5 minute read
5 minute read
With the complexities and intricacies of supply chain networks, the amount of data generated by a business’ supply chain is tremendous. With big data comes big insights, and organizations are becoming more reliant on this information for key decisions.
According to a PwC survey, highly data-driven organizations are three times more likely to report significant improvements in decision-making than those who rely less on data. And with 70% of supply chain leaders reporting more frequent and greater disruption, the need to unlock meaningful insights to inform strategy is stronger than ever.
That’s where supply chain analytics come in. Supply chain analytics uncover patterns and generate awareness that serves as the foundation for organizational and strategic decision making. Read on to learn why your company should be embracing supply chain analytics for an efficient and profitable supply chain.
Think of the many applications used along a company’s supply chain, from procurement systems to warehouse management to logistics tools. These systems and software generate massive amounts of data day in and day out. Supply chain analytics are the insights generated from the data. These insights, which take the form of reports and dashboards, are extremely valuable and equip leaders with knowledge to inform strategy and decisions.
Some companies invest heavily in supply chain analytics and have the technology and teams—such as an Enterprise Resources Planning (ERP) system—to analyze the data and make recommendations. Other organizations don’t have the tools or people in place, which is why they struggle to enact data-driven change.
Read: Data Management and the Supply Chain
There are four main types of supply chain analytics, with each playing a unique role in a well-rounded strategy.
Descriptive analytics are the most straightforward and widely used form of supply chain analytics. They reveal what happened or what is happening by tracking performance and patterns. Examples include looking at supplier lead times or measuring inventory levels.
Diagnostic analytics are aimed at figuring out why something happened. Diagnostic analytics often need technology such as artificial intelligence (AI) to replicate human thinking. An example would be if delivery trucks in a region are running late, diagnostic analytics could determine it’s due to severe weather in the area.
Predictive analytics provide insight into the future, using modeling and forecasting to calculate potential outcomes. Examples include using data from former trade regulations to anticipate the impact of new regulations or using inventory trends around past holidays to predict inventory needed at the next holiday season.
Prescriptive analytics are the most complex type of supply chain analytics, with less than 3% of companies utilizing them. Prescriptive analytics suggest actions most likely to have positive results, often based on findings from AI, neural networks, machine learning, and simulations.
For example, a company’s supplier could be at risk of going out of business, which analytics uncovered through examining that supplier’s performance history. Prescriptive analytics software would then suggest the company finds an alternative supplier with a better outlook.
A supply chain is like a row of dominos. If an issue arises, it can negatively impact the steps that come after it, meaning one minor error can lead to widespread operational problems and unsatisfied customers. Having an effective supply chain analytics strategy allows businesses to address potential concerns, increase their efficiency, and become more profitable.
Read: Supply Chain Trends & Insights Report: Transformation
Analytics help organizations identify and predict risks, so they can avoid disruptions, saving time and money. According to Gartner, 72% of executives reported the impact of disruptions to their supply chain has increased over the past three years. However, with more available and reliable data, organizations are leveraging analytics to foresee disturbances and formulate a response.
One of the first steps to increasing efficiency is gaining visibility into the current situation. With more than two-thirds of organizations reporting a lack of visibility across the supply chain, it’s difficult for businesses to know where and how to make improvements. That’s where analytics come in.
Analytics are especially helpful at achieving transparency in logistics and transportation. By using real-time data, analytics detect inefficiencies and suggest changes to move assets more effectively, optimize routes, improve fuel consumption, boost warehouse operations, reduce delays, and more.
Supply chain costs are more variable than ever. Organizations that use supply chain analytics can analyze expenses across the entire network, helping decide where to cut costs and pursue new opportunities. Analytics can tell when to adjust prices for maximum profit or suggest manufacturer or supplier changes to decrease operating expenses.
Predictive analytics using AI are growing in popularity for supply chain planning. Forbes reports 60% of global 2,000 manufacturers will be dependent on AI for their supply chain by 2024 and will experience productivity gains of over 20%. AI helps close the gap between supply and demand, allowing for improved demand forecasting. By calculating and analyzing customer, distributor, and supplier data, businesses can keep up with fluctuating demand levels.
Optimized inventory leads to improved service and a decrease in working capital. All four types of supply chain analytics are helpful to streamline inventory management.
Descriptive analytics create visibility into inventory levels, while diagnostic analytics identify shortages and overstocks. Predictive analytics warn if stock levels are too low or high based on demand, and prescriptive analytics can use those predictions to suggest action.
Customer expectations have never been greater. At the start of 2022, almost 90% of business leaders reported customer expectations have increased to all-time highs. Analytics take the guesswork out of what consumers want by reporting on trends and behaviors.
Amazon is an expert in using predictive analytics for innovative customer experience. With their patented “anticipatory shipping” system, the ecommerce giant analyzes customer data to predict what customers will want and when, enabling them to package orders even before they’re placed.
Analytics make it easier to gain insight into a company’s progress on sustainability initiatives. Organizations are using analytics to monitor their supply chain’s greenhouse gas emissions and energy consumption, so they can work towards lowering their environmental impact. Businesses can also track the ecological and social impacts of their suppliers and manufacturers to ensure they partner with likeminded organizations.
Read: Improving Supply Chain Sustainability: 5 Ways to Get Started
Supply chain analytics aren’t going anywhere. The supply chain analytics global market is estimated to hit $10 billion by 2025, and has a compound annual growth rate of 16%, according to a report by ADROIT Market Research. Supply chain professionals are also reporting that data analysis is their top technology investment priority.
However, with endless data to make sense of, and numerous supply chain analytics systems to choose from, leaders may be left overwhelmed and unsure where to begin.
That’s where Catena Solutions comes in. With an experienced Data & Analytics team, our consultants, operations leaders, and supply chain professionals help harness the right data to bring organizations the solutions they need. Applying our expertise in Business Intelligence, Business Process Optimization, and Technical Project Management, we’ll get your company on a path to embrace the countless opportunities of supply chain analytics.
Contact us today to learn more.