I was asked recently about what it takes to transform the supply chain planning process of a company. Having gone through this process a few times in my past, I thought it best to share my experiences and suggestions in the form of a discussion with Tapan Mallik during a webinar. This blog is a precursor to that webinar.
The right technology can provide a solution to maintaining optimal supply chain planning, but how can you justify the investment?
Industry analysts, big-time consultants, and your peers are all talking about technology, digital transformation, and the future of the supply chain. It can seem like a lot of noise given the day-to-day pressure you feel while working to ensure that inventory is on hand and positioned where it is supposed to be. With all you have on your plate, are you aware of the signs that it is time for a change?
Inventory Forecasting is the process in which the historical sales data, historical purchasing data, current demand planning, planned production, and distribution resource plan data are used for predicting inventory levels in a future time period.
For businesses, it’s a great opportunity to cash in on the seasonal spike in customer demand. However, every year it also poses a unique situation with equal challenges along with the opportunities.
Master Production Scheduling (MPS) plans for items that are independent (or direct) demand. Independent Demand is a demand that comes from Sales Orders, Service Orders, or forecasts on end items, i.e., items that we sell to customers. In RCCP, the principle assumption is that family-level assumptions are good approximations for the SKU level detail and the change in the mix will not have a big impact on the capacity projection.
With each storm, there comes a bevy of forecasts put out by different computer models. These forecasts begin about 10 days out and change as the storm gets closer and closer. This blog tries to extract some learnings from this process of forecasting.
When trying to forecast demand for the future, it is important to understand the variability in the underlying dataset.
Supply chain planning projects are often approved on the back of the promise of lower inventory levels. In a recent conversation, I was asked a more nuanced question: whether right-sizing inventory via better supply chain planning improves earnings before interest, tax, depreciation, and amortization (EBITDA). This blog tries to address this question.
Should we combine the positive numbers and the negative numbers as we approach the essential business of forecasting future demand? Let us think this through.