One-to-one relationships between Industry 4.0 technologies and Lean Production techniques: a multiple case study

Figures & data

Table 1. LP practices and techniques classified into six impact areas.

Areas	LP practices and techniques	Sources
Manufacturing Equipment and Processes	Value Stream Mapping (VSM)Process MappingSet-up time reduction – SMEDAndonPoka-YokeStandardised WorkStatistical Process ControlTotal Productive Maintenance (TPM)Elimination of Waste5S and Visual Management (VM)Lean Layout	Bai, Satir, and Sarkis (2019);Buer, Strandhagen, and Chan (2018);Bortolotti, Boscari, and Danese (2015); Sezen, Karakadilar, and Buyukozkan (2012);Saurin, Marodin, and Ribeiro (2011); Pettersen (2009);Shah and Ward (2007);Doolen and Hacker (2005);Shah and Ward (2003);Cua, McKone, and Schroeder (2001);Panizzolo (1998)
Shop-Floor Management	KaizenKanban/ Pull StrategyHeijunkaOne-Piece-FlowJust-in-Time (JIT)Takt TimeJidoka
Supplier Relationships	Long-term SupplierSupplier Involvement
Customer Relationships	Customer Involvement
Workforce Management	Multifunctional TeamEmployee Commitment
New Product Development	Standardisation of ComponentsDesign for Manufacturing

Table 2. I4.0 technologies or characteristics.

I4.0 pillar (Rüßmann et al. 2015)	Technologies or characteristics	Sources
Autonomous Robots	Collaborative RobotsAutomated Guided VehiclesSmart Machines	Romero et al. 2016Stock and Seliger 2016Davenport and Kirby 2016
Simulation	Of ProductOf Process	Rüßmann et al. 2015
	Digital Twin	Uhlemann, Lehmann, and Steinhilper 2017; Tuegel et al. 2011; Negri, Fumagalli, and Macchi 2017; Liu, Meyendorf, and Mrad 2018
Horizontal and Vertical System Integration	Interconnection between machines and MES/ERPData Sharing between Suppliers, Company and Customers	Lasi et al. 2014
The Industrial Internet of Things	RFID, Sensors, Tags, Global Positioning Systems	Shrouf, Ordieres, and Miragliotta 2014
	Real-Time scanning through Smartphone	Xu, Xu, and Li 2018
	Smart Bin	Thürer et al. 2016
Cybersecurity	Encryption, cryptography, virus scanners, signature scanner, ICT Anomaly Detection/IDS	Flatt et al. 2016
Cloud	Cloud systems for data sharing or for analytics application	Mell and Grance 2011
Additive Manufacturing	3D Printing	Dilberoglu et al. 2017; Herzog et al. 2016
	Smart Materials	Dilberoglu et al. 2017
Augmented Reality	Augmented RealityVirtual Reality	Büttner et al. 2017; Chavan 2014
Big Data e Analytics	Descriptive (and diagnostic)PredictivePrescriptive	Nguyen et al. 2018
	Artificial Intelligence	Kibria et al. 2018

Table 3. Sampled companies.

Code	Sector/products	Employees	Turnover (euro)	Size	Note
Case A	Manufacture of fabricated metal products	60	9,5 M	SME
Case B	Manufacture of electromechanical and electronic components	36	10,5 M	SME
Case C	Manufacture of electromechanical and electronic components	240	43 M	SME	Manufacturing lighthouse (The World Economic Forum)
Case D	Manufacture of electromechanical components	102	25 M	SME
Case E	Manufacture of pumps	250	100 M	LARGE
Case F	Manufacture of cableways	3500*	1,21 B*	LARGE
Case G	Manufacture of health care sealing solutions	270	45 M	LARGE
Case H	Manufacture of electrical equipment	5.869*	2,344 B*	LARGE	Lighthouse plant of the cluster ‘Fabbrica Intelligente’ (Italian Ministry of Economic Development)

*Since data of the manufacturing company were not available for this case, we reported here data of the entire group.

Table 4. The enabling effect of LP practices and techniques on I4.0 technologies or characteristics.

Area	LP practices and techniques	Enabling effect on I4.0	I4.0 Technology or characteristic	Cases
Manufacturing Equipment and Processes	Value Stream Mapping	It allows the identification of the pivotal areas in which invest with I4.0 technologies		C; E; F; G; H
		(ii) It allows the definition of the value-adding tasks to be standardised in electronic Job Element Sheets (JES) visualised on monitors at the working stations. The electronic JES allow the operator to insert information up to date in the MES and ERP promoting the integration	Vertical Integration	B; C; E; F; G; H
	Process Mapping	It allows the identification of the process flow, and therefore of the materials, WIP and information flows, essential to the design of the MES/MOM for the integration or to integrate to the ERP	Vertical Integration	A; B; C; D; E; F; G; H
	Set-up time reduction – SMED	No direct effect identified
	Andon	No direct effect identified
	Poka-Yoke	No direct effect identified
	Standardised Work	No direct effect identified
	Statistical Process Control	The Overall Equipment Effectiveness (OEE) concept defines data to be collect and analysed, namely all the data related to availability, performance, and quality	Descriptive Analytics	B; C; E; F; G; H
	TPM	No direct effect identified
	Elimination of Waste	PDCA and A3 to design new tools able to facilitate the introduction of I4.0 technologies (e.g. in case G A3 were used to design new carts able to facilitate collaborative robots in picking plaques from the shearing machine)	Autonomous Robots	G
	5S and Visual Management	The standardisation and optimisation of the warehouse facilitates the adoption of smartphones to manage materials: without rules and standards the smartphones would identify more inconsistencies than matches	Real-time Scanning with Smartphone	E; H
		(ii) The standardisation and optimisation of the warehouse facilitates the adoption of AR glasses to manage materials: without rules and standards the smartphones would identify more inconsistencies than matches	Augmented Reality	H
		(iii) Standardisation and optimisation of the workstations and the warehouse facilitate Autonomous AGV in the material handling	Autonomous AGV	E; H
	Lean Layout	In case E. a Lean Re-Layout in a straight-line cell facilitated the route of the autonomous AGV In case D, the introduction of a Collaborative Robot in the U-shape cell made the flow more balanced and continuous.	Autonomous AGV	E; D
Shop-Floor Management	Kaizen	Kaizen events facilitate the acceptance of I4.0 implementation by the workers and create shared occasion to bring new ideas (e.g. in case G, a multifunctional team designed the new cart above-described during a kaizen event)		C; E; F; G; H
	Kanban/ Pull Strategy	No direct effect identified
	Heijunka	No direct effect identified
	One-Piece-Flow	It facilitates tracking and tracing of pieces in real-time increasing transparency	Vertical Integration	C; E; F; H
	Just-in-Time	It facilitates tracking and tracing of pieces in real-time increasing transparency	Vertical Integration	C; E; H
	Takt Time	No direct effect identified
	Jidoka	No direct effect identified
Supplier Relationships	Long-term Supplier	The relationship with suppliers based on lean practices enables the Horizontal Integration, indeed companies can trust suppliers in sharing platforms about the real-time status of the material consumption and in letting them manage electronic Kanban. On the other hand, involved suppliers are more willing to give such service.	Horizontal Integration	E; H
	Supplier Involvement
Customer Relationships	Customer Involvement/ Customisation	No direct effect identified
Workforce Management	Multifunctional Team	Problem Solving conducted in multifunctional team and contests for innovative ideas enable the choice and the introduction of I4.0 solutions		G; H
	Employee Commitment
New Product Development	Standardisation of Components	No direct effect identified
	Design for Manufacturing	No direct effect identified

Table 5. The empowering effect of I4.0 technologies or characteristics on LP practices or techniques.

I4.0 pillar	Technologies or characteristics	Empowering effect on LP	LP principle or technique	Cases
Autonomous Robots	Collaborative Robots	They speed up One-Piece-Flow (e.g. in case H, a YuMi® robot is included in the production line for assembling disconnect switches one by one faster than human workers used to do allowing a more balanced line)	One-Piece-Flow	E; H
		(ii) They relieve workers from repetitive and alienating tasks (e.g. in case A collaborative robots substitute human workers in the repetitive action of picking metal sheet from the press)	Employees Commitment	A; E; G; H
	Autonomous AGV	They empower the supply of materials to workstations and supermarket managed through Kanban	Kanban	E; H
		(ii) They allow an itinerant pre-assembly that reduce machine tooling and promote JIT production. Indeed, the AGV visits all the stations of the supermarket according to the identified route for the specific product and the workers add every time a sequential component to the work in progress	SMEDJIT	H
	Smart Machines	They can track the material consumption empowering Kanban system	Kanban	C; D; E; F; H
		(ii) They can detect and separate products with defects (e.g. in case D, the operator cannot throw a good piece into the wastebasket and vice versa because the machine to mount clips on filter units recognises the defect through a camera and opens only the gate of the wastebasket. Moreover, the machine does not allow to go on until the wrong piece is discarded)	Poka-Yoke	D; H
		(iii) They can detect anomalies in the process through sensors or cameras and call for repair via an alarm on smartphones, smartwatches or screens	Andon	A; B; C; D; E; F; G; H
		(iv) They receive information about different materials, products, or work phase and prepare the right set-up of the machine's parameters (e.g. in case D, a smart machine represents the last and single station for the packaging phase. It recognises different orders with barcode scanning and gives workers the right components for the manual assembly)	SMED	A; B; C; D; E; F; G; H
		(v) They receive information about different work phases and displays the relative electronic JES on monitors	Standardised Work	A; B; C; D; E; F; G; H
Simulation	Product Simulation	It allows to understand if standardised components and modularity strategy can meet the customer requirements	ModularityCustomisation	A; B; C; D; E; F; H
		(ii) It allows to understand which configuration allows to reduce scraps, material consumption, and processing phases meeting the LP motto ‘do more with less’	Kaizen	A; B; C; D; E; F; G; H
	Process Simulation	It allows configuring the layout such as to empower the process flow In some cases, the process flow to empower is a One-Piece-Flow	Lean layoutOne-Piece-Flow – JIT	C; D; E;G; HC; D; E; H
		(ii) Scheduling Simulation is adopted to respect the JIT principle	JIT	E; H
	Digital Twin	It creates a virtual Obeya-Room in different plants to share information and work in Multifunctional Teams from distant plants	Obeya-RoomMultifunctional Team	H
		(ii) Machine DT Simulation allows to try different maintenance solutions in a safe condition and to forecast future maintenance (e.g. in case H, it is possible to replicate in VR electric panel for these purposes)	TPM	H
Horizontal and Vertical system Integration	Machines integrated with MES, ERP	The integration allows real-time information record and sharing that empower:
		Kanban system: material consumption is always recorded and I. in case of warehouse integration, it triggers pick-to-light systems (with smartphones or lights in the shelf) II. in case of autonomous AGV adoption, it directs the material handling	Kanban	C; E; H
		(b) JIT: the flow is transparent and there is real-time awareness of the machine's capacity and availability that allows to better react to plan deviations	JIT	C; D; E; H
		(c) Immediate communication of anomalies or breakdowns (in case C and H communicated also through wearable smartwatches)	Andon	A; B; C; D; E; G; H
		(d) Data collection to calculate real-time KPIs (especially all the components of the OEE, namely availability, performance, and quality)	Statistical Process Control	B; C; E; G; H
	Data shared with suppliers and/or partners through IIoT systems	A shared platform of real-time data empowers the supplier involvement (shared warehouse and KPIs situation)	Supplier involvement	E; G; H
		(ii) A shared platform of real-time data allows the supplier of transport and logistics to better plan its deliveries	Supplier involvement	G; H
		(iii) A shared platform of real-time data empowers the Kanban system directly managed by the supplier	Kanban	H
The Industrial Internet of Things	RFID, sensors, tags, global positioning systems	RFID adoption allows to recognise the die and set all the relative parameters up	SMED	G
		(ii) An RFID based tool management system avoids that the wrong tool is used by the machine and thus that the tool or the workpiece is damaged.	Poka-Yoke	A; F; H
		(iii) RTLS can map movement creating spaghetti charts or can create heat maps that give information about the need for re-layout	Lean Layout	D; G; H
	Real-time scanning with smartphone	It allows the Pick-to-light in the warehouse	Kanban	E
		(ii) It allows the real-time control of machines’ KPIs just pointing them with a tablet or a smartphone	Statistical Process Control	C; H
Cybersecurity	encryption, cryptography, virus scanners, signature scanner, ICT Anomaly Detection/IDS	No effect identified
Cloud	Cloud	No direct effect identified
Additive Manufacturing	3D Printing	It allows the production of small customised pre-series	Customisation	E
		(ii) It allows the production of prototypes to test if modular and standardised elements can meet customers requirement	ModularityCustomisation	A; B; C; D; E; F; H
		(iii) It allows the production of Poka-yoke tools (jig and clamps)	Poka-Yoke	A; E; H
Virtual / Augmented Reality	Augmented Reality	AR glasses are adopted for interactive maintenance instruction	TPM	A (project); H
	Virtual Reality	It allows a virtual walk through a simulated layout to test if the configuration allows a fluid process and comfortable motions	Lean layout	G; H
Big Data and Analytics	Descriptive (e diagnostic)	It provides real-time KPIs calculation, essential for the statistical control of the process (especially for calculating OEE)	Statistical Process Control	B; C; E; F; G; H
		(ii) This analysis helps in detecting defects, weaknesses and wastes	Kaizen	B; C; E; F; G; H
		(iii) Due to this analysis, it is possible to display real-time information in the visual board and discuss them during the 5 min meeting	Employee CommitmentWaste Reduction	B; C; E; F; G; H
	Predictive	It allows predicting maintenance actions	TPM	H
	Prescriptive	It allows to prescript a maintenance plan	TPM	H
	Artificial Intelligence	Genetic algorithm is adopted to optimise Heijunka (multi-objective genetic algorithm for levelling the workstations workload according to the Heijunka principle)	Heijunka	H

Figure 1. One-to-one relationships between LP techniques and I4.0 technologies.

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