No one can see exactly what the future will bring. With the right tools, however, anyone can see the past, and for businesses that can be a great start.
We live in a world where historical data is constantly being combined with predictive analysis to drive every aspect of our lives. Data creates patterns, patterns create trends, and trends can inform critical decision-making for a company. Business leaders need key information to make good decisions. Data analysis is how they get it. One of the key areas for a business where analysis is most beneficial is the supply chain. Proper supply chain analytics can help answer questions like:
How much inventory do we need this quarter?
How many orders need to be placed and when?
How long will it take to source needed materials?
Where can we streamline our process, boost efficiency, and save money?
With the right approach and analytical tools, any company can learn to harness the power of past data, supply chain analytics, and forecasting models to comfortably pave a road map to cost savings and long term success.
What is Supply Chain Analytics?
In a nutshell, supply chain analytics is the process of applying various statistical models (usually called demand forecasting models) to historical information generated by the supply chain in order to predict future trends.
This historical information used can be past inventory numbers, costs, shipping times, sales numbers, and more. A good supply chain analysis platform takes into account as much data as possible, even accounting for outliers and abnormalities.
The Benefits of Supply Chain Analytics
While the overall benefit seems clear—predicting future trends to help leaders make business decisions—supply chain analytics has a host of other benefits to include:
- Higher Return on Investment (ROI). A study conducted by Gartner revealed that 29% of businesses gained a higher ROI thanks to supply chain analytics. The study also stated that:
“In addition to expected ROI, there are major trends that drive the demand and support the business case for analytics, ranging from the supply chain organization’s prioritization of analytics as the most important investment, to the dissatisfaction with embedded analytics capabilities in current supply chain management solutions…Analytics is a must to cope with today’s complex supply chains.”
- Better Risk Identification and Mitigation.
Good business intelligence tools and supply chain platforms can reveal weak areas in the supply chain that can serve as single points of failure for your business. Identifying these risks is the first step to implementing backstops and having a plan in place in case your supply chain is interrupted or crippled.
- Streamlining inventory to your customer’s needs. What products do your customers order most often? Is one product more popular than others during certain times of the year? Through understanding past sales trends, a business can dynamically shape their inventory to have exactly what their customers want in stock without wasting resources on items that are not in demand.
Demand Forecasting Models
Forecasting models are the heart of supply chain analytics. Every model looks at historical data just a little bit differently in order to meet the varying needs of organizations. Some require a lot of data in order to be accurate, others can make do with less. Some are more suited to regular variance, such as seasonal changes, while others are designed to identify and handle outliers. Understanding the different models and which ones meet your organization’s needs is critical to getting good results.
For the most part, these models fall into two categories, qualitative and quantitative. Qualitative models take into account information that cannot be easily quantified, such as expert opinions, cultural and world events, and customer feedback.
Qualitative models are great when a business does not have a wealth of historical data to lean on. For example, a small start-up that has only recently gotten up and running may not have years of sales data, but they do have market research and surveys. A good qualitative forecasting model can help these new companies maximize their limited resources by making predictions based on their startup research and industry trends. A very popular qualitative forecasting model is the Delphi method.
Quantitative models, on the other hand, utilize hard numbers. When it comes to quantitative analysis, more data is better. The less starting information you have, the easier it is for outliers to skew the data. However, if you have years and years of information to base your predictions on, those outliers can be seen within the overall context of larger trends and patterns. Trend predictions and time series forecasts are common methods of quantitative forecasting models.
To dig deeper into the different types of demand forecasts that exist, check out our article on five methods to assure better accuracy in your demand forecasting.
Predicting the Future: Things to Keep in Mind
According to Paul Myerson of Industry Week,
“The supply chain is a great place to use analytic tools to look for a competitive advantage, because of its complexity and also because of the prominent role supply chain plays in a company’s cost structure and profitability. Supply chains can appear simple compared to other parts of a business, even though they are not. If we keep an open mind, we can always do better by digging deeper into data as well as by thinking about a predictive instead of reactive view of the data.”
As Myerson says, supply chains are complex, and thinking proactively is a practice in constant improvement. Supply chain analytics is not a “one and done” procedure that an organization can implement one day and reap the benefits of indefinitely. Instead, it is a continually evolving process. When utilizing any supply chain analytics platform, here are some things to keep in mind.
- Bad Data = Bad Forecasts
While some platforms are better at handling bad data and missing information, these things generally hurt the overall accuracy of the forecasts that these processes make. Low accuracy forecasts can breed doubt in the mind of the decision maker relying on those predictions. Good datasets need to be as complete as possible and validated to make sure they are accurate. Duplicate data is another common thorn that can get in the way of good analysis. When an organization is aggregating data across all its supply sources, there can be plenty of redundancy. Shipping and sales data can often be reported multiple times from multiple sources, so validation is critical.
- Keep It Real (Time)
Along with data validation, making sure your data is timely will also help keep forecasts relevant and useful. Continually updating datasets with information as it becomes available as opposed to relying solely on static, historical data makes for more actionable and intelligent results.
- There is No Perfect Algorithm
It may be tempting to look for one methodology that suits your business needs and then stick with it no matter what. However, doing so may leave your company missing out on many of the benefits that supply chain analytics brings.
For example, a start-up with no historical data might use qualitative methods at the outset of their operations but, as their sales grow and their supply chain evolves, they shouldn’t continue relying on the same methodology and making forecasts based on their (now outdated) market research. They now have concrete numbers to back their decision-making and can evolve their analysis to incorporate the new data.
Tweaking and fine-tuning an algorithm over time—or even changing algorithms entirely—to account for new data is a foundational aspect of a successful supply chain analysis program.
- Question Your Assumptions
While certain forecasting models are designed to take into account subjective information, the overall purpose of supply chain analytics is to highlight information that leaders and decision makers might not be aware of. This means that, many times, the forecasts will reveal something that runs contrary to what these leaders believe about their company. An organization willing to improve their operations through supply chain analytics must also be willing to be confronted with results and recommendations that run counter to their personal thoughts and opinions.
- Embrace Automation
Forecasting models have been around for decades, however, it is only recently that businesses have fully realized the benefits of supply chain analytics. Now, the future of business analysis lies in automation and Artificial Intelligence (AI), and Machine Learning (ML). These new methods for analyzing data are now becoming more widely available to every organization regardless of size. From informing good farming practices to predicting digital ad placement, AI and ML forecasting are making waves across every industry—they’re bound to be the next revolution in supply chain optimization as well.
If you want to learn more about supply chain analytics or how Crisp’s forecasts can benefit your organization, let’s talk. Reach out to us today, and we’ll help you create accurate & efficient forecasts for your organization.