The world and its economies are always in a state of flux. New trade regulations are put into place, disasters upset the flow of goods, stock markets rise and fall, and supply chains struggle to keep up with these chaotic changes. By implementing supply chain analytics and supply chain management software, businesses can attempt to capture and study the huge amount of data that accumulates as a product moves from A to B. As more and more enterprises adopt Big Data and analytical tools, supply chains are turning away from studying the past and instead, forecasting the future.
In this guide, we are going to dive deep into supply chain analytics and cover a number of topics:
- What Is Supply Chain Analytics?
- What Are Some Analysis Strategies?
- How Is the Practice of Analyzing Supply Chain Data Changing?
- What Are the Downsides?
- What Are Some Trends to Watch for?
- How Does Software Enable Analytics?
- How Do You Select a Platform?
What Is Supply Chain Analytics?
To begin, we should start out with a working definition. At its most basic, supply chain analytics help make sense of all the data produced by the various arms of a supply chain. This data is collected and displayed in a visually digestible manner, usually in the form of graphs or charts, and then used to plan and make decisions by managers.
Sounds simple, right? Perhaps it would be if not for the fact that supply chains are complicated, requiring a wide array of industries and people in order to function. Operations that are looking for insight on how to optimize their supply chains have a lot of ground to cover. The process of digging through the vast amount of data generated by a single supply chain is often referred to as a “treasure hunt,” an effective naming convention since there are so many places to look.
Succeeding in these treasure hunts is as difficult as it sounds without proper planning and the right tools for the job. Supply chain managers have numerous avenues to explore, each of them containing vital tasks that can be drilled into for valuable data. You’ll need a strategy to find the most relevant data without overwhelming stakeholders with too much information. It’s all well and good to have supply chain analytics working for you, but being delivered a mass of data that can’t be unraveled doesn’t do anyone any good.
Let’s take a look at some of the different types of supply chain analytics. These methods can be used to find critical data organizations need to optimize.
What Are Some Analysis Strategies?
Supply chain analytics gives us the ability to make decisions based on data provided by analysts, but how do they decide where to look and what methods to use? Analysts have to tackle all angles of a supply chain, from manufacturing to the final delivery. Luckily, there are a few methods in place for them to consult.
Descriptive analytics gives users a window into what’s happening within the supply chain right now. The data involved in this type of analytics will be relied on every day and will require corporate databases to store and recall. This type of analytics is focused on providing a single source of truth across the entire supply chain, including internal and external systems. Some of the things this type of analytics can uncover are:
- Customer-Product Matrix – A customer buys a product that could create an opening for another product
- Coefficient of Variation – The last 12 months of shipments can be used to calculate the average demand for a product
- Customers per SKU – The number of customers one product goes to. If a product that is difficult to make is purchased by many, it may be time to update sales strategies
- Alert Reporting – Logic tools can help create alerts for situations like stock-outs
- Safety Stocks – Descriptive statistics can help guide users toward which high volume products require backstock to avoid running out at inopportune times
These traditional business intelligence findings are then taken and displayed on an easily accessible dashboard. This type of supply chain analytics is also considered a hindsight tool; users can find out what happened in the past and how it relates to the future.
Much like the title suggests, predictive analytics are all about forecasting the future and preparing for changes down the line. This method is employed when a business is considering a future scenario to see how it would play out before diverting resources. Machine learning and AI are amazing inclusions into the world of analytics, but no algorithm is always right. Hence the need for human employees to shoulder the burden of choice and planning for the future.
In fact, a recent study shows that the number of companies currently using the predictive method has grown 76% from 2017 to today. This year, 30% of respondents said predictive analytics technology was currently in use. This is up from 17% two years ago. It comes as no surprise that more businesses are turning to predictive analytics to fine-tune their supply chains.
Let’s turn to an example of how predictive analytics can prevent a bad situation from becoming an absolute catastrophe. Let’s say there’s a distribution company currently operating successfully; all their trucks are on the go and product is flowing through normally. This company uses predictive analytics to compare past data to current happenings, anticipating demand and changes in the market.
One day, there is a fire at the manufacturing plant that supplies this distribution company with all their spare truck parts. But before the shareholders at the distribution company have even had time to meet, the predictive analytics system identifies a plan of action. It automatically places an order for critical truck parts from other manufacturers immediately.
Unfortunately for all other distributors, these replacement parts begin to jump in price across the market due to the fire massively decreasing supply. However, predictive tools identified this issue and allowed the system to place orders for parts before the market could change. The distribution company keeps functioning at minimal loss, while those that did not have these measures in place are paying high prices for necessary parts.
It makes sense that while there are still a few holdouts not employing this technology, the number of respondents to a study saying they would not adopt these tools dropped to 7% from 11% in the last two years.
Until more recently, this method of supply chain analytics has been called the “final frontier of analytical capabilities.” In a way, this nickname still works, as it’s the final result of a collaboration between descriptive and predictive analytics. Prescriptive analytics lives in the past, present and future, using data from them all to give businesses potential routes forward. By leveraging data from the past and comparing it with information from today, this method can highlight future opportunities or warn about incoming risks.
This type of analytical style uses a constant stream of structured numerical data as well as unstructured video and image information to act as a sort of crystal ball. Without technology like machine learning (the ability of a machine to move forward based on data without human interaction), this type of analytics would be unavailable. The major difference between this and predictive analytics is that the prescriptive method recommends a course of action to take.
Here’s the problem with prescriptive analytics, if you don’t know what questions to ask and how to react to the choices given to you, it is quite useless. There is so much data to be had that without proper filters, it’s simply too much to be helpful. However, organizations that are prepared to act gain a sophisticated edge over operations that do not employ analytics.
These types of analytical tools and strategies weren’t always around. Let’s take a look at how supply chain analytics have changed over the years.
How Is the Practice of Analyzing Supply Chain Data Changing?
Like most processes, supply chain analytics comes from humble beginnings. Before the advent of software like ERP and SCM platforms, data was often stored in spreadsheets or physical documents. You can see how compiling relevant data from all points of a supply chain into a spreadsheet or hand-written document would be rather inefficient. However, this type of data did not give businesses the competitive edge that analytics provides today. Sure, forecasting and demand planning are important to the overall health of a supply chain, but more needs to be done with this data.
The transition from analog methods of record-keeping and data analysis to the use of EDI and ERP management systems opened up many new doors for supply chain analytics. Management platforms let users build a web of connectivity that allows for the exchange of information across an entire supply chain, further improving planning and forecasting operations. These features were revolutionary as older versions of these management solutions did not include them.
Finally, in the past decade or so, data usage has transformed into business intelligence and advanced predictive analytics practices that provide supply chains with a way to use and understand the data they discover. A recent study has shown that businesses with optimal supply chains have 15% lower costs and a cash-to-cash cycle three times faster than supply chains without these types of analytics in place.
Time can only tell what features and tools are coming down the pipeline in the future, but we can make a few educated guesses. We’ll take a look at some future trends emerging in the analytics realm further in.
The advantages are apparent, but nothing is perfect. Here are some downsides to keep in mind when thinking about taking the plunge into supply chain analytics.
What Are Some Downsides?
According to a recent study, nearly 21% of supply chains say that data visibility is one of the biggest challenges they face. Unsurprisingly, setting up a well-oiled analytics machine with machine learning and AI inclusions can be difficult and costly.
One of the main pitfalls of supply chain analytics is the cost of entry. Operating at the highest analytical level requires some cutting edge technology on top of an already costly system. Depending on which arm (if not all) of the supply chain you are looking to optimize, costs could rise further. For example, warehouse management is a vital link in any successful supply chain.
For starters, a warehouse management system (WMS) is vital to get the analytics process rolling. Without one of these systems in place, there would be no way to interpret, display or even find the data you wanted.
Luckily, many leading warehouse management platforms come with tools built to start the analytics process. Depending on the system and its focus, the tools available will vary. So, we’ve got a WMS in place and it has the features we want in order to drill down and get to that juicy data. What next? As discussed earlier, having the ability to uncover the data and see what needs to be done is one thing. If our WMS and analytics tools discover that introducing warehouse automation is the best way to maximize efficiency, can we act on it? If funds are low and no action can be taken, then the whole exercise was something of a waste.
Achieving this level of data visibility and then having the manpower and funding to act on it is a tough goal for a small business to achieve. Budgetary concerns tend to constrain this type of in-depth analytics, but thanks to the meteoric rise of cloud-based systems, smaller organizations are getting their hands on great tech for affordable prices.
Now, much like our predictive analytics strategy, let’s peer into our crystal ball and spend some time in the future.
What Are Some Trends to Watch For?
The Digital Supply Chain
The “digital supply chain” is a term that is being tossed around with more and more frequency these days. Some say that this mythical digital supply chain will be the logistics concept of the year, while others say that the term digital has been overused to the point where it hardly means anything of real substance anymore.
These are two wildly conflicting opinions on the matter, and much of the negative chatter is warranted in one way or another. Before we can go much further, we have to discuss what a “digital supply chain” is. As a quick example, supply chains are growing, so much so that many involve an operation that spans the entire globe and interacts with multiple countries at once. If something were to go wrong at any leg of this expansive supply chain, money and time would certainly go down the drain.
If there were a disaster that shut down multiple roads and impacted supply chain operations in an area, a digitally enabled supply chain could access geo-enriched maps to find any suppliers that could possibly be affected. This digital environment gives managers the ability to act quickly and see what resources are needed to get the supplier up and running. Here, a fully digital supply chain shines with its resilience to disaster as well as its ability to quickly discover and react to problems.
The transition to a digital supply chain, while it sounds simple, requires more than just a simple transition from non-digital to digital. A multitude of next-gen technology is needed to keep a digital supply chain operating at maximum efficiency, including the Internet of Things, the cloud, artificial intelligence, machine learning and more, depending on the organization. It’s easy to see how the move to a completely digital supply chain is not only difficult, but extremely expensive.
Machine Learning Enhancements
Since 2018, machine learning and artificial intelligence have been hot topics of conversation in the world of supply chain management — and for good reason. An automated force that can accurately receive, translate, and make decisions on data it receives would be a major advantage for any supply chain.
Machine learning is helpful when dealing with large dynamic data sets, much like the data you would gain from supply chain analytics. This type of cutting edge tech can already be found in supply chains today, most obviously within analytics but also in the burgeoning market of warehouse automation and supply chain planning. Procurement operations also benefit from the adaptability of machine learning when attempting to deal with the constantly changing market of customer demand.
As time goes on, the supply chain community is still dragging its feet a bit when it comes to the inclusion of these bleeding-edge technological enhancements. However, machine learning is empowering artificial intelligence with the ability to make successful choices without human input.
More and more areas of the supply chain are finding that applying machine learning and artificial intelligence to their processes opens up a new world of optimization and decision making. As time marches on, much of the supply chain analytics world is excited to see how far machine learning can take automation and artificial intelligence.
We’ve discussed some analytical strategies, the pros and cons of supply chain analytics and even gazed into the future to identify some upcoming trends. Now, we can discuss how an SCM platform helps businesses make the most out of their analytical data.
How Does Software Enable Analytics?
As we discussed earlier, obtaining data about your supply chain via analytics professionals fantastic, but without the ability to act on said data, it is pretty useless to have around. A big stack of data with no tools to separate and understand it all is just that, a big stack of data.
In order to make the most out of the information pulled from your supply chain, the adoption of a management platform is the next logical step. Achieving full data visibility is an expensive challenge, but with so many platforms moving to the cloud, there are affordable options for those looking to dip their toes into analytics.
An SCM system can take information gleaned from the supply chain and prepare it for managers and stakeholders to view and base their decisions on. Relevant parties can then see where their supply chains suffer, what is not operating at maximum efficiency and then make choices on how to succeed through possible solutions provided by the active prescriptive analytics tools.
As far as solutions go, the focused or general analytics put into place are completely up to the manager deciding on the system. For those wanting to tackle the entire supply chain end-to-end, an SCM platform would probably be on their radar. However, warehouse, inventory, transportation and procurement management systems are also available to handle focused analytics are certain links of a supply chain.
The market for management systems can be pretty overwhelming. Luckily, our research team has put together a leaderboard and comparison report containing information about some of the top competitors in whichever market you are interested in.
Now that you are armed with some information, how do you find a management platform to begin your journey into analytics?
How Do You Select a Platform?
So, we’ve covered the ups and downs up supply chain analytics, but now you’re ready to get started yourself. Before getting lost in daydreams of bountiful business-optimizing data, you’ve got to get set up with a supply chain management platform. Without one, all of that data will go to waste, but how to pick one?
One of the first, and easiest, things you can do is start by asking yourself some probing questions about your organization. For example, how large is your business? This may seem like a simple question, but it can help get you started on the right path. Some systems are designed with certain sizes of firm in mind, while some are comprehensive enough to handle small and large businesses.
How much room is there in your budget for a new system? A smaller business with fewer resources can still remain competitive by adopting cheaper options like cloud-based SCM platforms. In fact, the cloud is continually rising in popularity thanks to its many advantages over systems that require on-premise deployment.
Think of the must-have tools your teams need and lay out the specific requirements necessary to fully streamline your supply chain. This way you know you are getting features that solve your pain points and don’t just add needless complexity.
Supply chains that have the tools available to make the most out of supply chain analytics are on the bleeding-edge of optimization. With technology continually improving, the world of analytics is primed to continue growing and providing supply chain managers with even more routes to success.
How has implementing supply chain analytics into your daily processes given your organization a competitive edge? Let us know with a comment below!