If you’re in the business world, you’re probably always looking for ways to streamline your supply chain operations. Luckily, supply chain analytics is here to help! By harnessing the power of data and analytics, companies can uncover valuable insights into their supply chain processes, pinpoint areas in need of improvement, and make informed decisions that can boost their bottom line. But like any new technology, there are hurdles to overcome when implementing supply chain analytics. In this article, we’ll delve into the benefits and challenges of this powerful tool, and discuss the architecture that’s necessary to successfully integrate it into your business.
- What is Supply Chain Analytics?
- What is the Difference Between Supply Chain Analytics and Supply Chain Management?
- Purpose of Supply Chain Analytics
- What are some examples of Supply Chain Analytics?
- Importance of Supply Chain Analytics
- Five Types of Supply Chain Analytics
- Benefits of Supply Chain Analytics
- Challenges in Implementing Supply Chain Analytics
- Supply Chain Analytics System Architecture
- Role of Big Data
What is Supply Chain Analytics?
Supply Chain Analytics (SCA) is the process of using data and analytics to gain insights into supply chain processes and operations. When it comes to managing a supply chain, having access to real-time data can be a game-changer. That’s where these analytics come in – it’s the process of collecting and analyzing data from every step of the production and distribution process to identify areas where efficiency can be improved. This means tracking everything from customer relations to inventory management, financial investments, transportation and delivery processes, and workflow processes. By having a bird’s eye view of the entire process, companies can quickly adapt to sudden changes in demand and find new ways to optimize their operations.
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The benefits of SCM analytics are numerous. By providing visibility into every aspect of the supply chain, companies can identify bottlenecks, reduce waste, and minimize delays. This leads to significant operational efficiencies, cost savings, and improved customer satisfaction. Additionally, by analyzing data from every stage of the supply chain, companies can uncover new insights and opportunities for growth.
To accomplish this, supply chain analytics collects, analyzes, and synthesizes data from a wide variety of sources. This information is then used to provide actionable insights into supply chain performance, allowing businesses to make data-driven decisions that can help improve their bottom line.
What is the Difference Between Supply Chain Analytics and Supply Chain Management?
While supply chain management and SCM analytics may seem similar at first glance, there are some key differences between the two. At its core, supply chain management is all about managing the flow of goods and services from the origin to the point of consumption. This involves coordinating and managing a wide range of activities related to production, distribution, and delivery.
On the other hand, SCA is all about collecting, analyzing, and synthesizing data to gain insights into supply chain performance. By harnessing the power of data, analytics can help organizations make more informed and efficient decisions that can drive significant operational improvements.
Purpose of SCM Analytics
SCA aims to provide insight and extract value from the large amounts of data associated with the procurement, processing, and distribution of goods. It is an essential element of supply chain management. It helps companies gather, assess, and act upon the data generated by their supply chains, making it possible for them to make not only quick adjustments but also long-term strategic changes that will give the business a competitive advantage. These analytics can also help identify known risks and predict future risks by spotting patterns and trends throughout the supply chain, increasing planning accuracy, and better predicting future demand.
What are some examples of Supply Chain Analytics?
Importance of Supply Chain Analytics (SCA)
SCA is important because it helps businesses improve their performance, reduce costs, and increase profits by using real-time and historical data to make better and faster decisions. It allows companies to gather, assess, and act upon the data generated by their supply chains, making it possible for them to make not only quick adjustments but also long-term strategic changes that will give the business a competitive advantage. As a result, these analytics help companies achieve a lean supply chain, prepare for the future, and better understand risks.
Five Types of Supply Chain Analytics
- Descriptive analytics: Descriptive analytics uses data to explain the past and current reality, by examining trends and relationship patterns. Examples of such analyses include the performance of a supply chain or the inventory levels of a warehouse.
- Diagnostic analytics: Diagnostic analytics focuses on understanding the underlying causes of issues or trends in past and current data. It is used to investigate why supply chain shipments have been delayed or sales goals have not been met, by giving insights into past performance that can be used to guide future decisions.
- Predictive analytics: Predictive analytics is a data-driven approach to forecasting and predicting future supply chain events, including demand for a product or potential delays in shipments. By leveraging past data and recognizing patterns, predictive analytics can help businesses make more informed decisions about their operations and optimize supply chain performance.
- Prescriptive analytics: Prescriptive analytics is a type of data-based technology designed to provide recommendations for optimizing decision-making. It uses data to suggest what actions should be taken in order to maximize the efficiency of supply chains, such as selecting suppliers or creating the ideal inventory.
- Cognitive analytics: Cognitive analytics is a powerful tool that relies on artificial intelligence and machine learning to process vast amounts of data. This analysis can generate actionable insights which allow businesses to optimize the performance of their supply chains.
Benefits of Supply Chain Analytics
SCA offers a host of benefits that can help businesses improve their operations and reduce costs. Let’s take a look at some of the key advantages of using supply chain analytics:
- Increased speed and efficiency: SCA enables businesses to optimize their supply chain processes, allowing them to move goods and services more quickly and efficiently. This can result in faster delivery times and improved customer satisfaction.
- Cost savings: By analyzing supply chain data, businesses can identify areas where they can reduce costs and eliminate waste. This can lead to significant cost savings and improved profit margins.
- Comprehensive understanding of operations: These analytics provide businesses with a holistic view of their supply chain operations. This allows them to identify bottlenecks, optimize processes, and make better-informed decisions.
- Real-time insights: With SCM analytics, businesses can monitor their operations in real-time, allowing them to identify and address issues as they arise. This can help improve overall efficiency and reduce the risk of disruptions.
- Improved social and environmental goals: By analyzing supply chain data, businesses can identify areas where they can improve their social and environmental impact. This can help them achieve their sustainability goals and enhance their reputation.
- Better visibility into supplier selection process: SCA can provide businesses with greater visibility into their supplier selection process, allowing them to make more informed decisions about their suppliers. This can help them identify the best suppliers and negotiate better contracts.
In summary, SCA is an essential tool for businesses looking to improve their operations, reduce costs, and enhance their sustainability efforts. With its ability to provide real-time insights and a comprehensive understanding of operations, businesses can make better-informed decisions and achieve greater efficiency and profitability.
Challenges in Implementing Supply Chain Analytics
Let’s take a closer look at some of the key challenges businesses may face when implementing supply chain analytics:
- Cost and maintenance of analytical software tools: Implementing these analytics requires the use of specialized software tools, which can be costly to acquire and maintain. This can be a significant barrier for smaller businesses.
- Need for experts: These analytics require a deep understanding of data analysis and supply chain management. Finding and hiring experts in these fields can be challenging.
- Isolation or silos: In some organizations, supply chain data may be isolated or siloed within departments, making it difficult to integrate and analyze.
- Location-specific insights: SCA may require location-specific insights, which can be challenging to obtain and analyze.
- Lack of top-management sponsorship and leadership: Without support from top-level management, implementing SCM analytics may be difficult or impossible.
- Lack of a scalable data foundation: Building a scalable data foundation that accurately represents the supply chain can be challenging.
- Limited talent and skills: Finding and hiring talent with relevant analytics skills can be difficult.
- Lack of clarity on the business case: Without a clear understanding of the business case for supply chain analytics, adoption may be slow or stalled.
- Resource constraints: Implementing such analytics may require significant resources, which can be a challenge for smaller businesses.
Implementing these analytics can be challenging, but the benefits it provides make it worth the effort.
Supply Chain Analytics System Architecture
Supply chain analytics is the practice of collecting and analyzing data from every aspect of a company’s supply chain to gain insights that can help optimize operations and profitability. This includes data from sources like enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, suppliers, product manufacturing and testing, quality management processes and shopfloor manufacturing. Additionally, third-party market data such as demographics and marketing information can be utilized to gain further insights. Real-time Business Intelligence (BI) embedded in SCM requires tight integration of manufacturing into analytics, so that information gathered is used to identify design issues, associated costs, and other features throughout the products’ lifecycle as shown in image below:
Figure 1: Data Sources Integrated for Real Time BI
Supply chain analytics is the process of using a variety of Business Intelligence (BI) tools to analyze large amounts of enterprise data in order to gain insights into an organization’s supply chain, manufacture, and purchasing operations. These BI tools allow businesses to link customer data with internal information such as operations, sales, and finance.
The components of BI include data warehouses, data marts, query and reporting tools, decision support systems, forecasting technology, document warehouses and management systems, knowledge management software, mapping tools and visualization technology.
Data warehousing stores the core data needed for a SCM analytics solution; data marts are specific to the requirements of individual departments; query and reporting tools provide multidimensional views for strategic planning; decision support systems enable real-time analysis; data mining helps uncover trends from unstructured data sources; document warehouses manage document information from various sources; knowledge management platforms provide insight on how knowledgeable employees use their expertise to optimize processes.
For example, technology like this allows organizations to keep track of inventory levels in real-time for better cost optimization decisions or offer customers more accurate estimates on when their orders will arrive.
The picture below shows basic understanding of SCM analytics systems architecture.
Figure 2: Supply Chains Analytics Systems Architecture
Role of Big Data
The role of big data in SCM analytics is to improve decision-making for all supply chain operations by combining data and quantitative methodologies. Big data analytics creates better decisions for all supply chain operations by expanding the dataset for analysis beyond the typical internal data stored in ERP systems. It is beneficial for solving the company’s problems at the speed required by the business and helps address various issues strategically and affect the supply chain. Big data analytics also promotes good communication between the manufacturer and the supplier to enhance the delivery time and adds to data-driven decisions to improve service levels and cut costs.
Supply Chain Analytics – Simplified
Supply Chain Analytics refers to the utilization of data and analytics to improve decision-making around a company’s supply chain operations, performance, and efficiency. This involves examining and enhancing processes like sourcing, manufacturing, logistics, inventory management, and delivery. Through the use of Business Intelligence (BI) tools, businesses can obtain critical insights into their supply chains, enabling them to formulate effective strategies for achieving cost savings and enhancing the customer experience.
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About the Author – Dr Muddassir Ahmed
Dr MuddassirAhmed is the Founder & CEO of SCMDOJO. He is a global speaker, vlogger and supply chain industry expert with 17 years of experience in the Manufacturing Industry in the UK, Europe, the Middle East and South East Asia in various Supply Chain leadership roles. Dr. Muddassir has received a PhD in Management Science from Lancaster University Management School. Muddassir is a Six Sigma black belt and founded the leading supply chain platform SCMDOJO to enable supply chain professionals and teams to thrive by providing best-in-class knowledge content, tools and access to experts.