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: info@lexer.co.jp

URL http://www.lexer.co.jp/en/