Every organization has components, commodities, and even finished products on stock. With these, they ride waves of high demand or supplier constraints. Inventories such as these constitute a huge management task, with ambiguous variables and high stakes. In fact, the effort needed to succeed in managing inventory is often so high, so specialized, that many larger companies seek external help. This is where inventory optimization companies come in. 

Given the break-neck pace at which the markets function, supply chain executives must think on their feet to stay ahead of the competition. Each day brings possibilities and factors, turning the decision-making job into an unenviable task. Making the correct decision requires an eclectic mix of experience and knowledge, the foundation of sound judgment. 

The self-driving supply chain: inevitable? 

The ultimate goal of any business is to deliver its product and service to the customer on time and in the right quantity. Supply chain management plays a significant role in achieving this goal. The supply chain of any business is a complex and dynamic network of integrated processes, people, and technology to fulfill customer demand. There are different players within the supply chain of any products: suppliers, wholesalers, manufacturers, 3PL service providers, etc. Successful supply chain management contains the efficient management of each part in which the right strategic, tactical, and operational decisions are taken and implemented at the right time.

Is your organization aiming for the moon but not even capable of setting realistic goals? Goal-setting can be the bane or the boon of your organization. Whether you can set realistic goals and targets for your business can make all the difference between success and failure.

After all, it is not just about the demoralization that employees and stakeholders face when the targets are not attained. It also means that you have wasted a lot of resources, time, and effort in chasing a goal that could not have been accomplished in the first place, resulting in the company losing out on achievable results that it could have realized. 

The concept of digital twins has been around for a long time in terms of modeling and simulation. However, the two are vastly different. The digital twin is a living-learning model, as described by Colin Parris, CTO of GE Digital. Thus, it is continuously running and updating itself based on data received from the subject of the twin. 

For complex supply chain networks in a VUCA world, expertise in supply chain planning is key to meet demand most efficiently and reliably possible. It is essential for a competitive advantage. Suppose you do not have this expertise in-house. In that case, supply chain planning as a service can be a good alternative by utilizing the expertise of specialized data scientists to increase the quality of your forecasts. 

With the rise of the age of data abundance, it is natural for technology like data infrastructure, analytics, and AI platforms that process such data to appear. These new technologies enable to access, transform, and analyze the vast reservoirs of data available to us. However, analyzing and processing data provides us with insights in dashboards and more information involving the worst-case, and such information is useless – unless it can be put to good use.

With the advancement and widespread use of the Internet, there has been an astronomical increase in both the number of data sources and the amount of data generated from each source.  

For a long time, supply chain specialists have been dependent on traditional forecasting models and techniques to estimate demand for their goods and services. Both the digital transformation and the fourth industrial revolution –industry 4.0– are advancing rapidly. This has a fundamental influence on the way human beings live and work. The introduction of digital identifiers and the internet of things (IoT) produced a huge amount of data. One of the main impacts of this situation is that supply chain management decision-making is increasingly driven by data instead of experience. However, the traditional strategies and methods used in the different parts of the supply chain impose many constraints that prevent gaining full advantage of data. 

Join Dr. Christoph Kilger and Dr. Boris Reuter as they discuss the supply chain performance gap, and where you can start closing it. Most businesses collect large amounts of data, yet supply chain analytics doesn't always translate into concrete measures that are executed. How can you start digital execution management that leads to real results?