Every day, business professionals hear and read about supply chain challenges. Late shipments, short shipments, extended lead times, customer order changes, and the like.
APICS and ASCM professionals designed computerized software planning systems for these moments. Yet, despite having been available for over 40 years, we still see some companies attempting to solve their supply chain problems with spreadsheets, muscle, and overtime.
Here are a few suggestions:
#1. Analyze and Reset Lead Times – Purchase lead times have gotten extended in reality, but does your planning system know this or is it using old, invalid lead times? If a purchased item’s lead time has gone from 6 to 8 weeks, it’s time to adjust. We recommend harvesting Purchase Order data from your ERP system and comparing actual, demonstrated values with those loaded in the item master file used by the planning system. Export them to a file and send them to your suppliers for confirmation. Sometimes that simple, collaborative exercise helps.
#2. Analyze and Reset Safety Stock Quantities – Safety stock guards against demand and supply uncertainty variability. The 2022 has hit us with a lot of both. There are many formulas for safety stock, but they all incorporate measures of variation in lead times and demand (usage). This data can also be exported from your ERP system. At a minimum, for each purchased and manufactured item, identify the range (minimum and maximum) lead time and weekly or monthly usage or sales if examining a saleable finished good.
Regardless of the specific safety stock formula your company chooses, wider ranges of lead times and wider ranges of usage will drive increases in safety stock quantities to maintain desired service and stock out levels.
If your company uses simpler Order Point (often known as min/max planning), recall that Order Points are set according to the equation OP = Demand During Lead Time + Safety Stock. The recalculation exercises above will help your company meet internal and customer demands.
If your company uses more advanced, time-phased planning techniques such as Material Requirements Planning (MRP) or Distribution Requirements Planning (DRP), resetting your lead times and safety stocks will also help, as well, but more needs to be done. We suggest the following exercises to shore up your MRP and DRP systems:
#3. Evaluate and Adjust Forecasting Models and Processes – Most forecasting models utilize sales history to forecast future demand. The question, however, is how much history. If a company uses 2 years of sales data, it’s using data from the outset of the Covid pandemic when fear was high. Demand was high for at-home goods, low for travel and leisure goods. Larger swaths of history smooth out spikes and troughs but, in the 2022 environment, we suggest using a limited amount of history to have a more market-responsive forecasting model, no more than 3-6 months of history during these turbulent times.
In addition to that simple exercise, now is a perfect time to see if your company is using the right model. Moving average forecasts are great for their simplicity but often miss or are late to detect trends or seasonality. Advanced forecasting software will run mathematical “competitions” that compare the fit of various models to sales history to determine the best fit.
Lastly, and this is the most crucial point, it’s crucial to align your company’s leadership’s business forecast with the grounds-up, nitty-gritty, item-based forecast. Suppose the last 6 months produced $25MM in revenue and 5% growth trend. Absent any new knowledge, mathematical models will project that data forward and produce item forecasts that, when summed, yield $26.25 MM for the next 6 months. Companies need to ask if this grounds-up forecast aligns with leadership’s budgets and Sales’ evidence from customer interactions. If leadership projects 10% growth, the historical data-based forecasts need to be increased, across-the-board or selectively. Forecasting is part science and part art.
#4. Evaluate and Adjust Purchase and Production Quantities (Lot Sizes) – Purchase and Production quantities, so-called “Economic Order Quantities,” are directly proportional to demand and ordering costs and inversely proportional to item unit costs and holding costs.
The classic EOQ formula is:
It’s important to consider each variable to determine if your company should be making or buying more or less of an item each batch.