In today’s complex business world, high-performing, resilient supply chains are crucial for success. However, businesses from various sectors face unique challenges that curtail their ability to meet the dynamic market needs and customer service expectations despite having a robust demand forecasting process in place. Reasons for inabilities can be traced down to an under-performing supply chain that lacks a basic scalable demand forecasting infrastructure. Moreover, such inefficiencies can also lead to shortfalls in forecasting accuracy, supply and demand planning, and production overhauls.
What is demand forecasting and why is it important?
Demand forecasting is a systematic method that involves predicting the demand for specific categories of products and services under a set of irrepressible, competitive forces. It helps businesses to gauge the popularity of products over a fixed period based on market and customer needs. Insights such as these enable businesses to ensure they maintain the right stock-levels required to fulfill consumer needs.
Demand forecasting plays a key role in business decision making across industries, such as planning the production, inventory optimization, purchasing raw materials, managing funds, and product pricing. Here are a few reasons why its important from a business perspective:
- Helps improve customer satisfaction
- Reduces inventory stock-outs
- Lowers safety stock requirements
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Adopting an analytics-based systematic approach to supply chain planning can help businesses boost supply chain performance, resulting in several other benefits. From reducing inventory costs to improving customer satisfaction rates, demand forecasting turns out to be an instrumental tool and a key aspect of supply chain management. It also helps organizations to improve order management, ensure OTIF deliveries, avoid unnecessary inventory expenses, and plan for future price fluctuations. By developing and implementing scalable demand forecasting models, organizations are more likely to achieve the desired outcome which eventually leads to better productivity and margins.
Demand forecasting challenges faced by businesses
As we move towards granular demand forecasts, its crucial to analyze several other external factors that influence market needs and customer purchase decisions. Though factors such as these may reflect in the seasonal trends, industry standards, and market dynamics that make up a time series, its crucial to incorporate them directly into the demand forecasting models. The challenges include:
- Data discrepancies
- Resource heavy analytics methodologies
- Inefficient processes and demand plans
To learn more about the strategies to improve demand forecasting accuracy, contact our supply chain analytics experts.
How demand forecasting can contribute to the success of your business
Today most leading businesses use ERP software to generate forecasts for their products and services. However, such systems are generally not flexible and cannot be modified to address the unique needs of businesses. And for this reason, it is now imperative for companies to deploy demand forecasting models that are tailored to the unique traits of each product category and geography. Better forecasts, in turn, will contribute to improvements in organizational and business performance, resulting in these benefits:
Enhanced customer service levels
Granular insights into factors impacting consumer demand and subsequent retailer ordering behaviors can help businesses in the effective deployment of inventory, ensuring higher fill rates, better on-time availability, and fewer stock-outs.
Fewer missed sales opportunities
Understanding the source of high demand will help you ensure that the right inventory is in the right place at the right time.
Better working capital management
Operating with the right amount of inventory can help companies better manage working capital, and this is bound to happen only when they are more confident about the generated demand forecasts.
Efficient production and manufacturing
A better understanding of demand will not just help optimize production schedules but will empower businesses to overcome challenges in manufacturing.
Enhanced resource utilization
Maintaining stocks that are just enough to meet the demand implies the products will be sold even before they reach their expiration date, in turn, minimizing wastage and spoilage of perishable stocks.
Automating the demand forecasting processes enables companies to allocate more time to focus on selling, and it also helps save time and effort required to oversee the forecasting process.
Request a free demo to learn more about the potential of AI for demand forecasting and supply chain planning.
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