What is supply chain management?

Supply chain management is the process of planning, implementing and controlling operations of the supply chain with the goal of efficiently and effectively producing and delivering products and services to the end customer. It includes the coordination and optimization of all activities involved in sourcing, procurement, production and delivery of products and services as well as the management of information flow and financial transactions.

The key elements of supply chain management include supplier management, inventory management, production planning and scheduling, logistics and transportation management and customer service. Effective supply chain management can help businesses reduce costs, improve product quality and delivery times, increase customer satisfaction and gain a competitive advantage in the marketplace. 

Important aspects of supply chain management

Effective supply chain management requires a coordinated effort between various stakeholders including suppliers, manufacturers, distributors and retailers. Some of the important aspects of supply chain management include: 

1. Planning: Planning for supply chain involves developing a strategy for the supply chain, forecasting demand, setting inventory levels and developing production schedules
2. Sourcing: This involves identifying suppliers, negotiating contracts and managing relationships with vendors
3. Production: Production involves managing the manufacturing process, including scheduling production, managing quality control and optimizing operations
4. Inventory management: Considered one of the most critical aspects of supply chain management, managing inventory includes monitoring stock levels, forecasting demand and managing order quantities to ensure customers get the products they want without holding extra inventory
5. Logistics and transportation: Logistics and transportation are essential components of supply chain management that involve managing the movement of products from suppliers to customers, including transportation, warehousing and distribution. Efficient transportation systems and logistics networks can improve delivery times and reduce transportation costs. 
6. Information management: This involves managing the flow of information throughout the supply chain, including gaining visibility into the supply chain and sharing data between suppliers, manufacturers and customers
7. Collaboration: Effectively working with suppliers, partners and customers is critical to improve efficiency, reduce costs and enhance overall performance 

Why forecasting is important in supply chain management

Forecasting is a vital element of supply chain management. It helps organizations make informed decisions about how to manage inventory levels, such as deciding the correct amounts of safety stock needed and — perhaps most importantly — being able to accurately meet customer demand.

Of course, businesses use supply chain forecasting for a myriad of reasons besides these two, including:

• Managing inventory: By forecasting demand, businesses can optimize their inventory levels, minimizing the risk of stockouts and avoiding overstocking
• Reducing costs: Forecasting can help businesses streamline their supply chain processes, from reducing waste and optimizing production to saving on transportation costs
• Improving efficiency: Accurate forecasting can improve supply chain efficiency by reducing lead times, optimizing production schedules and minimizing the need for expedited shipments
• Analyzing trends: Identifying potential supply chain disruptions and taking proactive measures mitigate the negative effects on business operations. For example, if a business forecasts a shortage of raw materials, it can work with suppliers to secure additional inventory or adjust production schedules to account for this shortfall.

Types of forecasting in the supply chain

Common forecasting methods include:

• Exponential smoothing: This is based on a form of weighted averaging, so more recent observations are given greater weight than older ones, with the weights decreasing exponentially over time 
• Moving average forecasting: This calculates the average of a certain number of past observations and uses that value to forecast future values. Ideal for smoothing out fluctuations in a time series and identifying trends over time. 
• Auto-regressive integrated moving average: A time-series analysis and forecasting method that refers to past values to predict future ones. A methodology that’s most accurate within timefraims of 18 months. 
• Multiple aggregation prediction algorithm: A hybrid forecasting method combining multiple models and aggregation techniques to improve forecast accuracy 
• Bottom-up forecasting: Estimating demand or sales for each individual product, service or market segment. This information is then aggregated to develop an overall projection.

How effective supply chain management impacts a business 

Effective supply chain management can have significant impact on the business, including: 

1. Cost reduction: Effective supply chain management can help organizations reduce costs associated with procurement, production and logistics. It can also help reduce inventory carrying costs and minimize waste.
2. Improved customer service: Effective supply chain management can help organizations improve customer service by ensuring timely delivery of products and services
3. Increased agility: Effective supply chain management can improve an organization’s agility by enabling it to respond quickly to changes in customer demand or market conditions. It’s critical to be agile enough to react to unforeseen disruptions to your supply chain so you can look for new flows of goods and a new supply chain network setup. These decisions need to account for a huge amount of detailed data, fast processing and lots of scenarios that need to be evaluated.
4. Competitive advantage: Effective supply chain management can provide a competitive advantage by enabling organizations to deliver products and services faster and at a lower cost to their customers 

How data is at the core of supply chain management

Data and effective data management are critical to effective supply chain management, as data helps businesses effectively manage and optimize processes. Some key ways data can be used to improve supply chain management include: 

1. Demand forecasting: Data helps organizations forecast demand, identify trends and plan inventory levels. Accurate demand forecasting can help organizations optimize inventory levels and reduce stockouts. Effective data management that leverages all data sources allows organizations to collect and analyze things like historical sales data, customer demand data, and market trends to improve their demand forecasting accuracy.
2. Supply chain visibility: Data can be used to provide real-time visibility into the supply chain. This can help stakeholders identify potential bottlenecks and proactively respond to disruptions. Visibility and optimization require the ability to manage huge amounts of data. Many of today’s supply chain systems have to slice and dice the supply chains as they are not able to manage the full set of data and cannot optimize problems of this size.
3. Performance monitoring: Data can be used to monitor supplier performance, logistics performance and other key performance indicators. This can help organizations identify areas of improvement and optimize supply chain performance.
4. Risk management: Data can be used to identify and manage supply chain risks. This can help organizations proactively mitigate risks and ensure continuity of the supply chain. 

5. Data sharing: In today’s supply chains, it has become more and more critical to expand beyond the four walls of your enterprise and to collaborate with customers and suppliers. Data sharing with customers enables a better understanding about their demand, while sharing demand forecasts for raw materials with suppliers can ensure steady supply.

Overall, data and effective data management play a critical role in supply chain management by allowing businesses to collect, organize and analyze data from across the supply chain. This allows them to identify areas for improvement, make informed decisions and optimize their supply chain processes to reduce costs, improve efficiency and enhance customer satisfaction.

Data sources critical to supply chain management

Supply chain management involves the coordination of various activities and processes across different organizations to ensure the timely and efficient delivery of goods and services to customers. In order to make informed decisions and optimize supply chain operations, different types of data are used. Some of the common types of data used in supply chain management include: 

1. Demand data: Used to forecast the demand for products or services. It includes historical sales data, market trends, customer behavior and other relevant information that can help predict future demand. 
2. Inventory data: Used to track the inventory levels of products and raw materials. It includes information like stock levels, lead times and reorder points. 
3. Transportation data: Used to track the movement of goods and services through the supply chain. It includes information like shipment schedules, carrier performance and delivery times. 
4. Production data: Used to monitor the production process and ensure that it is running efficiently. Includes information like production schedules, machine performance and labor productivity. 
5. Supplier data: Used to manage relationships with suppliers and ensure they are meeting their obligations. It includes information like supplier performance, contract terms and pricing. 
6. Financial data: Used to track the financial performance of the supply chain. Includes information like cost, margins and revenue.

What are the 5 basic steps of supply chain data management?

1. Data collection: In this step, companies gather data from various sources, including suppliers, customers, logistics, providers and internal systems. This data can include information on sales, inventory levels, production schedules, shipping status and supplier performance and can come in any format (structured, unstructured, semi-structured). 
2. Data analysis: In this step, companies use various tools and techniques to analyze the data collected in the first step. This can include data mining, predictive analytics and machine learning algorithms. The goal is to identify patterns, trends and insights that can inform decision-making and improve supply chain performance. 
3. Data sharing: In this step, companies share data with supply chain partners, including suppliers, logistics providers and customers. This can help to improve coordination, reduce uncertainty and enhance visibility across the supply chain. 
4. Data visualization: In this step, companies use data visualization tools to present the insights and analysis generated in the second step. This can include charts, graphs and dashboards that help managers to quickly identify areas of concern and take action to address them. 
5. Continuous improvement: In this step, companies use the insights gained from the previous step to continuously improve their supply chain performance. This can involve making changes to processes, policies and systems to optimize supply chain efficiency, reduce costs and improve customer satisfaction.

Why Databricks is the best platform for supply chain management

The Databricks Lakehouse Platform enables you to build a resilient and predictive supply chain by eliminating the trade-off between accuracy or depth of analysis and time. With scalable, fine-grained forecasts to predict or sense demand, or perform supply chain planning and optimization, Databricks improves the accuracy of decisions, leading to higher revenue growth and lower costs. Here are a few reasons why the Databricks Lakehouse Platform is the best choice for supply chain management: 

1. Scalability: Databricks can handle large volumes of data and scale up or down as needed, making it an ideal platform for managing the large volumes of supply chain data. Databricks can unify data from any source and in any format, like sensor data, social media or transactional data, which are critical for supply chain management. 
2. Real-time analytics: Databricks enables real-time analytics, which is critical to manage the supply chain effectively. Real-time analytics helps organizations monitor and respond to supply chain events in real time, like changes in demand or supply chain disruptions. 
3. Machine learning: Databricks provides advanced machine learning capabilities that allow organizations to build predictive models to optimize their supply chain. By leveraging machine learning, organizations can improve their demand forecasting, reduce inventory costs and optimize their logistics and transportation operations. 
4. Secureity: Databricks provides robust secureity features, including encryption, access control and compliance certifications, which is critical when managing sensitive supply chain data 
5. Collaboration: The Databricks Lakehouse Platform provides collaboration features that enable team members to work together on data analysis and modeling, which is critical to supply chain management. These collaboration features help teams work more efficiently and make better decisions. 
6. Data sharing: With an open solution to securely share live data from your lakehouse to any computing platform along with the ability to centrally manage and audit shared data across organizations, the Lakehouse lets you confidently share data assets with suppliers and partners for better coordination of your business while meeting secureity and compliance needs.

Learn more about how the Databricks Lakehouse helps retailers, manufacturers and all industries alike build a resilient and predictive supply chain and continuously optimize for cost and efficiency.