How To Improve Supply Chains With Machine Learning: 10 Proven Ways

10 Ways Machine Learning Can Transform Supply Chain Management

Supply chain efficiency has a direct impact on product quality from the beginning of the production process, when raw materials are procured, all the way to product delivery. Supply chain managers can optimize their supply chain to adhere to quality standards at every stage. The supply chain ecosystem includes everyone involved in designing, manufacturing, storing, and moving products and their components from inception to the end customer. A comprehensive audit will measure the value of a specific vendor and make it clear how readily other vendors can be brought on board to fill any gaps. For example, during the pandemic one global restaurant chain used predictive analytics capabilities built into its planning software to accurately anticipate ingredient shortages.

Vanvuchelen et al. (2020) developed an algorithm based on DRL to determine the replenishment policy of a group of collaborative companies under a physical internet network. Physical internet is an interconnected logistics network in which companies share freight and resources (Montreuil 2011). The proposed algorithm decides on the amount and the time of ordering and shipping considering the periodic review of joint replenishment policies and aims to minimize companies’ transportation, holding, and backorder costs. Yasutomi and Enoki (2020) presented a DL architecture that defines the position of an inspection device in belt conveyors. The inspection device consists of an inertial measurement unit (IMU) inside a moving object joined with an algorithm to find anomalies in the conveyor lines.

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In production and manufacturing, row materials’ demand plan usually deviates from the actual requirement and the plans should be revised several times (Pechmann and Zarte 2017). Having an accurate material demand forecast can reduce purchasing and production costs of supply chains (Tang and Ge 2021). The authors Tang and Ge (2021) proposed a forecasting model that uses sales demand and previous material demand time series data as input and determines material demand value as output for consumer products.

With the outbreak of Covid-19, a significant increase has occurred in the demand for various products especially healthcare equipment, so forecasting of these increasing demands enables countries to effectively plan and manage their limited resources (Koç and Türkoğlu 2021). Then they used the output of the Covid-19 growth rate forecasting as the input for demand forecasting of grocery, electronic, fashion, and automotive products and services during a pandemic condition. Koç and Türkoğlu (2021) presented a DL-based model to forecast the demand for medical equipment and the outbreak spread in turkey. ML streamlines inventory planning by analyzing historical data and current trends to generate accurate demand forecasts.

Using other supply chain or deep learning related keywords or different filters may lead to finding more papers that can be considered as a future research direction. Artificial neural networks (ANNs) originated from human-brain behavior (Deng and Yu 2014). A unit or node is the basic computational element (neuron) of ANN that gets inputs from external sources.

Examples of Supply Chain Optimization

A graduate degree like the Master of Science in Supply Chain Management Online from the University of Tennessee, Knoxville, is one of the best ways to learn the advanced skills that top companies seek. Let’s explore the facets of sound inventory management for companies that hire the right talent with a well-rounded education in supply chain management. Conversely, efficient inventory management can boost profitability, enhance visibility, and improve operations by keeping a steady inventory flow. The supply chain is the web linking together multiple functions, including logistics, production, procurement, and marketing and sales (Exhibit 1).

Performance forecasting Container logistics is a high-cost industry in which any improvement in decisions to be made at different levels can lead to a huge cost reduction. One of the issues of this industry is container throughput forecasting (Shankar et al. 2020). Container throughput can be considered one of the important key performance indicators of any port (Awah et al. 2021). In this regard, the authors Shankar et al. (2020) used a DL network to forecast container throughput using data from the Port of Singapore.

A systematic review of the research trends of machine learning in supply chain management

Technology advances are allowing manufacturers to move beyond just optimizing their own facilities, processes, products, and logistics. They’re also connecting manufacturers with suppliers as well as with third-party data that can warn about port shutdowns, weather events, impending labor strikes, and other factors that could impede deliveries. And among time-series forecasting methods autoregressive integrated moving average (ARIMA) has been the most tested method that could not perform as good as DLs.

Other risks include the inability to respond quickly to supply chain disruptions, longer-than-needed production cycles, delays in product delivery, and poor customer service. Another class of DL methods has been introduced in 2014 is GAN (Goodfellow et al. 2014). This network can learn deep representations without greatly annotated training data by obtaining backpropagation signals using a competitive process involving two networks (Creswell et al. 2018) in a zero-sum game. A generative model aims at studying a set of training examples and learning the probability distribution that generated them (Goodfellow et al. 2020).

Demand Forecasting and Inventory Management

Interestingly, the covid-19 is also one of the frequently used keywords that shows the current situation caused by this pandemic has affected supply chains considerably and can be addressed by the use of DL algorithms. Machine learning can revolutionize various aspects of the supply chain process, from demand forecasting to inventory control. In this section, we delve into some of the key areas where machine learning can drive significant improvements in supply chain network optimization. Digital twins enable supply chain management professionals to test the impact of a change in a zero-risk virtual environment before implementation in the real world. Maltaverne says they can be used to design supply chains, analyze scenarios, build knowledge and optimize operations. Users can create proactive optimizations based on real-time signals — demands, markets and geopolitical — and, when incidents happen, either anticipate or react immediately via contingency plans or ad-hoc recommendations.

The entire organization becomes more agile and customer-centric, leading to an increase in revenue of 3 to 4 percent. Given the rapid-fire shifts in demand due to the pandemic, there is a real risk that traditional

supply chain planning processes will be insufficient. Companies run the risk of product shortages, increased costs from stock, inventory write-offs, and related inefficiencies up and down the value chain. Machine learning can assess customer requirements and optimize the upstream supply chain. Supply chain managers need to identify the best locations for factories, warehouses, and distribution centers and the optimal flows between those locations.

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The research reviews the cases where Machine Learning Techniques are being used in Supply chain optimization. Your phone chimes and you have a delivery notification of your most awaited product, right on time! But ever wondered how businesses manage to get the right products to the right place at the right time? And with the integration of machine learning, supply chain management is becoming smarter and more efficient in an increasingly challenging business environment. The classification is the process of dividing the data into distinct categories based on certain constraints (Kumar and Verma 2012).

Using such techniques can reduce the amount of food waste, save limited resources, decrease food production costs, and finally result in more sustainable food supply chains (Biggs et al. 2015). Another important issue of the food supply chains is food safety and one way to assure consumers about it is by displaying required information on the product pack label. However, labeling mistakes or legibility problems may occur in the packaging process (Thota et al. 2020). To address this problem, Thota et al. (2020) proposed a DL domain adaptation system that identifies and verifies the existence and legibility of use-by-date information on package labels.

Quality management comes in the second stage, followed by financial management, product classification, and inventory management in the third stage. To make it clearer, we briefly describe machine learning supply chain optimization the main work of each paper based on the SCM problem addressed in the paper. To do so, we carefully studied selected papers and summarized their problems in the following paragraphs.

The classification is done by feature concatenation of data captured from two imaging means including visible-light and hyperspectral imaging systems. Their proposed method can classify fruit maturity into six stages and can be useful in defining optimal harvest time. Jagtap et al. (2019) proposed an automated IoT-based system to determine the total amount of waste through advanced image processing and load cell technologies.

In this category, Zhao and You (2020) introduced a framework for robust chance-constrained programming based on a generative adversarial network to minimize the total cost of a supply chain. The total cost consists of capital cost and the costs of procurement, operation, inventory, and transportation. The proposed framework designs the supply chain network and operations under demand uncertainty. Chen et al. (2021) used DRL to develop a blockchain-based framework to manage the production and storage of agri-food products with maximum profit. By the use of this framework, considering the demand and cost, factories can cost-effectively supply retailers and retailers can highly satisfy consumers’ demands.

• Another company, a coffee wholesaler based in Europe with a history of growth through acquisitions, faced huge challenges around financial and supply chain consolidation.
• Moreover, considering the target audience including both researchers and industry managers, providing the details of DL algorithms was impossible and just general explanations to familiarize both of them with the process of using DL algorithms in the SCM were brought forth.
• No background required, though some general knowledge of supply chain will be helpful.
• DL algorithms have also been applied for sales forecasting because of the ability of these methods to effectively consider the patterns and context-specific non-linear relationships between critical factors (Liu et al. 2020).
• It focused on planning, including product blending and inventory management, to significantly increase throughput and improve margins.
• DNNs, by employing deep architectures in ANNs, are capable of representing learning functions with more complexity when the number of layers as well as units in a layer increases (Liu et al. 2017).

However, RNN has difficulties in capturing long-term dependencies due to the vanishing and exploding gradient problem during the training process (Wang et al. 2018). To address this issue, the gated recurrent unit (GRU) was proposed giving the RNN a long-term memory (Liu et al. 2020). The other common RNN is Long Short-Term Memory (LSTM) network (Hochreiter and Schmidhuber 1997) which provides memory blocks in its recurrent connections (Pouyanfar et al. 2018). The LSTM can be effectively used to forecast complex time-series data (Punia et al. 2020). Garillos-Manliguez and Chiang (2021) applied seven multimodal CNN architectures for classifying fruits based on their maturity.

It is stated that this work is part of a broader project aiming to provide a low-cost biometric identification tool to be used along the entire lobster supply chain. Wang (2020) presented a classification method for online tourism products that provides a reference for developing a personalized data-driven recommendation system in the tourism supply chain. The method analyzes the information of user reviews and tourism products/services reviews to generate personalized recommendation lists. Hybrid forecasting In the last subcategory of forecasting, Khan et al. (2020) integrated new technologies of IoT and blockchain with DL to develop an optimized supply chain provenance system in the food sector. Then, the extracted text features, financial market data, and historical data related to the oil market were used to forecast the price, production, and consumption of oil with several prediction techniques. The three-field map summarizes the relationship among top keywords, top authors, and top title words.