Supply Chain Management
Supply chain professionals in today's competitive marketplace are trying to build integrated supply chains that are efficient, productive, and flexible while being able to handle massive amounts of data. Supply chain personnel often estimate demand based on historical data. However, the recent advent of machine learning algorithms has put at our disposal new tools that can achieve amazing forecast accuracy on common industrial demand datasets in a short period of time. This allows you to quickly achieve good performance in predictive accuracy. These models can learn many relationships that regular statistical models cannot. Traditional statistical approaches use previously established models to make forecasts based on previous demand. The problem is that these models have not been able to meet the demand experienced previously.
A key benefit of data science is increased accuracy compared to traditional methods. The more data that is analyzed about how to forecast your supply chain, the more likely you are to create reliable forecasts.
Improved management:
Managing your supply chain is not easy. You need to identify timely and cost-effective insights. The goal of data science is to use supervised and unsupervised learning to identify the characteristics and factors that contribute to effective management.
Increase efficiency with less effort:
Using machine learning and data science techniques enables horizontal collaboration between different transportation and logistics networks. This not only increases supply chain efficiency, but also reduces potential hazards.
Statistical science and machine learning excel at recognizing patterns, whether based on data insights or visual data. Therefore, it is useful to check the condition of physical assets in the supply chain. Machine learning can predict new product sales and demand as companies launch new products. Statistical models help in sophisticated demand forecasting, taking into account various market factors.
Supply chain data scientists perform analytics that help predict and manage risk. Here are some potential use cases for data science in SCM:
Supply chain data scientists perform analytics that help predict and manage risk. Here are some potential use cases for data science in SCM:
1. Materials
In most factories, finished products start with raw materials. Fruits, flowers and latex are examples of plant-based raw materials. Leather, wool and milk are made from animals. Minerals are mined (oil, metals, minerals). Data analytics can be used to improve materials management functions such as procurement, quantity, storage, safety and quality control. The report also evaluates the impact of inputs on production and assesses the quality of the final product.
2. Procurement
The term "procurement" refers to the process of obtaining products and services from vendors. Finding suppliers, negotiating prices, submitting orders, paying for goods, tracking shipments, and keeping records are common parts of the supply chain process. Collecting and analyzing procurement data with the aim of generating business insights and making more informed decisions is the primary focus of procurement analytics. It makes sense to keep an eye on the purchasing process and assess aspects such as consumable prices, product quality, and supplier relationships.
3. Fuel and transportation prices
Ships, trucks, trains and planes are all used to move goods through the supply chain. With the help of data scientists, we can predict and visualize the best modes of transportation. Shipping plans, shipping routes, backhaul routes, and transportation compliance are all calculated using different forecasting algorithms.
Data analytics can help manufacturers with their own vehicle fleets save costs and improve productivity. Fuel consumption data can be collected and analyzed using telematics devices and on-board computers. Businesses can save gas by promoting safe driving practices and stocking up on fuel-efficient vehicles.
4. Discounts and Tariffs
Companies that source materials internationally are often subject to trade restrictions. A tariff, for example, is a charge levied on an imported product. Products made from certain raw materials can be more expensive than they would have been without government regulation.
The impact of price increases or decreases on your business can be better understood using data analytics. Plus, learn from the past and gather information about your customers. Use this information to set prices that reflect the value of your products and increase sales.
5. Supply and demand in the market
Data scientists can predict future demand levels by analyzing historical sales data combined with real-time data. Machine learning and predictive analytics are often used to assess the variables driving customer demand and their potential impact on the business.
Make better business decisions with accurate demand forecasting and planning support. It provides insight into how demand at various points of distribution can be influenced by factors such as consumer preferences, competitive dynamics, and company-specific manufacturing and promotional efforts. To do.
6. Inventory Management Issues and Practices
Appropriate inventory, quantity, and inventory can all be determined using data analytics. This simplifies material and product demand forecasting, inventory management, and supply monitoring.
In addition to consumer behavior, supply chain management data scientists can also provide information on product and sales channel performance. This helps businesses avoid shortages and excesses, fulfill orders faster, maximize revenue and profits, and increase customer satisfaction.