An innovative machine learning model for supply chain management

In 2014, Girshick et al. (2014) combined Selective Search algorithms with CNNs to determine Regions of Interest (RoI), for object detection. However, in their proposed method the two parts of the network were not trained at the same time, resulting in a tedious training process (Wang et al. 2019). To improve the speed and accuracy of training, then Fast Region-based CNN (Fast R-CNN) (Girshick, Fast R-CNN 2015), Faster R-CNN (Ren et al. 2015), and Mask-RCNN (He et al. 2017) were proposed. Kong et al. (2021) presented a multi-stream CNN architecture named as MCF-Net to classify crop species in practical farmland scenes. The CSPNet backbone network, three parallel sub-networks, and cross-level fusion layers are all parts of this architecture.

Quality management software can unify disconnected quality systems—common at large manufacturers—and give quality managers the flexibility to accommodate shortened production schedules. A good quality control system will adapt to rapid change and improve as it gathers more data. Few doubts remain about the importance of a responsive and agile supply chain after the COVID-19 pandemic. Creating a responsive and agile supply chain as well as benchmark inventory management capability is vital to modern-day supply chain management. With careers in our discipline more in demand than ever before, professionals with advanced knowledge of inventory management abilities find themselves in excellent positions. Machine learning dives into historical sales data, market trends, and even things like social media sentiment to predict what customers will want next.

Optimize Your Supply Chain with AI and ML

The best solution is integrated technology that provides up-to-the-minute inventory tracking, sales forecasting, cash flow management, delivery logistics, and customer behavior. This end-to-end visibility is essential for any company looking to optimize its supply chain. One of the biggest cost reduction opportunities related to supply chain optimization comes from precise inventory control, which encompasses demand forecasting, inventory tracking, and product storage.

For example, only 25 percent of respondents working with a system integrator reported that their objectives and the system integrator’s incentives are aligned. Other issues include a lack of internal alignment, as well as a struggle to build a compelling case for the investment. About 35 percent of respondents said the impact delivered from their planning system fell short of expectations, 45 percent cited that a project was not delivered on time, and 26 percent reported budget overruns. Table 5 shows DL algorithm categories (D2) used in papers either as a single algorithm or as part of a hybrid architecture.

Amazon: Streamlining Warehouse Operations and Enhancing Customer Experience

All of these processes use historical information and machine-learning methodologies to create a clear view of the entire supply chain, so that COOs can optimize for specific variables. For example, an ideal solution would maximize product availability and production capacity, while also lowering the total cost to serve. In addition, it would be able to model potential future scenarios, machine learning supply chain optimization with predictive planning to simulate the impact on the supply chain, along with the specific implications of various mitigation measures. Optimizing warehouse, production, and logistics processes can reduce infrastructure costs, in some cases by reducing the amount of space needed to produce the same number of products or by allowing manufacturers to increase rates of production.

• Carrying too little inventory can mean customers are left waiting for their orders, possibly causing them to buy from a different manufacturer.
• An effective member selection method is an important basis for smooth dynamic supply chain operation.
• Less than one-third of companies perform an independent diagnostic at the outset, but this exercise can ensure companies have an accurate list of all the value-creation opportunities.
• Chuaysi and Kiattisin (2020) combined KNN classifier with MLP on statistical and trajectory features of fishing vessels to enable their traceability at sea.
• And increased attention on the environmental impact of supply chains is triggering regionalization and the optimization of flows.

After collecting material, the steps of descriptive analysis, structural categories selection, and the evaluation of the collected material are presented in this section. With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams. She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

As described in the research methodology, in this paper, we explored the studies published in the Scopus database, however, it is suggested to the researchers go through other databases like Google Scholar, ProQuest, and Web of Science in order to have a more comprehensive review. In this section, we identify gaps in existing DL research when compared to the needs of real-world SCM problems. We believe that tackling these research gaps is critical if we want our systems to get deployed, used, and have positive and lasting impacts.

Too much product on hand raises storage costs; too little runs the risk of disappointing customers and distributors, who may go elsewhere to meet their needs. Supply chain planners also play a role in determining the location of factories, warehouses, and distribution centers—all aligned with data around the availability of raw materials and the location of customers. The results of this study showed that, although there were 43 papers for DL applications in SCM in the last 5 years, the DL applications in SCM were still in a developmental phase, because DL applications in the supply chain are still quite blurred. From these tables it can be concluded that DL algorithms can lead to better results compared to machine learning techniques. In terms of time-series forecasting methods autoregressive integrated moving average (ARIMA) has been the most tested method (Weng et al. 2019a, b; Piccialli et al. 2021; Koç and Türkoğlu 2021; Bousqaoui et al. 2021; Mao et al. 2018) that could not perform as good as DLs. 9 illustrates the relationship between Dl algorithms, supply chain problems, and the investigated industries.

Route Optimization

As global markets become more interconnected and competitive, businesses must continually improve their supply chain planning and execution to maintain their competitive edge. Supply chain optimization techniques are evolving to address the needs of modern businesses, focusing not only on cost reduction but also on agility and customer satisfaction. In the complex world of supply chain management, there’s a hidden force with the potential to revolutionize how organizations operate and compete. This invisible game-changer is machine learning, an advanced technology that, when harnessed effectively, can create a seismic shift in the way supply chains function. The CPG industry has long relied on traditional processes to manage supply chains and operational performance, but the pandemic has upended many (if not most) of these efforts. Consumer sentiment has changed dramatically, with a marked shift to value and a greater focus on essential products.

Even if you are not familiar with supply chain fundamentals, the rich data sets that we will use as a canvas will help orient you with several Pythonic tools and best practices for exploratory data analysis (EDA). As such, though all datasets are geared towards supply chain minded professionals, the lessons are easily generalizable to other use cases. Machine learning can predict when something is about to go wrong, allowing for repairs before a breakdown occurs.

Two DRL techniques applied in the reviewed papers are Deep Q-Network (DQN) proposed by Mnih et al. (2015) and Proximal Policy Optimization (PPO) introduced by Schulman et al. (2017). Meisheri et al. (2021) employed both the DQN and PPO methods to determine the optimum replenishment decisions for retail businesses under uncertain demand, having multiple products with different lead times and cross-product constraints. Vanvuchelen et al. (2020) used the PPO to develop joint replenishment policies in the Physical Internet industry.

Connecting supply chain partners—mainly manufacturers, suppliers, distributors, and retailers—is crucial to making informed, timely decisions amid supply chain disruptions and demand fluctuations. The only way to make those connections is for all supply chain members to use integrated systems to share real-time information—all the more important for complex supplier and retailer networks with multiple participants in different geographies. Companies turn to contract manufacturers to lower costs and penetrate new geographic markets, handing off all or certain parts of their production to domestic or overseas specialists.

Machine learning for supply chain optimization isn’t just a theory; it’s changing the game right now. A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented.

Organizations increasingly need to pull data across the value chain from intelligent sensors, programmed to identify critical events, assess their impact, and adjust planning and control variables. Similarly, software capable of modeling the implications of various disruptions is also vital. Today’s algorithms can analyze a company’s network of suppliers and determine the total impact if a specific supplier goes down. Shajalal et al. (2021) proposed a classification model using DNN to predict whether a product order would be back ordered or not. In this context, they handled the class imbalance problem using four different techniques.

• Yasutomi and Enoki (2020) presented a DL architecture that defines the position of an inspection device in belt conveyors.
• Beyond the traditional focus on efficiency and cost reduction, machine learning offers a fresh perspective, enabling supply chains to become more agile, resilient, and customer-centric.
• The integrated implementation of the SCM system is finalized using the SSH framework.
• There are a few other industries that are still in the early stages of utilizing DL techniques to improve supply chain performance, which might be dangerous.

Machine learning can analyze various data sources, such as news and social media, to spot early warning signs of trouble. To manage the complexity of today’s supply chain, new solutions need to be smartly designed and adapted to specific business cases. This alignment enables companies to tackle key decision-making points with an adequate level of insight while avoiding unnecessary complexity. However, implementation can require significant time and investments in both technology and people—meaning the stakes are high to get it right. Reza Toorajipour holds MSc in international business administration at Shahid Beheshti University. His research interests include business model innovation, supply chain management and industry 4.0 technologies.

Forecasting is the process of taking historical data and using them as inputs to predict future trends (Bousqaoui et al. 2021). Supply chain managers need short-term to long-term forecasts to effectively make operational, tactical, and strategic decisions (Punia et al. 2020). DL algorithms by having high levels of abstraction are expected to improve the accuracy of the prediction process (Mocanu et al. 2016). Machine learning-driven supply chain optimization enables businesses to provide more responsive service, resulting in higher customer satisfaction. Maintaining optimal inventory levels and reducing lead times means that companies are able to make their products more readily available to meet customer demand, enhancing the overall shopping experience.