March 2018 | Commentary | IT Matters: Logistics & Supply Chain Technology

5 Reasons to Make Demand Modeling Part of Your Supply Chain Design Process

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Jeff Metersky, Vice President of Solutions Strategy, LLamasoft, 734-418-3119

Predicting customer demand is incredibly difficult. Economic indicators, weather, seasonal influences, and industry trends constantly change and need to be continually factored in to your analysis. This insight helps improve the design of your supply chain to better account for changes in demand.

In today's marketplace, it's crucial to understand your customers' buying behavior, as well as have the ability to quantify what factors impact their buying habits. An inability to do so forces myopic supply chain decision making, which puts your supply chain at risk.

Decision makers consider demand a key input in supply chain design and sales and operations planning activities. Many organizations, however, lack confidence in their demand data. Other individuals feel they don't have the analytical background to correctly interpret this data.

Making Predictions

To solve this problem, demand modeling technology has emerged as a critical capability for businesses that want a visually interactive way to have more confidence in, and a better understanding of, their predictions—predictions that can help them make better informed decisions and establish more effective supply chains.

Here are five reasons why you should consider demand modeling as a key component in your supply chain design process.

  1. Demand is a key driver of your future supply chain design. One important factor that impacts your supply chain is the level, location, and timing of demand. Make sure your business has a solid grasp on future demand to ensure sufficient capacities are in place and products are deployed appropriately.
  2. Demand modeling allows you to explore alternative theories of what makes it happen. Testing for alternative cause and effect theories and scenarios is critical when modeling a supply chain. The ability to try different demand scenarios and determine appropriate designs, policies, and responses enables you to prepare for potential issues that may crop up along the way.
  3. This approach enables accurate scenarios and sensitivities. A deeper understanding of demand provides higher confidence in design recommendations, rather than hedging your bets on estimates. This allows you to better quantify changing trends and detect the specific stages of the product lifecycle at any point.
  4. Support a repeatable process to capture changes with demand modeling. Factors that influence demand are dynamic. Having a repeatable process enables better supply chain design and makes it easier to recalculate scenarios as variables change. Because it's repeatable, you don't have to start from scratch for adjustments.
  5. This solution eases many typical demand challenges. 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. By factoring in demand throughout the process you can be more certain of your predictions and act with confidence if demand changes.

Ultimately, having a better understanding of customer demand can mean making more confident decisions. Factoring demand modeling into the design process makes supply chains more robust, flexible, and dynamic. This makes it easier to adjust quickly if there is a change in demand driven by underlying factors.

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