What are the benefits of Supply Chain Visibility?

Supply chain visibility reveals what is really happening end to end. This knowledge can expose weak areas of a company’s supply chain process, as well as areas to improve. Companies can identify the root cause of a problem instead of fixing a symptom of the problem. The best way to manage demand volatility is to improve downstream supply chain visibility. Once a company has supply chain visibility, it can also use leading indicators to further analyze and improve its supply chain process. Leading indicators help to foreshadow future changes. For example, rather than using historical traffic data to plan routes, Google Maps uses cellphone location data (a leading indicator) to predict real-time traffic congestion data. Murano Corporation is offering a better approach to consistently predict market needs accurately. Our supply chain visibility software will use data to improve supply chain resilience while minimizing high inventory levels or inventory stock outs when customer demand is not readily available.  

What happens when there is no Supply Chain Visibility?

When there is no supply chain visibility, a company has a lack supply chain management. For example, when there is no visibility into the supply chain process, there is no way for a company to see or analyze inventory levels, anticipated production, or material in transit. This lack of information also causes decreased synchronization between customers and suppliers. Without enough data, it is difficult for supply chain members to collaborate or improve the supply chain process.

In some industries where supply chain collaboration is limited and there is minimal repeat business, the only legal and ethical option is for suppliers to use publicly available data (“Big Data”) and their customers’ actions (“leading indicators”) like anonymous cell phone location and credit card transaction data, satellite images, traffic data, social media chatter, weather data, Google searches, geopolitical, hospital admissions, shipping data, etc. to determine future customer demand. Even though this data is publicly available, the manufacturers do not have the financial appetite or technical know-how to use the data, and data mining and fusing for AI is not their core competency.

Very few industries share future demand and current customer inventory levels with their suppliers. Combined with a volatile, uncertain, complex, and ambiguous (VUCA) environment, this is a double whammy resulting in shortages. A lack of supply chain visibility leads to decreased forecasting ability. Supply patterns do not match demand patterns when demand signals are unclear or misleading. The suppliers either guess or use historical data to generate sales forecasts. Forecast inaccuracy is unavoidable because of inherently unreliable forecasting algorithms and bad human generated data. Historical data is irrelevant in a highly VUCA environment, leading to a phenomenon known as Bi-Modal Inventory Distribution. Inventory levels that are too low cause stock outs, back orders, and lost sales, while high inventory levels take up too much inventory space and money.

Bi-Modal Inventory Distribution


Guessing or relying on sales forecasts generated from historical sales data can lead to stock-outs and excess inventory, a supply chain phenomenon known as Bi-Modal Inventory Distribution. The opportunity for improvement is to switch market prediction from relying on lagging indicators or historical data like past sales to leading indicators which will eliminate forecast accuracy errors and reduce excess inventory and stock-outs.