A manufacturing company has 300 active customers. Of those, 285 have slightly different configurations of its “standard product.” The sales team builds every quote from scratch because there is no standard offering. The head of production can't name a single product that is manufactured exactly the same way every time. The CFO can't break down revenue by product category.
This isn't an extreme case. It's the reality of many midsize B2B companies that have grown organically by saying yes to every customer request.
Why product portfolio chaos happens
Product portfolio chaos almost always develops the same way: The company starts with a core product. One customer wants variant A. Another wants variant B. Sales says yes. Engineering builds it. Over the years, the number of variants multiplies without structure and without anyone asking whether a new variant actually makes sense.
The result: a de facto portfolio of 50+ SKUs that no one fully understands, with overlaps, redundancies, and gaps.
What product portfolio chaos costs
- Sales efficiency: Without standard offerings, every quote takes longer. Every single one. Average quote preparation time in companies with unstructured portfolios is far longer than in companies with structured portfolios.
- Production and procurement: No economies of scale. Every variant requires its own parts, its own setup, and sometimes its own suppliers.
- Marketing: How do you communicate what you offer if you can't explain it yourself in three sentences?
- Customer service and support: Every customer configuration becomes its own product. Knowledge doesn't scale.
“A company that tries to do everything for everyone ends up doing nothing particularly well.”
How to structure a portfolio, even when it's chaotic
Step 1: Take inventory without preconceptions
What does the portfolio actually look like today? List every SKU, every variant, and every custom product. Then cluster them: What is genuinely different? What is only cosmetically different? What solves the same problem for the same customer?
Step 2: Analyze revenue and margin by SKU
Which products and variants generate 80% of revenue? The Pareto principle applies surprisingly often: 20% of SKUs generate 80% of revenue. The rest adds complexity without proportional returns.
Step 3: Identify opportunities for standardization
Which customer requirements could be handled with modules instead of custom manufacturing? A modular system (base product + options) can significantly reduce the number of variants while delivering the same or greater customer value.
Step 4: Create a transition strategy for existing customers
This is the hardest part: Existing customers with custom solutions need to be moved into the new system without losing their trust. That requires personal communication, a clear timeline, and, where necessary, transitional pricing.
1. Data: A complete SKU list, revenue and margin by item, and sales volumes
2. Decision authority: Who is allowed to eliminate variants? Without leadership backing, standardization fails because of internal resistance (Sales: “But the customer wants X.”)
3. Transition time: A realistic timeline. Standardizing complex portfolios takes 12–24 months.
4. Modular framework: Which base configurations will remain, which options make sense, and which ones will be discontinued?
When full standardization isn't possible
Some industries and customer segments genuinely require custom solutions: custom MedTech products, machinery for specific production processes, and complex IT systems. In these cases, the goal isn't 100% standardization. It's standard where possible, custom where necessary.
One framework: Define what is the same in 90% of all projects. That's your standard. Define what is specific in 30–50%. Those are your modules. Anything that occurs in less than 10% of cases is a custom solution, with a correspondingly higher price and longer lead time.
This structure turns product portfolio chaos into a commercial architecture: sellable, manufacturable, and easy to communicate.
Sources: Grønvald, Nørgaard, Christensen & Mortensen (2026), Quantifying the Overhead Impact of Product Variety, Production Engineering 20(3) · Grønvald et al. (2025), Quantifying the Effects of Modular Product Architectures, Applied Sciences 15(22) · Myrodia, Hvam, Sandrin, Forza & Haug (2021), Variety-Induced Complexity Cost Factors, Journal of Manufacturing Systems 60, 373–391 · Jiao & Tseng (1999), Product Family Architecture for Mass Customization, Journal of Intelligent Manufacturing 10, 3–20.
Published June 2026