Are Control Towers the Future of an Efficient Supply Chain?

In an effort to ‘get ahead’ and ‘build resilience’ we are seeing a spiked intrigue for control towers. As per the latest reports, investments in Supply Chain Control Towers are expected to surpass USD $10 bn. With data science being an enabler and the driving force behind its evolution, could this really define the future of an efficient and cost-effective supply chain? Dr. Lindner, a Supply Chain Visionary and Advisor at 3SC, breaks it down for us in this conversation.

1. In the recent past, we have seen control towers emerging right in the center of the industry. What attributes to that?

We are in a world of globally connected chains, where there are interdependencies between demand and supply. These are built on multi-tier constructs. In addition to this, we have seen multi-faceted disruptions and risks; for eg: strikes, natural calamities, economic imbalances that have given an opportunity for invention of new capabilities. Furthermore, people have realized that no one is doing it alone.

Enter control towers. If we go by basic definition of it, they provide the ability to plan, predict, monitor, recognize and react, hence simplifying and preparing well for the complexities of a supply chain. The need for it has arisen with the requirement of having an integrated assembly while utilizing the resources of third parties. We need an instrument where people can collaborate and function as an extended enterprise.

2. Many are still mulling over its implementation. Is this the right time to jump into it? And how?

The right time is now! People from the supply chains reference model have the right kind of thinking. They say – think big, start small and scale fast. So, let’s translate this through control tower.

Step 1 - would be to collect the pain points of the company. This is the comparison between the actual state and the strategic operation. If there is a gap – you have the requirement.

Step 2 - is if a company identifies its need for a software, eg: could be a shipment dashboard or transportation management – that gives access to streamline process via systems.

Next comes selecting either a software or a partner; and then finally looking at a proof of concept. You start with something with a limited functional & directional scope and if this is successful, there is close to 100% possibility that it will be successful on a larger model as well. Learn, adapt, evolve and then implement.

Back in the day, a concept like control tower could only have been viable for Fortune 500 companies but the advent of AI & ML technologies, new tools and entrants has democratized the situation. Historically, I would have only seen a USD$100 mn freight under management scope have the where-with-all to execute this; but now, companies with USD$10 mn freight under management also have started to build a convincing business case.

3. What would a control tower deliver in terms of capital efficiency?

People are doing value stream mapping that helps in identifying where the time is getting lost. In a very tactical effort – goods are ready to be finished, but documents or signatures are missing, and therefore they are not shipped, which affects the time lost by a whopping 50%. This is something, a control tower could help manage.

It also helps provide insights into the data that can be studied for network optimization. This helps in understanding the deployment of inventory which can help in reaching capital efficiency. Control tower typically goes together with cost-saving targets – which is between 10% - 20% that can be achieved, if implemented right, in 2-3 years.

4. How do you think AI & ML impacts control towers?

We can break down this evolution and intervention of technology into two parts – one, is where it is capable of replacing human work, and the second highlights brand new innovations, which could not have been imagined.

Control tower helps in ETL process – extract, transform and load. They can predict risks in addition to giving a causation as well, which is interesting to solve problems. They can propose interventions and identify and notify the stakeholders. So this is a fantastic journey of capabilities that increases efficiency. In procurement, for eg., they can predict cost drivers basis price benchmarks. If the buyer has a machine that tells them about a reasonable proof based on the distance, weight, frequency and the general direction of the cargo, it is brilliant.

5. What is it that 3SC is doing that could be the next big in terms of evolution in this space?

There are new entrants in the market, like 3SC, that come packed with the power of data science. The dealings with data transformation and data insights provided are far better than the historical ones. All the capabilities I have listed so far, can be done in a shorter implementation time; and shorter implementation time runs into costs. So, the implementation of 3SC is by far more cost efficient. The timeline of implementation hurdles has been reduced to 2 months from 1 year, which is why medium caps are also opting for it.