Why companies are struggling with supply chain risk forecast models today and what they can do about it
Agility and flexibility in the supply chain are more vital for corporate performance than ever. The lack of reliable and accurate disruption forecasting in your supply chain can seriously impact your revenue, reputation, and value in the eyes of your shareholders.
Many Fortune 1000 companies are looking to create resiliency in their supply chain. Organizations that proactively address the challenge of forecasting, thereby mitigating risk in their supply chain, will be ahead of the curve on the competition and better serve their customers.
Millions of dollars later, why are so many organizations still struggling with forecasting supply chain risk?
All corporations must make decisions in management of their supply chain risk, which is why they have teams of people to help address this challenge. Hopefully, they can identify gaps where they can make changes to reduce risk, increase profitability and deliver resilience to their supply chain. Some companies opt to work with 3rd party consulting firms to add expertise beyond their immediate staff.
Additionally, off-the-shelf software platforms are not readily available or easily deployed to help manage these risks.
The upstre
am supply chain can be large and complex. It includes all the sourcing of raw materials before distributors sell finished goods to final customers, which means many global firms rely heavily on their sourcing partners to support their supply concerns. One of the most significant risks associated with this strategy is the extended supply chain complexity.
Let’s illustrate this. A typical purchase order lifecycle may last between 2 months to 18+ months, depending on the industry. Changes to the schedule or adjustments due to disruptions may take months to resolve the issue and receive parts from sourcing partners, which could be located as far as China, Europe, Asia, or South America. This means that with your suppliers, you wouldn’t get parts in time to adjust your schedule volumes or SKU mix within your selling window to compensate for forecast misses. The financial damage would be lost sales, lower revenues, and leftover products that didn’t sell.
In addition to all the responsibilities needed to maintain a resilient supply chain, teams must manage upstream supply chain issues with limited information. When you think about all the possibilities that can impact your supply chain, it’s vast and complex. Many events or triggers within the supply chain can cause delays or disruptions. When making risk forecasts, teams typically must understand a few factors.
What are the confidence levels regarding their forecasts?
Does the company currently use models with enough external data across the geopolitical, market, and supplier dynamics to demonstrate risk visibility across all these variables? If not, you’ll continue to find gaps in forecasting that are ineffective in solving the challenge. Looking beyond your internal supply chain to the macro, regional, and “unconventional” data is essential to make more accurate forecasts. Usually, the triggers you don’t expect will affect your supply chain the most.
How far into the future can teams forecast?
Several risk models can see between a few days up to 2-4 weeks in advance on the high end. With artificial intelligence breakthroughs over the past few years, AI models can reach several times the current forecast time horizons than current predictability models.
Since changes in suppliers, raw materials, locations, routes, and other factors can take weeks to months to adjust, the lack of timely, actionable data means your supply chain lacks the resiliency to take corrective actions.
What are the impacts of incorrect forecasts and disruptions?
From the manager to the C-Suite, disruptions are felt throughout the organization. One doesn’t need to read the headlines or watch the news daily to realize these impacts aren’t just missed revenue or inventory forecasts. It affects the share price, which increases a company’s cost of capital, and its ability to expand or acquire other companies, and may force the company to borrow to raise money.
Historically corporations have addressed these risks by taking a few actions:
- They’ve purchased millions of dollars in software designed to help track, step by step, the status and location of purchased part shipments. However, none of that is worth much when products are stuck in ships outside the port of Houston, for example. Current systems must provide proactive predictability, not just real-time visibility in this environment.
- They have gone from small risk teams in the pre-1990s to often small armies of costly logistics staff. Remember, as per the above, the larger the army, the more software licenses, training, and specialized expertise are required to support them.
- Due to the events of the past two years, many just-in-time inventory models have switched to a hybrid model between JIT inventory and just-in-case (JIC) inventory models. This is a difficult model to balance since JIC models provide companies with enough on hand production material to meet unexpected spikes in demand; the typical JIT model considers inventory as waste. The unfortunate effect is higher storage costs and higher overhead.
The bottom line is that none of these actions address the core cause of forecasting market disruptions but significantly increase fixed costs and should only be considered a stopgap.
To learn more about how Ceres Technology’s Nostradamus AI platform addresses the above issues, read our last article providing a solution brief on Nostradamus AI.
