supply planning
Machine learning forecasting: Why, what & how
With customer expectations and preferences changing faster than ever, a deep understanding of the customer demand is essential to making the right decisions about marketing spend, sourcing, inventory, production, transportation, staffing, and more. Critical business measures like turnover, capital expenditure, risk evaluation, profit margins, cash flow, and capacity planning are all reliant on accurate demand forecasting, which ultimately can help businesses to estimate the total sales and revenue for a defined future. Typically, demand forecasting includes activities such as supply planning, product manufacturing planning (e.g., sourcing, R&D), and financial planning. The critical aspect of these planning activities is to understand product demands from customers and how to fulfil those demands in the most timely and efficient way. By capturing the variability of future demand through forecasting, businesses can predict customer behaviors more accurately and meet their demands with a higher level of confidence and significantly reduced lead times from order to delivery.
Enhanced Planning Capability Enables Continuous Supply Chain
The future of planning is connected, intelligent, and continuous. Yet many companies remain so far away from this vision; it often seems unachievable. With many planning processes being so siloed and disconnected from execution, they can feel ineffective. Fortunately, evaluations of the planning landscape reveal many organizations are adopting technologies that move towards a de-siloed, network-based approach to planning. To optimize planning capabilities, it crucial to achieve this connection at the enterprise level as well as into the broader supply network.