In today’s competitive and complex market, it is tough to develop a proper forecasting model for fulfilling demand in the supply chain process. Most of the prediction techniques deliver unsatisfied outcomes. The main reason for this disappointment is the old technique used in such operations. These models are not designed to perceive continuous information from available data to make decisions. Machine learning is one breathtaking solution to face such problems. Its broader implementation in supply chain management can be seen as follows:
Table Of Contents
1. Better Decisions:
It is not easy to make the right predictions of every decision in the supply chain industry. Any decision, which is taken during the life cycle of supply chain management, is dependent on various processes and overall functioning of the industry. Machine learning with its analytical approach processes large data set to provide valuable insights for quick decision making.
2. Smart Machines:
ML in big data delivers the smart machines with provided instructions. A machine with low-level operating value is needed to improve with the practical solutions. These intelligent machines are sustained to perform the provided tasks effectively.
3. Supplier Quality Management:
Machine learning improves the supplier quality management with its powerful pattern recognition technique looking for supplier’s quality level and log creation. Generally, an industry is dependent on various suppliers to manage their plant units. The products or materials supplied to the industry needs to qualify few minimum criteria to assure the material quality and track of other dependent factors. Introduction of ML in industries maintains accurate report of every operation involved in the product hierarchy saving the extra effort and cost associated with it.
4. Improved Demand and Production Planning:
Machine learning is successfully implemented for demand planning but suppliers these days are focused on the use of ML to enhance their production planning. But when pointing out the scenario architecturally and culturally, the inclusion of ML in production planning is harder than in the case of demand planning. The global supply chain planning (SCP) market of 2 billion USD is moving to software as a service model (SaaS) from software license model lowering the upfront cost. Various applications with the power of machine learning are developed to meet the organizational objectives. One can enroll in Android Development Course to understand the basic to advanced implementation of ML in widely used applications.
The production planning is one critical phase holds 25% of the overall market. Production planning software concern with daily production planning in an industry as well as deciding weekly or monthly plans to increase up the production throughput. On applying ML to the supply chain management effectively, it identifies customer demand patterns and presents the proper solution for multiple scenarios, making an organization to stand steady among other competitors.
5. Maintaining Stock Levels:
In ongoing planning methods, organizations need to keep high stock safely almost all the time of emergency. However, implementing machine learning can help in monitoring various variables for maintaining an optimum stock level driving a secure future.
6. Inventory Management:
Machine learning can enhance the inventory management in various ways such as finding data that can affect inventory optimized results, well-maintained data, automating recognition, predict out stock etc. It reduces the process and admin costs by integrating the warehousing operations with in and out freights. Thus it achieves the improved connectivities between the warehouses, logistics and automated resources.
7. Accelerate Workflow:
ML helps in planning and scheduling maintenance works by aligning labor, resources, equipment, and timing. It runs planned and unplanned maintenance operations to improve the safety and environmental resulting in the certain optimal operations.
8. Pricing:
Price of a product relies on the various factors- from retails selling to the amount of manufacturing and used materials including logistics, labor or other used resources costs. Machine learning can simplify this procedure by individually examine all these factors.
9. Introducing a new product in the market:
A new product needs proper planning before its introduction to the market. Before starting the first phase production, marketing teams perform in-depth research of the product explaining its popularity or rejection rate in the market among the subjected audience. The conducted research is highly qualified which contains the background study of the product and consumers, but, can’t always direct an organization to a proper outcome. With ML involvement, all the concluded outcomes maintain transparency availing great chances for its verification.
Final Thoughts:
Machine learning, if correctly implemented at the right corners within right time, leverages supply chain organizations with multiple advantages. The developed accurate models with powerful demand forecasting capability eliminate the barriers for the planning department, making it easier and faster. Maybe in future, every business will include the technology as an indispensable tool in their operations.
| Published on Java Code Geeks with permission by Vixit Raj, partner at our JCG program. See the original article here: 9 Ways Machine Learning Is Revolutionizing Supply Chain Management Opinions expressed by Java Code Geeks contributors are their own. |
