Welcome to the complex world of product configurations in manufacturing, where every decision shapes the landscape of your offerings. In this fast-paced industry, you face numerous challenges in meeting customer demands, ensuring product quality, and staying competitive in the market. A major issue you face is configuring products to match your customers' unique needs while keeping efficiency and accuracy intact.
Building a customer-specific product involves considering many options: dimensions, standards and regulations, other unique features, and accurate pricing are just naming a few. Yet, with so many choices, navigating this complexity can be overwhelming and time-consuming. This is where different configuration methods come into play. These methods are not standalone programming languages like Java or Python. Instead, they are ways of structuring and managing data within a software system to facilitate product configuration. In this blog, we’ll address three types of configuration methods: Attribute-based, rule-based, and constraint-based, which are all strategies employed within Configure, Price, Quote (CPQ) software to help businesses manage product customization more efficiently.
Each of these approaches addresses specific challenges that you face as a manufacturing business. Attribute-based configurations provide a structured framework but can be cumbersome for complex products. Rule-based configurations offer flexibility but require a deep understanding of rules and constraints. However, when it comes to managing the complexity of manufacturing highly intricate products, such as agricultural equipment or industrial automation, constraint-based configurations emerge as the optimal choice. They provide clarity, minimize errors, and ensure that configurations are accurate and viable from the outset. This makes constraint-based configurations essential for manufacturing companies striving to stay ahead in today's competitive market.
Attribute-based configurations allow users to customize products by selecting specific features or attributes, creating a tailored solution that meets their exact needs.
Imagine configuring a product as if you were assembling a jigsaw puzzle. Attribute-based configurations lay out all the puzzle pieces on the table, each representing a unique attribute or option. Your task is to methodically fit these pieces together, forming the complete picture of your product. This method allows for a structured approach, providing a clear framework for understanding how different attributes interact and contribute to the final configuration. However, as the complexity of the product increases, so does the challenge of fitting all the pieces together. For products with extensive customization options, attribute-based configurations can become laborious and time-consuming, requiring meticulous attention to detail.
Nevertheless, for simpler products with well-defined attributes, attribute-based configurations offer a clear and straightforward path forward, guiding users through the configuration process with ease. Take a T-shirt, for example, with a size, a color, and a print. Due to the limited variations of your product, the jigsaw would stay relatively small and, thus, manageable. And that can come in very handy when you want to work with SKUs (Stock Keeping Units). In order to keep stock of something, you need to know all possible combinations; when the jigsaw puzzle stays small, that's totally feasible. Knowing all possible combinations allows you to discount configurations that you have an overstock of while, in turn, increasing the delivery time when a customer configures a variant with an insufficient stock level.
Figure 1. A visual representation of attribute-based configuration for available options of a T-shirt.
Figure 2. A visual representation of attribute-based configuration for available options of an Industrial Weigher Unit.
Explanation: The more complex the product, the greater the number of available options. There are far more possibilities than impossibilities, therefore, this becomes an inefficient way of defining your options.
Rule-based configurations enable users to customize products by following predefined rules and logic to ensure all chosen options are compatible and functional together.
Rule-based configuration is essentially a human interface on top of attribute-based configuration. Rule-based configuration relies on the same technique as attribute-based: defining what's possible. The difference here is that rule-based provides a visual interface to do so and to overcome challenges that are not so linear.
Imagine a complex jigsaw puzzle where each piece could potentially fit into multiple locations. Rule-based configuration is like being handed a rulebook rather than having all the puzzle pieces laid out. These rules act as your guiding principles, dictating which pieces can connect based on predefined criteria. It offers users flexibility, allowing customers to tailor the configuration to specific needs. Rule-based configuration introduces a visual system resembling a flowchart. This system guides users through potential paths and decisions towards a final configuration. It simplifies the management of variant flows, enhancing user intuition and easing the navigation through complex configurations.
Rule-based configuration, unlike attribute-based methods, stands out in complexity management by providing a nuanced approach. Picture navigating a jigsaw puzzle with irregular edges; each decision influences subsequent choices, just like the dynamic environment of rule-based configuration. Users follow predefined rules, much like finding the right fit for each puzzle piece, with each decision impacting the overall outcome. Despite its adaptability, defining all potential configurations remains labor-intensive, limiting its suitability for products with highly intricate configuration needs. In essence, while offering flexibility similar to fitting puzzle pieces into multiple locations, rule-based configuration demands attention to detail and may not suit every configuration scenario.
Figure 2. A visual representation of rule-based configuration for available options of a T-shirt.
Figure 3. A visual representation of rule-based configuration for available options of a T-shirt and a matching Hat.
Explanation: Due to the fact that a Blue-Colored Hat can be combined with multiple options for T-shirts, the attribute-based configuration method is no longer an efficient way to define these options, nor the rule-based method where you’re required to define the paths to all possible options,
Constraint-based configurations let users customize products by setting specific limitations or requirements, ensuring that the final product meets all specified criteria and works correctly.
Let’s expand our jigsaw puzzle analogy, because if configuration methods were as simple as a two-dimensional jigsaw puzzle, you would not be reading this blog to begin with. Your ability to serve your customers the perfect solution knows almost no bounds. If your products could be compared to a jigsaw puzzle, it would be one where almost all the pieces could be interchanged; instead of a single puzzle piece fitting into a single place, it fits almost everywhere. Your products are complex because, when it comes to customization, so much is possible, not because so much is impossible. So why then would it make sense to use a configuration method that forces you to define what’s possible?
Now try fitting these limitless possibilities into an attribute-based or rule-based system. Try fitting all the different places a puzzle piece can go into a table or flow chart. You’ll soon realize you’re using the wrong configuration method because it would complicate selling even the simplest products, and that’s not what Elfsquad does. Instead of defining the possibilities, you should be defining the impossibilities, because that’s where your inefficiencies and troubles come from. Elfsquad saw the value of this method and embraced it from the beginning; it’s called Constraint-Based Configuration.
This method provides unparalleled clarity and significantly reduces the risk of errors throughout the configuration process. Users can navigate through the available options knowing exactly what combinations are feasible, minimizing the chances of selecting incompatible choices. Furthermore, the constraint-based method allows you to keep your products’ details and rules separate. This makes it easier to manage and grow your CPQ system. If the details and rules are tied together, this can cause various issues that limit your options. Simply put, constraint-based configurations offer a structured and efficient approach to managing the most complex product configurations.
Both attribute-based and rule-based configurations indirectly force you to compromise what makes you unique. By requiring you to define everything that's possible, you’re left with no choice but to try to make your products simpler. When manufacturing increasingly-complex products in a constantly-changing market, you know that isn’t an option. At Elfsquad, we understand how constraint-based configurations can revolutionize the way products are tailored to fit customers’ needs. That's why we built our CPQ software using this method. It helps us ensure efficiency and accuracy in meeting your customers’ requirements. And for this approach, using AI-based technology (an AI solver) is essential; it simplifies the process and makes sure we get the best results.
The AI solver we built at Elfsquad is lovingly referred to as Archer. Without getting too technical, Archer helps by looking at all the rules and limitations you've set, kind of like the boundaries of the jigsaw puzzle we mentioned. Then Archer finds the ideal combination of options that fit within those boundaries. It's like having a puzzle master who knows exactly which pieces can go where, so you end up with a perfect solution every time.
But at Elfsquad, we’re always one step ahead. We’ve already developed Archer 2.0.
Archer 2.0 offers a significant improvement over traditional constraint-based configurations because, simply put, it has the most intelligent computing capabilities available. Users can still input constraints just like they would with traditional configurators, but Archer 2.0 offers a significant enhancement. Instead of merely providing configurations based on the input constraints, Archer 2.0 immediately determines and presents the best configurations and optimal routes in real time. It’s truly next-level AI optimization thanks to the constraint-based configuration method. Just another way we are reshaping the IT landscape for manufacturers around the world.
Figure 3. A visual representation of constraint-based configuration for available options of a T-shirt and a matching Hat.
Explanation: When it comes to highly-complex products, constraint-based configuration is the only method which allows you to efficiently manage all possible options (and their paths) by defining what's not possible instead of what is,
In conclusion, attribute-based and rule-based configurations are not substandard methods, they are just more suitable for other use-cases (not complex manufacturing). Attribute-based and rule-based methods are like already knowing the solution to a jigsaw puzzle. But because you need to remember where every single puzzle piece goes, these methods are only useful for smaller puzzles compared to constraint-based configurations. If constraint-based configurations are implemented effectively, and supported by a great AI-solver (like we have at Elfsquad), then the puzzle can be updated and adapted in real-time. This lets you easily learn the solution of increasingly bigger and more complex puzzles.
Whether following a set of rules, defining boundaries within constraints, or piecing together a puzzle, Elfsquad has your back. Our CPQ solution was built specifically for handling highly-complex products and processes. Why settle for less?
If you’re ready to see constraints-based configurations in action, our Business Development Executive Tristan would love to fill you in. Schedule a meeting directly with Tristan here.