How much has the supply chain evolved over the years? For the modern supply chain, things like delivery automation and digital models are essential staples, while AI and blockchain are threatening to further disrupt the industry.
Developments in technology have changed how every functional area of the supply chain works — from purchasing to demand planning to customer service. With the global supply chain undergoing a digital transformation, analytics is now a must for modern logistics teams.
What Is Supply Chain Analytics?
Supply chain analytics uses information and quantitative analysis methods to help you make better decisions across the various activities in a supply chain. Analytics tools track metrics like key performance indicators (KPIs), transform information into insights through data visualization, and can recommend action steps based on standards, trends, and patterns. Accurate supply chain analytics extends traditional supply chain analysis capabilities and adds automation and the capacity for crunching big data.
Analytics is a broader category of tools and processes that can be applied to various industries and endeavors. Supply chain analytics, naturally, applies specifically to the supply chain.
Applying Different Types of Analytics To Supply Chain Management
There are three main types of analytics, depending on what type of information they provide.
Descriptive analytics usually tracks KPIs that show you how well your processes are doing. Technically, you can also see historical performance, but it’s called descriptive analytics because it’s meant to show you data that illustrates current performance, as opposed to projecting or extrapolating how current trends will work for you in the future.
If you need to get a bird’s eye view of your organization or just functional parts of it, you turn to descriptive supply chain analytics and focus on the areas you want to analyze. Descriptive analytics can use real-time data, so you can always have an in-the-moment view readily available.
One step further from descriptive is prescriptive analytics. As its name implies, this type of operations analytics prescribes steps and tweaks to optimize your supply chain efficiency. Prescriptive analytics can automate intelligent decision-making, but only for minor concerns.
If you’re experienced and well-versed in different types of supply chain analysis, you can take a look at descriptive analytics and then plot simple optimization tasks based on what you see. This is essentially what prescriptive analytics does. With more powerful technology like machine learning and data science, prescriptive analytics can help with deeper supply chain planning.
Predictive analytics allows you to forecast how current trends will evolve over a certain period of time. Obviously, predictive analytics needs a broader and deeper view of your supply chain networks as well as additional external market forces to perform analytical techniques for forecasting. Technologies using big data backed by AI are commonly used.
The idea is to give you a snapshot of what's likely to happen based on the information you currently have so you can make better-informed decisions. Different projections are valuable for different parts of the supply chain, e.g., demand forecasts can be used to tweak fulfillment operations.
The impact of analytics on your operations depends on the type you use and how well you use it. While the performance of strong supply chain analytics ultimately depends on you, it’s practically inevitable that you'll make use of these tools in today’s landscape.
Why Effective Supply Chain Analytics Is So Impactful Today
Modern businesses rely on a steady stream of increasingly large amounts of information — this is especially true for supply chain companies. Supply chain analytics tools are supposed to help make performance tracking and analysis easier, but really, there’s practically no other way to do these things effectively given how much information you need to work with in the industry today.
Supply Chain Is Evolving With Innovative Technology and Techniques
Various local and global public policy changes are driving trends in supply chain technology. Just a few of the most impactful challenges molding supply chains include:
- Rural area population and wealth growth
- Sustainability concerns and carbon footprint regulations
- Economic restructuring
Technology inevitably becomes crucial to surmount these challenges and meet certain goals. You’ve probably noticed over the last few years, for example, that real-time data collection has become even more of a linchpin to successful supply chain strategy or that more and more companies are hiring increasingly bigger data teams as a technological and HR investment.
Inventory Management and Fulfillment Performance Are Under Stress
COVID-19 has undeniably wrought chaos on local and global supply chains, putting enormous stress on the industry as a whole. In a 2020 survey on warehouse and distribution center equipment, 69% of respondents said improving inventory control was the second-highest area of adjustment thanks to the pandemic.
Inventory management and fulfillment have seen unusual patterns during the height of COVID-19. Only modern applications and tools could hope to efficiently address concerns like the best locations for order fulfillment and allocation. While the pandemic is hopefully a once-in-a-century occurrence, the event has still reflected how much modern industry relies on effective tools like supply chain analytics.
Advanced Analytics Positively Influences Supply Chain Performance
Again, big data is primarily the culprit driving analytics adoption and is also the reason why using analytics has become the norm. Research has shown that technologies like analytics with AI and machine learning capabilities are priorities in global supply chain management. These help with the decision-making process, enhance contextual intelligence, and assist in asset and inventory management. They are also invaluable in predicting demand and inventory maintenance needs, proving useful in areas like:
- Optimizing purchases, inventory, and sales
- Planning assortment based on customer demand
- Appropriate allocation and order fulfillment
All of these point to the same conclusion: Data analytics in supply chains matter more than ever.
Make Data-Driven Decisions and Collaborate in Real Time To Improve Your Supply Chain Operations
Supply chain data analytics can power data-driven decisions and improve collaboration throughout the supply chain in real-time. At a certain scale, there’s simply too much data to crunch without analytics. You’ll need the right tools to implement a supply chain strategy successfully, sooner or later.
This is where platforms like Canvas come in handy. Canvas can generate different data visualizations to help you with descriptive analytics for your supply chain, letting you stay on top of the most useful metrics and make better strategic decisions.
There’s no better way to make the correct calls than reliable data analysis, and Canvas can help you garner the insights you need at a glance. Canvas helps logistics teams use their spreadsheet skills to explore data, build beautiful dashboards, and share them with your team. Sign up now and see how Canvas can bolster your supply chain strategy.