Make Better Decisions with Supply Chain Analytics (2023)

The supply chain is a critical piece of the puzzle for business success, because it directly affects a company’s ability to provide a positive customer experience while also accounting for many of the expenses that affect overall profitability. The supply chain is a network between suppliers, a business and the end user, covering everything from raw materials sourcing to delivery to the end consumer.

Given the towering importance of the supply chain to businesses, many have stepped up their supply chain management (SCM) efforts. They’re looking for any opportunities to make processes faster, cheaper and easier in that long journey from a raw materials supplier to the end user. This is especially relevant as supply chains have only become more complex over time—companies work with a growing number of international partners and face escalating pressure to deliver their products as quickly as possible.

Supply chains involve many different activities, people and organizations, which produces an immense amount of information. This is where supply chain analytics come in—they can turn that overwhelming amount of data into digestible dashboards, reports and visualizations that influence key decisions and lead to better results. Easy access to these analytics has become critical in a landscape that continues to grow more competitive.

Video: What Is Supply Chain Analytics?

What Is Supply Chain Analytics?

Supply chain analytics is the analysis of information companies draw from a number of applications tied to their supply chain, including supply chain execution systems for procurement, inventory management, order management, warehouse management and fulfillment, and transportation management (including shipping). A supply chain is like dominoes: each step in the network affects the one that follows it, and ultimately any issues at any stage could impact the ability to meet customer expectations.

Each piece of software mentioned above may have its own reporting capabilities that shed light on that specific step in the supply chain, like predicted lead times for suppliers, current safety stock levels at the warehouse or orders fulfilled per hour, for example. But supply chain analytics are most powerful when all these systems are integrated, usually via an Enterprise Resource Planning (ERP) system. The ERP itself or a separate application can then present and illustrate data from across your global supply chain through dashboards or reports.

This gives employees a comprehensive view of this logistics network and enables them to understand the upstream and downstream effects of a specific disruption. They can then respond quickly in a way that mitigates the issue as much as possible. For example, some systems can analyze the data in real time and send out alerts to signal potential problems before they turn into a bigger issue.

What Is the Role of Supply Chain Analytics?

Supply chain analytics make it possible for companies to gather, assess and act upon the data generated by their supply chains. It allows them to make not only quick adjustments, but long-term strategic changes that will give the business a competitive advantage. Because supply chains often span the globe and can include hundreds of different entities, managing this information manually or via spreadsheets is almost impossible. At the very least, it is extremely inefficient.

A few examples of supply chain analytics include demand planning (using historical data and other factors to predict what customers will order); sales and operations planning (manufacturing and/or purchasing the goods an organization needs to cover forecasted demand); and inventory management (tracking sell-through of items and which SKUs it needs to replenish). Each of these activities can increase the overall efficiency of business operations, which can lead to sizable cost savings. For instance, more accurate demand planning means you avoid overspending on procurement while also avoiding both stockouts and excess inventory (which can turn into obsolete inventory). So your business is keeping costs down while still offering a superb customer experience that will help you stand out from the competition.

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Supply chain analytics continue to take on a more prominent role in the daily operations of today’s most successful businesses. Organizations are paying closer attention to these numbers than ever before and using various analytics techniques to optimize each link in this network.

What Are the Types of Supply Chain Analytics?

There are four primary types of supply chain analytics that companies should consider right now to build more efficient operations that could save time and money. Here’s a brief description of each:

Descriptive Analytics

Descriptive analytics looks at what happened in the past. They can identify patterns in historical data. This information could come from both internal supply chain execution software and external systems that offer visibility across suppliers, distributors, various sales channels and customers. Analytics can compare the same type of data from different periods to identify patterns and hypothesize potential causes of changes.

A manufacturer may review a descriptive analytics dashboard on a daily basis and discover half of its deliveries to distributors are running late. Leaders at the company can then investigate that problem further, and learn that a snowstorm hitting the region where that group of distributors is located has slowed down its trucks.

Predictive Analytics

Just like it sounds, predictive analytics help companies predict what could happen and the business impact of different scenarios, including potential supply chain disruptions and other outcomes. By forcing leaders to consider these possible scenarios before they happen, they can be proactive rather than reactive. They have time to prepare a strategy for an expected spike or fall in demand, for instance, and can react accordingly.

Looking at that same manufacturer, it may review the latest economic projections from the Federal Reserve and anticipate sales will fall by 10-20% in the next quarter. With that in mind, it orders smaller quantities of raw materials from its suppliers and cuts back hours for part-time workers for the next month.

Prescriptive Analytics

Prescriptive analytics combine the results of descriptive and predictive analytics to suggest what actions a busines should take now to reach its desired goals. This type of analytics could help companies tackle problems and fend off major supply chain disruptions, potentially by evaluating both their own information and that of partners. Since prescriptive analytics are more complex, they require more robust software that cans swiftly process and interpret a lot of data.

Prescriptive analytics may tell the manufacturer that one of its key suppliers in Southeast Asia is at risk of going out of business within the next year. A consistent history of late orders, reduced capacity and declining economic conditions in the region all point to this outcome. In response, the manufacturer could request a meeting with the supplier’s executives to figure out if they’re in financial trouble and how it might be able to help. If there’s no clear resolution, the business can start vetting other suppliers to replace this one before it’s too late.

Cognitive Analytics

Cognitive analytics try to replicate human thinking and behavior, and they can help organizations answer difficult, complicated questions. These analytics are capable of understanding things like context when interpreting results. To do this, cognitive analytics relies on artificial intelligence (AI), specifically machine learning and deep learning, that allows it to become smarter over time. This can greatly reduce the amount of work required by staff to produce these reports and analyses, and empowers employees beyond the data science team to pull results and understand them.

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With its AI-enabled software, the manufacturer may be able to automate much of the work that goes into demand planning. The solution could process all available data, as well as internal and external factors, to come up with highly accurate, detailed suggestions for the amount of each product it needs to produce for the upcoming quarter to meet demand. This reduces the additional expenses that come with making more inventory than necessary or lost sales from failing to meet demand.

Why Is Supply Chain Analytics So Important?

Supply chain analytics help organizations across all industries make better, faster and more informed decisions about their business operations. In that way, it delivers real and lasting value for the companies that use it.

These reports and dashboards help companies identify and understand their potential risks, improve their planning, optimize their inventory management and better meet their customers’ high expectations. For example, analytics software could flag risks by noting a specific transportation provider has repeatedly been late delivering shipments late over the last month. It can not only pick up on this pattern, but indicate the likelihood of continued delays. Additionally, the solution can quantify the impact of such a delay, including the number of potential late deliveries and cost of chargebacks/returns.

Analytics can improve planning with more accurate forecasts, which in turn lets you put all the operational pieces in place to meet the expected volume. If a retailers sees a steady uptick in sales, and the holidays are approaching, it may place larger purchase orders with suppliers and add more contractors at its warehouse so it’s ready for a surge of orders during the crucial holiday stretch. If there are any suppliers that lack the capacity to accommodate these larger orders, the retailer can find alternative options while it still has time.

Many businesses consistently have too much or too little inventory, and neither is ideal. Excess inventory leads to higher-than-necessary inventory carrying costs, while running out of items means lost sales. Analytics help strike the right inventory balance to keep costs as low as possible without stockouts. The system might trigger an alert for SKUs that are running low based on the typical lead time for that supplier. Sales trends can also help the operations team decide which items warrant additional warehouse space and which could be kept in low quantities or phased out.

Together, all of these metrics and numbers help businesses meet customer expectations. A hiccup at any point in the supply chain can negatively impact the customer experience and potentially lead them to buy from a competitor. There are also analytics directly related to the customer experience that companies could track, like on-time delivery rate or order accuracy rate, to identify and address any concerning trends.

History of Supply Chain Analytics

In the not-so-distant past, analytics were mostly limited to statistical analysis that helped organizations forecast demand and a handful of important KPIs that measured the success of a business. That started to change in the early 2000s, following widespread adoption of ERP systems that centralized data and had business intelligence features that allowed companies to better understand the performance of their supply chain networks. This helped businesses anticipate problems and lower expenses while still satisfying customer demand.

Since then, continued advances in technology—with the popularity and capabilities of cloud-based platforms have helped companies unify and analyze all supply chain data. The cloud has made it far easier to share information with external parties, as well, which has stoked more collaboration between supply chain partners like suppliers, distributors and retailers. This provides another valuable layer of visibility, as various stakeholders throughout the supply chain can receive real-time alerts about delivery problems, delays from suppliers, quality control issues and other changes that affect them. Today, businesses can also take advantage of more recent inventions like machine learning and cognitive computing to gain deeper insights and more accurate predictions.

While cloud solutions and other advancements have helped companies manage their supply chains more effectively, many find themselves with an overwhelming amount of data. In 2017, the average supply chain had 50 times more data than it had in 2012, but businesses were analyzing only 25% of that information, according to IDC. This is more than any human could process, which is why relying on technology that can automatically handle this has become essential.

(Video) Make better decisions with proactive risk mitigation to create a resilient supply chain | OD160

Benefits of Supply Chain Analytics

As we’ve noted, the benefits of accurate supply chain analytics are profound and lasting. They can help at every link in the supply chain by finding patterns and revealing other valuable insights. They can uncover opportunities for process improvements and call attention to problems operations leaders may not have seen coming. This ability to pinpoint existing supply chain risks and foresee future ones coming may be the most valuable benefit of analytics, as these disruptions can have a big impact on the bottom line.

Access to real-time analytics also helps firms gain a better grasp on their profitability, avoid stockouts, reduce late shipments and adapt to shifting customer preferences. This information helps businesses optimize their deployment of resources, and that leads to cost savings. In absence of this data, many of these decisions are left to guesswork and rely only on basic, historical data.

As many organizations strive to be “data-driven,” supply chain analytics represent a critical step toward this goal. Put simply, company leaders can make better decisions when armed with detailed supply chain information and reports.

Challenges of Supply Chain Analytics

One of the foremost challenges of supply chain analytics is a relatively high barrier to entry. For those that currently lack the systems to gather these insights, purchasing the technology could be a significant—though worthwhile—investment. Relying on spreadsheets, email and point solutions to gather and review this critical data simply won’t cut it. Businesses need supply chain management systems that can track goods from raw materials to final delivery. To take full advantage of this data, they may also need an analytics solution that can turn reams of data into useful reports and visualizations.

In addition, a business needs to have strong processes in place to collect all the necessary data. Information from across the supply chain should be stored in a central database, which requires reliable integrations. Only with data flowing smoothly from all relevant systems can an organization understand the current status of and outlook for its supply chain.

Another challenge is the skilled labor that may be required to build and interpret certain analytics. While software can make analytics far more approachable for supply chain employees, most of whom do not have a background in data science, it’s still worth considering whether you have the right people to support this effort. Training on the analytics solution may be all that’s necessary. For the most part, this a bigger concern for larger companies that want to capitalize on the latest emerging technology to gain deeper, more advanced insights into their supply chains.

Features of Supply Chain Analytics

As supply chain analytics take on a higher-profile role within many organizations, leaders may be wondering what they should look for in solutions. Research group IDC has come up with five features to look for in supply chain analytics, which it’s labeled the “five Cs”:

  • Connected: Any supply chain analytics effort starts with data, so it’s critical that the solution has access to all pertinent sources of information. These data connections start with the ERP and any complementary business systems, and extend to any other technology your business uses to collect information, like Internet of Things (IoT) devices.
  • Collaborative: Supply chain partners are critical to your success, and businesses should not lose sight of that. They should collaborate with their suppliers and, if possible, customers to find ways to enhance products or processes. Cloud solutions make it easier than ever for these parties to exchange mutually beneficial ideas and information.
  • Cyberaware: As businesses continue to add more software and connected devices, the risk of cyberattacks and the chances of a successful attack have increased. Companies need to realize this and lean on internal cybersecurity resources or outside experts who can assist them in equipping all systems that plug into their analytics with the necessary protections.
  • Cognitively enabled: Cognitive analytics, which as noted earlier use AI to draw their own conclusions, will surely take on a bigger role in supply chain analytics in the years to come. Cognitively enabled analytics help companies quickly understand the full effects of a disruption and prioritize their actions in response. Such a solution will only become more effective over time, opening the door for additional automation.
  • Comprehensive: One-off insights or reports only go so far. Analytics software must provide extensive and thorough observations for an organization to realize the full potential of these tools. To do that, the solution needs not only extensive functionality, but the scalability to still provide immediate results as it handles increasing amounts of information.

Supply Chain Analytics Software

As the speed of business continues to increase and the world’s supply chains become longer and more complex, analytics are more important than ever. That’s why a growing number of companies are using supply chain analytics software to gain more accurate insights faster, improving decision-making and reducing risk.

At a basic level, this software takes a vast amount of logistical data from your end-to-end operations and turns it into dashboards that managers can easily access and understand. Managers and executives can then make recommendations, decisions or adjustments based on that information. Specifically, this software can help managers maintain optimal inventory levels, fulfill all customer orders in full and on time, procure the goods they need to make and/or fulfill those orders and improve overall profitability. By automating many of the manual tasks previously required to deliver on these objectives, it also frees up managers to focus on other value-added tasks.

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More advanced analytics software goes beyond in-depth reporting capabilities, supporting the prescriptive and cognitive analytics described earlier. This functionality will be especially valuable to larger companies that spend millions annually on supply chain-related expenses and have much to gain.

Future of Supply Chain Analytics

The supply chain has become a hub of innovation in recent years as many companies discover this is an area of their business ripe with opportunities to cut costs and improve the customer experience. Analytics will be a key tool in realizing many organizations’ goals for greater supply chain visibility and transparency. The global market for supply chain analytics is projected to exceed $10 billion by 2025 and has a compound annual growth rate (CAGR) of 16%.

Although prescriptive and cognitive analytics remain out of reach for some smaller companies due to the human and capital resources required, that’s already changing and they will continue to become more attainable in the near future. Leading providers of supply chain software for the emerging and mid-market sector are already incorporating AI into their systems to give smaller businesses the same advantages enjoyed by enterprises.

In the years to come, supply chain analytics will pull from an even larger pool of data as companies continue to digitize their operations and adopt IoT devices in factories, warehouses, trucks and more. And to turn that ever-growing set of data into digestible insights, technology providers will leverage various technologies that fall under the umbrella of AI. It’s the only way for companies to actually benefit from the tremendous volume of information coming from their supply chains.

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Choosing a Supply Chain Management

Any supply chain analytics initiative will likely fall flat without a supply chain management platform. This software manages your supply chain from end-to-end, including supplier management, procurement, warehousing and storage, picking and fulfillment, shipment/delivery, and reverse logistics. Since the supply chain management solution manages each piece of this network, it provides the data necessary to take advantage of supply chain analytics. Some SCM solutions even have built-in analytics.

Not all supply chain management software offers the same features and functionality, which is why businesses should take care in selecting a solution that meets their current and future needs. Companies should factor in data reliability, ease of use and return on investment (ROI) when selecting a platform that will help them build more efficient, stable supply chains. Once companies select software that suits their operations, they can start taking advantage of the many benefits that come with powerful supply chain analytics.

(Video) Using Supply Chain Analytics to Power a Digital Supply Chain

The resilience and cost-effectiveness of a supply chain can make or break a business. Supply chain analytics can bolster your business in both of those areas, which is why they’ve come into greater focus for today’s industry-leading companies. Today’s supply chain analytics solutions already have impressive capabilities, and with future advancements will only become more of a game-changer for businesses across all industries.


How does supply chain analytics help in decision making? ›

Supply chain analytics refers to gaining insight and extracting value from the large amounts of data associated with the procurement, processing, and distribution of goods. Supply chain analytics are important for companies to fully digitalize, build an autonomous supply chain and enable real-time decision making.

How supply chain analytics can enhance decision making in supply chains? ›

By analyzing customer data, supply chain analytics can help a business better predict future demand. It helps an organization decide what products can be minimized when they become less profitable or understand what customer needs will be after the initial order.

What are the 3 areas of analytics that can contribute to decision making? ›

There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.

What type of analytics helps in making decisions to achieve the best outcome? ›

Predictive analytics

Predictive analytics allows organizations to predict different decisions, test them for success, find areas of weakness in the business, make more predictions—and so forth.

Why is analytics important in decision-making? ›

Learning how to analyze data effectively can enable you to draw conclusions, predictions, and actionable insights to drive impactful decision-making.

How data analytics are used in the decision-making process? ›

Data Analysis Helps You Make More Informed Decisions

This allows you to create targeted campaigns that are tailored specifically to customer needs and preferences. Additionally, it helps you anticipate future customer needs so that you can plan ahead and stay one step ahead of the competition.

How can data analytics improve decision making? ›

Data analytics, if used properly, provides a competitive advantage over other companies in the industry by enabling organizations to identify new opportunities and leverage their insights to make strategic decisions. Data analytics programs are evolving as the digital transformation of companies progresses.

What is the importance of supply chain decisions? ›

The ultimate goal of effective supply chain management is higher profits through improved customer satisfaction and a lower cost of doing business. Profits are healthier when costs are controlled and reduced wherever possible. Operating costs go down when raw materials and production costs go down.

Why Big Data analytics is important in supply chain management? ›

Big data analytics creates better decisions for all supply chain operations by combining data and quantitative methodologies. It broadens the dataset for analysis beyond the typical internal data stored in ERP systems. Furthermore, it employs strong statistical tools to analyze both new and old data sources.

What are the 4 types of data analytics to improve decision-making? ›

Now, let us discuss an essential question, 'Data analytics is categorized into how many types? '. There are four different data analytics types that we need to learn about: Descriptive, Diagnostic, Predictive, and Prescriptive.

What are the four 4 types of decision analysis phase? ›

The traditional decision analysis cycle consists of four phases:
  • basis development.
  • deterministic sensitivity analysis.
  • probabilistic analysis.
  • basis appraisal.

What does business analytics use to help managers make better decisions? ›

It is the practice of using data and statistics to analyse trends to gain insight into a company or its products/services. Businesses use this information to make decisions on how they want their companies or products/services to run, and they can use these insights to evaluate their success or failure.

What is analytical decision-making skills? ›

Analytical skills are problem-solving skills that help you parse data and information to develop creative, rational solutions. An analytical person in the workplace focuses on making sense of the facts and figures and using logical thinking practices to identify a fix.

Which analysis is used for decision-making? ›

Decision analysis (DA) is a form of decision-making that involves identifying and assessing all aspects of a decision, and taking actions based on the decision that produces the most favorable outcome.

Why is data analytics important in solving problems? ›

Analytics can be used to solve issues across a myriad of different complexities, like sinking revenues, inefficient risk and fraud reporting, poor KPI management, plummeting marketing ROI and more.

What are the 5 major decision areas of supply chain management? ›

Supply management is made up of five areas: supply planning, production planning, inventory planning, capacity planning, and distribution planning.

What is the most important goal of supply chain management? ›

The main goal of supply chain management is to manufacture products and deliver them to the end consumers. However, providing the product is not the only goal; the quality of that product also matters. You should provide consumers with a product that offers the best value possible.

What is the most important benefit of supply chain management? ›

Primarily, it facilitates and optimizes the flow of products, information, and finances, allowing companies to create better relationship value and improve overall business efficiency.

What is the main purpose of data analytics? ›

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.

What are the four areas of supply chain analytics? ›

There are five common types of supply chain analytics that create a hierarchy of capabilities for distribution leaders, supply chain managers and other decision makers:
  • Descriptive analytics.
  • Predictive analytics.
  • Prescriptive analytics.
  • Diagnostic analytics.
  • Cognitive analytics.
Jun 18, 2021

What are the 3 most important sources of data for effective decision-making? ›

Sources of Data
  • Observation Method.
  • Survey Method.
  • Experimental Method.

What are the five steps in decision analysis? ›

Decision making is the process of making choices by identifying a decision, gathering information, and assessing alternative resolutions.
  • Step 1: Identify the decision. ...
  • Step 2: Gather relevant information. ...
  • Step 3: Identify the alternatives. ...
  • Step 4: Weigh the evidence. ...
  • Step 5: Choose among alternatives.
Jun 8, 2022

What are the five 5 key steps of data analysis process? ›

  • Step One: Ask The Right Questions. So you're ready to get started. ...
  • Step Two: Data Collection. This brings us to the next step: data collection. ...
  • Step Three: Data Cleaning. You've collected and combined data from multiple sources. ...
  • Step Four: Analyzing The Data. ...
  • Step Five: Interpreting The Results.
Mar 16, 2020

What are the 4 key approaches to great decision-making? ›

When you make decisions, there are four decision-making styles that you can use. There's an Autocratic style, a Participatory one, a Democratic style, and a Consensus-based decision-making style.

What is the best decision making model? ›

As mentioned above, the rational model works best when making complex decisions. Before implementing the rational model, ensure you have all relevant information accessible and time scheduled with your team to work through the steps.

What are the 4 R's of decision-making? ›

Aligning the Four Rs of Decision-Making: Results, Resources, Restrictions, Risk.

What is an example of decision-making process? ›

One of the most typical examples of decision-making in management is to take a call on production facilities. As your business expands and demand grows, you will be forced to increase your production capacity. The next step would be to decide how much capacity installation is required to meet demand effectively.

What are 3 types of decision-making? ›

Decision making can also be classified into three categories based on the level at which they occur. Strategic decisions set the course of organization. Tactical decisions are decisions about how things will get done. Finally, operational decisions are decisions that employees make each day to run the organization.

What is big data analytics and how it will help improve business decision making ability? ›

Big Data analytics is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Big Data analytics provides various advantages—it can be used for better decision making, preventing fraudulent activities, among other things.

What companies use data analytics to make decisions? ›

The message here is bell-clear: data analytics is the lifeblood of successful businesses.
From a few you've heard of, to a couple you definitely haven't, here are seven interesting companies using data analytics:
  • Tesla.
  • Tesco.
  • DeepMind.
  • HR Wallingford.
  • Spotify.
  • Nintendo.
  • Bristol Myers-Squibb.
Dec 23, 2021

How to improve decision-making? ›

Steps to Improve Your Decision-Making Skills
  1. Review Strong Decision-Making Skills. ...
  2. Take Your Time. ...
  3. Start with the Desired Outcome. ...
  4. Weigh the Pros and Cons. ...
  5. Get a Second (or Third) Opinion If You Need It. ...
  6. Use Past Experience as a Guideline. ...
  7. Measure the Results. ...
  8. Learn from Your Mistakes.

How do you make better decisions? ›

Tips for making decisions
  1. Don't let stress get the better of you. ...
  2. Give yourself some time (if possible). ...
  3. Weigh the pros and cons. ...
  4. Think about your goals and values. ...
  5. Consider all the possibilities. ...
  6. Talk it out. ...
  7. Keep a diary. ...
  8. Plan how you'll tell others.

How do you answer analytical thinking interview questions? ›

How to Answer Analytical Skills Interview Questions
  1. Gather data to inform your decisions.
  2. Assess both positive and negative situations to improve your processes.
  3. Are able to develop processes.
  4. Evaluate information through critical thinking.
  5. Think through problems to find solutions.
  6. Set and achieve goals.

What are the examples of decision analytics? ›

Examples of Decision Analysis

These might include traffic at the proposed location on various days of the week at different times, the popularity of similar shopping centers in the area, financial demographics, local competition, and preferred shopping habits of the area population.

How does business analytics improve decision-making? ›

Business analytics enables you to improve decision-making by supplying you with information that can assist you in making better decisions and having more confidence in the outcomes of those decisions.

How can demand and supply analysis help managers in decision-making? ›

Similarly, with the study of the supply of the product, if the supply increases with a constant demand level, the producers can decrease their price level to attract the consumers' demand. Therefore, the study of supply and demand helps in the decision-making for raising the revenue.

How could you use data analytics to help inform your decision making? ›

The following steps can help you make better decisions when analyzing data.
  1. Know your vision. Before you can make informed decisions, you need to understand your company's vision for the future. ...
  2. Find data sources. ...
  3. Organize your data. ...
  4. Perform data analysis. ...
  5. Draw conclusions.
Oct 3, 2022

How important is business analytics in problem solving and decision making? ›

Even though there are endless data analytics applications in a business, one of the most crucial roles it plays is problem-solving. Using data analytics not only boosts your problem-solving skills, but it also makes them a whole lot faster and efficient, automating a majority of the long and repetitive processes.

Why Data analytics is important for supply chain? ›

Supply chain analytics provides significant benefits across the board for a company's supply chain operation. When used correctly, it allows companies to convert data into actionable reports, dashboards, and visualisations to achieve better results through: Better decision making in a company's supply chain operations.

How is data analytics used in supply chain? ›

The most significant advantages of using data analytics in SCM are: Demand Forecasting: It helps identify customer purchase patterns and predict future demand for products in the market. Inventory Visibility: Such analytics promotes transparency in inventory management by allowing businesses to keep track of suppliers.

How do you manage supply chain effectively? ›

5 Tips for Efficient Supply Chain Management
  1. Find Dependable Suppliers. One cannot emphasize enough the importance of finding the right suppliers. ...
  2. Invest in Employee Development. ...
  3. Continuous Improvement. ...
  4. Leverage New Technologies. ...
  5. Improve Returns Management.
Sep 17, 2019


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