So even a small variation in, say, data input for demand for a particular SKU can lead to large variability in how successful a supply chain executes the task of getting the right inventory to the right place at the right time. And the data may not even be wrong, just not precise enough. If my understanding of the origin of Lorenz s initial insight is correct, he found vast differences in a weather forecasting model when he used a number that went out to three decimal places instead of six. This does not mean that supply chain forecasting will fail, only that it is not perfectible. One of the critical, though often misunderstood, elements of chaos theory is that chaos is not random.
Systems perform according to rules.
Cause and effect are observable and measurable. Although it may seem a contradiction in terms, chaos is ordered. A system can be managed. Efforts to improve inventory forecasting and replenishment strategies are essential to reduce the number of unpredictable variables in supply chain systems. Accelerating the speed of information through the supply chain and compressing cycle times throughout the chain are crucial to reducing uncertainty.