If I were asked to name supply chain executives’ three main challenges, I would mention:
- Managing complexity resulting from customized demand (the lot of one) and multi-tier networks of product and service suppliers
- Increasing agility resulting from volatile demand, fast-changing market conditions, and geopolitical instability
- Generating value and changing the perception of the supply chain function from a cost element to a key competitive factor
Taming such a three-headed dragon is a daunting task. Luckily there are some capabilities that can help to overcome these hurdles:
- Visibility: It might seem obvious, but if you cannot (quickly) see the entire picture of your business, it won’t be easy to spot where bottlenecks will impact your supply flows.
- Collaboration: With so many players in the game, the ability to synchronize the network is essential. Nothing works like a bi-directional collaboration model.
- Data-driven processes: Gut-feeling decisions are rarely the best in terms of overall value generation for the company. Without a proper data-driven approach, the capacity to make wise decisions quickly is limited.
It’s all about the maturity to become an intelligent control tower
If you are in supply chain, you might raise your eyebrows and think that these capabilities are already well established and mastered within your company. That could be the case, but let’s do a deep dive on your level of maturity.
Let’s take visibility, for example. It can be after the fact (i.e., I see that an event has been performed), real-time (i.e., I see the event happening) or predictive (i.e., I can predict the event before it happens). The level of maturity can determine radically different approaches with regard to the challenge you’re trying to address. For example, having real-time info on inventories can help avoid a stock-out or allow immediate actions towards the clients.
The same can be applied to collaboration, which can be operational (e.g., order and invoice collaboration), tactical (e.g., vendor-managed inventories), or strategic (e.g., co-innovation). It’s proven that the deeper the collaboration model, the higher the value generated from a relationship.
The data-driven approach is mostly unexploited: it’s about moving away from pure reporting capabilities and leveraging data while the process is being executed (real time) and likely supporting the execution of the process in a co-pilot mode. Not only does this allow you to act faster but also to make the best decision, considering all the elements that otherwise would be ignored (e.g., decisions based not only on cost but also considering simulations on profitability and customer-retention effects).
Control tower vs. intelligent control tower
Considering these challenges, the focus is shifting on how to sense and respond to market dynamics rather than expecting to make very accurate forecasts in such complex scenarios. In this context, the concept of a logistics control tower has been established to gather the right set of capabilities to control and direct the execution of the end-to-end order fulfillment process. Moreover, as the omnichannel paradigm takes over, having the capability to orchestrate in real time the materials flows, those managed internally and by third-party logistics, is becoming more and more important. We could say that the mission of a traditional control tower is to provide visibility on end-to-end processes, automate cost control, and allow performance measurement to drive improvements.
But what if the control tower could be integrated with all company processes and not only concentrate on order fulfillment? What if on top of the real-time visibility we could have simulation capabilities supporting decision making? What if we can also track the status of goods? Here’s an example.
A truck is transporting finished products from a manufacturing plant to a distribution center. Inbound activities at the warehouse are triggered as the truck enters a predefined geo-fenced area. The pallets are entered automatically into quality inspection, and the pallet sensors record an anomaly in the temperature profile. Even before the goods are received, the order date is updated and communicated to the customer, and a substitution is proposed based on real-time inventories and the customer’s priorities.
What does that mean from a business-improvement standpoint? Faster processes (inbound activities triggered before the truck arrives), better quality (sensors on pallets), and improved customer satisfaction (real-time information and alternative options).
How to build an intelligent control tower
Building an intelligent control tower requires different capability layers integrated with the company’s digital core. Of course, it is not only a matter of implementing new technology. Business processes must be re-engineered and organizations reshaped. Obviously, we want to know an event in real-time. However, knowing who is going to act and what business rules to take into account are just as important.
Where to start
First and foremost, it’s important to realize that this framework is a reference and not a one size fits all. Depending on the maturity level and the compelling issues to be solved, a different target model could be required as a different roadmap to consider.
The general advice is to start small but quickly on pilot scenarios to identify the key elements for success and be prepared to scale. Design thinking methodologies can help identify the value cases and prototype the solution in two weeks. Afterward, a pilot can begin using the lean startup approach (over two to three months) and then scale gradually using the agile methodology.
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