SUPPLY CHAIN

ARTIFICIAL INTELLIGENCE

Supply Chain AI/Enterprise AI  

There are several legacy demand forecasting solutions currently in the marketplace. They all feed lagging indicators like historical sales data into one or more forecasting algorithms that identify seasonality, recognize patterns, and forecast future sales. The legacy solutions also make following flawed assumptions:

Supply Chain AI | Enterprise AI  

There are several legacy demand forecasting solutions currently in the marketplace. They all feed lagging indicators like historical sales data into one or more forecasting algorithms that identify seasonality, recognize patterns, and forecast future sales. The legacy solutions also make following flawed assumptions:

#

1

2

3

4

5

Assumption 

Future has some type of correlation with the past and there is no randomness in the future.

Reality 

Past performance does not guarantee future results and history rarely repeats itself. 

Historical sales data is timely, accurate, detailed, and complete 

Enterprise data available in legacy systems that have been generated by humans using keystrokes is “muddy” and of poor quality to be used for prediction. 

All the required data is available within an organization and there is no need to rely on external data 

On the contrary, information on future sales drivers reside outside an organization unless they have initiated a marketing campaign.  

Location matters. It matters because shipping lead times can vary by distance and production schedules need to be adjusted accordingly. Old paradigm discounted location data. Sales data was considered only along two coordinates: quantity and time. Location data is another important dimension and the process is more than a simple two-dimensional analysis.  

Only forecast quantity and horizon is important and not location forecasts 

Is a “static” process performed infrequently and on-demand and not updated in real-time 

​

​

​

​

Before Google Maps, drivers referred to paper maps or printed directions only when they needed directions and were lost (on-demand) and assumed that the directions were static. Maps and directions once printed could not account for changes like detours, road constructions, delays etc. and could not be changed. Legacy demand forecasting solutions use a similar static approach. Future sales drivers are dynamic and constantly evolving requiring a computing power to replace humans for data gathering, pattern matching, assessment, and predictive powers. Humans are best suited for making decisions in ambiguous situations.

​

Due to these flawed assumptions, all forecasting algorithms and legacy demand forecasting solutions are bedeviled by forecast accuracy errors. A far better approach is to use leading sales indicators, or in other words identifying customer behavior, communication, and actions, environmental, geopolitical, and medical triggers leading to a purchasing decision.

​

Murano Corporation’s supply chain AI software is a leading indicator-based market prediction engine with neural network capability that is self-learning and operates in a secure cloud. This enterprise AI integrates supply chain data with leading indicators and advanced artificial intelligence techniques to achieve digital transformation.

​

​

​

​

​

​

​

uses automation for problem solving, continuous learning, and pattern matching. The key innovation in Supply Chain AI is predictive intelligence which is invaluable in using data to foresee disruptions, forecast demand, inventory planning, and utilize resources more efficiently. 

Supply Chain Artificial Intelligence (AI)

Are Manufacturers Ready for Supply Chain AI Today? 

Unfortunately, 99.5% of manufacturers today are not ready for Supply Chain AI. With older and disconnected technology, many manufacturers' and suppliers' data is not easily shared with one another. Integrated data and connected technologies are crucial for Supply Chain AI. Another issue within US manufacturing is the growing need of skilled workforce and overwhelming amount of work to do. AI can help through automating employees' extra workload, ensuring they are not overworked and assisting with employee retention. 
 

Reduce Invoicing Errors

Invoicing errors caused by suppliers over-billing and/or under-shipping raw materials to manufacturers is far more common than people realize. Supply Chain AI can more accurately and consistently prevent invoicing errors using advanced and sophisticated pattern matching.

​

Reduce Inventory Costs

Supply Chain AI can more accurately predict raw material outages helping buyers and planners reduce excess inventory and potential shortages.

​

Reduce Defects

Supplier Quality issues are another major challenge facing manufacturers today. Supply Chain AI can help manufacturers better collaborate with their suppliers, pattern matching to detect repeat failures, and predict future quality issues.

​

​