Conventional wisdom says it takes three to five years and tens of millions of dollars to digitize a corporation's supply chain. However, a few companies have reaped major benefits--including higher revenue and customer retention--with a faster, cheaper approach. It involves assembling available data; using analytics to understand and predict customers' and suppliers' behavior and optimize inventory, production, and procurement; and adding automation to revamp or introduce processes. The transformation requires three main initiatives: replacing consensus forecasts with one unified view of demand, changing one-size-fits-all supply strategies to segmented ones, and creating a plan to continually balance supply and demand and manage deviations or disruptions.
The case discusses supply chain risk management: how to leverage the structure of a supply network to assess its resilience, focusing on the consequences of disruption rather than the causes, as is traditionally done. The proposed framework, presented in a 2014 HBR paper by Simchi-Levi, Schmidt and Wei, describes how a consultant, Ann Van Delft, applies this framework when assessing the supply chain risk for her client a large telco internet service provider. Telco had recently experienced several outages due to equipment malfunctioning and a shortage of spare parts, resulting in the loss of some customers to its competitors. Ann was hired to deal with these issues as senior management felt the outages were damaging Telco's brand, and would deter new customers.
Identifying the optimal prices for products was once a time-consuming process. That's changing as businesses start to take advantage of advances in machine learning, increases in computing speed, and greater availability of data.
Traditional methods of managing supply chain risk require estimations of how likely a disruption is to occur. For fairly common risks--poor supplier performance, forecast errors, transportation breakdowns--the traditional methods work quite well. But it's a different story for rare, high-impact events such as megadisasters, pandemics, and political upheavals. These risks are hard to quantify using traditional models, and as a result, many companies do not adequately prepare for them, which can have calamitous consequences when catastrophes do strike. A new model allows managers to quantify the "impact" of a supply chain disruption on a company's operational and financial performance, rather than focusing on the "cause" or "likelihood" of the disruption. This type of analysis obviates the need to determine the probability of any specific risk's occurring--a valid approach since the mitigation strategies are equally effective regardless of what caused the disruption. In this article, the authors describe how companies can use the model to reduce their exposure to all types of supply chain risk.