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Supply Chain Modeling: What Does the Future Hold?

Many organizations struggle to effectively coordinate supply with demand, especially in uncertain markets. However, supply chain leaders are discovering that supply chain modeling is providing the answers they need to compete more effectively. Research performed by Aberdeen shows that 54 percent of Best-in-Class companies use predictive and prescriptive analytics to identify trends and business opportunities compared to only 31 percent of all other companies. Vendors use artificial intelligence and machine learning to construct supply chain models that allow companies to better understand supply chain performance as well as to orchestrate the right decisions to maximize opportunities.

What Is a Supply Chain Model?

In its simplest form, a supply chain model mirrors the organization’s supply chain. It is a series of mathematical formulae that represent inputs, conversions and outputs. The model includes practical business constraints, such as capacity, contractual agreements, regulation and financial limitations, to closely reflect real-world limitations. At first glance, it may seem this model has some similarity to how ERP mirrors and manages an organization’s supply chain. While this is superficially correct in terms of following rules for how transactions are processed, it’s very different in its use of techniques such as statistical modeling, linear programming, optimization, heuristics and exact algorithms, which all help determine future outcomes and identify the right decisions.

Predictive Modeling Helps Identify Customer Demand

Most types of business intelligence are backward looking, so their use in helping predict the future is limited. What’s needed is a model that takes into account past performance but which also interprets trends and outside influences from internal and external data. Such a model can determine what’s likely to happen in future; for example, when a particular product range will reach the point in its life cycle that sales decline. This is called predictive analytics, and it’s a powerful tool for understanding future trends based on real data.

Improved Decision Support with Prescriptive Modeling

The mistake many make is to assume that prescriptive analytics is all that’s needed for improved decision making. While prescriptive analytics points to the need to make a decision, of itself, it doesn’t provide clues to the right decision, especially if there are many possible outcomes. This is where prescriptive analytics comes in. A prescriptive analytics solution has two key features. Firstly, it has a model that emulates the business. Secondly, there are solver programs that use exact algorithms to analyze multiple options to determine the right decision. For example, you could use prescriptive analytics to determine the most cost-effective facility for manufacturing a new product.

Finding Rational Answers to Complex Supply Chain Challenges

Predictive and prescriptive analytics offer data-driven solutions to complex problems. While predictive analytics give an insight into the future, prescriptive analytics helps you influence the future by making data-driven decisions. It’s this ability to guide rational and informed decisions that makes prescriptive analytic modeling such an important technique. It’s a brilliant way to harness the information contained in your data and to assure future success of your organization.

Innovative Supply Chain Modeling Techniques

As companies embrace the benefits of prescriptive analytics, so solution providers are adapting their software to make it easier to code. Many offer easy-to-use industry-specific solutions, often cloud based, that suit well-defined fields such as retail supply chains.

Vendors such as IBM and other large groups offer code-based linear programming modeling solutions that are powerful but which need the services of relatively large teams of OR specialists and data scientists. Others, like River Logic’s Enterprise OptimizerĀ®, use intuitive drag-and-drop programming that does away with the need for coding and writing complex mathematical equations. With the shortage of experienced data scientists, it seems likely that drag-and-drop solutions such as used by River Logic will become increasingly popular.