Achieve Rapid Results with Market-Driven Parts Variety Reduction

By Stephan Wöhe

Manufacturing companies are often faced with regularly optimizing their product portfolio, thus eliminating productivity and profit killers. Achieving this optimum is the goal when developing new products and managing the existing product portfolio.

While it's widely acknowledged that modular product architectures are often the most effective and efficient development strategy, the question of achieving tangible results within a short timeframe remains. How can we ensure measurable outcomes in less than 12 months?

Typically, the development of a resilient, sustainable modular product architecture can take one to two years. However, market-driven parts variety reduction emerges as a practical and applicable solution to bridge this time gap. It aims to minimize changes to the existing product range and architecture while reducing part variety and optimizing costs.

Market-driven parts variety reduction can be used as preparation for immediate modularization. In this way, initial potential can be leveraged in the short term, and a comprehensive modularization project can be accelerated.

The results of this method can be seamlessly integrated into the modularization project, ensuring an efficient implementation of modularization in your company. This is a perfect interplay of methods, providing reassurance about the effectiveness of the process.

In this blog article, we would like to show you exactly how this works.

Essential Data Integration Without Altering Product Architecture

Data is the basis for market-driven parts variety reduction. The more data is available, the more aspects can be included in the optimization. This also means that the result will be more beneficial for your company.

In the simplest case, you only have the sales data for your products, the description of the products and their equipment, and the associated parts lists. If you combine this data in a structure, you can already derive valuable insights.

Simple ABC analyses can be carried out based on this information. As a result, all C products are often removed, for example. However, this method also carries risks, as a section of the product range that is important for other A customers is often lost. The discontinuation of C-products does not guarantee that the parts, components, and suppliers that cause the most significant problems in the supply chain will also be eliminated.

Market-driven parts variety reduction avoids this mistake by integrating additional perspectives from development, production, and purchasing. It also attempts to prevent the loss of product features through clever packaging. The customer still gets what he asks for but with less complexity for your organization.

Number of Product Variants to Parts and Turnover


Differences to Variant Management and Product Mining

Provide data on products, customers, prices, parts, production/assembly, and suppliers, if possible. In that case, the number of possible optimizations and the quality of the statements will increase. In contrast to variant management or product mining approaches, the market perspective is set and leading in optimization in our market-driven parts variety reduction.

It's a well-established fact that customer-oriented companies tend to be more profitable than those that are less focused on their customers. However, the key to success lies in balancing this customer orientation with other optimization approaches. Unlike modularization, market-driven parts variety reduction doesn't alter the product architecture, but rather optimizes the product and parts portfolio within the existing structure, thereby maximizing profitability.

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How Does Market-Driven Parts Variety Reduction Works? 

Market-driven parts variety reduction statistically records and evaluates relationships between data on products, customers, parts, and suppliers. The graphic below shows different types of information and which documents link them together.

Example of Data Analysis Structure

If the data is already available in a structured form and source and the connections are known, pivoting the data structure can already provide helpful insights. However, given the rarity of this situation, we have turned to graph analysis and modern artificial intelligence methods. These tools enable us to develop relationships, identify clusters, and evaluate data points or relationships using machine learning, ensuring the most accurate and insightful results.

Use of AI in the Context of Market-Driven Parts Variety Reduction

The first step is identifying possible data sources, optimizing data quality, and, if necessary, filling data gaps and optimizing redundancies. In principle, almost any data source is suitable. Only images and drawings pose a more significant hurdle for analysis with AI.

The data, meticulously prepared in this manner, is then subjected to the precision of machine learning methods. This allows for the identification of patterns that serve as the basis for optimizations and simulations.

The models are built and applied based on the derived optimization strategy, and the results are evaluated. We usually use several strategies in parallel and investigate any overlaps. Based on this interim result, the models and analyses are optimized. Finally, the findings are translated into measures and implemented quickly.

Delivering Impact in Just Months

The most striking aspect of market-driven parts variety reduction is its swift impact. Experience has shown that within three to six months, it can streamline and optimize the supply chain, with over 90% of the options remaining available. This efficiency is further demonstrated by the fact that 50% of the products are streamlined, and 25% of the parts are eliminated. The resultant reduction in complexity significantly affects development, purchasing, and the supply chain, leading to a noticeable decrease in associated costs. In fact, you can expect a cost reduction of around 2% of total costs.  

As market-driven parts variety reduction keeps the existing product architecture the same, the potential is lower than that of effective modularization.  

It's important to note that this method is not a replacement for modularization, but a valuable complement. Its significant acceleration of the design and implementation of complete modularization is a testament to this. The preliminary work it does allows for the early use of findings on required product features and their characteristics, with great validity. Depending on the scope and quality of the data model created, the results can be transferred to our PALMA system to manage the standardized scope. This effectively limits further growth in the complexity of offers and parts, a crucial aspect of supply chain optimization. Alternatively, the cleansed data can be fed back into existing ERP, PLM, CPQ or other systems.

Leveraging Market-Driven Parts Variety Reduction as a Step Toward a Modular Product Architecture

Manufacturing companies must constantly optimize their product portfolios to improve productivity and results. This applies to the development of new products and the management of existing product portfolios.

In this article, we have shown how market-driven parts variety reduction can quickly help as a first step.

Modular product architectures are often the most efficient development strategy. However, developing a sustainable modular product architecture can take one to two years. market-driven parts variety reduction can be used as an intermediate step to modularization. It not only enables fast results (3-6 months), but also offers significant cost reduction (2% of total costs), a potential financial gain that should pique your interest.

Market-driven parts variety reduction uses data from various sources (e.g., sales, development, production, purchasing). It analyses this data using AI-based processes and identifies optimization potential. The results of market-driven parts variety reduction are incorporated into modularization and accelerate it.

In summary, market-driven parts variety reduction is a sensible intermediate step towards modularization. It enables quick effects and accelerates the development of a modular product architecture.

The procedures described in this blog article are based on the analysis of product architecture data. The potential of this method is also depends on the quality of the available data. In addition to this article, we recommend our webinar “Best Practices to Define and Manage Product Architecture Data”. You can download a recording of the webinar below.

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Want to know more? 

If you find this topic interesting and want to know more about how we can help with market standardization or modularization, please get in touch with me directly. I'll be happy to set up a meeting to further the conversation. 



Stephan Wöhe

Senior Manager