Driving operational growth and increasing supply chain efficiency in today’s ever-changing business world is a top priority for many companies. In order to grow in a rapidly evolving marketplace, organizations must possess the ability to use advanced algorithms and data analytics to generate the next wave of supply chain value.
With the abundant data available and the advanced technologies now accessible to help harness it, organizations must establish effective strategies to curate data, produce and validate models and ultimately glean actionable insights to better manage their business.
But how does a business know if it’s getting the most out of its data? Below are some of the ways that data science is impacting supply chain operations and how an organization should be leveraging information to create greater, strategic business value.
Data Science is Simply a Process – Business Value is the Goal
When it comes to “big data”, it can be easy for an organization to focus on creating content with limited purpose, so it’s very important to first determine blind spots and gaps. In addition, it’s important to understand what specific improvements can be made to enhance your business, and how data can help you get there. When kicking off any data science journey, an organization should have an end business goal in mind and understand what resources it has at its disposal to make improvements and ultimately drive operational growth.
The “Hierarchy” of Data Science Value
Good hierarchies define foundational layers that must be met before an organization can achieve a higher level of value. The hierarchy – from the lowest level to highest – of data science value includes:
- Established and Trusted Sources of Information: Every organization creates huge amounts of data. However, this is only the starting point. Data has to be curated before it can be accepted as a decision-making input.
- Monitoring and Communication of Information: At its most basic function, data science should be able to decipher trends within an organization as well as patterns surrounding external industry factors. This information can help identify ways to enhance a business both in the short and long term.
- Callout Remedies and Corrective Actions: Data science should be used to identify specific callouts for action tied to new and existing processes. For example, does your organization understand how your service and cost equation with providers is achieving value as supply and demand changes in the market?
- Identify Early Warnings Signs: Data science models will play a significant role in getting in front of potential disruptions or obstacles before they happen; for example, the ability to link continuous moves or sense potential service disruptions. The ability to react to any problems before they happen can help your business save both time and resources.
- Integrate Automation and Decision Making: At its highest function, data science should help train systems at scale to facilitate decisions and integrate information within and between systems. This way, these data systems can continually provide new opportunities for an organization to learn and adapt.
Beware! Don’t Put Blind Faith in AI and Machine Learning
When implementing advanced technologies such as AI and machine learning into a data science process, it’s important to recognize both value and potential complications. Organizations should always:
- Know the accuracy of the models. 999% versus 90% has huge consequences at scale.
- Understand the implications and costs of model failure. The opposite of accuracy is also the frequency of failure – and failure has a penalty – so this must be considered.
- Recognize the benefits and limitations of data science. Just because the model is labeled “advanced” does not always mean it is good. Proof of concept and agile development are critical for getting an acceptable answer quickly and at a lower cost.
- Understand the relevance of historical training data to current systems. The past has to have some connection to the current or future state. Any significant changes in data representation will invalidate prior modeling efforts.
Positioning for a Future of Greater Business Value
The supply chain world has a bright future, and data science models can be very effective if implemented correctly. But if a supply chain organization is not focused on its core business objectives first and only focused on the proliferation of content, the approach will likely fail.
Matthew Harding has over 20 years of supply chain and transportation industry experience. Matthew has held leadership roles supporting consulting, technology, data analytics and 3PL services. Prior to Transplace, Matthew led a market intelligence service for shippers and 3PLs supporting well over 200 clients globally processing over $70B into actionable market intelligence for its clients across all major trucking modes. Matthew has also held roles leading consulting, transportation management systems implementation, procurement and manufacturing. Matthew is also a frequent author and industry speaker providing commentary on market conditions and their impact on the industry. Matthew’s education includes a bachelor of industrial and systems engineering with honors distinction from Georgia Tech, and was a Supply Chain Fellowship recipient from MIT, where he earned his masters degree from the Supply Chain Management Master’s Program.
