressure from shorter, more disruptive innovation cycles in the wake of technological advances. The resulting compression exacerbates the conflict between maintaining a current portfolio and delivering the innovative, high margin products that are the lifeblood of any business. There are three main implications.
- First, the tech industry increasingly sets consumer expectations, whether by annually upgraded mobile phones or social media applications that are tweaked almost every week. Yet for companies offering physical products, rapid development limits the extent to which they can test new products and product features against their core customers’ needs. With software accounting for a rising proportion of product value, the mismatch between physical product updates and the digital world is causing research and development operations to rethink the ways they create.
- That leads to the second major implication. Companies are taking a page from the tech sector by adapting agile software development methodologies to product development. They are focusing on minimum viable product design practices and constant, iterative improvement, rather than spending years developing a perfect product that could be outdated before it can be launched.
- Third, more and more products must integrate with other platforms and systems, in order to include features that both consumers and industry see as standard. In the automotive sector, for example, consumer technology companies have been competing intensely to extend their ecosystems to the daily drive. But this level of integration requires the participation of more stakeholders in the product development process, with major process and structural changes in R&D so that it can cope with approaching challenges around innovation. By understanding the current portfolio’s costs and market coverage, companies are better able to create a structured approach to developing new offerings.
Electric vehicle manufacturers are already successfully deploying software based solutions to typical hardware issues. Traditionally, automakers that wanted to offer additional power for a higher price had little alternative but to change the drivetrain physically by installing a turbocharger, for example, or offering a higher capacity engine. Now it’s possible to offer different power profiles, at different prices, or the same battery configuration, including under a pay per use model for range extension.
Finding the right complexity balance
There is no single, successful approach to product portfolio management. But some companies are already coping with these challenges by applying advanced analytics, adapting processes and roles, debiasing decision making, and putting the customer at the centre of product portfolio development.
Rationalising portfolios through advanced analytics
The use of advanced analytics in a portfolio management context is especially well suited to quantify technological distinction within the product portfolio. In other words, how a given product’s components compare to those in the rest of the portfolio. Two applications of this approach illustrate the potential benefits in overcoming limitations endemic to traditional product portfolio rationalisation.
Focus on low performance, not necessarily low sales. The first scenario addresses the all too common disappointment that results from focusing only on culling the lowest selling products especially across many different product families. Despite the time and effort spent, internal complexity may barely budge. The underlying reason? What truly drives complexity isn’t simply the number of product variants, but the number of components an organisation must develop, source, assemble, and maintain to support those variants. If the pruned products have better selling counterparts that use a majority of the same components, the complexity remains even as revenue disappears.
This is exactly the sort of problem modern network optimisation algorithms were designed for. These algorithms analyse component reuse across products, and identify the optimum combination of products to be pruned in order to release the most components while minimising foregone sales. The result identifies and optimises products with poor cost performance ratios, rather than just products with low sales.
Companies can customise this exercise by allocating true complexity costs to sets of components, counting not just direct costs, such as inventory and related capital costs, but also harder to quantify expenses, such as associated R&D spend and production line investments. Commercial vehicle manufacturers have used this approach to reduce the number of components they use by 20 percent while affecting just 5 percent of sales.
The sheer number of components, together with the often manual process of entering and classifying component data, creates significant challenges when an organisation wants to identify components that are similar, but not identical. Machine learning algorithms are ideally suited to this challenge, especially when trained over many months on comparable data. They are not only able to identify clusters of similar components, but can also use component similarity to highlight unexplained pricing discrepancies and point out more competitive suppliers.
One approach is to create unified data pools by combining line item spending with bill of material, material master, supplier, and engineering data from different systems and to use this to uncover clusters of similar parts with materially different prices.
Companies have more need than ever to rebalance their product portfolios so that complexity creates value rather than destroying it. They also have more tools at their disposal, with more power to create the right products that serve their customers’ needs.