Demand forecasting for the modern supply chain
Demand forecasting refers to the process of planning and predicting goods and materials demand to help businesses stay as profitable as possible. Without strong demand forecasting, companies risk carrying wasteful and costly surplus – or losing opportunities because they have failed to anticipate customer needs, preferences, and purchasing intent.
Demand forecasting professionals have specialised skills and experience. When those skills are augmented with modern supply chain technologies and predictive analytics, supply chains can become more competitive and streamlined than ever.
Why is demand forecasting important for modern supply chains?
In the wake of the pandemic, companies are in an exceptionally fast-moving business climate. Customer behaviours and expectations are evolving quickly and as more and more businesses adopt optimised supply chain practices and cloud-connected business networks, competition is getting fierce. Demand forecasting is important to the supply chain because it helps to inform core operational processes such as demand-driven material resource planning (DDMRP), inbound logistics, manufacturing, financial planning, and risk assessment.
How does demand forecasting work?
At its best, demand forecasting combines both qualitative and quantitative forecasting, both of which rely upon the ability to gather insights from different data sources along the supply chain. Qualitative data can be curated from external sources such as news reports, cultural and social media trends, and competitor and market research. Internally-sourced data – such as customer feedback and preferences – also contributes greatly to an accurate forecasting picture.
Quantitative data is typically mostly internal and can be gathered from sales numbers, peak shopping periods, and Web and search analytics. Modern technologies employ advanced analytics, powerful databases, and use artificial intelligence (AI) and machine learning to analyse and process deep and complex data sets. When modern technology is applied to qualitative and quantitative forecasting and predictive analytics, supply chain managers can provide ever-increasing levels of accuracy and resilience.
Demand forecasting methods
Depending upon the industry, the customer base, and the volatility of the product, demand planning professionals use the following forecasting methods:
- Demand forecasting – macro-level: Macro-level demand forecasting looks at general economic conditions, external forces, and other broad influences that may disrupt or affect the business. These factors help inform businesses of regional and global risks or opportunities, and keep them be aware of general cultural and market shifts.
- Demand forecasting – micro-level: Demand forecasting at the micro level can be specific to a particular product, region, or customer segment. Micro-level forecasting is especially attuned to one-off or unexpected market shifts that might lead to a sudden spike or plunge in demand. For example, if experts are predicting a heat wave in New York and your company makes portable air conditioners, it may be worth the calculated risk of preemptively bumping up your inventory buffers in that area.
- Demand forecasting – short-term: Short-term demand forecasting can be at the micro or macro level. It is usually done for a period of fewer than 12 months to inform day-to-day operations. For example, it may involve consulting with the company’s sales and marketing teams to see if they’re planning any promotional or sales events that might cause a demand spike.
- Demand forecasting – long-term: Long-term demand forecasting can also be micro or macro, but typically looks ahead longer than one year. This helps businesses make better-informed decisions about things like expansion, enterprise investments, acquisitions, or new partnerships. When businesses give themselves a year or more to analyse and test markets, they can get a more robust picture of what kind of demand trends they can expect when they set up shop or launch products in new countries or regions.
Three steps to get started with demand forecasting
Here are three simple steps to help you establish good supply chain planning strategies and demand forecasting best practices:
- Let demand forecasting be what it is.
Demand forecasting is an important backbone in the supply chain planning process and underpins a lot of other processes. It can therefore be tempting for businesses to let demand forecasting become a catch-all practice that is bent and wedged in to support various other supply chain planning functions.
When used properly, demand forecasting has clear purpose: it predicts what, how much, and when customers will purchase. Other supply chain functions – like S&OP, inventory optimisation, and response and supply planning – deliver complementary capabilities within an integrated business planning system. If these tools are used for the specific functions they’ve been designed for, demand forecasting tools can get on with what they do best.
- Demand forecasting software loves data, data, and more data.
When supply chain technologies – particularly those dealing with demand and inventory forecasting – are powered with AI and machine learning, they get better, more accurate, and more insightful the more data you feed them. Don’t only rely on backward looking data like past sales or past product performance. Look to additional sources like news, politics, social trends, and customer insights.
Today, data doesn’t have to be linear and simple to be analysed effectively. Modern data management tools can curate and process large and complex data sets. And AI and machine learning bring speed and intelligence that not only allows for advanced and predictive analytics, but also learns from experience and cumulative data input.
- Budget and plan accordingly to optimise demand forecasting.
Supply chain planning requires a realistic and strategic approach to be at its best. Legacy practices and workflows are hard to adjust, and people tend to resist change. But in the end, improved demand forecasting and supply chain planning can increase profitability and reduce risk and loss while providing your supply chain team members with a more streamlined and efficient working experience. By earmarking budgets and team resources early on, businesses can help support better buy-in and a smoother rollout of their supply chain optimisation plans.
Get more competitive with predictive analytics and demand forecasting
Every step you take towards the digital transformation of your supply chain gets you that much closer to the visibility and efficiency you require in today’s competitive business climate. Work with supply chain managers and team leaders across your business to start breaking down silos and learning where the biggest risks may be hiding – as well as the greatest opportunities for long- and short-term wins. Then speak to your software vendor to learn more about integrating supply chain planning solutions into your operations.
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