In earlier years, demand planning and forecasting have been primarily based on statistical analysis and quantitative performance metrics. In addition, spreadsheets were used to hold data from various supply chain actors.
Enterprise Resource Planning (ERP) and Electronic Data Interchange (EDI) systems were adopted by businesses by the 1990s to connect and exchange data among supply chain partners. These technologies helped firms design, plan, and forecast while facilitating quick access to data for analysis.
Businesses started utilizing business intelligence and predictive analytic tools in the 2000s. With these technologies, companies could learn more about how their supply chain networks were operating, how to improve their decisions, and how to optimize their networks.
- Predictive Analytical Modelling
Accurate demand forecasting in supply chain management has several advantages, including reduced holding costs and ideal inventory levels.
Businesses can profit from predictive analytics for demand forecasting by using machine learning models. This artificial intelligence (AI) models are skilled at spotting obscure trends in past demand data. Supply chain machine learning can be used to identify problems before they affect corporate operations.
A robust supply chain forecasting system ensures the company has the resources and knowledge necessary to address any threats or challenges. Additionally, the effectiveness of the reaction rises in direct proportion to how quickly the company can address issues.
- Inventory Management
Inventory control is essential for supply chain management because it enables businesses to deal with and respond to unforeseen shortages. For example, no supply chain organization would want to cease production while starting a search for a new supplier. But, on the other hand, they wouldn’t want to overstock either because it starts to hurt their revenues.
Finding the right balance between timing the purchase orders to keep operations running smoothly and avoiding overstocking the things they won’t need or utilize is a critical component of supply chain inventory management.
- Maintaining Quality
Maintaining a double check on Quality and safety becomes a major challenge for supply chain organizations as there are increasing pressures to deliver items on time to keep the supply chain assembly line going. Accepting defective items that don’t fulfill quality or safety standards could pose a significant safety danger.
Furthermore, challenges and hazards that readily snowball throughout the entire supply chain, generating serious problems, might result from environmental changes, trade disputes, and economic pressures on the supply chain.
- Real-time visibility for better Customer Satisfaction.
Visibility was recognized as a persistent issue facing supply chain businesses in a Statista survey. Visibility and tracking are crucial for a successful supply chain organization, and it is continually searching for new technology that will guarantee improved visibility.
Deep analytics, IoT, and real-time monitoring are just a few examples of machine learning approaches that may be leveraged to significantly increase supply chain visibility, allowing organizations to alter the customer experience and meet delivery obligations more quickly. Machine learning models and workflows accomplish this by assessing historical data from many sources and then identifying links between the operations along the supplier value chain.
Amazon is an excellent illustration of this, as it uses machine learning methods to provide its users with fantastic customer service, which is accomplished via ML, which gives the business insights into the relationship between product recommendations and subsequent client website visits.
- Last-mile tracking
As its effectiveness can directly affect a number of sectors, including customer experience and product quality, last-mile delivery is a crucial component of the entire supply chain. Additionally, data reveals that 28% of all delivery expenses in the supply chain are attributable to last-mile deliveries.
By considering many data points regarding the methods individuals use to enter their addresses and the overall amount of time it takes to deliver the goods to specific places, machine learning in the supply chain might present excellent prospects. Additionally, ML can be a great help in streamlining the procedure and giving clients more precise information about the status of their shipments.