TOC
1. Introduction
2. Understanding Supply Chain Management
. Definition of SCM
. Stages of Supply Chain Management
. Benefits of Effective SCM
3. Common Challenges in Supply Chain Management
. Material Scarcity
. Lack of Supply Chain Visibility
. Transportation Costs
. Data Security
4. Advancements in SCM through Data Analytics
. Planning and Forecasting
. Risk Management
. Improved Customer Satisfaction
5. Key Areas of Data Analytics Utilization in SCM
. Demand Forecasting
. Supply Chain Visibility
. Supplier Performance Assessment
. Logistics Optimization
6. Conclusion
Ever imagined how easy it would have been getting real-time insights of the whole supply chain process, starting from collecting raw materials to delivering finished products, without manually getting involved. Well, today it is possible with the help of data analytics.
For instance, an e-commerce platform, with 2+ million customers and having 13+ million orders on personal care products, food and beverages faced unproductiveness in supply chain. This includes frequent stock outs leading to delays. To get a solution, the company partnered with Prescience Decision solution . As a solution they developed an analytics-based forecasting system using past 3 years of data and advanced model techniques such as linear regression, XG Boost, and ARIMAX, and Python and SQL. It showed 95% of weekly forecast accuracy for inventory costs, fleet sizes etc.
Understanding Supply Chain Management & its Importance
Supply chain management (SCM) means the management of an entire workflow from collecting raw materials to turning them into finished products and timely delivery to the final customers. Effective supply chain management includes 5 major stages: planning, sourcing, production, distribution, and returns. In a global supply chain, these stages go through different sets of networks, including manufacturers, suppliers, retailers, wholesalers, etc. It becomes very important to optimize these networks to get the work done on time.
Supply chain management is important in the modern world because it helps organizations cut down on costs, expenses, and waste and make them profitable. According to a study, it says that businesses with advanced SCM capabilities are 23% more profitable than the ones without SCM.
In this blog, we will understand what is supply chain management, what are its challenges, and how data analytics will play a major role in improving it.
Common challenges in supply chain management
At times, effective management of the supply chain can fall due to several reasons, such as shortage of material, unexpected delays, fuel costs, supply chain visibility, etc. Let’s look into some of the common challenges that are witnessed in SCM.
Material scarcity: One of the basic needs for manufacturers is to produce goods; that becomes a challenge when raw materials are not available. The scarcity of basic raw materials such as glass, plastic, lumber, and metal can cause delays in the production. According to the 2023 hub supply chain resilience report, of 334 respondents, 61% choose scarcity in raw materials as a top challenge. This becomes one of the reasons for unexpected delays and can be resolved by keeping the communication alive within the vendors and suppliers.
Lack of supply chain visibility: Supply chain visibility is a core logistics activity to keep an eye on the movement of goods, vehicles from warehouses to the customer’s doorstep. Organizations without advanced capabilities might find it difficult to track each and every components, supplies, delivery time, finished goods, etc.
Transportation Costs: The transportation/shipping costs increases due to several reasons, such as sudden delivery demands, fuel costs, not having the right shipping containers, etc. This might lead to huge expenses for businesses. To tackle this issue, companies can integrate TMS(transportation management system), a software that will help organizations track the movement of goods and optimize the routes.
Data security: Data management is a crucial part of SCM. This includes data interoperability and connection. As different stakeholders use different technologies, it becomes difficult to consolidate all of them together. To tackle this, a unified platform can be integrated. Additionally, the SCM holds sensitive data, which leads an easy way into data breaches and cyber-attacks.
Supply Chain Management Through Data Analytics
Planning & Forecasting: With the help of data analytics, planning and forecasting the future outcomes has now become easy. Predictive analytics analyzes the past purchasing history and order patterns, thus helping businesses to be prepared when getting high product demands.
Risk management: In SCM, risk can be managed by assessing each and every point of different stages of SCM. Data can be analyzed from warehouses to understand the stock availability, suppliers on time availability, logistics operations, and so on. Moreover, to mitigate the external risks, data can be collected from global health data and weather conditions, to mitigate risks.
Improved Customer Satisfaction: Customer data can be collected through different sources such as demographic, transactional, operational data etc. These data can be utilized through predictive analytics models. This helps businesses to understand the preferences of customers, helping to tailor more personalized products catering to customer needs.
Key Areas where data analytics is utilized in supply chain
As data analytics benefits SCM in various ways, there are certain key areas where utilizing data analytics will help manage the supply chain more efficiently. This includes demand forecasting, logistics optimization, supplier performance, logistics optimization, and so on.
1. Demand Forecasting
Predictive analytics, subset of data analytics used for demand forecasting. Demand forecasting in simple terms means, the ability to foresee the demands of customer by analyzing data. This includes past purchases, historical data, buying patterns, etc. This is also termed as demand analytics. Predictive analysis includes several methods, such as liner regression analysis, time series analysis, and machine learning algorithms. These predictive analytics models constantly gets refined based on the new data they receive over a period of time.
Accurate demand forecasting also helps in inventory management. It helps in reducing the chances of overstocking or understocking of products.
2. Supply Chain Visibility
Supply chain visibility helps in reduce unwanted disruptions, increase communications, and better customer service, etc. With the complexities of modern supply chains, having a clear visibility has become a necessity. Real-time analytics provide end-to-end tracking and identify patterns and insights at different stages of the supply chain.
3. Supplier Performance Assessment
Supplier performance assessment is important for several reasons: to check if the products are getting delivered on time, prevent delays, reduce costs, etc. The assessment is based on a few key KPIs, such as on-time delivery, quality of product, pricing terms, risk, and stability. To evaluate these KPIs, organizations can set up scorecards (numerical value to measure how well they performed). These data can be analyzed and tracked using spreadsheets or software. Data analytics helps identify unsure patterns and supplier underperformance, which helps in making quick decisions.
4. Logistics Optimization
Logistics optimization helps businesses identify transportation routes, costs, delivery times, and so on. Using big data analytics, businesses can analyze large volumes of data from different sources. One of the key benefits includes the ability to track real-time insights from GPS trackers, sensors, etc. This provides organizations the ability to make quick decision.
Summary
Active supply chain management (SCM) has become a necessity in the world of business. Even though there are challenges, integrating data analytics can help address these problems and provide uncomplicated and accurate decision-making capabilities. In the complexities of today’s data-driven environment, for any business to stay ahead, adopting data analytics is crucial. In the supply chain, from demand forecasting to inventory management and risk mitigation, data analytics plays a pivotal role, leading to long-term growth.
Moreover, Prescience delivers specialized data engineering and analytical services tailored to customer needs to optimize data infrastructure to ensure that businesses possess accurate and relevant data for their analytic needs. Prescience helps organizations improve their data pipeline and customize their data warehouse for businesses to leverage the full potential of their data.
Prescience Team
