Distribution network optimization has become a white-hot topic these days as “the Amazon effect” leads businesses to evaluate how quickly they can get products to customers. 

Warehouse optimization modeling exercises examine the upside of being closer to customers versus the downside of carrying more inventory in more locations.  The biggest mistake companies make in this area is relying too much on the modeling software itself to provide an answer to the question: “How many warehouses should I have and where should they be?”

Lean warehousing quality improvement programs have driven billions of dollars’ worth of value across corporate supply chains. They are the road map to faster, better and cheaper. But, if you’re not careful, these programs can become weighed down with bureaucracy, adding time and cost to your quality improvement efforts.

About 15 years ago, I changed industries from healthcare to logistics. As I am an IT professional, you might think it was a jump from an antiquated, paper-based world to a high-speed, technologically advanced one. Actually, I’ve been surprised at the extent to which the exchange of supply chain data in the logistics world remains old-school or simply manual. 

“Streamline.”  “Optimize.”  “Deep Dive.”  These buzzwords are some of the worst offenders in terms of overuse and annoyance. But despite the eye-rolls they trigger from business professionals these days, they are actually pretty good words.  

They are a competitor of sorts, but I love what Big Brown does for the supply chain. They do a great job using their resources to inform and educate the public about our industry. Everyone knows UPS, because just about everyone (I'm assuming) has had some interaction with their service at least once in their lives. People probably think of UPS first as a package delivery company. However, the delivery of that package involves so much more.

There has been a lot of talk in the industry publications about "big data." How can companies use terabytes and terabytes of data to gain supply chain insights… how do you accumulate it… how do you clean it… and how do you analyze it? There is no doubt that more data - properly stored and analyzed - adds value.

Data warehouses are centralized data repositories that integrate data from various systems, applications, and sources. The data warehouse is an environment separate from the operational systems, and it is completely designed to provide for analytical and ad-hoc reporting, queries, and data analysis.  Because the data warehouse is in an isolated environment, queries, data mining, and analysis can be performed without any impact on the operational systems.

Over the past few months, a catchy phrase has juiced up the marketplace -- "BIG DATA."