As of today, an increasing number of enterprises use BI tools and advanced analytics methodologies to gain real-time insights into business data. Using data-driven analytics techniques in different fields enables organizations to evaluate their business performance and pinpoint areas for improvement. At the same time, it aids in optimizing business process management strategies with intelligent decision-making.
Data analytics has become even more critical for manufacturing companies that thrive on fulfilling the needs and expectations of their customers. A majority of manufacturing companies implement advanced analytics techniques to efficiently manage their inventory, warehouse, sales, and CRM operations. However, they often overlook the importance of data analytics in supply chain management.
In this post, we shed light on the significance of implementing data analytics features in supply chain application development.
The term ‘analytics’ represents the ability of a system to make data-driven decisions using advanced algorithms and data visualization techniques. That said, supply chain analytics deals with the effective management of data associated with supply chain operations. Understandably, supply chains produce enormous amounts of data on an everyday basis. According to experts, this data holds a hidden value that can provide ample information about the existing SCM strategies and their efficacy.
However, a major amount of supply chain data is unstructured. Thus, we require advanced software tools to make sense of this data and extract hidden patterns to gain valuable insights. Besides, the use of data analytics in supply chain management fosters the use of cognitive technologies such as artificial intelligence and machine learning.
There are several different types of data analytics methodologies that manufacturing companies can leverage to optimize their supply chain processes. That being said, we can broadly categorize supply chain analytics into four main types:
Capacity planning is the process that aims at determining the production capacity of an organization with respect to changing market demand. It enables manufacturers to maintain a perfect balance between the procurement of raw materials and their manufacturing capacity to meet sales demand. The availability of advanced analytics features has significantly improved capacity planning by providing valuable insights into inventory, warehouse, and supply chain operations. Most importantly, with effective capacity planning, businesses can develop a good understanding of dynamic market conditions. It also helps understand the latest market trends and aids in demand forecasting through insightful demand-supply curves.
Sales and operations planning, also known as S&OP is an advanced analytics methodology that mainly focuses on improving financial planning and operational efficiency. It is often called integrated business planning (IBP) and enables operations managers to pin down financial challenges while overseeing operations’ bottom line. With effective sales and operations planning, manufacturing and supply chain companies can streamline financial management and improve production planning.
Business intelligence, often referred to as historical data analytics, helps you gain insights into the past performance of a supply chain. It includes several crucial parameters such as sales ratios, stock levels, past consignments, inventory turnover, and the likes. BI software development services focus on identifying the aforementioned parameters to provide a complete performance evaluation. Based on the information provided by BI tools, manufacturing companies can optimize their operations to achieve higher levels of productivity. It further helps them accelerate their processes, enhance production capacity and amplify their profits and sales benefits.
Demand forecasting is yet another critical feature of data analytics in supply chain management that deals with the forecasting of future events. It incorporates advanced techniques such as predictive analytics, predictive forecasting, and data mining to predict future events with remarkable accuracy. With these features, warehouse managers can accurately predict demand for a particular product or commodity that would be required for frictionless supply chain operations. At the same time, it enables enterprises to predict future outcomes of a business decision, thus helping them with intelligent decision making.
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All things considered, it is quite evident that implementing data analytics in supply chain management would help organizations amplify their profits through effective production planning. A Gartner survey revealed that 29 percent of companies reported higher levels of ROI post implementing data analytics in their routine operations. Whereas, only four percent of companies reported no ROI or ROI below the expected scale. The data indicates that data analytics should be a staple element of the supply chain application development process regardless of the industrial use cases. At the same time, manufacturing and supply chain companies should also focus on BI application development to derive maximum benefit from their data resources.
We are a 360-degree ERP development company that provides complete enterprise solutions for cross-industry business use cases. Our development team is skilled at implementing data analytics in supply chain management processes to streamline complex SCM operations with reduced manual strain. We use open-source ERP platforms like Odoo, OFBiz, ERPNext, and Opentaps to develop data-driven supply chain software solutions. Our BI software development services let you scrupulously optimize your operations and achieve business goals with intelligent decision-making. To know more about supply chain software development services, visit our website or drop us a line at [email protected].