Archive for Business Intelligence

Modern Business Intelligence for E-commerce Shipping

Organizations must properly execute their e-commerce shipping strategy to survive in today’s market. Business Intelligence is critical to proper execution, providing opportunities to significantly shift, reduce, and manage global transportation costs without sacrificing service.

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Using Big Data to Improve Your Supply Chain

The term “big data” has been one of the hottest buzzwords over the last couple of years, and that’s not a big surprise. Statistical science and data analytics can help organizations (of any size and type) obtain historical measurements and generate actionable information that increases supply chain visibility, drives operational efficiency and supports overall growth. But knowing the importance of having access to data is only the first step. Organizations need to have the right […]

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The Citizen Data Scientist

Mike Guilfoyle of ARC has been doing some interesting writing on predictive analytics. One of the terms he uses is the “citizen data scientist.”  I like the concept, and will explain Mike’s thinking in this article. But let me provide a little context first.  It would be easy for a supply chain executive, or any business manager, to ask what is the difference between “predictive analytics” and “forecasting.”  Forecasting is based on traditional mathematical and […]

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eCommerce Company uses Artificial Intelligence to Improve their Value Chain

Plated is using  machine learning and artificial intelligence to improve diverse operations across their value chain. These operations include product development, customer engagement, sales, and forecasting.

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The Peril of Circular Reasoning in Machine Learning and Forecasting

Machine learning engines can take data on forecast accuracy and use that data to automatically improve the forecast model. However, three is a rub. Lost sales is a key piece of data on the accuracy of the forecast, but lost sales is generated with a demand forecast. This is circular reasoning. But that does not mean this analysis is without value.

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Business Intelligence Can Deliver Positive Supply Chain and Business Results, Using Information Already Being Captured Through Existing Processes

Logistics professionals have access to tons of data. However, it may be challenging to get the exact information needed quickly so that it becomes actionable, driving strategic decision making, rather than a historical review of tactical actions and transactions. To seek a better understanding of daily transportation operations, an important first step is implementing a supply chain business intelligence (BI) solution that offers greater insight and helps drive strategic, proactive decision making. If a company currently […]

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The Arms Race to Leverage Machine Learning in Supply Chain Planning

Artificial intelligence (AI) is hot.  Over $4 billion in venture capital has been invested in AI firms just in the US. But supply chain planning software companies, with their cadre of operations research Ph.Ds who have been modeling complex problems for decades, may be better poised to solve many complex business problems than the hot new Silicon Valley firms.

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A Practical AI Platform? What a Concept!

I’ve been briefed by several providers of AI platforms. I’m left uneasy by these briefings. It is as if Artificial Intelligence is like a magic wand, wave it and your problems disappear. But Teknowlogi appears to have a practical AI Platform, the first I’ve come across.

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Machine Learning with Data Overload

Just because we have a massive junkyard of data in a data lake does not mean we have the right data to answer key supply chain questions like: “How do I improve production rate, reduce maintenance costs, or improve product quality in my factory”. Machine learning with neural networks is a promising technology for extracting useful models for data, but typically needs very specific data that is not likely to be found in a data lake.

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Robust Benchmarking of the Demand Management Process is Possible

Robust benchmarking is difficult. It is now possible to do robust benchmarking of the demand management process. That is much harder to do than you might think.

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