A review of digital assistants in production and logistics: applications, benefits, and challenges

Figures & data

Table 1. Summary of related literature reviews.

Review	Review type	Focus area	DAs considered	Sample size
von Wolff et al. (2019)	Systematic literature review	Digital workplace	Chatbots	52
Wellsandt, Hribernik, and Thoben (2021)	Narrative review	Benefits of using DAs in production	DAs	Not clearly identified
Pereira et al. (2023)	Systematic literature review	Characteristics of technical assistance design principles in Industry 4.0; specific services offered by VA; application challenges of VA	Softbots	9
Rabelo, Zambiasi, and Romero (2023)	Narrative review	Control of shop floor composed of cyber physical systems	Softbots	Not clearly identified
Colabianchi, Tedeschi, and Costantino (2023)	Taxonomy development	Conceptual architecture, taxonomy and design guidelines for manufacturing chatbots	Chatbots	Not clearly identified
Our study	Systematic literature review	Human-DA collaborative tasks in the areas of product design, production, maintenance, quality management, and logistics.	DAs	69

Table 2. Overview of different types of DAs.

Digital assistant type	Description	References
Conversational agent	A dialogue system that can support spoken and written natural language as input and output.	van Pinxteren, Pluymaekers, and Lemmink (2020); Knote et al. (2019)
Chatbot	An application system that provides a natural language user interface for human-computer interaction. It can be rule-based or AI-based, and it can be chit-chat or task-oriented.	Følstad and Brandtzæg (2017); Adamopoulou and Moussiades (2020)
Intelligent personal assistant (IPA)	A software agent that helps in interfacing with machines, computers, databases, and other information systems as well as managing commitments and performing tasks in a human-like interaction.	Myers et al. (2007); Santos et al. (2018)
Virtual assistant (VA)	A new way to interact with humans naturally, which can be done in a cognitive, collaborative, and linguistic human-machine manner, to automate routine tasks partially or entirely to increase the productivity and comfort of the users so that they can devote time to other tasks.	Maedche et al. (2019); Schmidt et al. (2018)
Softbot	A new type of frictionless human-computer interaction by skipping fixed and predefined menus commonly accessed via a keyboard and mouse. It is a virtual system that automates tasks and helps humans in the execution of tasks with variable levels of intelligence, autonomy, and proactivity.	Rabelo, Romero, and Zambiasi (2018); Rabelo, Zambiasi, and Romero (2023)
Avatar	Computational agents that represent the real embodiment of people in cyberspace, including human-like auditory and visual characteristics or traits. It can assist users by recording their actions and inputs to the appropriate database or retrieving technical information or historical data for the user.	Brade et al. (2020); Lampen, Liersch, and Lehwald (2020)

Figure 1. Conceptual framework of human-DA interaction in production and logistics.

Showing the framework that consists of five main elements: (1) production and logistics areas, (2) human-centricity, (3) DAs, (4) human-DA collaborative tasks, and (5) benefits and challenges of adopting DAs developed based on the socio-technical systems theory.

Table 3. Groups of keywords used in the literature search.

Group	Keywords
A	Digital assistant*, AI-based assistant*, AI based assistant*, Personal assistant*, Smart assistant*, Autonomous agent*, Interactive agent*, Conversational agent*, Conversational system*, Conversational interface*, Chatbot*, Chatterbot*, Chatterbox*, Dialog system*, Dialog agent*, Dialog assistant*, Digital agent*, Smart speaker, Speech based assistant*, Speech-based assistant*, Voice assistant*, Text-based assistant*, Text based assistant*, Virtual agent*, Virtual assistant*, Virtual coach*, Avatar*, Software robot*, Softbot*, Operator assistance system*, Worker assistance system*
B	Product design, Product development, Engineering design, Production, Manufacturing, Assembly, Machining, Maintenance, Quality management, Quality assurance, Quality control, Quality inspection, Quality check, Logistics, Supply chain, Warehouse*, Order pick*, Material* handling, Inventory management, Fleet management

Figure 2. Literature search and selection protocol.

Showing the literature search and selection process including filtering for relevant works according to inclusion and exclusion criteria and snowballing which ended up in a final sample of 69 works.

Figure 3. Application of DAs across different production and logistics areas.

Showing the applications of DAs in the production and logistics literature where production and maintenance are the most popular areas where the literature investigates the use of DAs in reducing human effort while performing different types of tasks.

Table 4. Benefits of adopting DAs in production and logistics.

Benefits			ID
Human	Reduce cognitive workload	Decrease information overload	[15, 21, 28, 40, 46, 47]
		Reduce unnecessary memory of task procedures	[42, 49]
		Enhance decision-making capabilities	[11, 15, 18, 21, 23, 24, 27, 43, 44, 45, 53, 54, 66]
	Reduce physical workload	Enable hands-free operations	[17, 25, 26, 35, 36, 37, 39, 40, 42, 43, 44, 45, 51, 68]
		Improve motion ergonomics	[61]
	Improve psychosocial condition	Increase joy of use and engagement	[3, 4, 7, 15, 18, 21, 23, 24, 27, 28, 29, 34, 37, 39, 53, 54, 68]
	Improve human skills	Improve quality of learning and training	[3, 13, 24, 28, 33, 34, 36, 42, 48, 49, 50, 51, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69]
Task	Improve task efficiency	Facilitate data collection and visualisation	[2, 3, 4, 5, 6, 8, 11, 12, 13, 14, 15, 17, 19, 21, 23, 24, 25, 28, 29, 33, 34, 37, 39, 40, 42, 43, 44, 45, 48, 49, 50, 53, 60, 63, 66, 67]
		Facilitate seamless interaction with machine/information system	[2, 3, 5, 7, 9, 10, 11, 12, 13, 14, 15, 21, 24, 25, 26, 27, 28, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 49, 50, 52, 53]
	Increase task flexibility	Increase flexibility in completing tasks	[2, 3, 13, 15, 21, 24, 25, 26, 27, 28, 37, 39, 43, 44, 45, 48, 56, 64, 65]

Table 5. Challenges of adopting DAs in production and logistics.

Challenges		ID
Technological challenges	Speech-to-text (STT) processing reliability	[10, 16, 18, 39, 40, 53, 58, 68]
	Text modelling accuracy	[15, 43, 69]
	Training data transparency	[43, 53]
	Data interpretability	[15, 43, 53]
Implementation challenges	Noise in industrial environments	[8, 26, 35, 39, 50, 51, 53]
	Effectiveness test of DAs in real-world scenarios	[3, 4, 7, 17, 21, 22, 23, 24, 37, 39, 43, 45, 59, 69]
	Compatibility with existing legal and ethical framework	[4, 9, 16, 53, 57, 58, 59]

Figure A1. Publication numbers over time and per source.

Showing the timeframe and outlets of the papers contained in the sample.

Table A1. Methods adopted in the literature sample.

Paper type		Number	Percentage
Conceptual		10	14.5%
Total		10	14.5%
Empirical
	Qualitative	29	42.0%
	Quantitative	24	34.8%
	Mixed method	6	8.7%
Total		59	85.5%
Overall		69	100%

Table A2. Overview of the literature sample.

Task types	Production and logistics areas	ID	Authors	DA category	Input from human	Output from DA	Automation level of task (referred to Endsley 2017)	Joint use with other technologies
Information management
	Product design
Product/Engineering information consultation		1	Choi, Hamanaka, and Matsui (2017)	Chatbot	Text	Text, graphic	Batch process	Social network
		2	Trappey et al. (2022)	Chatbot	Text	Text, graphic	Batch process	VR
	Production
Machine/information system interaction		3	Kassner et al. (2017)	Chatbot	Text	Text, command to robot	Action support	DT, Cloud, Social network
		4	Ziegeler and Zuehlke (2005)	Avatar	NA	Avatar	Information cueing	–
		5	Mantravadi, Jansson, and Møller (2020)	Chatbot	Text	Text	Decision support	–
		6	Do and Jeong (2022)	Chatbot	Text	Text	Batch process	RPA
		7	Hüsson, Holland, and Sánchez (2020)	IPA	Speech	Speech, text, graphic	Decision support	–
		8	Gärtler and Schmidt (2021)	VA	Speech	Text, speech	Batch process	–
		9	Bousdekis et al. (2022)	DA	Speech	Speech	Automated decision-making	–
Shopfloor information management		10	Afanasev et al. (2019)	Dialogue assistant	Speech	Speech	Shared control	DT, CPS, Cloud
		11	Loh et al. (2023)	Chatbot	Text	Text	Batch process	–
		12	Reis et al. (2022)	VA	Speech	Speech	Action support	–
		13	Jwo, Lin, and Lee (2021)	VA	Speech	Text, graphic	Decision support	IoT
		14	Penica et al. (2023)	Chatbot	Speech	Speech, graphic	Decision support	AR, IoT, RFID,
		15	Abner et al. (2020)	Softbot	Text	Text, graphic	SA support, action support, decision support	IoT, Cloud
		16	Wellsandt, Foosherian, and Thoben (2020)	DA	Speech	Speech, graphic	SA support	DT, Cloud, IoT
	Maintenance
Maintenance information management		17	Kernan Freire et al. (2022)	CA	Speech	Speech	Shared control	RFID
	Logistics
Demand information management		18	Hsiao and Chang (2019)	DA	Speech	Speech	Automated decision-making	–
		19	Murciego et al. (2020)	IPA	Speech	Text, speech	Batch process	Cloud
		20	Angelov and Lazarova (2019)	Chatbot	Text	Text	Batch process	–
		21	Kern et al. (2006)	IPA	NA	NA	Supervisory control	–
Collaborative operations
	Product design
Collaborative human-AI product development		22	Makokha (2022)	DA	Speech	Speech	Automated decision-making	–
	Production
Production planning and control		23	Rabelo, Romero, and Zambiasi (2018)	Softbot	Text, speech	Text	Decision support	CPS
		24	Rabelo, Zambiasi, and Romero (2019)	Softbot	Text, speech	Text, speech	Automated decision-making	CPS
		25	Rabelo et al. (2021)	Softbot	Text	Text, graphic	SA support, Action support, Decision support	DT, Cloud
		26	Li and Yang (2021)	VA	Speech	Speech, graphic, command to robot	Action support	–
		27	Schwartz et al. (2016)	Softbot	Speech, motion, eye movement	Speech, graphic, avatar, command to robot	Action support	DT, IoT, Motion tracking, Eye tracking, Object tracking
		28	Longo, Nicoletti, and Padovano (2022)	DA	Speech	Speech, text, graphic, video	Decision support	AR, VR, MR, DT, IoT
Joint task performance		29	Zimmer et al. (2020)	Chatbot	Text, haptic	Text	Action support	–
		30	Li and Wang (2021)	VA	Speech, motion	Speech, command to robot	Automated decision-making	Facial tracking, motion tracking, eye tracking
		31	Chen et al. (2021)	Chatbot	Speech	Speech, graphic	Batch process	Computer vision, objects tracking
		32	Chiu et al. (2021)	Chatbot	Speech	Speech, graphic	Batch process	Computer vision, objects tracking
		33	Talacio et al. (2021)	IPA	Speech	Speech, graphic, video	Shared control	Computer vision, motion tracking, objects tracking
		34	Behrendt and Strohmeier (2021)	DA	Speech	Speech, video	Rigid system	Motion tracking, eye tracking, objects tracking
Human-robot collaboration		35	Li et al. (2021)	VA	Speech	Speech, command to robot	Action support	–
		36	Li, Zhang, et al. (2022)	VA	Speech	Speech, command to robot	Action support	Computer vision
		37	Li et al. (2023)	VA	Text	Text, command to robot	Action support	–
		38	Li, Hansen, et al. (2022)	Dialogue assistant	Text, speech	Text, speech, command to robot	Automated decision-making	–
		39	Li, Chrysostomou, and Yang (2023)	VA	Speech	Speech, command to robot	Action support	–
		40	Ye, You, and Du (2023)	Chatbot	Text	Text, command to robot	Shared control	VR
	Maintenance
Collaborative maintenance		41	Nyrkko et al. (2007)	DA	Text, speech	Text, speech, graphic	SA support	–
		42	Ade et al. (2020)	Chatbot	Speech	Speech	Shared control	–
		43	Wellsandta et al. (2020)	DA	Speech	Speech	Action support	–
		44	Wellsandt et al. (2021)	DA	Speech	Speech, graphic	Blended decision	–
		45	Wellsandt, Klein, et al. (2022)	DA	Speech	Speech, graphic	Blended decision	–
		46	Aceta, Fernández, and Soroa (2022)	Dialogue assistant	Speech	Command to robot	Decision support	–
		47	Aceta et al. (2022)	Dialogue assistant	Speech	Speech	Decision support	–
		48	Abate et al. (2008)	Avatar	Speech, motion	Avatar	SA support	AR, object tracking
		49	Zambiasi et al. (2022)	Softbot	Text, speech	Text, graphic	Shared control, rigid system	AR
		50	Serras et al. (2020a)	Dialogue assistant	Speech	Speech, graphic	Action support	AR
		51	Serras et al. (2020b)	Dialogue assistant	Speech, motion, eye movement	Speech, graphic	Automated decision-making	AR, motion tracking, object tracking
		52	Fleiner et al. (2021)	CA	Speech, motion	Text, speech, graphic	Action support	Facial tracking, motion tracking
	Quality management
Augmented quality control		53	Bousdekis et al. (2021)	DA	Speech	Text, speech, graphic	Decision support	–
		54	Wellsandt, Foosherian, et al. (2022)	DA	Speech	Text	Action support, decision support, rigid system
	Logistics
Order picking assistance		55	Wang et al. (2020)	Avatar	Motion	Avatar	Information cueing	AR, object tracking
Knowledge transfer
	Production
Production process/procedure training		56	Tanaka et al. (2003)	Avatar	Speech, motion	Speech, avatar	Action support	Motion tracking
		57	Kernan Freire et al. (2023b)	DA	Text, speech	Text, speech, graphic	Decision support	–
		58	Kernan Freire, Foosherian et al. (2023)	DA	Text, speech	Text, speech	Decision support	–
		59	Kernan Freire et al. (2023a)	DA	Text	Text, graphic	Decision support	–
		60	Pace et al. (2019)	Avatar	Speech, eye movement	Avatar	Shared control	AR, VR
		61	Lampen, Liersch, and Lehwald (2020)	Avatar	Motion	Avatar	Information cueing	AR, motion tracking
		62	Brade et al. (2020)	Avatar	Motion	Avatar	Information cueing	VR
		63	Rooein et al. (2020)	Chatbot	Text, speech	Text, video	Decision support	–
		64	Casillo et al. (2020)	Chatbot	Text	Text, graphic	Rigid system	–
		65	Clarizia et al. (2021)	Chatbot	Text	Text, graphic	Rigid system	–
		66	Longo, Nicoletti, and Padovano (2017)	DA	Text, speech	Speech, graphic	Action support	AR, VR, DT, IoT, Cloud
	Maintenance
Maintenance process/procedure training		67	Zhu et al. (2014)	IPA	Text, speech, haptic, motion, eye movement	Speech, graphic, video	Blended decision	AR, motion tracking, eye tracking, object tracking
		68	Barbosa et al. (2018)	VA	Haptic	NA	Shared control	DT, IoT
		69	Zambiasi et al. (2023)	Softbot, avatar	Text, motion	Text, speech, graphic, avatar	Rigid system	Cloud, metaverse

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Data availability statement

Data is available on request from the authors.