Demand sensing is a new method of forecasting, which uses mathematical techniques on real-time information to calculate a forecast of demand with higher accuracy. This is based on the up-to-date realities of the inventory management environment. The higher accuracy of demand sensing reduces forecast error by a significant margin, compared to traditional and conventional forecasting techniques.
Inventory optimization is a technique of balancing service-level objective functions or goals and financial constraints for a large distribution of items or SKUs while considering demand and supply variability in the mind.
Demand sensing and inventory optimization are linked areas in inventory management and also a part of the digital core in any enterprise. In the discussion below, we will see how they are linked and what benefit they can produce for the enterprise when they are managed effectively.
Demand sensing is a little different from demand forecasting. In demand forecasting, traditional statistical forecasting techniques are used and sales and marketing forecasts are combined in it and compared to make consensus demand plan. In demand sensing, it is the next-generation forecasting techniques used that greatly improve current levels of forecasting by applying an updated set of mathematical tools, which gives emphasis to day-to-day demand information and creates an accurate forecast of near-term demand horizon based on the current realities. This increase of forecast accuracy and demand planning helps enterprises manage the effects of fluctuation in the external environment and gain the benefits of a demand-driven and more efficient operations, higher service levels, and translated financial benefits. This includes top-line growth, higher profit margins, and lower inventory levels which positively impacts many KPIs.
Inventory optimization has predominantly two parts – optimization of safety stock and optimization of cycle inventory. Though cycle inventory is optimized predominantly by linear programming method, safety stock is the direct function of forecast error and service level. Safety stock is calculated by multiplying service level value and Std. Deviation of forecast error during replenishment period. With the usage of proper demand sensing technique, the forecast error will come down drastically and the safety stock quantity will reduce. This will result in a reduction in working capital and higher inventory churn or movement urn and low inventory carrying cost.
If demand sensing and inventory optimization techniques are used together efficiently, it will allow the enterprise to function in the make-to-forecast (MTF) model which will ensure higher forecast accuracy, higher inventory turn, low inventory carrying cost, and low cash-to-cash (C2C) cycle time.
The digital core of connected systems should manage these functions hand in hand to get the desired benefit.
This is one of the key propositions made by HCL’s Operations 360 Propositions.