How AI solutions development can help transform supply chain management?
Supply chain plays a critical role in ensuring success of many businesses in various industry today. With digital transformation happening across all business industries, supply chain management thus requires the same attention on digitization to meet consumers’ demand. And together with the rise of mobile devices, IoT devices (internet of things) and sensors used in stores, warehouse, and transportation vehicles, etc. results in an inter-connectivity and frequent flow of information and data between those devices – leading to an abundance of available data with lots of potential for improvement, particularly for supply chain management.
Thus, simply having access to data in a central place is not adequate to keep up with rising market condition in business competition. In order to extract useful information out of those vast amount of data, it is required of business organizations to incorporate the right technologies and expertise into businesses which are AI/Machine Learning and Data Analytics in this case. With AI, business will be able to find pattern and derive insights out of data, and use it to support decision making as well as recommendations for action.
A transformation in supply chain make it possible for to reduce downtime, boost capacity, ultimately lowering costs and improve profitability. Equally importantly is that by applying AI in supply chain, businesses can actively plan and continuously improve their supply chain management at various levels across business, making each and every single customer visit at store a pleasant journey. Considering that consumers are now ultra connected and in-control of their shopping demand, creating a shopping demand that is more complex than ever before.
The role of Machine Learning in changing supply chain management for the better
In this day and age, almost all businesses, especially retail ones, have access to data and some might have a lot more compared to others. However, without AI and Machine Learning technology to analyze and interpret the right data - which is collected from various sources across organizations - companies wouldn’t be able to leverage such data to make better-informed and profitable business decisions.
AI offers retailers a competitive business advantage, specifically with machine learning development solution that is trained to analyze and study from vast amount of business data put in. This enables retail business to evolve beyond just dashboard and report, providing AI-recommendation and predictive analytics for demand forecasting etc. Such recommendation and predictive analytics, which could possibly be implemented across various areas of the supply chain, allows retailers to better keep up with the constantly rising market and consumer demand.
In a typical model of supply chain management, there are various points of interactions such as warehouse, transportation vehicles, physical stores etc. where each plays an important role in ensuring the efficiency of supply chain management. Hence, collecting and aggregate as well as interpret those data from those interaction points is critical to determine what information is relevant or high-risk in solving the problem of potential business disruptions in supply chain. For example, a weather disaster such as flood, or storms might cause a delay in transportation of stocks leading to an event like out-of-stock which is one of the most common problems encountered by retail stores. The ability to incorporate machine learning into supply chain management (by using weather data) to improve transportation planning is one of the many use cases that can help business to address potential interruption point in supply chain. Another prominent use case of AI in supply chain is to incorporate Machine Learning in demand planning applications to improve demand forecast by incorporating various factors and data such as store traffic data, competitors pricing data
With a portfolio of legacy business applications, some potential disruptions might not be addressed efficiently as such traditional systems do not have a capacity to find pattern in data and/or produce actionable insights into intelligence that can be implemented across the supply chain in a streamlined and coordinated way, which is in itself the competitive advantage that is much needed for retail businesses.
Actively respond to disruptions in the supply chain, with AI.
As mentioned previously, many retailers find that insufficient level of inventory due to planning error or inaccurate inventory data which lead to unfulfilled orders are among the biggest pain points that many retailers often encounter and also heavily invest in to develop solutions to address such pain points. Retail organizations always seem to be in need to find a solution in order to predict and/or identify whether these kinds of mismatching exist or may arise, so that, they are well prepared in advance to resolve such issues. AI/Machine Learning technology has the potential to support retailers in solving some of the common problems found in inventory management. For instance, computer vision combined with radio frequency identification (RFID) can help retailers in automating inventory management. Specifically, RFID tags can be used to check items into a storage facility and may extend across any points in the journey. This helps maximizing on-shelf items availability.
Advantages over traditional supply chain
Compared to a traditional model, AI allows companies to become a lot more active in their supply chain management by being able to identify and predict relevant factors (in advance) which may cause business interruption in order to support making better business decision. Today, supply chain comprises of multiple and various stakeholders with a network inter-connected between many devices, making it become a lot more complex compared to how it used to be a decade or two ago. As a result, the risk of downtime happening at various points in the supply chain can get multiplies become even bigger. A proper executed plan would require precise co-ordination across the right points through the supply chain. At the physical stores and warehouse where execution happen, businesses can start to collect data in bulk amount in order to apply AI/Machine Learning solutions in order find the relevant information which helps improves planning as well as addressing any potential issues which may arise in execution.
Moreover, upon taking in the recommendation and predictions delivered by AI system, new data on the effectiveness of such AI recommendations in execution can be collected for further analysis making the new system capable of an active learning for continuous improve based on feedback-loop. This transforms the old supply chain into a brand new one with capacity for active planning and continuous improvement, bringing more flexibility and efficiency for businesses at various levels across organizations.
For a legacy business system, collecting and analyzing large amount of data could prove to be highly challenging or even impossible to do right, yet this is entirely possible with advanced machine learning models. Ultimately, all this makes supply chain with AI miles ahead when compared to the traditional model.
Improvement in shopping experience for retail customers
The transition into a new, on-demand digital economy will require retail businesses as well as other stakeholders in the supply chain including manufacturers, and transportation suppliers to adopt an innovate strategy in order to stay competitive. Based on business data, AI/Machine Learning could offer retailers highly relevant and localized insights, enabling a better planning and execution process. Machine Learning helps aggregate and analyze data from incongruent sources and provides new information, at the same time eliminating non-relevant information, offering insights to guide employees on executive action. Additionally, together with data that is of high quality, Ai is capable of detecting and predicting distortions in inventory level data, thus alleviating on the inventory disruption problem. With machine learning system that is capable of learning and improves, retailers can transform various areas of supply chain management which ultimately boost business efficiency, and thereby improving on profitability.
Conclusion:
In the age of digital transformation, improving the overall efficiency of supply chain management is critical to ensure success for business, especially the ones that are in the retail industry. Many retailers operate within a very narrow profit margin as a way to stay competitive, thus an improvement in supply chain, even at the slightest, can have a significant and positive impact on overall business profitability.
Given the vast amount of data becoming more and more available in the operation of logistics, warehousing, and store, being able to leverage such data for actionable insights to enhance operational performance can provide a huge competitive edge for businesses that do it right.
