D B S －１８－０４｢The Evolution of Production Systems from Industry 2.0
through Industry 4.0｣
Yong Yin
Graduate School of Business, Doshisha University
Kathryn E. Stecke
School of Management, University of Texas at Dallas
Dongni Li
School of Computer Science, Beijing Institute of Technology, China（summary）
December 2018

This paper discusses production systems with a focus on the relationships between
product supply and customer demand in the context of Industry 2.0 - 4.0. One driver of
production evolution is changes in customer demand over time, which is categorized into
several dimensions. Major production systems – flow line, Toyota production system(TPS), job shop, cell, flexible manufacturing system, and seru – have been developed andapplied to supplies to match different demand dimensions over time. For eachproduction system, two questions are addressed: what and how. Comparisons between
seru with TPS and cell are given. The possibilities of a future smart factory equipped withinternet of things (IoT) are discussed. The demand dimensions of Industry 4.0, theproduct architecture change in the automobile industry, and the impact of 3D printing areelaborated. Potential applications of lean and seru principles for Industry 4.0 are
presented.

Keywords: Seru; Flow line; TPS; FMS; IoT

DBS Discussion Paper Series supported by the OMRON Foundation.DBS

The Evolution of Production Systems from Industry 2.0 through Industry 4.0

Yong Yin
Doshisha University
Graduate School of BusinessKarasuma-Imadegawa, Kamigyo-ku, Kyoto, 602-8580, Japan

Kathryn E. Stecke
Ashbel Smith Professor of Operations Management
University of Texas at Dallas
School of Management
P.O. Box 830688, SM30
Richardson, Texas 75083-0688, USA

Dongni Li
Beijing Institute of Technology
School of Computer Science
Beijing 100081, China

1

The Evolution of Production Systems from Industry 2.0 through Industry 4.0
Abstract
This paper discusses production systems with a focus on the relationships between product supply
and customer demand in the context of Industry 2.0 - 4.0. One driver of production evolution ischanges in customer demand over time, which is categorized into several dimensions. Major
production systems – flow line, Toyota production system (TPS), job shop, cell, flexible
manufacturing system, and seru – have been developed and applied to supplies to match different
demand dimensions over time. For each production system, two questions are addressed: what
and how. Comparisons between seru with TPS and cell are given. The possibilities of a future smart
factory equipped with internet of things (IoT) are discussed. The demand dimensions of Industry
4.0, the product architecture change in the automobile industry, and the impact of 3D printing areelaborated. Potential applications of lean and seru principles for Industry 4.0 are presented.
Keywords: Seru; Flow line; TPS; FMS; IoT
1. Introduction
The 20th and 21st centuries are ages of industry (Crainer, 2000) in which manufacturing isimportant. Industries have and are still undergoing three industrial revolutions (sometimes called
Industry 1.0, 2.0, and 3.0). A continuous concern for manufacturing firms is the mismatch
between supply and demand within value chains. This paper reviews and focuses on therelationship between product supply and customer demand in the context of Industry 2.0 - 4.0.
Many factors affect supply-demand relationships, such as volume, variety, time, quality, price,brand, and design. For each Industry X.0, this paper only considers primary demand dimensions
that are important for customers. Customer demand is represented by various dimensions andsupply is realized by appropriate production systems.
Industry 1.0 (from the 18th to 19th centuries) brought human activities from focusing on
agriculture to the industrial society. The demand for industrial products in Industry 1.0 had only
one dimension – product volume. We can call this demand environment as the Simple Market. In

2Industry 1.0, supplies were smaller than demands. Outputs of industrial products could not satisfy
the demands from society. A central idea in Industry 1.0 came from the economics of Adam Smith’s
Wealth of Nations, in which price was described as an automatic tool to adjust mismatch between
supply and demand. If supplies were smaller than demands, prices rise. If supplies were larger
than demands, prices fall. Product variety was very low and most commodities were agricultural
products, so price adjustment was a good tool for balancing the supply-demand mismatch at thattime. Adam Smith is considered the “Father of Economics” and Wealth of Nations was the firstpublication on modern economics.
Industry 2.0 (from the end of the 19th century to the 1980s) was the period when industrial
products burgeoned both in volume and variety. Major technological innovations included
electricity, electronic and mechanical devices, and cars. Products of Industry 2.0 are still widelyused today. A milestone of Industry 2.0 was Frederick Taylor’s The Principle of Scientific Management,
which was the first publication on modern management theory. Taylor is considered the “Father ofManagement”. The demand during Industry 2.0 had two dimensions – volume and variety. We cancall this demand environment as Stable Market. Two innovators, Henry Ford and Taiichi Ohno,
practiced and extended Taylor’s theory. Ford addressed the shortage of supply in product volumes
by using mass production assembly lines. Ohno addressed various customer interests in productvarieties by developing the Toyota production system (TPS), the precursor to lean.
Industry 3.0 (from the 1980s to today) is characterized by technological innovations such aschange from analog to digital, which had big impact, especially on the electronics industry. The
product architecture of most electronics products changed from integral to modular, accompanied
by a dramatic reduction in average product life cycles. The demand for products during Industry
3.0 increased to three dimensions – volume, variety, and delivery time. We can call this demandenvironment as Volatile Market, which caused flow line and TPS malfunctions. Flexible
manufacturing systems (FMSs) and serus are used by industries as supply approaches to match thethree dimensions of demand.

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Industry 4.0 is an initiative with technology innovations such as internet of things (IoT), bigdata, electric vehicles (EV), 3D printing, cloud computing, artificial intelligence, and cyber-physicalsystems. Industry 4.0 has attracted attention from governments, industries, and researchers. Many
aspects of Industry 4.0 are unknown and uncertain, such as the demand dimensions of customers
and the future product architecture of electric vehicles. The next section provides a literature
review on the evolution of production systems.
2. Literature Review
Production systems have evolved through several paradigms. A number of studies havediscussed the evolutionary path of production systems from different perspectives. Mourtzis andDoukas (2014) classified manufacturing chronologically as craft production, American production,
mass production, lean production, mass customization, and global manufacturing. They suggestthat each production system is operated based on pull and/or push modes. For example, American
production, mass production, and lean production push. Craft production and global
manufacturing pull. Mass customization uses both push and pull.
Hopp and Spearman (2001) reviewed manufacturing history through management evolution.
They begin in 4000 B.C., when Egyptians coordinated large-scale projects to build pyramids,
through James Watt inventing the steam engine. Adam Smith published Wealth of Nations around1770. Fredrick Taylor published The Principles of Scientific Management and Henry Ford introduced
the first moving automotive assembly line in Highland Park, Michigan in the 1910s. Taichi Ohno
published Toyota Seisan Hoshiki on the Toyota production system in 1978. Hopp and Spearmanemphasized the scientific mechanisms underlying each production system. For example, they
investigated the influences of system variabilities (from customers, in-process, and suppliers) to the
manufacturing processes by applying mathematical tools.
Many studies focus on the evolution of lean production. Fujimoto (1999) summarized theevolutionary path of TPS by investigating Toyota’s organizational capabilities in manufacturing. He
proposed a three-layer model to interpret how TPS gradually and cumulatively evolved through4the three layers of routinized manufacturing capability, routinized learning capability, andevolutionary learning capability. Holweg (2007) investigated the evolution of the research at the
MIT International Motor Vehicle Program that led to the conception of the term lean production.
He presented a timeline of key events within Toyota, the dissemination of lean production outside
Toyota, and major publications and concepts of lean production. Hines et al. (2004) reviewed theevolution of lean production theories. They applied McGill and Slocum’s (1993) four typeclassification of organizational learning to classify the evolution of lean production theories into thefour stages of cells and assembly lines, shop-floor, value stream, and value systems.
This paper focuses on the evolution of production systems in the context of supply-demand
relationships. A short value chain with three players – component supply, final product assembly,
and market demand – is analyzed. Historical changes in demand dimensions from Industry 2.0 to
3.0 are given. Industrial production systems are discussed and compared. For final productassembly, assembly line, TPS, and seru are analyzed. For component supply, job shop, cell, and
FMS are discussed and compared. We highlight the differences between seru and conventional
manufacturing cells that are based on group technology. The what and how of each production
system is addressed. For example, what is seru production? How should a seru production system
be constructed and managed? To the best of our knowledge, this is the first paper that uses
supply-demand relationships to analyze the drivers for production system evolution over time. We
summarize the demand drivers of Industry 2.0 and 3.0, and predict the demand drivers in the
future Industry 4.0. The possibilities of a future smart factory equipped with IoT are discussed.
3. Flow Line, TPS, Job Shop, and Cell for Industry 2.0
The struggle for the Simple Market (with the one dimension of product volume) of Industry 1.0
was the shortage of supplies, which were generated with craft production implemented byworkshops of families and/or small communities. This shortage of supply difficulty was addressedby using mass production in Industry 2.0.
The Stable Market of Industry 2.0 had the two dimensions of product volume and product5

variety. In the beginning of Industry 2.0, the pressure was how to increase product volume withlow cost. By using mass production, Ford matched high volumes with its Model T assembly line
innovation. An assembly line for mass production is a system that can supply large productvolumes with low cost (the question of what). The main enablers (the question of how) of anassembly line include dedication of the assembly line (i.e., no product variety, one line for eachproduct model), standardization of components and operations, specialization of workers andequipment (i.e., one worker and equipment for each operation), short operation times, and theattempt to balance workloads. In the early 1920s, Ford had 2/3 of American automobile market
share. The price of a Model T was reduced from $850 in 1908 to $250 in 1924. From the 1920s,
customer desires were becoming diverse. A single model (the black Model T) no longer satisfied
all customers. Alfred Sloan (1964) of General Motors addressed this problem by using a divisionalorganization structure (one division for each model). In contrast, the organizational structure ofFord was flat. In 1940, Ford’s market share fell to 18.9% and GM rose to 47.5%. The performance ofGM was good, but the real champion that could satisfy both volume and variety simultaneously
became Toyota.
Krafcik (1988) coined the term lean for the TPS created by Ohno (1988). TPS is an integrated
system that can supply products to meet both requirements of product volumes and productvarieties. Lean is a production concept that is investigated by researchers and imitated by many
companies. Many research and practical papers, reports, and books were published in various
media to describe TPS and lean. The underlying management principles and theoreticalmechanisms of lean TPS are well-known. Excellent analysis and review papers on lean are deTreville and Antonakis (2006), Hines et al. (2004), and Narasimhan et al. (2006). The evolution
from mass to lean TPS productions illustrates the trade-off between efficiency and flexibility (Adler
et al., 1999; de Treville et al., 2007). The what and how problems related to TPS are next described.
A TPS is an integrated production system that generates products to satisfy requirements ofvolumes and varieties simultaneously with minimum resource waste. This definition coincides

6with Krafcik (1988), who defined lean as “Requires the use of the less of anything through the
production of the product including less of labor, space, tools investments and time, which canlead to keeping the less inventory and achieving few inventory defects, resulting in variety and agreater amount of production”. Two critical cores of Krafcik’s definition are “the less of anything”
and “resulting in variety and greater amount of production”, which are the essence of ourdefinition.
A large number of TPS enablers have been reported. Some of the core enablers include
just-in-time material system (JIT-MS), seven wastes, heijunka, multi-skilled workers, quick setup
and changeover, and keiretsu. The TPS copes with product variety with a mixed-product-model
assembly line, which can accommodate different models simultaneously. Quick changeover andsetup are required to achieve high flexibility and heijunka is applied for high efficiency. Workers are
partially cross-trained, which means that each worker can perform more than one operation.
JIT-MS (i.e., the right components, in the correct place, at the right time, in the exact amount) is
applied to control material flows. A scientific analysis of the lean-enabling technique is the factory
physics of Hopp and Spearman (2001). They emphasized a balance among capacity utilization,
work in process, and variability. For example, if variability in a system increases, either work inprocess must increase or capacity utilization must decrease. de Treville and Antonakis (2006)
employed this understanding of factory physics to emphasize that lean is realized by maximizing
capacity utilization and minimizing buffer inventories, thus reducing system variability (related tosupplier, customer, or in-process). Shah and Ward (2007) made a similar point that a TPS isachieved by eliminating wastes by concurrently minimizing or reducing variability.
Mass and TPS assembly lines assemble final products. The parts and components of products
are usually produced using job shops and/or cells and/or FMSs, in which the part variety is usuallyhigher than the final assembled product variety. A job shop tends to manufacture small lots of avariety of parts. Most parts in a job shop require a long setup time between each operation and aprocess sequence of machines. A job shop is created by locating similar machines together,7

resulting in a functional layout. For example, drilling machines are usually contained in one area
and grinding machines in another area. In a job shop, flexibility is high and efficiency is low.
Cellular manufacturing (CM) is an application of group technology. One objective of CM is toefficiently cope with the production of a high variety of parts. Cells are converted from job shopswith functional layouts to improve efficiency (Yin and Yasuda, 2006). A cell consists of a machinegroup and a part family. The first step in CM system design is the identification of part families andmachine groups to form manufacturing cells so as to process each part family within a machinegroup with minimum intercell movements of parts. This identification is referred to as the cell
formation problem. Parts in the same family have similar machining sequences. Machines in a cellare arranged to follow this sequence. In this way, parts flow from machine to machine in theirprocessing sequence, resulting in an efficient machining flow that is similar to an assembly line. For
each part family, the volume of any particular part may not be high enough to utilize a dedicatedcell. The total volume of all parts in a part family should be high enough to utilize a machine cellwell. CM attempts to flexibly accommodate high variety and simultaneously efficiently takeadvantage of flow lines (Celikbilek and Suer, 2015) .
4. FMS and Seru for Industry 3.0
The Volatile Market of Industry 3.0 has the three demand dimensions of product volume,
product variety, and delivery time. Delivery time is the time-period from when a customer places
an order until the customer receives the product.
Product life cycles of electronics products decreased during the Industry 3.0 period. In Japan,
the average life cycle for electronics products is about six months (Yokoi, 2014). This motivates
manufacturing firms to consider responsiveness as a primary objective. TPSs cannot adequately
achieve responsiveness. The average life cycle of automobiles was around four to six years (Sato,2015). Toyota’s production planning and control period is around two months (Aoki, 2012).
Customer waiting times can be relatively long. For example, in 2016, Toyota’s delivery time for a
Prius averaged six to eight months (Nikkei-Business, 2016). Lean’s failure in responsiveness has

8been partially answered by management scholars. For example, Wessel et al. (2016) noted that “allcompanies’ internal systems – their metrics, resource allocation processes, incentives, approaches torecruitment and promotion, and investment strategies – are set up to support their existing
business model. These systems are generally well established and extremely difficult to change,
and they often conflict with the needs of digital business models.” This is similar for lean, which
was designed to reduce demand variability in the two dimensions of volume and variety. It isextremely difficult to change a current system, for example, a mixed product assembly line, to fit
the desire to reduce demand variability in three dimensions.
FMSs started production in the mid 1970s to early 1980s. An FMS is an integrated,computer-controlled complex of automated material handing and computer numericallycontrolled machine tools that can simultaneously process medium-sized volumes of a variety ofpart types. A fully automated FMS can attain the efficiency of well-balanced, machine-pacedtransfer lines, while utilizing the flexibility that job shops have to simultaneously machine multiple
part types (Stecke and Solberg, 1981; Stecke, 1983; Browne et al., 1984).
A seru production system is an assembly system that has been adopted by many Japaneseelectronics companies. The first English paper on seru production is Yin et al. (2008), which
describes and analyzes the success of seru production systems in Canon and other Japanesecompanies. The underlying management and control principles of seru are given in detail in Stecke
et al. (2012), Yin et al. (2008, 2017), and Liu et al. (2014). Roth et al. (2016) review the last 25 years of
OM research and provide eight promising research directions. One research direction mentionedthat “seru production systems are more flexible than Toyota’s production system, and they represent
the next generation of lean production that has recently been introduced to operations” (pp. 1476).
Seru was explicitly created as an alternative to the TPS because TPS had malfunctioned in an
innovative industry where the primary objective is responsiveness (see Stecke et al., 2012 and Yin
et al., 2008, 2017 for details). Seru began in 1992 when Sony replaced a video camera assembly linewith several compact serus. Soon after, Canon dismantled its assembly lines to adopt seru

9

systems. Sony and Canon are considered pioneers in implementing the seru production system.
Huge benefits have been reported from seru production. For example, seru can reduce lead time,
setup time, WIP inventories, finished-product inventories, cost, required workforce, and shop
floor space. Seru also influences profits, product quality, and workforce motivation in a positiveway.
An important performance of the seru production system is that it can quickly respond to
manufacture product varieties with fluctuated volumes, matching supply with Industry 3.0’s
demand. By applying seru, delivery time is reduced. Variety and volume are easily handled. For
example, Sony Mexico’s variety and efficiency increased 650% and 30%, respectively. Similarly,
Canon’s variety and efficiency increased 200% and 300%, respectively (Stecke et al., 2012; Yin et al.,2017).
The following discusses the two questions (what and how) related to seru production systems. A
seru is usually an assembly system that consists of some (usually simple) equipment and one ormore workers that produce one (sometimes more) products. A seru production system consists of
one or more serus. Serus within a seru system are quickly reconfigurable, i.e., they can beconstructed, modified, dismantled, and reconstructed frequently in a short time.
There are three types of serus, called divisional serus, rotating serus, and yatais. They represent
the evolutionary development of serus. A divisional seru is a short, often U-shaped, assembly line
staffed with several partially cross-trained workers. Tasks within a divisional seru are divided into
different sections. Each section is in the charge of one or more workers. A rotating seru is often
arranged in a U-shaped short line with several workers. Each worker performs all required tasks
from-start-to-finish without interruption. Tasks are performed on fixed stations, so workers walkfrom station to station. A worker follows the worker ahead of her or him, and is also followed by
the worker behind her or him. A seru with only one worker is called a yatai.
The TPS-based assembly line became inefficient because of an inability to change veryfrequently to match small-volume demands. The typical seru creation process in Sony and Canon10

can be summarized as follows (Yin et al., 2017). Assembly lines were dismantled and replaced withdivisional seru systems through resource co-location and removal/replacement, cross training, andautonomy. The technique of karakuri (involves procedures to discover and appropriate the usefulfunctions of expensive equipment into inexpensive self-made equipment) is applied to replace
expensive dedicated equipment by inexpensive self-made and/or general-purpose equipment thatcan be duplicated and redeployed as needed by serus. As cross-training progresses, divisional serus
evolve into rotating serus and yatais.
The management and control of a seru production system can be described as follows. A
management principle called just-in-time organization system (JIT-OS) is developed to match
supply with demand. JIT-OS (i.e., the correct serus, in the right place, at the appropriate time, in theexact amount) is an extension, or upgrade, of Toyota’s traditional JIT-MS (i.e., the correctcomponents, in the right place, at the appropriate time, in the exact amount). Their mechanisms are
similar. The main difference is the focus from components to organizations (i.e., serus). The typical
implementation of JIT-OS is as follows. Configure the seru production system so that the correct
serus are in the right place, at the appropriate time for the set of products to be assembled. This
involves either the relocation or relayout of current serus or the creation of new serus for both new
products or model changes. Then determine the appropriate number of serus and/or number ofworkers within serus to handle the various required product volumes. Yin et al. (2008) and Stecke etal. (2012) illustrate practical cases of JIT-OS implementations in Canon and Sony. Yin et al. (2017)
show that most JIT-OS problems are NP-hard. Efficient approaches to solve these problems need to
be developed. In summary, JIT-OS is a key mechanism to help serus achieve responsiveness.
Reconfigurability of serus is a prerequisite to implement JIT-OS.
5. Comparisons of Seru with the TPS and Cells
Differences between seru and TPS and cell are now discussed. The differences are emphasizedfrom the four aspects of strategy, operations, technique, and performance. There are two types ofcells, GT-based cells that machine parts and assembly cells. Johnson (2005), Sengupta and Jacobs

11(2004), and Gong et al. (2011) discuss assembly cells.
Differences from the strategic perspective are as follows.
1.

Objective: A primary objective of both TPS and a GT cell is to deal with product variety (e.g.,
TPS’s mixed product line and a cell’s part family). A secondary objective of the TPS and a cell is
to cope with product volume. A primary objective of seru is to pursue responsiveness. A
secondary objective is to cope with product variety and volume.

2.

Position within a value chain: A GT-based cell is usually designed to machine parts. Serus,
TPS lines, and assembly cells are assembly systems, usually in the final stage of production. A
supply chain can be constructed by using GT cells or FMSs in the upstream and serus, TPS, orassembly cells or lines in the downstream.

3.

Conversion process: A GT cell is converted from a job shop (from a functional layout). A
seru is converted from a traditional assembly line.
Differences from the operations perspective are as follows.

4.

JIT-OS: JIT-OS manages and controls a seru system. JIT-OS cannot be applied to TPSs orcells since they are not quickly reconfigurable.

5.

Virtual organization: When business environmental uncertainty becomes high, cells
encourage the use of virtual cells. Serus can cope with environmental uncertainties bycombining JIT-OS and reconfigurable serus.

6.

Autonomy: Serus emphasize responsible autonomy that enables worker teams in serus toschedule production, but workers are not allowed to carry out procedures however or wheneverthey choose (Yin et al., 2017). In contrast, cells emphasize choice autonomy that enables workers tohave some freedom concerning procedures and timing (Hyer and Brown, 1999).
Differences from the technique perspective are as follows.

7.

Group technology: Cellular manufacturing is an application of group technology. A GT
cell machines a part family that contains a few similar part types. In contrast, serus can bededicated to a single product or to several products for a short production time, and then be

12dismantled and reconfigured.
8.

Heijunka: The TPS and an assembly line need heijunka to balance the workloads among
different products. Serus do not need heijunka since most serus are dedicated for a short periodof time before reconfiguration.

9.

Equipment: Compared to TPS and cell equipment, the equipment of serus is simple.

10.

Karakuri: Karakuri is a prerequisite for creating seru systems. Karakuri is not used in the TPS
and GT cells.

11.

Cross-training: Seru systems encourage completely cross-trained workers (e.g., yatai).
Workers in the TPS and GT cells are partially cross-trained.
Differences from the performance perspective are as follows.

12.

Reconfigurability: Serus are reconfigurable and not fixed. TPS and assembly lines are fixedand not reconfigurable.

13.

Concurrent operations: For a specific product, a seru system can be concurrent or parallel. A
product can be assembled simultaneously by several serus. In contrast, TPS and assembly cells
are not duplicated. Products are assembled with other products together in a mixed TPS line.
Products are assigned to a part family and machined in a single GT cell. Therefore, a seru
system is more reliable than a TPS or cellular manufacturing system because of redundancy.

Key points of the differences between seru and TPS and cells are summarized in Table 1.
Table 1. Seru versus TPS and cells

Strategy

Operations

Seru

TPS

GT-based Parts Cell

Assembly Cell

Objective

responsivenessvarietyvarietyvariety

Supply Chain Position:assembly

assembly

parts supplierassembly

Conversion Processassembly line--

job shopassembly line

JIT-OS

yes

no

no

no

Virtual Organization

no

no

yes

yes

Autonomy

responsiblenochoicechoice13

Technique

PerformanceGroup Technology

no

no

yes

yes

Heijunka

no

yes

yes

yes

Equipment:

simplegeneral/specialized

generalgeneralKarakuri

yes

no

no

no

Cross-training

completely

partially

partially

partially

Reconfigurability

yes

no

no

no

Concurrent Operations

yes

no

no

no

6. Potential Manufacturing for Industry 4.0
Industry 4.0 was an initiative established by the German government in 2012 (Kagermann etal., 2013) to maintain its strong competitiveness in manufacturing industries. Similar promotions
are advocated in other industrial countries. For example, the United States proposed a smart
manufacturing plan (Smart Manufacturing Leadership Coalition, 2011) and suggested connecting
everything with the Internet using IoT (Porter and Heppelmann, 2014, 2015). The Japanese
government published “Society 5.0” – a smart system that covers smart community, smart
infrastructure, smart factory, and others. China created a plan of “Chinese Manufacturing 2025” tofoster Chinese manufacturing shifting to high value-added and becoming a global leader.
Most keywords in the above initiatives of different countries are technology related. These
include sensors, IoT, big data, cloud computing, artificial intelligence, automation, robots,
cyber-physical systems, 3D printing, and electric vehicles. Some potential effects of thesetechnologies for future manufacturing have been discussed (Kersten et al., 2017a, 2017b; Ivanov etal., 2016; Theorin et al., 2017; Liao et al., 2017; Thoben et al., 2017) in the three layers of data anddata collection and communication devices, physical structures of smart factories, and advanced
data analysis to support factory operations.
Many analysts and policymakers think that the rise of big data analytics and mobile
technology should spur the development of smart cities. Lim and Mack (2017) see a less optimistic
urban future, because digitization and crowdsourcing may undermine the foundations of a14

megacity economy. They consider 3D printing as a disruptive new technology, because it has
transformed the economies of scale into economies of one or few. Many Asian manufacturing
centers can expect to see widespread disruption to their economies and work forces. Robots,
artificial intelligence, and the human cloud can make the notion of offices obsolete. Industries must
adapt to technological change and start to plan for a disrupted future.
Schröder et al. (2014) analyzed supply chain risk management in the context of Industry 4.0.
With some technologies of Industry 4.0 such as cloud computing and cyber-physical systems, a
supply chain can become more flexible and transparent. On the other hand, supply chain
management may be faced with new challenges. Increased data volume and availability inreal-time require new infrastructures and approaches to handle information. The connection ofhumans, objects, and systems may allow dynamic, real-time improved, self-organizing, andcross-company value creation networks. More autonomy may be given to production systems.
Decision-making competences may be transferred from a hierarchical organized system to adecentralized, semi-autonomous collective of machines, equipment, operators, and mobile devices.
Relevant management and control systems should be developed to adapt to modifications in
hardware, software, and communication technology of Industry 4.0.
The changes and requirements in production management principles, in particular, changes incustomer demands in the era of Industry 4.0, are not clear. This section attempts to begin to fillthis gap. First, Industry 4.0 literature that discusses customer requirements is reviewed. Then, areal case, Xiaomi, is used to demonstrate possible future customer demands. Finally, the evolutionof customer demands and how this evolution could reshape production principles are discussed.
6.1. Smart Manufacturing for Industry 4.0
An important competitive advantage for a company is its capability to realize individual
requirements of diverse customers. Companies pursue this competitiveness by applying masscustomization (MC), which attracts attention from researchers and practitioners since the late 1980s(Da Silveira et al., 2001; Fogliatto et al., 2012). MC is a strategy that creates value by some form of15

company–customer interaction at the product design, fabrication, and assembly stages to createcustomized products (Kaplan et al., 2006). MC should provide enough product variety andcustomization that nearly every customer can find exactly what they want (Pine, 1993).
It can be difficult to effectively implement MC (Gilmore and Pine, 1997; Salvador et al., 2009;
Fogliatto et al., 2012). Manufacturers should apply appropriate management and control principles
to organize manufacturing systems to realize individual customer requirements. A manufacturingsystem should be efficient, flexible, responsive, and able to find an appropriate management andcontrol principle, hopefully quickly (Zawadzki and Zywicki, 2016). Traditional manufacturingsystems may find it difficult to achieve these requirements. For example, the TPS is flexible andefficient. Effective approaches have been developed to suggest better management and control
principles (Hopp and Spearman, 2001). But its responsiveness is slow. Seru can respond to
customer requirements with high efficiency and flexibility. Useful seru management and control
principles (i.e., JIT-OS) need further development. Currently, most JIT-OS applications are basedon factory managers’ experiences.
Because of the importance of MC and the difficulty to realize MC with current manufacturingsystems, some research suggests that smart manufacturing under the environment of Industry 4.0
may be a key to help implement MC strategy. Zawadzki and Zywicki (2016) suggested smart
product design and production control for efficient operations in a smart factory to realize an MC
strategy. A desire for customized products in combination with decreasing product life cycles begs
for organizational structure changes from TPS to self-improving smart manufacturing systems thatcan utilize data to quickly react (e.g., reconfigure) to personalized customer orders (Brettel et al.,2014). These management and control problems are usually NP-hard (Yin et al., 2017). Somestudies have suggested efficient mathematical models that use big data to manage and control
manufacturing processes in smart factories (Ivanov et al., 2016, 2017).
6.2. Xiaomi Case
Smart products in the era of Industry 4.0, like smartphones, can be connected for data16

exchange among users, makers, and possibly related third parties. Similar to the smartphone,connected products may consist of the three core modules (Porter and Heppelmann, 2015) of aphysical module (such as mechanical and electrical components), a smart module (such as sensors,
software, and microprocessors), and a connectivity module (such as protocols, antennae, andnetworks to product clouds). Most products of Industry 3.0 do not involve the third connectivity
module. The successful business case of Xiaomi is now provided to deduce some possible demanddimension changes from Industry 3.0.
Xiaomi is a Chinese smartphone maker who released its first smartphone in August 2011, andbecame the world’s 3rd largest smartphone maker in October 2014, following Samsung and Apple.
The rapid success of Xiaomi relied on its unique business model, which can be depicted as atriangle (Askci-Corporation, 2014). The three angles of the triangle are hardware, software, and theinternet, which are similar to our above description of a smart products’ physical module, smart
module, and connectivity module, respectively. Xiaomi’s uniqueness is in its Xiaomi-internet thatconsists of a Xiaomi cloud, Xiaomi chat, Xiaomi WIFI, and Xiaomi library. The Xiaomi-internet isan online network (and can be regarded as an IoT application) that allows customers tocommunicate with the Xiaomi staff, hardware and software suppliers, and other customers.
Customers’ specific requests are encouraged to be submitted to the Xiaomi-internet. These
requests are responded to quickly and then may be realized in hardware and/or software designs,
manufacturing, and internet service. Xiaomi customers’ loyalties and enthusiasm are high.
6.3. Demand Dimensions for Industry 4.0
Based on the literature review and Xiaomi’s case, we can deduce some future demanddimensions of Industry 4.0 as follows.


Variety: Companies may introduce multiple models for each product. There may be one ormore standard products consisting of standard modules. These standard modules consist of
hardware and/or software that provide standard functions for general customers. Somecustomer participation in product design can be important. There could be other standard

17

platforms where customers can suggest or request specific personal designed modules and/or
components to realize possible individual customization. A platform is an unfinished productthat consists of underlying core components or modules to form a common structure fromwhich derivative products can be efficiently developed and produced. We call the latter casecustomer participated individual customization (CPIC).


Time: In Industry 4.0, product life cycles may become more uncertain. The life cycle of aplatform may be short or long. In contrast, life cycles of individual modules that are personallydesigned to provide specific functions may be short because of possible frequent upgrades.
Requested delivery time may be short.



Volume: In Industry 4.0, volumes of standard products and platforms may be high or middle.
In contrast, volumes of personal designed modules may be very low. Volumes of standard
modules may fluctuate drastically with a wide range from low to high.
A future smart manufacturing system should be organized in conformance with the above

Industry 4.0’s customer demand dimensions. A possible configuration is given in Figure 1. The
construction of IoT and big data cloud allows communications among customers, assemblers,
suppliers, and other service providers. A smart manufacturing system may consist of two parts.
The first part is an information system (cloud computing and design of products and processes in

Figure 1. An example of a smart manufacturing system for Industry 4.0

18Figure 1 that can act as a brain of the smart manufacturing system. The information system
implements two tasks. Customized modules and/or components are articulated during productdesign to realize CPIC based on received specific customers’ requests. A supply decision is madeto meet (or not) customer desired product varieties, volumes, and times. The decision consists of acoordination policy for the supply chain and a management and control principle (e.g., JIT-OS) for
the smart factory. Advanced optimization and technologies such as artificial intelligence and deeplearning may be used to assist in finding an appropriate supply decision.
The second part of a smart manufacturing system is a physical system that consists of a smart
factory and its suppliers. The smart factory assembles or fabricates final products, platforms,
and/or modules. A smart factory can contain various production systems. A possible JIT-OS may
be generated by the information system and may be used to allocate customer orders to different
production systems to achieve demands with smooth production flows. Possible production
systems such as flow lines, FMSs, and serus can be differentiated in their applications as in thefollowing possible scenarios. For high production volumes and low varieties (e.g., standard
products, platforms, and modules), flow lines equipped with high speed machines, tools, androbots may be used to pursue scale efficiency. For medium production volumes and moderatevarieties (e.g., standard and/or customized products, platforms, and modules), FMSs or flow lines
may be used to pursue efficiency and flexibility with mixed product models. For high and/or low
production volumes and high varieties (e.g., customized products and modules, and possible
module upgrades) that require responsiveness (e.g., short lift cycle and/or delivery time), seru
production systems or FMSs may be applied.
7. Modular Design for Vehicles
During Toyota’s annual general meeting in June 2017, president Akio Toyoda introduced somenew challenges and opportunities, such as electric vehicles, automated vehicles, connected cars,and car sharing business models. Information technology companies such as Google and Apple
may become strong competitors in some of these applications (Nihon Keizai Shimbun, 2017). To

19

address these challenges and opportunities of electric vehicles, automated vehicles, connected cars,and car sharing business models, Toyota plans to intensify the following areas. Toyota plans toinvest in artificial intelligence and an in-house company system (that is, a business unit financially,
independent of the corporation) to quickly and flexibly take charge of business projects related tothese challenges and opportunities.
Deployment of the Toyota new global architecture (TNGA) is another one of theabove-mentioned areas. TNGA is a product design method that was developed by Toyota in 2015.
Toyota plans that half of its products in 2020 should be developed by using TNGA (Nikkan
Kogyo Shimbo, 2017). Similar design methods have been utilized by other automotive companies,
such as Renault and Nissan’s common module family, Volkswagen’s modulare querbaukasten(translated from German to modular transversal toolkit), and Volvo’s scalable product architecture.
The key concepts of all of these automotive design methods are the utilization of common
components and the adoption of modular architectures.
The trend of architecture change from integral to modular in the automotive industry has been
discussed in the literature. Fixson (2005) and Ro et al. (2007) discussed the impact of modularity on
automotive supply chains. They note that modularity can reduce new model development costs
and can act as a viable MC strategy for the automotive industry. Pandremenos et al. (2009)
reviewed modularity concepts for the automotive industry. They predicted that the productarchitecture of next generation vehicles will change from integral (with high production volume andlow flexibility) to modular (with middle to high production volume and flexibility). Cabigiosu et al.
(2013) conducted an experimental design to explore the component-vehicle interface definitions
adopted by assemblers and suppliers. They showed that the interface design is determined by the
degree of vertical integration, knowledge scope, and strategic focus of an assembler, as well as the
supply chain coordination mechanisms between the assembler and its suppliers. Lampon et al.
(2017) analyzed the European automotive manufacturers’ production networks and showed thatthe use of modular architecture can improve coordination by increasing manufacturing flexibility.

20

Product architecture can be defined as the way in which the functional elements of a productare allocated to physical components and the way in which these components interact (Ulrich andEppinger, 2008). Ulrich (1995) defined product architecture as follows: (1) the arrangement offunctional elements; (2) the mapping from functional elements to physical components; (3) the
specification of the interfaces among interacting physical components.
Product architectures are either modular or integral. Previous studies have shown that the
choice between these two architectures for a product is important and can be a key driver to a
manufacturing firm’s performance (Fine et al., 2005; Ramachandran and Krishnan, 2008). Examples
of modular architecture include electronics products such as desktop computers and consumer
electronics. In a modular architecture, the mapping from functional elements to physicalcomponents is one-to-one. The interfaces among interacting physical components are loosely
coupled. Most components of a modular product are interchangeable and the interfaces are
standardized. Advantages of modular architecture include an increase in product variety,components commonality, easy upgradability, and low cost.
Examples of integral architecture include luxury motorcycles, game software, and automobiles.
In integral architecture, the mapping from functional elements to physical components is not
one-to-one. The interfaces among interacting physical components are often tightly coupled. For an
integral product, a change in some functional element or component can lead a change to othercomponents in order for the overall product to work correctly. Modular architecture can realize local
product performance that can be achieved by using one or several components. For example, thespeed of a computer is mainly determined by its CPU. In contrast, global product performance
depends on many components and can only be improved through an integral architecture. For
example, the mass and/or size of a product are determined by almost every component within a
product. Function sharing and geometric nesting are design strategies that are frequently employed
to minimize mass and/or size (Ulrich, 1995; Yin et al., 2014). Advantages of integral architecture
include increased product performance, and the creation of unsubstitutable competitive

21

competences.
A possible trend in the automotive industry may be that gasoline vehicles may be graduallyreplaced by EVs. Gasoline engines and gasoline may be replaced by motors and batteries. Similar
to most electronics products, there is high possibility that the architecture of EVs could be modular(Nihon Keizai Shimbun, 2010; Kawahara, 2010; Uyama, 2013). Compared to a gasoline-engine
vehicle, the number of EV components may be reduced dramatically. For example, the number ofgasoline engine components is around 10,000 - 30,000. The number of electric motor components
may be around 80 - 100. More important, many components such as motors and batteries may
become standardized components in the automotive industry (Nihon Keizai Shimbun, 2010). Then,similar to the computer industry, core components (e.g., CPU) may be procured easily fromsuppliers and assembly operations of EVs maybe become simple.
Recall that in Industry 3.0, the electronics industry underwent an architectural change from
integral to modular. These motivated Japanese electronics makers to replace TPS production’s
mixed assembly lines with assembly serus to increase responsiveness. A similar change inproduction methods may occur in EVs. The variety of EVs may increase. Product life cycles may
shorten. Responsiveness could become a priority for the automotive industry. Production systems
using seru principles could become appropriate in the future.
8. 3D Printing
Garrett (2014) suggests that the impact of 3D printing may be disruptive and revolutionary, andthat the impact could last for several decades in the areas of manufacturing, value chains,environments, global economies, and geopolitics. 3D printing for real products is still expensive
and is mostly used to generate prototypes and mockups. Applications are expanding. According
to a survey of more than 100 companies, two-thirds were using 3D printing (Curran, 2016).
Among these two-thirds applications, 13.1% were producing products and/or components and53.5% were experimentations and prototype productions. Gebler et al. (2014) estimated that 3D
printing in manufacturing is expected to mature within the next 10 years and to change the input

22and output of the processes used to produce low volume, customized, and possibly somehigh-value products. An article (Tumbleston et al., 2015) in Science introduced possible technologies
for 3D printing that may speed the adoption of 3D printing in various industrial applications.
Some advantages of 3D printing for industry have been presented (Economist, 2011). For
example, 3D printing can print many geometric structures. It may simplify the product design
process. It is relatively environmentally friendly.
Some 3D studies for possible future applications are as follows. Dong et al. (2016) used amultinomial logit model to analyze the optimal assortment from a potential set of product variants
to compare a manufacturing firm’s strategy under two possible types of flexible production
technologies, traditional flexible technology and 3D printing. They found that 3D printing may
allow a firm to choose a larger set of variants than the optimal assortment using traditional
flexible technology without significant profit loss. Westerweel et al. (2016) considered the impact
of 3D printing on component design. They evaluated the lifecycle costs of components to findthat component reliability is crucial to the success of 3D printing.
Song and Zhang (2016) examined the use of 3D printing on a logistics system for spare parts
inventory design. They specified which parts should be printed when needed and which parts
should be stocked. When a demand for a part encounters a stockout, sometimes it can either bebacklogged or manufactured by using a 3D printer. They demonstrated that adopting 3D printing
may yield some cost savings and this impact may increase in part variety. A similar spare parts
inventory logistics design problem was discussed in a case study (Ivan and Yin, 2017), whichinvestigated a Russian car dealer that sells Japanese cars. Spare parts for the automobiles have tobe procured from Japan. By installing a 3D printer at the dealer in Russia to generate some simple
plastic parts, the dealer sometimes can reduce supply lead time.
The degree of individual customization may increase because a 3D printer can generate manygeometric structures. Such 3D printing may take longer and be more expensive than conventional
machining. 3D printed parts may not be as sturdy and might not meet tolerances. Supply lead time23

may be reduced by installing 3D printing locally. Product life cycles may shorten. Responsivenesscould increase.
9. Conclusions
Various types of production systems, such as flow line, TPS, job shop, cell, FMS, and seru, havebeen used with success. This paper reviews the evolution of production systems from theviewpoint of supply-demand mismatch or balance. Figure 2 summarizes the evolution of Industry
1.0 – 4.0 over time.

Figure 2. Time line of Industry 1.0 – 4.0
Customer demand is partitioned into different dimensions to describe the market
characteristics of Industry X.0. Appropriate production systems have been utilized to match
different demand dimensions over time. Comparisons of seru with the TPS and cells are provided.
Potential manufacturing for Industry 4.0, possible modular design for EVs, and the outlook of 3D
printing applications are suggested.
From early craftsmanship workshops to today’s modern factories, the practices and operationsof manufacturing systems have evolved over these 200 years. A study of the evolution of
production systems can provide hints for the possibilities of future production systems. Industry

24

4.0, EVs, and 3D printing are in their early stages. Many aspects of these areas are unknown anduncertain. Because of the interdisciplinary characteristics of the three areas of business, engineering,and information technology, studies have been performed from different perspectives. Futurestudies that incorporate and integrate these three areas are recommended.
Three future research directions are as follows. First, big data collection and evaluation should
become easier because of IoT. From Figure 1, the information system of a smart manufacturingsystem may have to generate a management and control system (e.g., JIT-OS) to allocate customer
orders for processing on different production systems. Most JIT-OS decision problems are NP-hard(Yin et al., 2017). Efficient approaches that may utilize advanced optimization or artificial
intelligence or deep learning need to be developed. Second, the customer demand dimensions ofIndustry 4.0 are uncertain and unknown. Industry 4.0 combines many technologies such as sensors,
automation, robots, and cyber-physical systems. These technologies may require changing theoperational procedures of a production system. How a production system adapts to an
environment with new technologies and customer demand dimensions has to be investigated.
Third, detailed case studies that are rigorous, deep, and insightful to explain how to create, manage,
operate, and maintain production systems in the context of Industry 4.0 is suggested.
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