At the start of the pandemic the demand (new patients) for PPEs went up dramatically. There were limited amount of supplies and surge in demand for N95 masks, ventilators, hospital beds etc. Generally high demand is a good thing for manufacturers. In case of hospitals, not really. Variability in demand and uncertainty in numbers create havoc.
Using simple queuing theory, they can guess how many of (say) ventilators, i.e. bottleneck resources, are needed. Like many industries the bottleneck resources shift depending on the mix in demand. This approach resulted in many patients waiting days in order to get to the badly needed bottleneck resources.
Many S&OP, if not all, behave the same way because they use rough modeling and high-level capacity analysis based on averages. As a result of this approach, supply cannot be planned accurately and user intervention is needed to make adjustments. Accurate modeling using S&OE together with resilient design of the supply chain and prescriptive algorithms can greatly improve operations, reduce lead-times and ensure on-time delivery.
Studies have shown that when resources are accurately pooled in modeling hospital bottleneck resources then waiting time for (say) a ventilator drops by about 60%. In manufacturing industries, when resources are modeled accurately rather than in a bucketed way, then the error due to averages of say processing times, lead-times or set-up times are removed. “Averages” are the cause of generating poor plans leading to constant manual adjustments by the planners.
With accurate modeling, executable plans are produced leading to precise execution of the plan and a realistic availability of alternatives when demand changes or other disruptions occur. For more information on accurate modeling of supply chains click Here.