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Investigate and Predict Demand: Five Reasons you Need to Consider Demand Modeling as part of your Supply Chain Design Process

By Vikram Srinivasan  July 11, 2017

Can you confidently predict your future customer demand? How does your business and external factors such as economic indicators and weather influence that demand across different time horizons (short, medium and long-term)? How might that information help improve the design of your future supply chain to better account for the changing demand?

In today’s demand-driven marketplace, it’s more critical than ever to understand your customers’ buying behavior as well as be able to quantify what factors impact their demand. If you can’t you may be putting your supply chain at risk with myopic supply chain decisions. Why is future demand different than you thought? What are the components and drivers of it? Is it long-term growth that’s changing and how might that impact your long-term capacity planning process? Is there SKU-proliferation? Having answers to these questions is significantly more important than the forecast itself as you design your future supply chain. You probably know that demand is a crucial input in supply chain modeling and S&OP activities, but many companies lack confidence in their demand data, perhaps due to missing information or “black box” applications that obscure calculations. Some analysts feel they don’t have the statistical background to correctly interpret this data. Demand modeling is emerging as a critical capability for businesses that want a visually interactive way to have more confidence in their demand predictions as well as effective supply chain strategic decisions for the future.

Here are five reasons you should consider demand modeling in supply chain design:

  1. Demand is a key driver of design of your future supply chain. One of the most important factors that impacts your supply chain is demand. Designing a supply chain requires a good understanding of future demand, its components and key drivers.
  2. Demand modeling allows you to play and explore with alternative theories of what makes demand happen. A deeper understanding of this demand provides higher confidence in design recommendations to better quantify changing trends, detect what stage of life cycle your product is at, and investigate cause and effect relationships between your demand and its influencers using powerful machine learning algorithms.
  3. Demand modeling enables accurate demand scenarios and sensitivities. Accurately quantifying which factors influence your demand enables you to explore more robust demand scenarios and sensitivities for a better future design state.
  4. Demand modeling supports a repeatable process to capture changes. Factors that influence demand are dynamic and having a repeatable process enables better supply chain design.
  5. Demand modeling eases many of the challenges with demand as part of supply chain design. Barriers such as demand complexity, inability to quantify demand influencers and lack of understanding of key demand characteristics often constrain accurate and effective supply chain designs.

As a natural next evolution in LLamasoft’s drive to make supply chain design faster, easier, accurate and more powerful for the end user, and expand the scope and benefit of design across the business, we built the Demand Guru demand modeling tool.

Better Understanding of Customer Demand Means Better Supply Chain Decisions

LLamasoft Demand Guru (Fall 2017 availability) enables visual exploration of key drivers of demand, accurate prediction and testing of alternate demand scenarios and sensitivities, and quick and easy access to external time series data as part of LLamasoft’s Data Cube to better predict demand with causative modeling. As part of LLamasoft’s end-to-end supply chain analytics platform, Demand Guru adds just minutes to the supply chain modeling process by automating demand modeling and delivers more confidence in short-term and strategic decision-making.

Interested in learning more? Sign up for our July 26 introductory webinar for demand modeling