Variant Management: The EFS Consulting MOPS Algorithm

Key Takeaways  

• Variant management means designing and structuring products and services so that the resulting diversity (e.g., different variants, parts, markets, or production processes) can be effectively managed using appropriate methods.

• MOPS (“Multiple Objective Portfolio Streamlining”) is an algorithm that calculates the maximum meaningful variant reduction potential.

• MOPS optimizes conflicting goals by creating a trade-off point where a minimal portfolio is calculated, providing a reduction baseline.

• A practical EFS example shows how MOPS can streamline a product portfolio by 15–45% while maintaining the same market and customer coverage.

What is Variant Management: Definition 

“Variant management includes the development, design, and structuring of products and services or product ranges in the company. The aim is to manage the complexity emanating from the product (quantity, parts, components, variants, etc.) as well as the complexity acting on the product (market diversification, production processes, etc.) by means of appropriate instruments.” (Schuh, G.; Riesener, M. (2018): Managing Product Complexity – Strategies, Methods, Tools; 3rd edition; Munich: Carl Hanser Verlag)

Variant management is essential to offer the broadest possible portfolio to be able to meet all customers’ requirements. As a result, the number of variants increases sharply, and costs have to be reduced to remain competitive. The goal of variant management is to keep only those variants in the portfolio that meet as many customer requirements as possible. At the same time, these variants should generate the highest sales and be economically most profitable. In other words: the best trade-off should be found in order to achieve the widest possible range of products and the highest possible profit.

Variant reduction is a central component of variant management. By eliminating redundant parts, assemblies, or product variants, complexity can be significantly reduced. This simplifies development, procurement, logistics, production, and sales alike. At the same time, reduction has direct cost effects on new developments, as development and tooling costs can be saved. In the long term, minimizing variants also helps lower ongoing complexity costs, including documentation, storage, procurement, and logistics.

This is exactly where the MOPS algorithm comes into play. It helps companies identify the maximum reduction potential and provides an efficient foundation for decision-making in variant management.

MOPS: The EFS Consulting Algorithm

As a supporting tool for variant reduction, EFS Consulting has developed the MOPS algorithm (Multiple Objective Portfolio Streamlining). MOPS simultaneously optimizes multiple, sometimes conflicting objectives in variant management. From a technical perspective, MOPS solves an approximated multi-objective set union knapsack problem. It then returns an approximation curve of the true Pareto front.

Usage of MOPS

MOPS is used in the case of conflicting goals in variant management. For example, if the number of parts should be minimized while the variant portfolio must be maximized, MOPS calculates a reduction baseline that achieves both goals as well as possible. This baseline serves as a starting point for EFS experts to develop the ideal solution for and with customers, for example, in stakeholder workshops.

How MOPS Works

For the optimal use of the MOPS variant reduction tool and to achieve the best possible results, the first step is to identify substitution possibilities. Additionally, product descriptions at the part level must be created and objective functions formulated.

1. Determination of substitution possibilities

To determine the substitution possibilities, a substitution matrix (transition matrix) is filled. This matrix defines which parts can be replaced by which other parts. Certain properties of a part must be preserved (e.g., the material), while other properties can be modified within defined tolerances (for example, the sheet thickness may be 1 mm thinner or thicker).

2. Creation of the product description

To create the product description, a list of parts is formulated on one side, and the product variants, built from these parts, on the other side. There must be a functional dependency between those two sides: You need part X and part Y to build variant A.

3. Formulation of the target functions

1. Minimization of the number of parts
2. Maximizing the variant portfolio

The objective functions set global assumptions, while the resulting constraints specify, for example, that certain variants – such as A and B – or a defined number of specific parts must be retained.

MOPS program run

A fast greedy algorithm is iteratively run through and makes the locally optimal decision at each step. In this case, the algorithm selects the part that can be removed at each step while maintaining the maximum number of producible variants. The transitions from the substitution matrix are taken into account. Elements of the portfolio that can be replaced are not considered lost. This step is repeated until a minimum number of parts is reached.

5. Result

The output is a set of heuristically generated compromises: the reduction baseline. This baseline is represented in a two-dimensional graph with the number of parts on the x-axis and the number of variants on the y-axis to make complex relationships understandable for all stakeholders. It forms the basis for the technical discussions and decisions.

EFS Consulting Case Study: Optimization of Tank Volumes

• Client: OEM
• Industry: Automotive | Commercial Vehicle

Starting Situation:

EFS Consulting was commissioned to optimize a Fuel Tank Portfolio. The goal was to minimize the number of physically available tanks. At the same time, the complete range from the minimum to the maximum tank volume should continue to be offered.

Measures & Impact:

In the first step, the constraints that the optimized tank portfolio must meet were defined:

1) The smallest and largest tank volumes must be maintained in order to ensure tank coverage over the entire range of different volumes.

2) The lean portfolio should be similar to the original one so that both sales and buyers can find their way around:

1. No new tanks or variants are to be developed.
2. Geometrically unique tanks with high Sales volumes are excluded from optimization.
3. Tanks in various materials (aluminum and steel) will continue to be offered.
4. Tanks may only be substituted within similar geometries: a tank with a low height may only be replaced by a tank with a low height. The same applies to tanks with high height.
5. Tanks that can be installed on both sides are preferred, as modular parts minimize the number of parts. This is an implicit requirement that the algorithm automatically follows.

3) What may be replaced: Tanks may be replaced by other tanks with a similar volume of ±50 dm3.

Based on the above requirements, the substitution matrix (see image below; values are to be understood in dm3).

The Output

The calculated tank portfolio reduction baseline, the output, was presented as a diagram (Figure 1 & Figure 2). Furthermore, a corresponding parts list was created, including qualitatively detailed reasons for the tanks that were excluded. The calculation revealed a significant parts reduction potential with only marginal limitations on the representable tank layouts.

Subsequently, further assumptions were incorporated at the customer’s request to create a maximally lean tank portfolio with the MOPS approach.

Conclusion

In combination with the corresponding technical expertise, the development of the MOPS algorithm enables EFS Consulting to create effective variant management solutions for customers quickly. The project case presented makes it clear that a strong reduction in parts does not necessarily needs to be in contradiction to a large variety of variants.

EFS Consulting sees the target potential for applying MOPS at a variant reduction of 15 % to 45 %, depending on customer needs and the scope of part optimization. At the same time, EFS Consulting provides its clients with a toolbox to build product documentation, visualize product structures, and validate existing documentation. Based on the EFS variant management approach, an efficient product portfolio is proposed, which specifically considers variant eliminations, relocations, and new developments.

FAQs

What is the EFS Consulting MOPS Algorithm?

MOPS is an algorithm that optimizes a product portfolio to meet customer demands with reduced, optimized variants.

What does EFS Consulting use MOPS for?

MOPS is used to optimize a product portfolio towards a goal, whereby constraints to be observed can be taken into account as constraints.

Do you still need EFS Consulting when using MOPS?

Yes, because MOPS is a tool used by EFS experts to increase productivity and must be optimized for each use case. Additionally, the real use case is more complicated than the abstracted optimization that MOPS calculates. At this point, EFS’s expert knowledge is required.

What challenges does EFS Consulting meet with MOPS?

MOPS enables the rapid design of a component portfolio that is influenced by various other components within the assembled product. For example, this includes the fuel tanks within the chassis layout of a truck. This allows EFS Consulting to effectively integrate the technical perspective and the client’s requirements equally.