While a key enabler for the modern supply chain, ERP is often mistaken as the single universal solution to meet every need. Whether the result of over-aggressive vendors or self-imposed rationalization to justify the large investment, the outcome is the same: disappointment with supply chain functionality. Invariably, the initial state of euphoria wears off with the realization that ERP provides a foundational platform, not the ultimate solution, at which point companies seek to bridge gaps in functionality by augmenting with best-of-breed solutions.
Demand planning is a classic example. Forecasting modules in ERP systems, such as SAP’s Advanced Planning Optimization (APO) or JDA, rely on time-series statistical analysis methods to create seasonal demand predictions. Fourier time-series mathematical analysis was groundbreaking when first developed in 1822, and later largely replaced by Holt-Winters exponential smoothing. However, aside from a plethora of model tuning parameters, the time-series methods employed by ERP have remained essentially unchanged in decades and are ill-suited for a modern, fast-moving supply chain. It is akin to relying on telephone books in a world of mobile phones and the Internet.
Hence, forecast accuracy for the consumer products industry remains a challenge, with average weekly forecast error (mean absolute percentage error) of more than 50 percent. Findings from the Terra Technology 2013 Forecasting Benchmark Study, encompassing $130 billion in annual sales from eleven of the largest multinational consumer products companies, reveal that one-third of items have less than the two years of history required for even the most basic seasonal time-series analysis. Furthermore, industry reliance on promotional activities to drive sales resulted in three-quarters of all items being promoted, distorting sales patterns for the one-third of items that do have two or more years of history.
Even without high levels of innovation and promotions, the traditional forecasting methods employed by ERP are by nature disconnected from current events that impact demand in unpredictable ways. This is problematic since today’s demand planners are faced with challenging new consumer and supply chain dynamics. Consumers and retailers have become more complex, technologically sophisticated and harder to predict. The protracted global economic downturn has made volatility the new norm, further distorting purchase behavior — pre-recession sales patterns have little relevance in countries afflicted with record high unemployment rates.
Fortunately, a myriad of real-time demand signals within manufacturer and retailer operations such as channel inventory, and point of sale (POS) data hold valuable threads of information that demand sensing can use to provide the best possible prediction of future sales. Augmenting ERP with demand sensing capabilities lets companies systematically analyze this data to accurately predict demand in fast-changing markets and make better inventory decisions, improving return on capital and promoting growth. It is not just theory — demand sensing is used to plan products in almost 160 countries around the world and has cut forecast error by 38 percent for roughly one-third of all North American consumer packaged goods shipments. Isn’t it time you looked beyond ERP for forecasting accuracy?