Simulation Integrated Manufacturing
If future were foreseeable
manufacturing would be simple.
If experiences were shared
manufacturing would be wise.
If thinking were united
manufacturing would be tough.
By adding simulation manufacturing really will be smarter.
�e key to all of this is the ‘Virtual’.
Sharing wisdom and power to innovate manufacturing.
Predictive manufacturing for smart manufacturing
hy and for what purpose “ Simulation Integrated Manufacturing” is required?
In the times of globalization to cope with the drastically and rapidly changing world of business,
transition to a new manufacturing concept is required. With IoT in rapid progress, production innovation e�orts
that take the regional industrial cultures into consideration are underway overseas,
such as the “horizontal division of work” in the Industry 4.0 framework in Germany
and the “high-value-added services” approach adopted by GE and other players in US.
However, just smart machine concept from Indstrie4.0 does not give you the practical production system and
a future of manufacturing. To move the manufacturing innovation forward, what we should try to do now
is not to follow implementing IoT but to leverage and maximize the inherent strengths
with “vertical integration approach” .
�is strength can be enhanced through latest technologies to develop a “predictive engineering” approach,
where problems can be foreseen and coped with before they even occur and changes and new trends
can be predicted and dealt with in an e�ective manner.
“Predictive engineering” refers to an upstream engineering approach where potential issues and problems are
exhaustively identi�ed and coped with in the production system planning stages.
With this, the best possible performance and quality can be assured in the planning stage.
�is is a next generation manufacturing concept where ICT (information and communication technologies),
IoT and the manufacturing strength are united into one.
W
How “Simulation Integrated Manufacturing” actually works
LEXER RESEARCH Inc. is currently promoting a new manufacturing concept called “Simulation Inte-
grated Manufacturing (SIM)” , that enhances engineering operations from organizational aspects through
predictive engineering techniques with production model. SIM enables stronger coordination of upstream
operations through production simulation and also drastically improves production management by
bringing IoT features to the production �oor. Furthermore, SIM fully transforms engineering operations
by replacing the conventional type of scattered operation structure with a more centralized and closely
coordinated one by production model.
SIM is mainly implemented in two operational areas; the upstream operations up to the start of mass pro-
duction and the downstream operations subsequent to it. What to do in each of these areas is brie�y
described by the following objectives:
�e actual steps to achieve the two objectives are explained in the following paragraphs.
Predictive Engineering / Vertical integration of production engineering operations
Predictive Production / Dynamically optimized production with massively parallel simulation system
PredictiveEngineering
PredictiveProduction
Smart Man Smart ManSmart Machine Smart Machine Smart Machine
Smart factory
Processdesign
Productplanning
Targetcosting Product
design
Factoryplanning
Procurement
Logisticsplanning
Floorplanning
Productionplanning
Predictive Engineering
Start ofmass
production
Predictive Production
Vertical integration ofproduction engineering operations
Dynamically optimizedproduction
ProductionModel
Vertical integration of production engineering operations
Upstream operations to manufacture a product start from project-level planning activities including
product planning, factory strategy planning and target costing, followed by more practical planning and
design activities including product design, process design, procurement and line planning, to prepare
and provide all that is necessary to start the mass production of the product. By con�guring these activi-
ties based on a common production system concept, they can be organically linked within a single pro-
duction model framework. Furthermore, the tangible elements of production, such as the product and
parts, equipment, tooling and layout, and the intangible elements such as the work and process, trans-
portation, logistics and production plans, can be put together through production simulation with pro-
duction model to assess if the relationship between the elements is practical and feasible.
To achieve the vertical integration of production engineering operations, various planning services can
be con�gured and evaluated in individual operation processes to determine their feasibility and e�ective-
ness, so that upstream operations can be made more e�cient and more e�ectively organized. �is is a
new engineering approach that shifts from the conventional fragmentary optimization attempts toward a
more comprehensive and business process-integrated optimization. In addition, extensive use of IoT
features will enable creating a production plant database called “Global Factory Repository” that can
fully synchronize with the actual production environment to facilitate production engineering activities.
Vertical integration of production engineering operations
To realize all of the above, it is not enough to merely make more produc-
tion models and improve functionality. We must go one step further, to
create production models that truly represent the actual production con-
ditions and to establish a system where expert knowledge is contained as
a thoroughly systematized set of methodology. �is should be the basis of
all engineering operations, and that is what “vertical integration of pro-
duction engineering operations” is all about.
ProductionSimulation
ProductionSimulation
ProductionSimulation
ProductionSimulation
ProductionSimulation
ProductionSimulation
Business process
Global Factory RepositoryFloor plan Line balance Parts shelf Inventory
Work team Worker skill Workability Power etc
Logistics Equipment Tooling Zig
Supplychain
EngineeringchainPrediction
Faculty
ProductionModel
IoT, Smart factoryIoT, Smart factory
Productplanning
Targetcosting
Productdesign
Factoryplanning
Processdesign
Procurement
Floorplanning
Logisticsplanning
Production
Predictive Engineering
PlantPlant
Plant
Dynamically optimized production with massively parallel simulation system
Once the mass production of a product commences, it is typically very common that unexpected issues and
problems such as machine failures, quality defects or emergency orders are experienced as a everyday occur-
rence. You would be required to resolve all such issues quickly to recover normal production state as soon as
possible. Such process of problem solving and recovery is called “resilience” , which, unfortunately, often
does not occur very easily in a production operation. Once production deviates from the established sched-
ule, recovery can be di�cult and time-consuming.
A “Dynamically optimized production system” , when faced with a problem such as an equipment failure
(serious or minor), emergency production order, non-delivery of parts or quality defect, immediately runs
real-time simulation to assess how serious the situation is and how it impacts the production. An optimiza-
tion process will then start that, by executing massively parallel simulation with production model, identi�es
and executes all possible remedies and improvements such as modifying the production plan, changing the
production line allocation or adjusting the workforce plan, so as to autonomously seek recovery or otherwise
control the situation as best as possible. In short, a dynamically optimized production system enables you to
e�ectively cope with variations, disruptions and turbulences experienced in a production operation through
active use of production simulation techniques with production model.
To put a SIM concept into practice, it is necessary that IoT features are available for use. On a human-oper-
ated type production line, a cyber-physical system is employed to monitor the work instructions received and
the work performances recorded to assure that production continues without problems. On the other hand,
an MES or smart machine will be utilized on a machine-driven type production line to retrieve work perfor-
mance data while production continues. If a work delay is detected, the massively parallel simulation system
with production model will run dynamic optimization in an intermittent manner, monitoring and correct-
ing the production plan and the production system as required to resolve the delay to maintain normal oper-
ation.
Dynamically optimaized production
MRPscheduler
Productionplanning
Work planning(initial)
Work planning(running)
Production control
Manufacturing
Plan is continuously
correctedthrough optimization
Workperformance
Plant A
Plant B
Plant C
Workimplementation
Optimization subject
Optimizationblade serverOptimizationblade serverOptimizationblade server
Optimization result
Optimizationsubject
Optimizationsubject
Optimizationresult
Optimizationresult
Massively parallelreal-time
simulation
Massively parallel simulation engine clusterMassively parallel simulation engine cluster
Workinstructions
Workinstructions
Predictive Production
ProductionModel
System Architecture of dynamically production with massively parallel simulation system
Role of “Simulation Integrated Manufacturing” in a manufacturing organization Simulation
IntegratedManufacturing
(SIM)
MES
ERP PLM
Global production strategy will be supported by powerful SIM technology.
Predictive Mining ManagerWork planManager Production plan real time optimize
Smart MachineSmart Machine
Smart Machine
Smart Machine Smart Machine
Engineeringchain
Supplychain
Cyber (virtual)production floor
Virtual Factory ( Production floor real-time status )
Physicalproduction floor
Production Execution Manager
Production plan Manager
MRP / Scheduler
Product designProduction preparation
Massively parallel simulation engine clusterpowered by GD.findi
ProductionModel
With conventional production simulators, each case of production process planning requires a specialist to
undertake the time-consuming process of developing a simulation program tailored to that individual situation.
On the other hand, GD.�ndi does not require programming to work. Simply by using our superior graphic user
interface, onsite factory engineering sta� can develop factory �oor plans or production process plans on their
own. �en they can immediately execute simulations. GD.�ndi has introduced such innovative engineering to
the manufacturing �eld.
By employing GD.�ndi, users can virtually construct not only manufacturing facilities, but also distribution cen-
ters so as to build production model for 'Simulation Integrated Manufacturing'. �at allows them to craft appro-
priate productivity-enhancement policies. �e results of such simulations facilitate visual con�rmation of opti-
mum layout, the order of production implementation, plant and equipment performance, inventory space, deliv-
ery routes and delivery methods, as well as team building among the work force.
�ese operations ensure fast and e�ective consensus building within teams. As a result, users can smoothly and
without delay implement factory �oor planning, equipment design, procurement, education and training of
sta�, as well as other steps needed to get production up and running.
Furthermore, not only can these results be con�rmed visually, they are also recorded numerically. �erefore, they
can be compared with draft designs using various types of KPI (key performance indicators). As this numerical
data can be used for computing product costs, compiling estimates, etc., that also facilitates the establishment of
appropriate prices.
What makes possible these outstanding results is the fact Lexer Research, the creator of GD.�ndi, has accumulat-
ed a wealth of engineering expertise over many years. GD.�ndi is a simulation technology guaranteed to deliver
manufacturing and logistical knowledge and solution methods from management circles to the factory �oor.
ProductionModel
In order to build SIM system or 'Production model'production simulator 'GD.�ndi' worksas core simulation engine
Simulation model set upservices
GD.findi
Project managementservices
GD.findi
Global Factory Repositoryservices
GD.findi
Optimization services(Option)
GD.findi
GD.findi Cloud services
Tokyo O�ce: 6F-Higashikanda-Towa Building, 2-3-3 Higashikanda, Chiyoda-ku, Tokyo, 101-0031
Tottori Headquarters: 2-98 Chiyomi, Tottori City, Tottori Prefecture , 680-0911
LEXER RESEARCH Inc.
Inquiry Tel: +81-3-5821-8003 Fax: +81-3-5821-8098 E-mail: [email protected]
URL http://www.lexer.co.jp/en/