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. 

If you ask today's business leaders, you will see that many would have a perfect answer on what to do strategically and how they organize execution. However, beyond the approaches and frameworks, there are many digital platforms to support the execution of strategies, tactical decision-making, and operational excellence. This article will shed some light on the differences and application areas of some of the tools. And, we'll give a definite answer on how to pursue and track improvements on the tactical level.

AI and ML are the two pillars of the digital revolution, enabling an immense productivity gain, that the corporate world is on the cusp of. Although interchangeably used, it’s important to differentiate: in a nutshell, AI or Artificial Intelligence is the concept of machines to carry out tasks smartly, whereas ML or Machine Learning refers to an application of AI to analyze data, to discover, to recommend, to forecast and to learn from large data sets. The supply chain is one of the primary industrial areas that stand to gain a lot from the successful incorporation and implementation of AI and ML, as showcased in the three use cases for predicting customer behavior, demand forecasting, or avoiding charge-back risks. 

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.

The supply chain is a highly volatile system. Therefore, the changes in the field are not just the frequent, temporary ups and downs of the market but also the movement towards a more digitized, dynamic, and customer-centric approach to business. Keeping up with these changes requires the supply chain management mechanism to keep constantly updating itself. Otherwise, some missing components may soon turn the mechanism into a legacy system that will lag.

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.  

The main aim of the digitalization of supply chain management is to simplify the management of the complex, interconnected processes of the supply chain. Moreover, for the digitalization of tactical decision-making, a digital model of the decision-making process is necessary. This is because every digital technology runs on rules, algorithms, and frameworks, learned or discovered.