Do you want to buy a Playstation 5? Get in line.

Severe supplier constraints and the resulting chip shortage have made things quite tenuous for fans of Sony’s latest gaming system. Plus, since semiconductor-based materials make up 40 percent of new vehicle costs and countless other consumer products, PS5s aren’t the only things in short supply. Manufacturers are facing massive shortages across their supply chains, which, along with as well as an utter failure to move swiftly to account for said shortages, has resulted in a market flooded with demand for an ever-dwindling supply. It is, in order words, a failure in supply chain risk management.

What is supply chain risk management?

In short, supply chain risk management is the act of calculating sudden shifts of demand across your supply chain, the risk of being incapable of accounting for said demand, and the resulting actions that your organization can take to mitigate these risks.

With supply chains now existing on a global scale, the number of drastic, unusual, and frankly sporadic incidents that can affect a supply chain are increasing. As such, the ability to be agile – understanding the likelihood of such crises and developing readiness plans for them – is paramount. Most companies split this concept of risk management into two camps – “macro” agility and “micro” agility.

Macro agility, as the name suggests, focuses on the big events that impact a supply chain. Think a sudden flood, a tropical hurricane, or, maybe, a global pandemic. Macro agility is all about anticipating the likelihood of major crises that could impact the supply chain and preparing readiness plans to tackle them. Since these are, by nature, rare and truly impossible to predict with any real certainty, it’s not so much trying to account for them happening as it is analyzing the probability and severity and doing everything possible to eliminate or avoid the resulting disruptions.

Micro agility, on the other hand, focuses on other kinds of supply chain risk–the more common things like supplier shortfalls, logistics problems, or pricing shifts. These are part of the everyday for most demand planners, and while they are much less severe than macro events, they can have a significant effect over time. Micro agility often requires smart supply chain planning that focuses on the here and now.

Supply chain risk management, therefore, is managing both these types of agility in conjunction: Focusing on your supply chain’s everyday challenges, while being cognizant of global changes and how they could affect your supply chain plan. It’s a necessary, and dare we say vital component of any digital supply chain management toolbox.

That’s nice…but when am I getting a PS5?

Managing supply chain risk is, unfortunately, not something that you can just turn on and fix all your problems at once. It needs to be implemented at the early stages of any organization’s strategy and rigorously maintained over time.

On the macro side, for Sony to be able to fix its supply issues, they would need to be able to go back in time and prevent the coronavirus pandemic from happening in the first place. Since that’s, well, unlikely, the other option is to try to identify the key points in their supply chain that fell apart and try to ensure they are better prepared for next time. Beyond, obviously, just fixing what didn’t work this time around.

On the micro side, there is a lot more supply chain practitioners can do to ensure customers get the products they want and need–before they give up or go to a competitor. One place to start is with demand modeling, which breaks down demand components into a series of internal and external factors, the demand stream, and looks at the impact of each factor on future demand. That includes all the influencers of demand at a granular and daily level for individual SKU-Locations.

Variability is a part of life when it comes to demand forecasting – in fact, not only is such variability perfectly normal, but you can actually predict it to make better decisions. Probabilistic forecasting, sometimes referred to as “uncertainty forecasting” is all about identifying the range of possible demand and the probability tied to each outcome. The end result helps manage risk and improves overall health and results across the supply chain.

Machine learning automation is another tactic more and more companies are using to better understand the impact of things like new product introductions, promotions planning, end-of-aisle displays, and price reductions, all while incorporating them into the forecast.

Now, obviously, PS5’s aren’t hurting for demand. But the various components that go into making it might be. Each individual piece of the production process is tied to its own forecast by that part of the supply chain, and is in turn tied to an inventory cost for Sony to keep in stock. Sony has to better calculate how many of each piece they would need to maximize production while minimizing inventory costs, while also accounting for the risk of not buying enough and their supplier running out of stock later on – impacting their ability to produce more PS5’s in the near future. This is a level of variability at every stage of the process, and is all part of the everyday “micro agility” of true supply chain risk management.

If they don’t…well…maybe they’ll finally have some stock again in 2023.

The source of this article is from ToolsGroup

By Matthew Kippen