Have you ever tried solving a jigsaw puzzle cardboard side up? While not theoretically impossible, it can be done with determination, time, and an understanding of how the puzzle is solved. Most jigsaw puzzles are rectangular, solving for at least the borders upside down does not take much effort. From there, time and perseverance is the best solution to solve the inside pieces. Building a supply chain network model without proper model strategy and design is like solving a jigsaw puzzle, upside down. Model strategy and design creates a foundation that adds color and depth to network design models that, otherwise, would be monotonous and void of true business value. Continue reading “Network Design: The Art of Simplicity is a Puzzle of Complexity”
The Problem “Visual excellence is that which gives the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space” – Edward Tufte. Often, it is easy to aggregate data and answer clients’ needs;however, creating a concise story from the visualizations and delivering suitable insights can be more difficult. Finding appropriate, effective, and simple visuals to best represent data is a challenging task that must be approached from multiple angles. In addition to incorporating user needs, it is imperative to apply best practice UI/UX to data visualization. In particular, to truly understand and respond to the user, we have to utilize heavily researched psychological factors within our dashboards. In this article, we address these challenges and help you create impactful visuals. We are in an ever-changing dialogue with data and we want to take you through the journey of combining these human tendencies with business intelligence and analytics to create a better finished product. Continue reading “Information Consumption and the Laws of Visualization”
You already know the best wayYou are the actual domain experts who have ingrained this intuitive problem-solving ability into your very fibers through significant effort over a long and sustained period of time. Machine learning without a domain expert is just that – a machine without intuition. Continue reading “Machine Learning is the second-best way of doing anything”
Artificial Intelligence is the key to harnessing the potential of the future. Articles detailing this have been circling the media universe for a while now but what is AI and why is it useful? To explore this beyond the oversimplified definition of “smart robots”, let’s start from the early origins.
The Turing EffectContinue reading “Artificial Intelligence – from Turing to Today”
As customers demand and expect their products “anytime/anywhere,” companies are shifting focus to implementing best in class Order Management & Fulfillment software to compliment their ERP, WMS, and TMS systems. Continue reading “OMS without BI is like a Compass without a Needle”
Keith Robbins joins Agillitics Team as Director, Supply Chain Program ManagementAtlanta –August 5, 2015 – Today Agillitics, LLC, announced that Keith Robbins has joined Agillitics as Director, Supply Chain Program Management. “We are all really excited to have Keith on board at Agillitics. Keith brings a wealth of experience in managing very complex technology projects. His leadership qualities are also a perfect fit with our continuous learning culture.” Receive up-to-date news directly from Agillitics on Twitter, LinkedIn, Facebook. Continue reading “Keith Robbins, Director Supply Chain Program Management”
In our last few posts we focused on the importance of bringing supply chain data into an Enterprise Data Warehouse (EDW) (http://bit.ly/SupplyChainED) and the value achieved (ROI) of doing so (https://bit.ly/2nqGv62). Staging and storing data enables essential descriptive and diagnostic analytics. Predictive analytics is a natural next step in the analytics maturity model.
Below is a visualization of the Supply Chain Analytics Maturity Model from Gartner. What type of analytics is your company currently implementing? We would love to hear from you.
Supply Chain Analytics Maturity ModelContinue reading “Supply Chain “Predictive” Analytics”
In our last post, we looked at the key reasons for bringing supply chain data into your company’s Enterprise Data Warehouse (https://bit.ly/2vw1SHh). Today we will take a look at the potential value and the compelling ROI that companies can achieve by embarking on this type of engagement. Continue reading “Supply Chain Data Rich But Insight Poor?”