Does Your Company Have the ‘Right Stuff’ to Embrace Advanced Analytics?

By: Henry Canitz – Director Product Marketing & Business DevelopmentPicture1

John Glenn became the first American to orbit the Earth on Mercury-Atlas 6 on February 20, 1962, just a few days after I was born. I grew up watching the Apollo Space Program launches including the six launches that sent humans to the moon and back. Like many kids back then I dreamed of being a test pilot and an Astronaut. I partially achieved that dream by becoming an Aerospace Test Engineer and working at Edwards Air Force Base where many of the early test flights by Chuck Yeager, Scott Crossfield, and other’s took place. On February 6, 2018, 56 years after John Glenn’s historic launch, SpaceX launched their Falcon Heavy rocket with Elon Musk’s Tesla Roadster and a dummy named Starman on a journey into the solar system. The Falcon Heavy is a new class of rockets that may allow man to colonize Mars and beyond. Today, space launches are routine with launches happening on a monthly if not weekly basis. Exciting stuff for someone who dreamed of being an astronaut.

It is also an exciting time to be a Supply Chain Practitioner. Like space exploration, the supply chain has become significantly more complicated over the last 25 years. Technological advances have simplified and automated a lot of routine processes while opening up entirely new opportunities. These new frontiers require advanced capabilities to drive business value such as cost reduction and customer service improvements. Analytics, for example, today is a routine part of a supply chain professional’s job. We can now analyze the end-to-end supply chain and quickly determine the best path forward. While speaking with practitioners at industry events it is quite apparent, some supply chain teams have the ‘Right Stuff’ to fully embrace advanced analytics while others are just beginning their journey.

Moving up the analytics maturity curve takes a combination of the right talent, processes and enabling technology. Unfortunately, the people component is often not adequately addressed. As supply chain planning incorporates more data, supply chain roles need to be redefined to support analysis and decision making. Just as Chuck Yeager had to acquire new abilities and skills to break the sound barrier, companies have to define new skills and roles to meet their envisioned advanced analytic enabled processes.

Below are a few of the new analytic roles for leading supply chain teams today:

  • Business Analyst: understands business needs, assesses the business impact of changes, captures, analyses and documents requirements and communicates requirements to relevant stakeholders.
  • Supply Chain Analyst: responsible for improving the performance of an operation by figuring out what is needed and coordinating with other employees to implement and test new supply chain methods.
  • Artificial Intelligence Specialist: work on systems that not only gather information but formulate decisions and act on that information. Software that determines Sentiment from Social Data is one example of the work of Artificial Intelligence Specialists.
  • Data Scientists / Big Data Analyst: analyzes and interprets complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision-making.
  • Database Engineer: responsible for building and maintaining the software infrastructure that enables computation over large data sets.

As our enterprise systems continue to produce volumes of data, we need to make smart decisions faster to drive the business forward. Does your current team have the ‘Right Stuff’ to embrace advanced analytics? What new roles do you need in your supply chain team? How should your team be organized to efficiently run the business while also driving innovation? Are your current supply chain systems sufficient to leverage new data sources and enable advanced analytics? Can you automate routine activities? These are just a few of the questions you should ask as you embrace all that analytics has to offer to keep your supply chain team engaged in value-creating activities.

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About the Author:

Hank Canitz Picture

Henry Canitz is The Product Marketing & Business DevelopmentDirector at Logility. To read more of Henry’s insights visit www.logility.com/blog.

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Machine Learning in Supply Chain Planning

Machine Learning in Supply Chain Planning - square

Machine Learning in Supply Chain Planning

Are we ready? And if so what’s involved?

The evolution to using artificial intelligence and machines that learn in supply chain planning is inevitable. In fact, there are early examples of the potential of AI to improve both supply chain planner efficiencies and provide better or optimized supply chain decisions. The question is, are we, as a profession, ready to embrace Machine Learning? If so, what does that mean and how do we get there?

Machine learning is a type of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. Machine learning computer programs teach themselves to grow and change when exposed to new data.

The latest Gartner Hype Cycle, published in July 2016, shows Machine Learning approaching the Peak of Inflated Expectations. Gartner predicts that mainstream adoption of Machine Learning is at least five years away, potentially ten. Still typing “Machine Learning AND Supply Chain Planning” into Google delivers more 16,000 results in less than half a second. This is a topic that supply chain planning people are thinking, talking, and writing about. In reality, the supply chain planning mindshare spent on Machine Learning is miniscule compared to that spent on reducing costs, improving customer service, and driving new revenue. Type “Cost Savings AND Supply Chain Planning” into Google and you get 2.5 million results.  One could argue that Machine Learning could contribute to meeting cost, revenue and customer service goals, but clearly there’s more focus today on basic supply chain planning capabilities like Demand Planning, Inventory Optimization, Sales & Operations Planning, and Supply Planning and Optimization.

One way to get started with Machine Learning is to look at your Demand Planning capabilities. For example, a “Best-Fit” forecasting algorithm automatically switches to the most appropriate forecasting method based on the latest demand information, ensuring you create the best forecast for every product at every stage of its life cycle. The algorithm evaluates forecast error each forecasting cycle and recommends or automatically selects the forecasting method that will produce the best forecast. “Best-Fit” forecasting is a basic form of Machine Learning.

Another example of today’s Machine Learning capabilities is found in software solutions that use algorithms to continually analyze the state of your supply chain and recommend or automatically execute plans to meet customer requirements. Optimization driven by algorithmic planning is an early form of machine learning that relies on a set of provided information (supply chain facilities and capacities, transportation lanes and capacities, customer service requirements, profit requirements, etc.) to automatically make optimal decisions.

One powerful example is the use of Multi-Echelon Inventory Optimization (MEIO) to automatically adjust inventory positions. The current inventory-to-sales ratio in the United States sits at 1.41, higher than any time since 2009 (right after the 2008 downturn). One cause of this glut of inventory is the emergence of omni-channel retailing. The “Amazon effect” of free and fast shipping, easy returns, and everyday low prices has changed customer expectations and the way products get into the hands of the consumer. It’s a struggle to keep up with the mind-bending rate of change that prevents companies from having the right inventory positioned at the right location to service customers. Companies that respond by increasing buffer inventory based on outdated information fail to attack the underlying issue of stock-outs, a fundamental shift in fulfillment. MEIO automatically seeks the optimal balance of inventory at the right locations, and provides optimal inventory parameters and positions by stocking location to establish optimal buffer locations and quantities. Embracing MEIO can reduce total inventories by upwards of 30% while maintaining or improving customer fill rates. MEIO is an example of basic Machine Learning that is available today.

Attaining the full benefits of Machine Learning will be an evolutionary process. We must learn to crawl, then walk, then run. The introduction of Machine Learning into most supply chain organizations will take years, but that shouldn’t stop supply chain professionals from planning for the future or taking advantage of some of the Machine Learning solutions available today. Implementing algorithmic planning and optimization technologies today builds the kind of expertise and experience that will ease the adoption of advanced Machine Learning solutions in the future.

Are you considering Machine Learning solutions?  If so, what steps are you taking to get there?

 

About the author 

Hank Canitz PictureHenry Canitz is The Product Marketing & Business Development Director at Logility. To read more of Henry’s insights visit www.logility.com/blog.