There is no doubt about the fact that the modern supply chain is rather complex, and business leaders need all the help they can get to sustain efficient operations. After all, whichever organizations can deliver goods most speedily and with the highest degree of customer satisfaction will be the ones to outpace competitors.
To ensure productivity, many transportation and logistics companies are turning to data analytics implementation since it can empower leaders with timely insights that boost decision-making abilities. But what exactly does data analytics in supply chain operations entail? That’s what we are going to find out today.
The Importance of Data Analytics in Supply Chain Operations
First, let’s get on the same page and establish, what is supply chain analytics? Well, the term simply refers to the different types of data analysis that are performed by various software tools in supply chain organizations. All to help identify patterns, provide extensive visibility into operational processes, and deliver recommendations.
Data analytics is immensely important in supply chain management because of the complexities involved in procurement, inventory control, order coordination, warehousing, and shipping. With so many simultaneously moving parts, it is imperative to rely on solutions that help keep track of it all.
Broadly speaking, data analytics can deliver the following benefits in supply chains:
- Optimize operations
- Improve inventory management
- Enhance shipment tracking
- Reduce costs
- Identify bottlenecks and ways of eliminating them
- Minimize machinery downtime
Types of Data Analytics in Supply Chain Organizations
Now that we’ve got the definition out of the way, let’s get a bit more specific. Namely, it’s time to discuss the various use cases of data analytics in the supply chain. While the four main types of data analysis that we outline below are cross-disciplinary, we will cover examples of each within the logistics sector specifically.
As the name suggests, descriptive supply chain analytics simply describe what has occurred. Through dashboards and reports, this form of analysis structures and summarizes key information about the supply chain.
For instance, descriptive analytics is used to answer the following questions:
- How have stock levels changed over the last month?
- How much fuel has been used by the fleet?
- How many returns have been processed in the last week?
- Under what conditions were the products transported?
As you can see, descriptive analytics display what has happened but offer no further information on any occurrence. Details on revenue levels, website visitor count, or the number of new leads acquired are also all based on descriptive analytics.
Diagnostic analytics takes things a step further by helping establish why something has happened. In other words, it assesses the cause-and-effect relationship between variables to discover connections between data and establish patterns that may have not been noticed before.
Hence, diagnostic supply chain analytics can answer the following questions:
- What causes frequent delays in truck dispatching?
- Why are shipments being delayed or lost?
- Why did the revenue drop 3% last month?
Typically, your supply chain analytics software would first deliver insights on what’s going on in the business overall and then apply data science capabilities to identify why it’s happening.
Now, we’ve reached some of the most exciting supply chain analytics tools. As the name implies, predictive analysis is all about forecasting what is likely to occur in the future.
Frequently, it incorporates artificial intelligence and machine learning algorithms that can go through myriads of digital information at an extremely high speed and deliver more accurate predictions.
Predictive analytics don’t only look at historical data, but often combine it with real-time information to provide the most reliable forecast about the future.
For example, many organizations are combining predictive analysis with IIoT technology by gathering data from connected devices installed in trucks or warehouses to forecast malfunctions and schedule maintenance before too much damage occurs.
So, to summarize, predictive analytics can aid supply chain operators with:
- Scheduling timely asset maintenance
- Anticipating shifts in consumer preferences
- Forecasting demand
- Predicting how regulatory changes might impact the business
Lastly, prescriptive analytics is the most in-demand form of analysis that supply chain tools employ because it is the most complex and beneficial one. Here, the software will go as far as suggest the next best course of action for you. Thus, ensuring that you can eliminate a problem in due time or capitalize on an emerging trend.
The reason it is the most complicated form of data analytics is because it requires that you implement all of the previous ones we’ve discussed as well and have good data management practices in place across your organization. As you can imagine, this level of thoroughness can require a lot of work. Especially, from organizations that have yet to move on from legacy systems.
However, this doesn’t mean that prescriptive analytics is unseen in supply chain operations. In fact, it is increasingly gaining popularity as businesses make real commitments to innovation.
So, if you choose to implement this technology, it’ll help you determine:
- Which is the best route for drivers to take amidst changing traffic conditions?
- How to optimize fuel usage?
- Is it a good idea to purchase a new warehouse?
- How do we ensure we always have enough stock with minimal additional costs?
Challenges in Supply Chain Data Analytics
You now know how supply chain analytics tools can help your business and the different use cases of these solutions. However, prior to jumping into development, it’s important to be aware of some of the difficulties you may face.
You see, some of the analytics you may want to run require a sophisticated software infrastructure. Plus, when you work with more and more digital information, your responsibility for ensuring its safety grows. So, let’s take a closer look at the challenges that supply chain data analytics may come with.
The amount of data generated by businesses is growing at an exponential rate. Every day there is more and more digital information generated, and if you want to capitalize on it, you’ve got to first manage and store it all effectively.
In this regard, many opt for data lakes or data warehouses implementation to make sure everything is stored securely and can be easily accessed for work. Of course, developing these systems is likely to take some time and investment, but it is an absolute must if you’re serious about leveraging data analytics.
Another major difficulty you may encounter when seeking to implement supply chain analytics software is the presence of various data silos. You see, in logistics and transportation, information is often distributed among many disparate systems — enterprise applications, CRM platforms, third-party tools, and the like.
With so many sources, it can be difficult for algorithms to identify patterns that emerge across data silos. Thus, it’s imperative to first integrate data from all relevant apps so that you can truly benefit from analytical solutions.
As you can imagine, making decisions based on outdated or incomplete information can result in major mistakes, missed opportunities, and even higher costs. So, it’s a good idea for your IT team to perform any data cleansing, aggregation, enrichment, and other quality assurance activities before you begin building data analytics platforms.
After all, if you can get rid of duplicates, inconsistencies, and other data errors ahead of time, there will be fewer issues to deal with down the line.
Lastly, whenever you work with extensive amounts of data, ensuring security becomes increasingly important. Across the entire data ecosystem, information must be safeguarded, no matter if it is in transit or simply being stored.
So, consider adding encryption mechanisms, two-factor authentication, and other practices that help prevent data leaks, theft, and destruction. Or, take it a step further and implement blockchain technology to leverage decentralization and cryptography for an added level of security.
Incorporate Good Data Practices
As you can see, there is a lot to keep in mind when it comes to data analytics in supply chain operations. You’ve got to know the objectives you’d like to achieve with this technology and how to deal with the potential challenge. However, you don’t have to go at it alone.
At Velvetech, we are pleased to offer extensive data analytics services to clients from all kinds of industries. Yet, we often work with transportation and logistics providers and are thus well equipped to help with your unique business scenarios.
So, don’t hesitate to reach out to our team via the form below and discuss your project with our specialists. We are always excited to take on new initiatives.