Optimization for Competitive Advantage

Manufacturers do not have it easy these days…

…the overall standard in today's markets are creating an increasingly competitive landscape. Companies are frequently pressured to cut costs while maintaining high standards. Often quality, price, and safety must be carefully considered.  For many industries, changes in regulatory standards loom over while these pressures exist. Adding to an already complex state, manufacturers have to consistently and carefully innovate to remain relevant - not just on their products, but also internal processes to strengthen their value chain while managing at the margins.

In short, to succeed in today's economy, companies must do more with less. "How?" is a perfect question that leads to exploring and understanding the concept of optimization.

Think about optimization as the process and methodology of finding the best solution out of numerous different options.  It is the way of getting more with less.

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To conceptualize a simple optimization scenario, let's take a Coca-Cola can as example.

How does Coca-Cola decide what would be the best can to produce, which is cost effective, and safe for human use?  This question deals with a goal in mind - an objective.

Producing an overly thick can is expensive as material is being wasted. At the same time, the can has to withstand a certain level of pressure and content volume. Lastly, to adhere to safety regulations, the can must be made of specific materials which are safe for storing liquids. 

In the optimization field, we call these requirements constraints - a condition that the final design must adhere to. In many scenarios, constraints may come from regulatory requirements, customer demands, or limitations in the manufacturing process.

The size, length, depth, and shape of the can are what we call variables - aspects of the design that we change and explore in order to determine the best solution to an objective.

Coca-Cola might ask additional questions such as "What is the most efficient way to deliver the products?"  or "Out of thousands of possible ways of producing the can, how do we find the best one? These types of questions relate to process optimization; yet the approach of considering constraints, variables, and objective remains the same.

Coca-Cola would be one among countless companies who think about their manufacturing process with optimization in mind. It is a strategic question that must be applied to different facets of the business.

Across many industry segments, these optimization questions appear frequently such as in Automotive, Aerospace, industrial machinery & components, and consumer packaged goods.  The Automotive sector is a perfect example of an optimized business - vehicles have become safer, lighter, and more efficient. The use and design of new materials yielded greater benefit to both the customer and themselves through better product design and a more efficient production process. Such Improvements were achieved by optimizing machine types, plant layout, production planning, and assembly process.

So how would Coca-Cola optimize a can design?

The traditional design process involves a lot of engineering intuition (based on past experiences and judgement), trial and error, and often times, developing a part again and again until a design seems to fit the criteria.

The challenge with the aforementioned approach is the lack of "ideal" and quantitative methodology behind the solution. As we initially discussed, companies must do more with less. Optimization is finding the best possible option, not just an "acceptable" result.

Imagine if Coca-Cola designed a can that was "just good" instead of optimal, by producing a can that was thicker than needed. The manufacturing costs due to the excess consumption of raw material would be higher, ultimately reducing margins and impacting the bottom line.

Another challenge is the cost of testing new designs. Producing numerous prototype can designs for experimentation may be expensive, but what if a company is prototyping an airplane winglet, an assembly line layout, or a medical device? Making multiple prototypes quickly becomes too expensive and time consuming. Engineers must achieve their design goals with minimal prototypes.

Performing computer simulations of components is a good first step, but they only demonstrate how a part may behave; they won’t inform you about the potential of your design. This means the engineering team will likely have to use similar strategies as they were with prototyping: refine the design, simulate, and repeat until they’re out of time or money. This process is often tedious, inefficient, and requires many man-hours.

This is where an optimizer comes in.

An optimizer empowers engineers and designers to consider the trade-offs between product attributes, while evaluating performance based on a goal (the objective in mind) and requirements (constraints).

In the Coca-Cola example, imagine asking the software to "find a can that uses the least amount of material, is able to hold 350 ml of liquid, withstand the pressure of carbonated contents, and handle the pressure of normal human grip". The engineer would then let the optimizer search and run in the background without manually adjusting various designs. After many automated runs, the optimizer would present the best solution along with analytics on the runs, and provide the engineer with insight on new design options.

Being able to optimize in such a formal and quick manner is key. We picked a simple example of a beverage can, which only has to satisfy a few requirements such as volume capacity and pressure withstood. The variables are very simple - how tall, wide, and thick the material needs to be. What about an optimization with 30, 50 or even 100 different variables?

It is nearly impossible to manually calculate the possibilities of such high variable problems. The typical solution in this scenario still falls on heavy simulation usage alongside engineering judgement. This is when an optimizer plays a bigger role as it helps engineers solve a complex engineering challenge: searching for the best design with so many different variables is like finding a needle in the Pacific Ocean.

The overall impact from leveraging an optimizer tends to result in either a better design (cheaper, safer, more effective) or a satisfactory design in less time (less simulation set up and less prototyping).Ergo the adoption of an optimizer can result in a  tremendously positive impact on your organization.

As outlined in this article, it affects your product and production line, the value your company offers to customers through product release, and is ultimately an important tool for competitive advantage. That might sound too good to be true, but trying a strong optimizer is worth the effort. Now, due diligence is always the key. Not all optimizers are created equal so be sure to check out our upcoming article on "How to Evaluate a Design Optimizer" by the Empower Operations team.

We hope this inspires your team and company to learn more about what an optimizer can do for you.

Contact us today for free consultation and to learn more.