Tag Archives: Data Mining

Since Covid people who never heard the term “supply chain” have become painfully aware of what it means and how deeply it impacts their lives. It doesn’t take a viral pandemic to create supply chain disruptions. A factory fire, a natural disaster, or resource scarcity — everyday occurrences — can all lead to items disappearing from shelves.

The recent formula shortage was largely due to a single factory being temporarily shut down.

Product shortages can be a significant hardship for families all around the world. In this article, we talk about how data mining can add stability and predictability to supply chain management.

First, What is Data Mining?

Data mining is the practice of looking at large quantities of information already stored in a database to retrieve new insights from it. Basically, it’s the process businesses use to create actionable knowledge. In the context of supply chain management, the data could pertain to anything from consumer habits, transportation routes, product development, or resource excavation.

Every single action that takes a raw resource out of a mine or jungle and turns it into a product on your shelf creates information. More information than any human (or, for that matter, any room of humans) could ever examine in two lifetimes.

With data mining, data processing, and data analysis, that information can be tamed and channeled toward productive means.

Supply Chain Threats

What variables currently threaten supply chain management? Because there are so many steps taken to turn raw material into a physical product, many variables can interrupt the process. Perhaps there is a storm that halts excavation. A viral outbreak that pauses work at a factory.

Disruptions in the transportation sector. Maybe the demand for a product is so much higher than anticipated that it becomes impossible to manufacture it at an appropriate pace.

All of these scenarios can lead to supply chain disruptions. Through data mining, however, many of them can be mitigated or avoided outright.

Understanding Supply

Let’s say (with unfortunate accuracy) that there is a recession projected to sweep through the country in the not-so-distant future. Naturally, financial downturns can have a significant impact on the way people shop.

But how can stores and supply chain managers use this information to make sure that there is plenty of the things people need and a relatively modest amount of things that will go largely ignored?

Data!

Using historic shopping data, supply chain managers can get a vivid forecast of how people are likely to behave during the next recession. This might mean deemphasizing the production and supply creation of luxury items and focusing more on putting staples on the shelves.

Fleet Management

The transportation industry is an enormously important component of supply chain management. Using IoT (internet of things) and data, fleet managers now enjoy unprecedented control over their routes. Maps, even GPS-driven maps, tend to be relatively limited in how granular they get. Route recommendations mostly factor in distances. Even programs that account for speed limits, etc. do so for the benefit of personal vehicles.

Trucking is a different animal. Does this route include a short overpass that the truck will need to detour to get around? Maybe the road winds, requiring a large vehicle to slow down to a crawl.

With historical route data, mined through telematics technology (sensors, mostly) fleet managers now get automated reports that recommend the best routes for their trucks. These recommendations not only factor in arrival times, but can also be calibrated to make recommendations most likely to preserve the condition of the vehicle.

Transportation companies run more efficiently. Products arrive at their destinations on time. It’s a win for everyone.

Adjusting the Chain

In a post-Covid world, one needn’t stretch their imagination to imagine a scenario where something could go wrong within a supply chain. Delays and shortages can happen after only a single break in the chain.

With data, supply chain managers can make reasonable forecasts about potential disruptions, and plan accordingly. Already, the supply chain management industry has moved toward keeping a healthy supply of alternative production lines — often closer to home — so that they can pivot immediately into new solutions when problems arise.

With robust access to data, supply chain managers can receive quicker insights as to when they should reach for these solutions.

The result? Fewer disruptions, and significantly more consumer stability.  No more months and months of waiting for a new refrigerator or oven.

*This article is written by Andrew Deen. Andrew has been a consultant for startups in almost every industry from retail to medical devices and everything in between. He implements lean methodology and is currently writing a book about scaling up business. You can follow him on Twitter @AndrewDeen14.

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When businesses begin to scale, many of the challenges they face result from added operational complexity and lack of visibility. As organizations start investing more in their business’s functional components, such as inventory production, warehousing, logistics, and order management processing, communication silos and departmental disconnects begin to appear, blurring certain efficiency lines across the company as a whole.

To combat these operational challenges, many companies often rely on Key Performance Indicators (KPIs) to help them make informed decisions about their business as a whole. However, operational data in itself isn’t always useful in its raw state. Organizations typically need to take additional steps to make their system data informative and actionable. This is where data mining comes in.

What is Data Mining?

According to Consumer Notice, data mining is the process of turning raw data points into useful and actionable information. By collecting, sorting, and analyzing large amounts of data from various sources all at once, data mining helps companies discover valuable patterns and trends in their business operations.

Discovering and refining these data points manually across multiple systems would be time-consuming and inefficient for most businesses. Data mining simplifies the process exponentially and provides organizations with the unified data transparency they need to reduce their costs, improve client relationships, reduce operational risks, and increase revenues.

How Does Data Mining Work?

Much like the refining processes of metals and materials, data mining involves several stages before a final product can be achieved. When applying data mining in business analytics, the method uses the following six stages of progression:

• Business Understanding — Identification of business goals and recognize the overall outcome of the data mining process the organization wants to achieve.
• Data Discovery — The implementation of tools and solutions designed to understand all raw data formats and sources for the business and their relevance in the data mining process.
• Preparation — A key part of the “refining” process, often using AI-driven tools to convert unstructured raw data formats into forms that people can understand and quantify.
• Modeling — Several techniques are used to quickly and efficiently sort through several large databases simultaneously, identifying relevant trends and correlation analysis patterns.
• Evaluation — A series of human and machine-driven quality control processes to ensure data is properly mapped and accurately compiled.
• Deployment — Raw data has been fully refined and is now formatted to be used in near-limitless applications, whether represented as metrics, reports, or other digestible and actionable forms.

Data mining plays a fundamental role in business intelligence platforms and will continue to drive the future of data analytics as a whole. As businesses rely more and more on fast, actionable data to inform decisions around their growth and sustainability, data mining solutions will continue to be more readily adopted by all industries. In fact, data mining technology itself has already created roles within organizations such as data analytics specialists and data scientists who dedicate their professions to extracting and presenting new information to the business.

Data Mining in Supply Chain Operations

Management of a supply chain can be a daunting task for organizations of any size. Whether it’s running production facilities, coordinating shipping and logistics, managing inventory across multiple warehouses, or processing and tracking large volumes of orders, supply chains are made up of many individual components, each of them needing their own calibration efforts.

One area of supply chain management that heavily impacts business operations is product transportation. This is especially the case in the current landscape of remote business operations, now heavily reliant on shipping services and efficient vehicle routing. However, while the need for streamlined and profitable logistics coordination efforts has never been higher than it is now, many companies still use outdated and disconnected processes to keep things running.

Legacy logistics planning and tracking processes are often made up of many manual processes and riddled with routing problems that need solving. Some of these problems include inefficient shipping planning that leads to missed deadlines, a lack of visibility between shipment origin and destination, and unknown volumetric capacity or maintenance statuses of vehicles. With so much of these logistics processes disconnected from other critical components of the business, streamlining this data collection with other key business metrics is essential to ensure long-term business sustainability.

The data mining process does this by helping organizations create a unified view of all areas of the business. This is achieved through actionable reports highlighting key performance indicators, giving them the insight they need to improve how they manage product sourcing, efficient execution of their transportation network, and streamline all supporting workflows in and outside of the office.

The use cases of data mining are nearly limitless and can be applied to all business areas. However, by mining for data in supply chain operations, organizations can achieve the visibility they need to balance business efficiency, compliance, and profitability all in one place. This leads to a much more scalable business growth path and ensures long-term sustainability down the road.

*This article is written by guest author Beau Peters. View more of Beau’s articles here.

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