EPEC 4.0: an Industry 4.0-supported lean production control concept for the semi-process industry

Figures & data

Table 1. The Stability-flexibility paradox in the S-PI (based on (Spenhoff, Semini, and Powell 2016).

Stability	Flexibility
Limited product variety Fixed demand volumes High demand predictability Stable demand Fixed production schedule	High product variety High demand volume variability High demand uncertainty Short-term demand Short-term demand changes

Table 2. Comparison of cyclic lean production control concepts.

	Stability	Flexibility	CODP
EPE (Powell, Alfnes, and Semini 2010)	x	−	MTS
Product wheel (King 2019)	x	−	MTS
Classic rhythm wheel (Packowski 2013)	x	−	MTS
Breathing rhythm wheel (Packowski 2013)	(x)*	(−)**	MTS
High-mix rhythm wheel (Packowski 2013)	−	x	MTS/MTO
Lean RfS (Glenday and Sather 2013)	−	x	MTS

*Variable cycle length needs to be monitored to maintain stability.

**Only volume of fixed scheduled products can be adjusted.

Table 3. Design proposition.

Component	Description
Context (C)	A semi-process industry metal parts producer is facing a trend of higher product variety and demand variability that requires greater flexibility from the production system. The production system is challenged by this requirement to produce high-volume serial parts and low-volume spare parts on the same production lines.
Intervention (I)	The proposed intervention is to adapt the lean production practice Heijunka as a cyclic planning concept and to integrate the cyber-physical systems technologies of Industry 4.0.
Mechanism (M)	An adapted and Industry 4.0-supported Heijunka concept can establish stability for the production system and can enable more flexibility to address higher product variety and demand variability. This concept can enable the production of high-volume serial parts and low-volume spare parts on the same production lines.
Outcome (O)	The expected outcome of the intervention is an improved and synchronized flow of information and flow of material.

Table 4. Critical production control characteristics of the case company.

	Main product variants		Additional product variants
	Serial parts	Spare parts I	Spare parts II
Product variety: Casting	20 variants		180 variants
Product variety: Extrusion	200 variants		1,800 variants
Product variety: Forming	300 variants		2,700 variants
Demand volume per order	10,000–25,000 pieces	50–2,000 pieces	20–500 pieces
Demand pattern	regular, stable, short-term volume changes		sporadic, uncertain, short-term rush-orders
Contracted delivery period	4–7 years		15+ years
CODP	MTS	MTS/MTO	MTO

Figure 1. The EPEC 4.0 production control concept.

Figure 2. Cyclic master schedule example (simplified).

Figure 3. Detailed cyclic schedule example (simplified).

Figure 4. The EPEC 4.0 cyber-physical systems architecture (based on Lee, Bagheri, and Kao (2015).

Spenhoff, P., M. Semini, and D. J. Powell. 2016. “Investigating Production Planning and Control Challenges in the Semi-Process Industry, the Case of a Metal Parts Producer.” Paper presented at the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia.

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King, P. L. 2019. Lean for the Process Industries: Dealing with Complexity. Second Edition. New York: Productivity Press, T&F.

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Packowski, J. 2013. LEAN Supply Chain Planning: The New Supply Chain Management Paradigm for Process Industries to Master Today's VUCA World. Boca Raton: CRC Press.

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Glenday, I., and R. Sather. 2013. Lean RFS (Repetitive Flexible Supply): Putting the Pieces Together. Boca Raton: CRC Press.

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Lee, J., B. Bagheri, and H.-A. Kao. 2015. “A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems.” Manufacturing Letters 3: 18–23. doi:10.1016/j.mfglet.2014.12.001.

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