Having access to an accurate forecast is very beneficial for businesses. If used correctly, it can provide better margins, increase market shares, and many other positive results. At a more tactical level, it can help reduce the costs associated with meeting the customer demand and make the supply chain more efficient.
Learn how a simple binomial model can help anticipate the future including COVID-19 breakthrough cases just as models help a firm estimate it's future.
Classical Supply Chain Management Confronts its Quantum Revolution – the Path to Rapid Intelligent Response (RIR)
COVID-19 direct and ancillary events have made clear that uncertainty is an inherent part of the demand-supply network structure. Every firm, on a regular basis, faces “risk situations" such as manufacturing excursion, unexpected new demand or loss of demand, component supplier interruption, etc. This has placed risk management and rapid intelligent response (RIR) front and center in SCM discussions.
In a previous blog post, we discussed how a high or low value of Coefficient of Variation (CV) impacts the first or second term of safety stock. Today we decided to put this to the test using real customer data - here we will discuss our findings.
In this blog we briefly cover some key insights for successful time series forecasting: (a) Profiling the Shape of the Curve is the first stage, and the first step is assessing if the time series is stationary. (b) The forecast method identified must capture the shape and be able to project the shape across time. (c) There are limits in historical and no amount of “fancy math” can overcome them.
Some time ago, I had been trying to help a business improve its statistical forecasting. We tried different parameters and different forecasting algorithms but the statistical forecast for about half of the products could not be improved no matter what we tried. We decided to do a deep dive to understand the reason.
Isn’t the goal of a successful retailer to give the customer what they want, when they want it? And isn’t today’s customer changing their mind on what they want quicker than ever? Quick trend changes are driven by this customer and retailers must be flexible to survive...
Data Science Without Modeling Impact is a Path to Disaster – Simulation to Explore the Impact of Group Size on COVID-19 Spread
In this blog post, we will briefly review some examples of being “COVID-19 adrift” with just data and then focus on the primary task – demonstrating how modeling can be used to understand the impact of group size on COVID-19 spread.
Digital Transformation is a popular term being used in today’s supply chain world. This can mean different things for your organization based on your company’s maturity. In this blog, you will learn the five key points to consider in a supply chain transformation journey.
Insight from Applied Statisticians for Forecasting: Is It Worth the Effort and the Mirage of Random Variation?
In this blog, we will illustrate through an example of these potential pitfalls (unanchored, random variation, and narrow metrics) and potential negative impact on a firm.