Supply Chain Modeling_What You Need to Know

Supply Chain Modeling: What You Need to Know about AI, ML and ERP

When it comes to supply chain modeling, one size does not fit all, but all of them benefit from strides made in the areas of artificial intelligence (AI) and machine learning (ML). AI can solve problems like delays and disruptions with human-like ingenuity, while ML also imitates the human brain, using data and algorithms to learn and improve accuracy.   

Supply chain modeling is a vital component for developing and validating supply chain and distribution strategies to meet business objectives such as cost containment, maximized efficiency and productivity, and meeting customer expectations for fulfillment and delivery.  

Which supply chain model you use and how it is implemented can have an impact on the future of your business. If you rely on your ERP alone to model your supply chain, you’ll be facing an uphill battle unless you also take advantage of AI and ML

The 6 Different Supply Chain Models 

There are currently six supply chain models that fit various business needs for efficiency and responsiveness, including: 

  1. The agile model: For businesses that have unpredictable demands for their specialty and made-to-order items 
  1. The continuous flow model: For mature and stable industries that face little fluctuation 
  1. The custom configured model: Works best for products that have diverse configurations 
  1. The efficient chain model: For businesses in competitive markets that require end-to-end efficiency 
  1. The fast chain model: For companies specializing in trendy and short-lifecycle products 
  1. The flexible model: Best for handling peaks and valleys in demand 
  2. The Supply Chain Operations Reference (SCOR) Model: a strategic model to establish standards for continuous improvement within the supply chain management system

Whichever model is used, certain requirements must be met. The focus should be on modeling a supply chain that meets not only today’s specific requirements but can also adapt to future growth, disruptions, and other changing circumstances. 

The Importance of Supply Chain Modeling 

Your goal should be customer satisfaction, because satisfied customers are repeat customers. This means you not only have to develop the supply chain model that works best for your business, but you must also be able to evolve that model as conditions change. 

Supply chain modeling with AI means you’ll be able to draw insights and recommendations from machine analysis of vast amounts of data, far more than human analysts can process. ML uses self-improving algorithms to optimize supply chain processes, predict new opportunities, and forecast challenges. These are both things your ERP alone can’t do. 

Why ERP Alone Isn’t Enough 

Your ERP system may unify the processes needed to run your company, but it was never designed to support supply chain modeling.  

Your ERP is intended to facilitate the flow of communication and knowledge sharing, systems integration for improved productivity and efficiencies, and increased collaboration across teams and departments. It simply doesn’t provide the necessary analytical depth of information management capabilities to unlock all the insights contained in your data, nor can it learn from that data to future-proof your supply chain. 

AI and ML Offer Solid Benefits in Supply Chain Planning and Operations 

It’s predicted that, in the future, AI and ML will significantly disrupt current supply chain models. AI is seen as the basis for autonomous planning that uses internal data as well as that from vendors, customers, and other relevant sources.  

Significantly, 61% of executives who introduced AI into their supply chain systems decreased costs by 53% and increased revenue.  

When you implement ML in your supply chain modeling, your decision-making processes can be optimized significantly by analyzing huge data sets and applying intelligent algorithms. Human intervention can be minimal. Once you set up the parameters, AI algorithms can do everything a human can do – except make mistakes.  

AI and ML can analyze data from past years to predict demand and factors that cause supply chain fluctuations. This improves supply chain forecasting by providing a detailed analysis of all influencing factors that are much more reliable than traditional methods. This informs every supply chain decision, including those regarding vendor relationships, workforce planning, and how to provide the best customer service.  

By using past data as well as new information, AI and ML can predict future disruptions, so you can plan contingencies instead of being blindsided. 

With AI and ML, you can fully exploit your data to optimize supply chain models that allow your business to work faster, better, and smarter. Reduce costs, mitigate risk, boost responsiveness, and improve the customer experience with built-in flexibility that will carry you well into the future.  

Download our report on how Logility enhances the value of supply chain planning beyond the capabilities of today’s ERP systems here