Optimization, Yes Or No?

Optimization, Yes Or No?

I’ve been working with Supply Chain solutions since the early 2000s. It was a time that we did not many tool options but in general, the ones available, were very good.

The challenge at that time was more around user interface, integration with legacy systems, customization options, etc. (by the way, some of these problems are still present today in some cases)

One of the most difficult topics to deal with at that time was related to how setup your supply chain model, network, business rules and whether to use optimization or not in order to achieve best results.

At that time, I was mostly working as a consultant and had several clients in many different industries dealing with the same questions and trying to come up with a solution that was simple enough to maintain and still generating relevant data to support their decision-making process.

When looking to optimization methods, comparing many different parameters and variables to support business scenarios, it is never easy to find the right balance of complexity vs. expected results. Usually in order to get more relevant results, the number of parameters will grow and that impacts directly in the execution time.

Given that most companies run those optimization processes every night, along with data updates, backups, etc., the execution window gets limited and, in most cases, there is a need to limit the running time of those, to fit into the time dedicated to every process step. That also going to impact on the results since the optimizer most likely will not have enough time to come up with the optimal plan based on all parameters. Also, the required hardware to support those solutions was very expensive and usually a bottleneck that we had to take into consideration.

This balance between model complexity, parameters, execution time and expected results was always very difficult to handle, especially in large companies where the number of SKUs was very high.



Time has passed and many new solutions are now available, especially with this new generation of Cloud Supply Chain tools. Hardware is no longer a bottle neck since most clients are leveraging service providers to handle any demands in this space and scaling up your platform is a much easier and less costly process.

With those new technologies, we see a change in perspective from some vendors, based on the way to approach optimization. Some of them are still developing sophisticated algorithms (and customizing those when needed) but others chose to go to a completely different direction. There are solutions that are more focus on updating the results based on events that happen in the supply chain, allowing users to run scenarios and choose the solution that best address any challenges they face. The ability to compare scenarios and/or use more data points (as financial figures as an example), have opened a new level of interaction where the users can directly influence on how to react to a new event.

Some of the available solutions are also enabling Artificial Intelligence to support this process, proposing actions to the user based on event driven scenarios and reducing the time to react in case of exceptions.

If I think about all the different solutions, industries and business models that I had to support either as a consultant or user, the number of implementations that were fully successful using sophisticated optimization algorithms is quite limited. It all depends again on the modeling complexity and, most importantly, the ability of the users to understand, interpret and making decisions based on the output provided by those tools.

The maintenance of those models is also very complex and with the amount of activities we still have running in a daily basis, it requires a lot of coordination and attention to detail in order to obtain good results.

For those reasons, I’ve learned to be more cautious when seeing or proposing the use of complex optimization tools. Not that they do not work… on the contrary… the problem is that sometimes we thing that we just need to push a button and everything will come up perfectly right, which we know is not the case in most of the times.

" The ability to compare scenarios and/or use more data points (as financial figures as an example), have opened a new level of interaction where the users can directly influence on how to react to a new event"

I am a strong believer that Change Management is a key element on any implementation, especially to help on user adoption of the new processes and solutions. By helping the users during the transition to the new model, we need to make sure that all of those variables are considered and we know we are capable of using any new functionalities (e.g.: optimization).

So, no matter if you choose to use any optimization tools or not, the chances of getting good results are less related to the solution itself, but rather to how complex is your Supply Chain model and, most importantly, our users’capabilities of dealing with that.

Weekly Brief

Read Also

5 Ways to Conquer Supply Chain Chaos

Todd Williams, President of KINEXO

Building a Supply Chain to Meet Customer Expectations

Scott Lane, Senior Vice President & General Manager, CCLS

How Robotics And Real-Time Data Are Upending Freight Logistics

Chris Farmer, Founder & CEO, SignalFire

Using Technology To Navigate supply Chain volatility

Jeff Kanterman, Regional Vice President - Transportation Management at NFI

Catering To The Evolving Demands Of The Modern-Day Customers With The New E-Commerce Ecosystem

Frank Gambish, VP of Transportation and Logistics, NRI Distribution

The Electrical Age

Josh Rennert, Director Air Freight, The Americas, Morrison Express