Artificial intelligence in operations management and supply chain management: an exploratory case study

Figures & data

Figure 1. Artificial Intelligence as an umbrella term.

Table 1. Links between AI Methods and Applications in SCM.

Business partner	Business purpose	Applications in SCM	AI method	Reference
Supplier	Supplier selection	Supplier selection/Partner selection	Machine Learning; Expert System; Artificial Neural Network	Cavalcante et al. 2019; Soleimani 2018; Wu and Barnes 2014
		Minimizing supplier risk/Forecasting SMEs’ credit risk in supply chain finance	Machine Learning	Zhu et al. 2019; Soleimani 2018
Manufacturer	Order intake	Order-picking system	Expert System	Manzini, Gamberi, and Regattieri 2005
		Chabots and intelligent assistants for operational procurement	Natural Language Processing	Davenport 2018
		Automatic spare part and service orders	Reinforcement Learning	Hellingrath and Lechtenberg 2019; Mortazavi, Khamseh, and Azimi 2015
		Spare part demand forecasting	Neural Network	Amirkolaii et al. 2017
	Quality control	Inventory control	Expert System; Reinforcement Learning; Artificial Neural Network	Paul, Azeem, and Ghosh 2015; Singh and Challa 2016
		Automated quality control	Machine Learning	Tellaeche and Arana 2013
	Production planning and control	Production optimisation /production capacity improvement/lead time reduction	Machine Learning	Min et al. 2019
		Monitor and predict the conditions of the cutting tool and the bearing	Neural Networks; Machine Vision	Lee et al. 2019; Soleimani 2018
	Services and Maintenance	Fraud detection/prevention	Machine Learning	Davenport 2018; Baryannis, Dani, et al. 2019
		Call Centre Automation (Switchboard)	Speech Processing	Brynjolfsson, Rock, and Syverson 2018
		Condition-based maintenance (predictive maintenance)	Recurrent Neural Network; Convolutional Neural Network	Lee et al. 2019
	Smart connected products	Situation aware products	Natural Language Processing	Jung, Hur, and Kim 2018
		Autonomous products	Deep Learning, Reinforcement Learning	Brynjolfsson, Rock, and Syverson 2018
Logistics	Transportation	Accurate demand forecasting (Reduce the Bullwhip Effect)	Neural Networks	Singh and Challa 2016
		Post-processing monitoring	Neural Networks	Kousiouris et al. 2019
		Self-driving cars	Machine Vision	Brynjolfsson, Rock, and Syverson 2018
		Vehicle routeing and scheduling	Evolutionary Computation	Levy 2018
	Warehouse	Voice picking	Speech Processing, Neural Network	Miller 2004; Gupta and Jones 2014
		Robot picking	Machine Learning, Robotics	Hengstler, Enkel, and Duelli 2016
		Automatic container unloading	Robotics	Hengstler, Enkel, and Duelli 2016
		Virtual Assistants	Speech Processing Neural Network	Hengstler, Enkel, and Duelli 2016
		Object inspection	Machine Vision	Jarrahi 2018
		Fault detection	Neural Network	Hengstler, Enkel, and Duelli 2016
Retailer	Sales process	Customer clustering	Machine Learning, Neural Networks	Knoll et al. 2019 Hellingrath and Lechtenberg 2019
		Detecting process errors	Neural Network	Hengstler, Enkel, and Duelli 2016
		Suggesting better products and service	Machine Learning	Brynjolfsson and McAfee 2017
Customer	Customer interfaces	Customer requirements management	Machine Learning	Lyutov, Uygun, and Hütt 2019
		Chatbots and intelligent assistants for customer services	Natural Language Processing	Dwivedi et al. 2019
		Get insights from the consumer preferences	Machine Vision	Choi, Wallace, and Wang 2018

Figure 2. Process for developing and deploying AI models.

Figure 3. Links between different data sources and their use cases in the supply chain.

Figure 4. The approach of exploratory research.

Figure 5. Configurator system for sales.

Figure 6. Centralised cloud-based AI servers in several factories to deliver better production plans as a service.

Figure 7. Feature detection demonstration for product quality control based on machine vision and DNN.

Figure 8. Automated spare part orders generated by smart connected product.

Figure 9. Matching business processes, potential use cases and data sources connected for AI implementations.

Table 2. Case overview.

	Case 1: Sales configuration	Case 2: Production planning and control	Case 3: Quality control of products	Case 4: Spare parts and maintenance orders
Organisation	Sales, configure-price-quote	Sales/Operations Planning	Production	Service
Primary method	Rule-based system with constraint satisfaction programming	Genetic algorithm running optimisation	Deep Neural Network conducting visual inspection	Clustering analysis and anomaly detection with machine learning
Objectives	Automation of quote processing, replacing human knowledge with artificial intelligence to process offers and orders	Ensure that all orders are processed on time and maximise capacity utilisation of production	Perform a thorough quality inspection for all parts in the manufacturing phase	Processing condition analysis of assets in the field (installed base) and placing automated maintenance and spare part orders
Persons involved and how role has changed	Salespersons – focus on customer communication; product knowledge and rules are maintained by AI	Production planners – daily scheduling is conducted by automated machinery sending order information to central cloud scheduling AI	Quality assurance can focus on corrective actions rather than operative inspections	Service planning – service calendars are maintained by AI
Key performance indicators	Number of orders Order quality Speed to process offers	Capacity utilisation Later orders	Number of inspections / hour Type A/B errors	Life-cycle costs of asset Number of rush orders

Table 3. Case AI data, learning and control.

	Case 1: Sales configuration	Case 2: Production planning and control	Case 3: Quality control of products	Case 4: Spare parts and maintenance orders
Data	Product data, sales offers and orders from ERP	Production orders	Production camera stream	Smart machine collected data from the EDGE
Closed-loop learning	Analysis of customer offers and orders in terms of product parameters	Information updates from factory, machine unavailability triggers re-optimisation of the schedules	Camera data is collected from production, manual retraining needed	Measuring equipment behaviour from IoT data
Learning adjusts	Product parameterisation suggestions based on clustering	Production schedule	New product failure types, improved detection	Condition- based maintenance

Figure 10. Key application areas.

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