Making Sense of the Complex: In Supply Chain Planning and Analytics

Line Art

Supply chain planning and analytics are becoming increasingly complex and so are the associated planning practices. Companies often make sense of the complexities manually using Excel spreadsheets or older styles of systems, which can lead to version control issues and human errors. Constantly changing variables, such as supplier deliveries, quality issues, or disruptions in the supply chain, add to the complexity making the planning process endless, time-consuming, and sometimes virtually impossible.

Some variables can be planned for and some cannot. While we cannot plan for trade wars, strikes, bankruptcy, endemics, or natural disasters; we can plan for what customers want, how much they want, and when and where they want it. Before we can plan to supply those customers, we need to predict what their desires and needs.

What happens if desires and needs are not predicted accurately? Inventory can increase, which can cause slow inventory turnover, which leads to inventory obsoletion. Increasing inventory might seem like a straightforward way to keep customer service levels high, but this is not always the case. When customers become less satisfied, they tend to look for alternatives. We want to operate right at the threshold of being able to react to customer needs while not being late to fulfilling those needs.

Without the proper tools, companies tend to become reactive, which is a suboptimal way to run a business from a delivery and cost perspective. The planning technologies that are available today can handle the complexity of supply chain planning and analytics by applying advanced analytical techniques, such as Probabilistic ForecastingMonte Carlo SimulationsDemand Sensing, and Cohort Analysis. When supply chains are disrupted, these tools can look at and model alternatives to enable executives to make the best business decisions.

So, if those techniques are mapped into a tool that can be automated, have machine learning capabilities, and use artificial intelligence, then we have a base for which to drive improvements within our company. This minimizes the risk to our business and minimizes the capital investments due to last-minute decisions while being able to use the broadest, most accurate, and most up-to-date information to make plans more proactive rather than reactive.