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How AI Enables Manufacturers to Improve Supply Chain

February 24, 2022
Data strategy
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How Manufacturers Need to Respond to Volatile Market Conditions

How much should you charge for a product? The simple answer is: more (hopefully much more!) than you paid to make it. But what if the cost of your raw materials changed every day–by a lot?

Over the past few years, manufacturers across the globe have seen some of the most volatile conditions in economic history. And during this same time, the processes for producing goods have been at peak complexity. For this blog post, we interviewed Pawel Dadura, CTO of, who works with manufacturers to help them introduce AI solutions to make better supply chain decisions. Here he discusses one of his latest focuses: transportation cost optimization.

What is transportation cost optimization?

Pawel: Transportation is one of manufacturers’ biggest, externally-dependent costs, and thus serves as one of the greatest opportunities to improve revenue.

The transportation costs can be split into fixed and variable costs involved in the transportation process. Optimization can be achieved either by reducing fixed costs (for example, by outsourcing transportation to specialized logistic companies) or by optimizing variable costs (for example, by optimizing routes and carrier structures).

Many of the factors that impact transportation are volatile, and thus complex and constantly changing. Understanding the source of these volatile factors should go beyond typically Supply Chain considerations and be considered in the context of Revenue Growth Management (RGM), yet it seldom is.

How does this relate to Revenue Growth Management?

Pawel: Revenue Growth Management means optimizing profitability by increasing revenue–either by shipping more things to the market or by increasing prices. But one part of the equation that is typically ignored is the variability of costs. RGM assumes fixed costs, which is a huge assumption anywhere, but it’s a particularly bad oversight in this industry, where the costs depend on quite volatile conditions.

The last two years have proved this assumption to be quite problematic. Costs have been more variable than ever before–think about border shutdowns, fossil fuel costs, energy, and all the upmarket raw goods cost. Revenue Growth Management departments need to factor in these volatile costs; otherwise their efforts to improve revenue could prove futile.

RGM and supply chain management teams need to talk to one another to work on joint solutions. Supply chain can’t just optimize cost, and RGM can’t just optimize revenue – they’re really two sides of the same coin.

Can you give an example of the oversights of both departments when they don’t talk to one another?

Pawel: On the one hand, imagine a Revenue Growth Management team deciding to increase prices in a market with decreasing spending power – that’s just not going to work. On the other, Supply Chain Management might decide to outsource transportation entirely because it's cost-efficient. But look at what’s happening now–transportation is significantly impacted by the cost of fuel, the change in consumer demand (more people ordering from home), and labor shortages. The result is a deficit in trucks for anyone who doesn’t have their own fleet. In the UK, there’s a deficit of 80,000 trucks. How can you execute your RGM strategy if the supply chain is disrupted?

Your specific focus is solving transportation cost issues (among others) with AI. Can you tell us more about that?

Pawel: The best solutions are typically AI-driven. Let’s take an example. Ten loads need to be transported from San Francisco to Albuquerque. How are they going to do it?

Let’s say they decide to outsource and work with external parties, because your own fleet of vehicles isn’t sufficient. You can then choose between either:

  1. Outsourcing to a carrier service who’s been working with you for ages at a fixed price. They’re totally reliable but also expensive.

  2. You can put your request out “on the open market,” auction-style, and see which provider will give you the best price. It’s not as reliable as your trusted carrier service, but there is a chance that it will be less expensive.

A better way is based on a behavioral model: Collect the history of the company's contracting decisions, incorporate customer reactions to price changes and different market events, and also competitor reactions to the market. An AI model can incorporate many, many more features into decision-making than you can on your own. Based on a variety of factors, the model provides a recommendation for how the company should contract their transportation - with this or that carrier service. The model recommends a choice of contracting solution.

This behavioral, AI-based model can evolve together with the market. Then, rather than having a fixed decision, you have a smart ongoing prediction.

That’s just one example, though. Use cases range from predictive fleet maintenance to network design to real-time vehicle tracking and dynamic route optimization. For all of these, data blending and connectivity is an important starting point. You want all these departments and external logistics companies to connect their systems to get a broader look at what’s going on.

The methods are out there, and most data science teams can find ways to apply them – the difficulty is in getting the rest of the organization to trust these solutions. Many people are afraid or distrustful of AI, so using something like KNIME to explain what’s actually happening can build a lot of trust in these solutions. Also, getting a partner who knows how to apply (and explain) AI techniques to these challenges can help with some of the change management.

Anything not covered in the questions that you’d like to add?

Pawel: The decision to take advantage of data across departments and methodologies is becoming even more pressing, due to the pandemic and the rise of inflation. You really can’t wait to postpone investments into tools and platforms that can help drive timely decisions on SCM and RGM. Otherwise they’ll be leapfrogged by competition in the best case, or face extinction in the worst case.

Spikes in commodity prices – mainly fuel and energy for the topic we’re discussing – combined with global supply chain disruptions and changing customer buying patterns makes cost of goods sold (COGS) highly volatile and thus it impacts fundamental assumptions of RGM modeling.

With trusted partners who understand both sides of the coin, companies can make a step change in their capabilities and navigate the volatility of current times with confidence, leaving the storm with growth.