Predictive Analytics in Healthcare: It's Not Happening
Predictive Analytics with Big Data
Predictive Analytics Key Component of Customer Experience Management
Predictive Analytics in Higher Education
How to Transition from Business/Industry IT into Higher Education...
Leebrian E. Gaskins, CIO, Texas A&M International University
Leveraging Predictive Analytics to Power Customer Data &...
Dean Abbott, Co-founder & Chief Data Scientist, SmarterHQ
Improving Predictive Analytics with Data Visualization
Fadi Elawar, Technical Consultants Team Lead, iDashboards
Unlocking the Potential of Predictive Analytics in the Supply Chain
By Jesse Laver, Vice President Global Sector Development, Technology, DHL Supply Chain
Now, we are on the verge of a new era in supply chain management in which the supply chain is poised to become more than a strategic asset for driving business transformation. This happens when supply chain data is aggregated and consolidated with data from other sources to better understand consumers and markets, increase agility and drive innovation across the business.
This is the potential of the predictive supply chain, the next major evolution in supply chain management.Most businesses are just beginning to tap into this potential. According to a recent Accenture survey, 97 percent of executives report that they understand how big data analytics can benefit their supply chain; but only 17 percent say, they have implemented analytics in one or more supply chain functions. This is changing quickly and the opportunity is now for organizations to capitalize on the data being generated in the supply chain to transform the business.
Building the Foundation
The precursor to the predictive supply chain is the descriptive supply chain, whereby descriptive information and analytics systems are used to capture and present data in a way that helps managers understand, what is happening.
Descriptive analytics comprise business intelligence systems, such as supply chain dashboards and scorecards, which enable ad hoc queries, data visualization and geographic mapping tools, to tell a story with the data. Utilizing these tools, companies can manage the day-to-day operation of their supply chain to become more agile and cost-effective.
Many industries, such as technology, have already made significant advances on this front and have an integrated descriptive supply chain in place. They are using the descriptive supply chain to better manage inventories and speedup product movement in response to dynamic market conditions. These improvements are significant and have elevated the value of the supply chain within the organization; however, as powerful as they are, they still leave the organization in the position of reacting to change, rather than anticipating it.
The Predictive Supply Chain
The predictive supply chain is about consolidating, mining and analyzing data to make predictions about the future both within the supply chain and beyond it, enabling organizations to shift from reactive to proactive management.
Data mining and other analytics are forming an emerging field of supply chain data science that has the potential to drive an evolution toward predictive operations
Today, many organizations are making strategic decisions using only historical data, which is like driving a car using only the rear-view mirror. Predictive analytics expands their visibility to the front as well as the rear.
The applicability to industries such as technology, in which demand can be extremely volatile, is particularly powerful. Imagine if business management could access real-time sales data and related costs by region and product at any time. Then, based on this information—as well as data from external sources—dynamically adjust production schedules, marketing initiatives, sales promotions, inventory positions, stock locations and transportation routing. The result is a predictive enterprise better able to capitalize on market volatility while avoiding excess inventory, improving service, shrinking product obsolescence and improving profitability.
Big Data and the Supply Chain
Studies of organizations that have used data effectively, documented numerous benefits, including higher revenue, improved customer service, more successful product launches and higher quality.
Most significantly, companies that do a better job predicting demand can improve margins by 1-2 percent.
In any global company, the supply chain is one of the largest sources of big data. It carries and produces information that affects almost every other area of the business. That should put the supply at the center of any organization’s big data initiative.
By layering analytic techniques and tools onto their existing descriptive information architecture, organizations in dynamic markets can reduce inventory, start to sense and shape demand, streamline networks, improve agility, responsiveness, and generally get out ahead – not just of their supply chain but of their business as a whole. For example, a manufacturer of consumer electronics with access to a real-time sales analysis by region, along with shipping costs and sales projections, could dynamically adjust production schedules, sales promotions, stock locations and transportation routing to capture sales, avoid excess inventory, improve service, shrink product obsolescence and improve the bottom line.
This requires going beyond the internal borders of the company to include external sources of data, both structured and unstructured, that can influence demand and distribution. One example is social media. Supplementing real-time sales data with industry trends and breaking news provides a more complete perspective on demand, as when a celebrity shows up at a major event using a distinctive product or a new product introduction generates unexpected media coverage. When that data can be captured and factored into the analysis, it enables more accurate and dynamic predictive modeling.
According to a Deloitte and the Material Handling Institute (MHI) study, which surveyed 400 supply chain professionals from across industry verticals, less than 25 percent of respondents have adopted predictive analytics to date, although that number is expected to climb to 70 percent over the next three to five years. While only 24 percent of surveyed companies currently use these types of systems, 38 percent cited them as a source of competitive advantage. Clearly, now is the time to begin to evolve toward the predictive supply chain.
Enabling the Evolution
Data mining and other analytics are forming an emerging field of supply chain data science that has the potential to drive an evolution toward predictive operations.To take advantage of this opportunity, supply chain professionals need to ensure they have a descriptive supply chain solidly in place while plugging into organizational big data initiatives and getting smart about the new generation of business intelligence, data visualization and artificial intelligence tools. The result promises to be more powerful than a better supply chain. It is the ability to create profit, growth and value on a sustained basis.