Is your organization “gaming” supply chain planning?

We live in one of the most volatile times in the modern supply chain era, and end consumers are experiencing the impact. In addition to labor shortages, the pandemic, geopolitics, and more, businesses are establishing behavior patterns that intensify the effects of these initial tensions – most notably, panic buying—prompting business buyers to “game” the planning process.

During periods of supply chain volatility, organizations usually look for ways to secure supply for their customer base. To avoid stockouts and revenue loss--and “game the system” --organizations will order significantly more products. This action exacerbates weaknesses in the planning process.

So how can you mitigate and reduce the effect of this gaming on the supply chain? My three suggestions are:

1. Focus internally first
2. Implement the correct practices
3. Adjust before you leap

Focus internally first.

First, you must assess what you can control within your four walls. What are your strengths and weaknesses compared to industry standards? Use these answers to uncover gaps across people, processes, and technology.

The most common inadequacies in planning organizations range from outdated, offline, and neglected processes to a lack of automation and technology.

When it comes to policy, ensure that minimum order quantity, minimum and maximum batch sizes, reorder points, safety stock, and other practices are up to date. Standardize procedures, decision trees, and other if/then problem-solving techniques to mitigate disruptions to the planning process. You might also experience unforeseen volatility in demand and capacity constraints due to fragmented or siloed communication among customers, sales, operations, and marketing.

In addition to policy, focus on your data and technology assets. Expand collaboration metrics and data sharing with your customers as part of a more significant expansion of your technological ecosystem. How can you reduce the fragmentation of technology while increasing connectivity across platforms? For this, look to any challenges in your data syncing or legacy systems, including in your audit processes. If you cannot run basic simulations to analyze a given scenario’s impact on sales, production, and deliveries, you are falling behind.

Implement the correct practices.

Consider this myth: “If we just buy this technology or implement machine learning, we can solve all of our problems.” The reality is that companies need to have leading-class practices in place before considering complex and expensive technology solutions.

Before moving forward in your automation and technology journey, you will want to establish proper governance. This will be useful for maintaining and updating inventory policies and routinely running segmentation and other analyses to right-size inventory and optimize inventory policy. Regularly review key collaboration metrics in the sales and operations planning process to promote collaboration with customers. With collaborative planning, forecasting, and replenishment (CPFR) processes and consensus demand planning, you will be better equipped to develop robust demand forecasting capabilities with root-cause analysis to understand why forecast errors occur, see volatility impacts, and address potential bias. Also compliance and risk management policies can be aligned across supply chain partners and linked to vendor and customer collaboration initiatives. With these practices in place, you can strengthen collaboration among supply chain partners facilitated by a supply chain control tower. This helps facilitate real-time decision-making across the end-to-end supply chain.

A synchronized technological landscape that utilizes external and internal integration points to collect and aggregate information can accelerate an organization’s decision-making, reporting, and analysis.

Adjust before you leap

Companies often want to jump straight to a technology solution without fixing fundamental issues that machine learning or a control tower cannot solve. Addressing these issues first will maximize the benefit of the technology solution. Cleaning up raw data to be complete or enhancing data captured from transactions is key to building a foundation for analytics. Once that foundation is in place, it’s time to explore the power of ML.

For example, anomaly detection engines powered by AI and machine learning have been used for several years with a high degree of accuracy. These help pinpoint outliers for organizations, especially for those establishing their planning process.

Machine learning models also allow a company to pull in signals that predict the “true” demand for a given customer or region. These add a unique layer of analysis compared to historical statistics-based models which focus on moving averages. Traditional models typically are unable to react to unusual shifts in demand.

Once you have calibrated your AI and machine learning processes, you can begin to explore a suite of cutting-edge options to mitigate risk and streamline efficiency in the planning process.

With these steps in place, you can be on your way to more accurate and efficient supply chain planning.  If you’d like to discuss these ideas further and learn how KPMG can help you improve your processes, please contact me.