Airplanes stay reportedly off course due to extreme weather conditions, turbulence, or other factors. But the carriers keep them on track through macro-level data-informed adjustments like turbulence relay data, climate analysis, etc. But what follows when these tiny adjustments don’t make it? Too dynamic, right?
The same is with supply chains. Yes, on a micro-scale, cross-border clearances, traffic, or hold-ups are known challenges on any route. But success often comes down to the driver experience, the information they have for route planning, or how fast it reaches them. It even trickles down to inventory levels, supplier performance, and supply-demand ratio. Data-driven insights and decisions are the navigators of the modern supply chain and how it establishes its route to success.
Although it’s true, each type of data has its own advantages and disadvantages – and, so the right context is key to achieving optimal results.
Collecting and analyzing data from the various segments of a supply chain can help businesses identify pain points, benchmark operations, and make more informed decisions. For example, by understanding the demand for a product and adjusting production accordingly, businesses can reduce the risk of out-of-stock situations and adjust inventory levels accordingly. In addition, by accurately forecasting customer demand, businesses can mitigate the risk of undersupply and disruptions in production.
The use of data-driven decisions helps reduce enterprise risk in financial, operational, and compliance areas. By understanding the various sources of enterprise risk and how data-powered decisions reduce them, businesses can improve their overall operational efficiency and resilience. For instance, by managing inventory levels, businesses can reduce the risk of stockouts and associated costs, while optimizing warehouse space utilization.
In the supply chain, data-driven decisions are used across various segments, including production planning, procurement, and inventory management. By properly interpreting collected data, companies can make informed decisions about how to best allocate resources and optimize their operations. For instance, having access to data that reflects growing or waning demand for one product over another can help businesses adjust capacity utilization and product mix accordingly.
Effective supply chain management is key to the success of any business, and data-driven decisions are essential for achieving optimal supply chain performance. Actually, it boils down a challenge into its accessible and core elements – to a point where reliability and accuracy become palpable. And then it’s easy to build from there to live up to the business goals. It’s always easy to scale up a micro-solution into a macro one, but condensing a macro to work on a smaller scale seems irrational. Supply shortages, ETA hassles, or non-standard disruptions are classic macro-level problems that can be solved at a micro level.
By building a strong data profile, we can better picture the leakages across a supply chain and the critical points of failure – a readily actionable risk profile that helps preempt and contain disruptions beyond the “gut feel” actions.