How to use descriptive stats in supply chain analytics?

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Supply chain analytics (SCM) is an ever-growing field, and one of the most significant areas is demand planning. As a supply chain professional, you have to consider various scenarios regarding supply and demand. You’re required to make decisions that ensure the proper utilization of resources while ensuring supply-demand balance. You need to make rational, data-driven decisions that can increase production, minimize stock-outs, and lower prices for customers. In order to achieve these, you must have access to real-time demand data,

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How can I describe the statistical data that provides insights into the supply chain’s efficiency, quality, reliability, and safety? How can I convey the information in a way that readers can immediately understand its significance? I’ll explain the advantages and drawbacks of different types of statistical data and then suggest a way to apply descriptive statistics in supply chain analytics. A well-designed supply chain has a significant impact on the organization’s overall efficiency, quality, reliability, and safety. To maintain such performance, managers and analyst

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I started my career in logistics and shipping in a small logistics firm. Later, I had the opportunity to join a company in the logistics domain, and I developed my expertise in analytical reporting, particularly in supply chain analytics. So, I will start by highlighting some of the common descriptive stats used in supply chain analytics. Descriptive stats refer to any numerical, meaningful, quantitative measurements of events or variables in a supply chain. Some of the common descriptive stats used in supply chain analytics are: 1. Demand and

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In supply chain analytics, descriptive stats are commonly used to depict the supply chain’s relationship with various customer segments. Supply chain data, on the other hand, is crucial to identify potential customer segments that can influence the supply chain. go to this website Supply chain data includes various data points, such as demand, inventory management, delivery dates, pricing data, and so on. These data points are interconnected and create a complex supply chain network, which can help identify customer segments. According to a survey, 82% of companies are leveraging

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Now tell about the use of descriptive stats in supply chain analytics: It’s a crucial aspect of supply chain planning that requires an intimate knowledge of supply chain data, and there are two ways to approach this: with descriptive and prescriptive data analytics. With descriptive stats, you can identify and explore patterns in data to identify opportunities, identify inefficiencies, and track performance, but this type of data analytics does not provide a roadmap to the future. On the other hand, prescriptive data analytics helps businesses make informed

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As supply chain analytics experts, we have all seen data points from various sources that may seem confusing or not easily understandable. This makes our task challenging, particularly when we have to analyze data that is not visual and not quantitative. But, the thing is we need to use statistics in supply chain analytics. Here is the trick: In supply chain analytics, we use descriptive stats to create insights. Descriptive stats are those that provide simple, intuitive descriptions to help us understand the situation. For example, if you are analyz

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I have been conducting supply chain analytics research studies for 15 years now, focusing on inventory optimization, order fulfillment, and production planning. I always knew that descriptive stats, such as mean, median, and mode, were an important part of supply chain analytics but never thought about their practical implementation in analytical reports. Today, let me share my experiences and some practical tips for using descriptive stats to add more value to your supply chain analytics reports. First, let’s get to the important point: descriptive

How to use descriptive stats in supply chain analytics?