Introduction
In today's competitive business landscape, retail and consumer goods companies strive to improve performance, increase efficiency, and gain a competitive edge. An efficient, high-performing supply chain is critical to success. Demand forecasting – the process of predicting future customer demand using historical data, market trends, and other relevant factors – plays a crucial role in these efforts. However, forecasting customer demand can be challenging, and companies often maintain excess inventory to protect against inaccurate forecasts, driving inefficiency. This article explores the importance of demand forecasting in optimizing key business processes, and analyzes its impact on various functional areas, and its role in increasing efficiency.
The Challenges of Forecasting
Many companies use specialized software to develop their forecasts, but most forecasting tools employ generic algorithms based on the assumption that demand can be predicted uniformly across industries, product lines, and geographies. They often fail to identify the drivers of consumer demand and cannot adapt to changing conditions and evolving consumer behaviors. This approach does not account for the "noise" caused by retailer behaviors, such as order policies and advance buying for large promotions, which can diminish the forecast's quality.
Additionally, companies often adopt manual "consensus" processes involving sales and operations teams. This consensus-building process leaves companies vulnerable to competing interests within the organization, skewing the outcome of the demand-planning process. Sales teams tend to provide optimistic forecasts to meet aggressive financial targets, while manufacturing and operations teams seek stability, lower inventory, and less waste.
In contrast, an analytical approach based on hard data allows for fact-based trade-offs and decision-making, highlighting gaps between financial performance targets and sales and operating plans early in the planning cycle.
Building a Better Forecast
Companies need forecasting tools that consider market and consumer dynamics and are tailored to the unique traits of each company, product category, and geography. Improved forecasts can enhance organizational and business performance, and generate the following benefits:
1. Supply Chain Management and Assortment Optimization
Accurate demand forecasting enables supply chain management and assortment optimization to run more efficiently by:
Reducing missed sales by ensuring the right products are available at the right time and place (20-30% reduction in stockouts)
Boosting additional sales through tailored product assortment at each sales point, catering to the specific needs and preferences of the local market
Lowering working capital and obsolescence by reducing inventory holding (10-20% reduction in carrying costs) and safety stock levels (20-30% increase in inventory turnover)
Enhancing supplier negotiations and partnerships, leading to volume discounts, preferential treatment, and improved product availability
2. Production Planning Efficiency
Improved demand forecasting increases efficiency in production planning through:
Streamlined resource allocation, avoiding overproduction or underproduction, reducing waste, and ensuring optimal use of labor, raw materials, and equipment (10-15% waste reduction)
Improved production scheduling, minimizing production bottlenecks, reducing lead times, and ensuring timely product delivery to customers (15-20% decrease in lead time)
Enhanced capacity planning, enabling businesses to adjust production capacity utilization based on projected demand, preventing underutilization or overextension of resources (10-15% cost savings)
3. Financial Planning and Budgeting Efficiency
Precision in demand forecasting improves financial planning and budgeting efficiency by:
Providing more precise revenue projections, allowing businesses to create budgets that better align with expected income and expenses, reducing the need for frequent adjustments
Helping businesses manage cash flow more effectively by anticipating fluctuations in demand and planning for variations in cash inflows and outflows accordingly
Enabling better decision-making regarding investments and capital expenditures by providing a clearer understanding of future demand and potential return on investment
4. Marketing and Sales Strategy
Demand forecasting enhances marketing and sales strategy by:
Supporting dynamic pricing strategies, optimizing prices based on demand levels, customer segments, and competitive factors, leading to higher profitability (2-4% increase in gross margin)
Increasing customer service levels by providing a deeper understanding of consumer demand and subsequent retailer ordering behaviors, enabling companies to deploy inventory more effectively for higher fill rates, improved on-time availability, and fewer stock-outs
Identifying market trends and consumer behavior patterns, enabling businesses to tailor their offerings and marketing efforts to better meet customer needs and preferences
Improving sales force planning by providing insights into when and where demand is likely to be highest, allowing businesses to allocate sales resources more effectively and maximize sales opportunities
How LENS can help
In LENS’ experience, a more accurate understanding of future demand is one of the most effective ways to improve business performance. In our work with retail companies, we have developed a forecasting methodology that accurately predicts demand and can help companies optimize pricing, reduce inventory, optimize assortment, and minimize obsolescence.
Our solutions consider each client's specific market situation, industry, business model, geography, marketing, and pricing strategy. By incorporating hundreds of factors and learning from past data, we offer a comprehensive market overview that fosters improved decision-making. This customized approach ensures the effectiveness and relevance of our demand forecasting solutions for each client's unique needs.
Take, for example, a major retailer with over 40,000 SKUs struggling with excess inventory and generic forecasting tools that failed to account for their specific products and markets. Utilizing various machine learning methodologies, we developed a customized consumer-demand forecasting model, incorporating factors such as sales seasonality, pricing and promotions plans, store micromarket characteristics (e.g., socio-economic levels), cross-product elasticity, and competitor presence.
This approach led to a highly accurate and robust forecasting tool, improving sales accuracy from 61% to 84%. As a result, the retailer experienced an 8% reduction in obsolescence, a 3.5% decrease in missed sales due to stockouts, and a reduction of inventory turnover by 9 days. This impact represents a return on investment of over 15 times the initial investment in additional profits. See case study below:
Conclusion
Demand forecasting serves as the foundation for optimizing key business processes, including supply chain management, pricing and promotions, production planning, financial planning, and marketing and sales strategy. By accurately predicting customer demand, companies can allocate resources more effectively, reduce costs, and improve customer satisfaction. The increased efficiency and impact of demand forecasting in each area demonstrate its significance for achieving success in today's competitive business environment.