Demand forecasting has the potential to transform how businesses manage their supply chains. However, achieving the true accuracy of demand forecasting remains a problem. Despite the sophisticated retail planning software, the risks remain which threaten the accuracy of forecasts.
Many factors distort demand and create a situation that makes it very difficult for a business to avoid costly mistakes. These include inconsistencies in historical data, ever-changing consumer behaviors, seasonal fluctuations, and unexpected external disruptions.
Advanced supply chain intelligence platforms can assist in overcoming these challenges. It will use granular data for demand forecasting and enable businesses to adapt to ever-changing market conditions.
Here’s how granularity can lay the foundation for more efficient and responsive supply chain intelligence, resulting in accurate demand forecasting.
The Role of Granularity in Supply Chain Intelligence
Supply chain intelligence uses granularity to analyze data at different levels in order to predict demand. There are product or brand-level data, geographic locations, customer segments, and even periods. With this finer information, businesses can identify demand changes and help to make the right supply chain decisions.
Granularity is not just about getting data; it is about getting the right form of data at the right level. It helps to:
- Identify Micro Trends: Dividing data into smaller categories to detect subtle demand changes.
- Make Informed Decisions: Granularity enables precise decisions at the SKU or hyperlocal level.
- Maintain Optimal Inventory Levels: Businesses can better align supply and demand, minimizing excess inventory and missed sales.
Broad or overly complex forecasts fail to deliver value without the right level of granularity.
Understanding the Hierarchies of Granularity
Granularity enables the retail planning software to analyze various levels of data before forecasting the demand. This data is often organized hierarchically to allow businesses to view demand from both macro and micro perspectives. These hierarchies include:
- Product Families: Categorize products in a hierarchical structure to get a clearer picture of what is going on at the SKU level of forecasting. Take the whole product category and cluster them into families like electronics and apparel then into groups like smartphones and laptops.
- Time Intervals: Businesses can use time-based granular data to make both long- and short-term decisions. Long-term forecasts take into account quarters or fiscal years, whereas short-term decisions calculate replenishment rates based on weeks, days, or even hours. This information aids in the identification of seasonal patterns, such as daily foot traffic spikes or increases in weekend sales.
- Market Segmentation: Analyzing granular data at the geographic level can improve hyperlocal precision in demand forecasting. Due to region-specific trends, demand will vary across geographic regions and customer segments. For example, the demand for winter clothing in a cold-weather region will differ significantly from that in a tropical climate.
These hierarchical layers enable businesses to accurately forecast demand at finer levels, ensuring that the supply chain runs smoothly.
Deciding on the Right Level of Granularity
The degree of available data is very important in determining the accuracy of the demand forecast. Because, it is possible to have too much information within which decision-makers lose sight of important features of demands, as well as not enough information on the contrary.
Businesses can decide on the level of granularity based on their business objectives. For instance, consider the below factors when deciding on the level of granularity in supply chain processes or specific industries:
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- Inventory Management: Over-forecasting at a broad level based on product family may lead to stockouts of individual SKUs. Under-forecasting will lead to stock accumulation which costs capital and storage space. Hence, at the SKU level, forecasting enhances the effectiveness of managing Inventory.
- Capacity Planning: On the other hand, capacity management strategies of manufacturing plants or warehouses may not need SKU-level detail but would be useful to have the product category-level forecast.
- Retail Industry: It requires a high level of granular data tracking such as demand based on product, store location, and even time of day.
- Manufacturing Sector: Tracking at the production level is enough for the manufacturing sector, where the focus is on optimizing production schedules and supply chain efficiency.
Bottom Line
Identifying granular demand patterns ensures that demand forecasting in the supply chain intelligence platform works as it is supposed to. However, getting the right level is the key and businesses can achieve it by following some best practices. It entails aligning the granularity with the decision-making process, taking into account supply chain lead times, and making use of available data.
Furthermore, regular reviews and adjustments to granular demand patterns ensure that they remain relevant as market conditions change. Balancing detail and data granularity enables businesses to improve forecasting, optimize supply chains, and meet changing customer demands.
