What are the common inventory planning and forecasting errors and how to avoid them?

The most common mistake is overweighting subjective vs objective inputs and data into near term and intermediate forecasting. Long term forecasting is much more speculative thus justifies more subjectivity both in terms of internal and external factors/variables.

Very common mistake is for companies to accumulate and import all sales order transactions as demand into the SCP or IBP so they will generate a baseline forecast for that data. But in almost all cases companies have dozens of sales order types. Many are offering, only financial, internal transfers, consignment or bill back. You need to scan the sale order type list to ensure the demand you export is the demand you wasn't to forecast and replenish.

My experience at Longhorn and other restaurants I owned the best inventory system, invoice automation, purchasing and menu costing is www.DiningEdge.com It does all and easy to use, even shows you best and worst sellers and their app is fantastic. I highly recommend it.

I made sure my count was dead on doing several check and counts before my actual inventory . I wrote off any outdated items and old code numbers that weren’t used anymore in both my trucks and warehouse. I looked for discrepancies before my inventory . Then I had a base to work with and we stayed with it all year .

Another thing to watch, if you are in a manufacturing environment, is to make sure your Bill of Material or Kits are hyper accurate. Conducting a regular BoM/Kit review is a must, in my opinion.

Knowing which mathematical method is correct depends upon the tangible goods being measured. Seasonality and trends are not two words meaning the same thing. Seasonal patterns and trends are separate components within the historical sales (demand signal). Think of seasonality and trends as separate variables within a forecasting model. Imagine a hardware store in Wisconsin which stocks snow shovels. When forecasting snow shovels exponential smoothing removes seasonality to expose the trend and ignores wintertime driven sales demand. However exponential smoothing does help identify long term, year over year trends. Regression can work well for forecasting high level aggregation such as corporate wide sales forecast.

In a fast growing tech electronics company, the best/worst/most likely scenarios was a great way for our supply chain team to improve the sales forecast process. Previously, Sales was forecasting "best case" but supply chain was budgeted as "worst case" - always leading to finger pointing and customer backorders. By making all three scenarios transparent - the costs and the risks involved, as well as the customer impact - we achieved alignment internally and more importantly, a higher customer in-stock rate.

Logistics have shown to be a very disruptive factor. As seen recently from the effects on Covid-19 how economies and businesses were grossly affected by logistical challenges. In forecasting it would be worthwhile to plan alternative routes and transportation to get the product to location, identifying risk and putting mitigating options in place in advance should help in avoiding panic and overpaying.

When coordinating, try to push the demand signal data as far up the supply chain as possible. Small variations at the point of consumption create wild swings at earlier segments of the supply chain. This observation is commonly referred to as the bullwhip effect.

I would also add... train, train, train your inventory staff. Evaluate their accuracy continuously. Empower them to be more involved in the process and reward that behavior.

Early warning systems can help prevent stock-outs. Monitor the consumption rate against the expectations from the forecast. If you've consumed 80% of your demand forecast in 50% of the demand period, action may be needed to adjust.