whitepaper planning and scheduling

7/28/2019 Whitepaper Planning and Scheduling

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WHITEPAPERREAL TIME PLANNING & SCHEDULING

Planning and scheduling involves the whole company. Startingfrom a strategic sales forecast, the production schedules of the

factories and single resource rosters are developed.

This is an important task for achieving competitive advantagesthrough operational excellence in the manufacturing process.State-of-the-art rules-based schedulers with semi-manual re-

organization of tasks are not able to support factories inreaching this operational excellence. iFRP goes beyond that,

being the only true optimizer that calculates the bestproduction schedule in real-time.


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by iFactory Inc., 2010, All rights reserved.

Real TimePlanning & Scheduling

Table of Contents

1. Preface ............................................................................................................................... 31.1 Benefits of iFRP: Intelligent Scheduling in Production Planning............................. 3

2. The Evolution of Planning and Scheduling ................................................................... 32.1 Planning and Scheduling: The Basic Concept .......................................................... 32.2 Why is Planning and Scheduling so Important? ....................................................... 52.3 Planning and Scheduling in General A Historic Overview................................... 9

2.3.1 Material Requirement Planning (MRP) .............................................................. 92.3.2 Manufacturing Resource Planning (MRP II) ..................................................... 102.3.3 Advanced Planning and Scheduling (APS) ....................................................... 102.3.4 Detailed Production Scheduling (DPS) .............................................................. 11

3. iFRP Goes Beyond State of the Art ............................................................................ 123.1 iFRP Intelligent Scheduling................................................................................... 123.2 Intelligent Scheduling How iFRP Works.............................................................. 13

3.2.1 Scenario description ........................................................................................... 133.2.2 Conventional Solutions....................................................................................... 143.2.3 Example: Changing Factory Restrictions......................................................... 143.2.4 iFRP Intelligent Scheduling .......................................................................... 143.2.5 Advantages over current approaches .............................................................. 14

4 Summary ................................................................................................................................ 17


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1. Preface

1.1 Benefits of iFRP: Intelligent Scheduling in Production Planning

Planning and scheduling involves the whole company. Starting from a strategicsales forecast, the production plans for individual factories are developed. Thematerial flow within each factory has to be controlled and production orders needto be allocated efficiently to individual machines in order to meet the desiredgoals in output, capacity utilization, cycle time, and efficiency.

Inefficient planning and scheduling is often the cause of the gap between requiredand actual plant performance. Improper scheduling can cause high lead times dueto synchronization problems or the occurrence of dynamic bottlenecks. These cancause delays on quoted delivery times. If a machine runs out of material due tobad scheduling, then production capacity is lost and delivery dates may be missed.

Effective planning and scheduling can be a difficult task. Standard planning andscheduling solutions often have problems in utilizing the full capacity of a plant.They use heuristics (rules-based) systems that do not consider all relevantconstraints and cannot react quickly to unexpected events. In many cases they aremerely enhanced monitoring tools or they emulate the job of a manual planner.Even if they are faster than a human planner, their heuristic approaches do notreach the plants full potential. Often, they can only handle simple optimizationcriteria, such as due date or priority.

iFRP is a unique real-time, event-based factory scheduler. The analytical iFRP

solution does not rely on heuristics and calculates a true optimum in real timeallowing factory staff to react quickly to unexpected events.

2. The Evolution of Planning and Scheduling

2.1 Planning and Scheduling: The Basic Concept

Planning and scheduling in general is a company-wide task. It starts with a salesforecast and goes down to each single machine on the factory floor. Due to its

complexity, planning and scheduling has to be supported by software mainly ERPand MES.


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Shop Floor scheduling depends on different data and functionality then ERP.

The graph below shows the planning and scheduling pyramid within a company.

Planning and scheduling is an integrated task. Each layer in the scheduling pyramiddepends on every other layer.

In the planning and scheduling process, the upper layers (demand forecast, supplychain management and factory resource planning) determine when and where acertain quantity of a particular product is needed. ERP modules can perform thistask. The factory floor needs to ensure that the factory output is in line with theseforecasts. This; however, is not typically an ERP task. ERP systems simply do nothave access to all relevant factory data. For example, ERP systems do not knowthe current state of machines (operational, maintenance status, off-line, etc.) onthe factory floor or many of the constraints (machine capacities, staffingrequirements, maintenance schedules, recipe management, etc.) which govern theproduction process. This data is available in MES systems. Ideally, the ERP system

delivers the production targets and receives resulting production plans meetingthese targets from the MES layer.

So what happens on the shop floor and why is ERP-based scheduling insufficient?

The factory floor deals with production orders. A production order is the basic lineitem of a production plan. Initially (prior to production planning) it consists of aproduct with an amount, due date, and list of machines which can process it.Eventually (after production planning is complete) it contains a scheduled startand end date and a selected machine on which it is to be processed.


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The production planning process must take into account all relevant constraintsincluding the current state of the factory floor (what is produced where) and meetall other process requirements. Often, the number of constraints and theirdynamic nature makes scheduling difficult. Existing solutions often use rules-based

heuristics and the parameterized knowledge of factory staff to perform thescheduling task. These solutions imitate a human planner. Results from suchsystems may produce executable production schedules. But they rarely utilize thefull potential of a factory.

Planning and scheduling should not merely keep a factory running. It must be atool for reaching operational excellence. It must find production capacities hiddenby the constraints within the process and help in reducing setup times, meetingdelivery targets, optimizing energy and staff use, leveraging existing resources,and much more.

2.2 Why is Planning and Scheduling so Important?

Planning and scheduling utilizes efficiency levers like energy savings or setup timereduction. An efficient planning and scheduling tool directly contributes to thecompany's financial performance.

At the company level, cash flow and profitability are essential. Planning andscheduling can address the appropriate levers at the plant level to influence thecompanys overall performance.


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The diagram below shows some of the efficiency levers of planning and schedulingand how they influence the company's performance.

The impact of scheduling on plant performance and utilization can have a great

effect on operational excellence.

In the following figure we show how factory success measures can be influenced bythe optimization levers which planning and scheduling controls. This should give anidea of the important role which scheduling can play in utilizing and controllingthese levers.

Inventory ReductionNot all resources in a factory work at the same rate. Some machines arenewer and faster, some can handle exotic products which may make themslower in certain cases. In addition the speed of machines is influenced by a

wide variety of factors, e.g. product variants or events like late delivery ofraw materials. These aspects cause different types of bottlenecks whichimpact the manufacturing process.

Static bottlenecks include machine capacities and are known upfront. Theydo not move across the manufacturing process or the shop floor.

Scheduling controls levers which directly influence the factory and company

performance


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They do not need to be predicted and even simple production planningsolutions take these bottlenecks into account.

Dynamic bottlenecks only appear at certain times depending on the specificproduct variants which are currently produced. They may move across theprocess and shop floor and are difficult to predict. Few production planningsystems take these bottlenecks into account.

Event driven bottlenecks appear when an unexpected event occurs. This canbe the break down of a machine or the loss of products due to quality issues

or an unexpected high-priority customer order. These bottlenecks areinherently unpredictable and require quick reaction on the factory floor.Production planning systems must have a real-time capability in order tosupport factory staff reacting to unexpected events. Only quick andappropriate action can minimize the impact of an event-driven bottleneckon the production process.

The standard approach to dealing with bottlenecks of any kind is themaintenance of inventory (safety stock) for all or at least for the mostcritical materials as well as the use of longer-than-necessary set up timesto buffer unexpected delays. Efficient production planning, i.e., planning

which can effectively and more detailed set-up matrices deal with everytype of bottleneck manages production with significantly less inventory thana standard approach.

Increase of ThroughputPoor production planning can result in unnecessary idle times for resourcesand worse unnecessary setup times. Another reason for idling machinesis the use of buffer times in order bridge small deviations from theschedule. This is intended to ensure that material and resources areavailable when needed. Allowing for these buffer times also simplifies the


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manual calculation of a production schedule by human factory staff. Oneadvantage is a certain degree of predictability of quoted delivery dates. Butthis advantage does not come without cost. The same safety margins whichguarantee delivery dates also push these delivery dates back. The diagrambelow highlights this behavior.

A sophisticated planning and scheduling solution can reduce idle times byoptimizing setups and reducing safety buffers while maintaining the same

degree of predictability. Sales staff can commit to earlier delivery dates andstill expect them to be met with a high degree of reliability.

Eliminating idle times also results in cycle time reduction and improvesfactory throughput.

Reduction in Operating CostThe reduction of idle times frees capacity hidden in the productionprocess. More can be done with less. Planning and scheduling helps toreduce inventories by producing materials just-in-time. Optimizedresource allocation helps to save energywhich will become an increasinglyimportant factory as energy cost are expected to go up in the future.

Another lever is the reduction of waste. In some factories the productionprocess cannot be stopped arbitrarily. Machines must produce something orthey must be shut down for extended periods of time, for example, at theend of the year. If production is poorly planned a factory may be forced toproduce waste just to keep machines running. An efficient scheduler canreshuffle waste production to the planned, end-of-year shutdowns, thusavoiding their production entirely.

Rule

based

scheduling

Component 1

Component 2

Component 3

Component 4

Manufacturing Days1 2 4 6 9 12 16 203 5 8 11 15 19 237 10 13 17 21 24 2614 18 22 251 2 4 6 9 12 16 203 5 8 11 15 19 237 10 13 17 21 24 2614 18 22 25

Final Assembly

iFRP

Component 1

Component 2Component 3

Component 4

Manufacturing Days1 2 4 6 9 12 16 203 5 8 11 15 19 237 10 13 17 21 24 2614 18 22 251 2 4 6 9 12 16 203 5 8 11 15 19 237 10 13 17 21 24 2614 18 22 25

Final Assembly

Quoted Delivery DayValue adding manufacturing step

Non value adding time

Quoted delivery dates can be confirmed to customers to customers, even if buffer andidle time is reduced


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2.3 Planning and Scheduling in General A Historic Overview

Scheduling solutions of the past 30 years were unable or only partly able to assistmanagement in utilizing plant capacities. In the following paragraph we will

discuss the most important scheduling approaches with their strengths andweaknesses.

2.3.1 Material Requirement Planning (MRP)

Material Requirements Planning (MRP) is a production planning and inventorycontrol system used to manage manufacturing processes. It creates a productionplan by decomposing the bills of materials into sub-products and uses averagedproduction times for each process step. Combined with an infinite capacityscheduler MRP can deliver duty rosters for specific machines.However, MRP has some major drawbacks:

Average production time: The system uses average production times foreach production step. Improvements on the shop floor are not generallyconsidered in MRP scheduling. They only lead to longer lead times, becauseeach step must take the same average production time, even if it is in factperformed faster. If on the other hand a single step takes too long, thenthe entire schedule may become obsolete.

Unlimited capacity: MRP calculations are based on the assumption thateach resource has unlimited capacity. This is an unrealistic assumption. Aproduction plan created based on this assumption does not fit the realitieson the shop floor.

Missing feedback: There is no closed-loop feedback between theparticipating modules of MRP, making it hard to react to internal andexternal events which may be disrupting the production process.

No shop floor connectivity: MRP has no interaction with the shop floor;therefore, it cannot react to events. MRP is located at the ERP layer whichuses a different data architecture than shop floor systems, whichconsolidate their data at the MES layer.

Rules-based (heuristic) approach: MRP uses rules in order to calculate aproduction schedule. This is necessary due to efficiency issues. But these

Before midst 80sMidst 80s

midst 90s

Midst 90s

2005From 2005 From 2006

MRP

DPS

iFRP

MRP IIMRP II

APSAPS

Chronological sequence of planning and scheduling concepts


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rules can become invalid when events occur or the factory is changed in anyway.

2.3.2 Manufacturing Resource Planning (MRP II)

MRPII systems consist of finite capacity scheduling (FCS), capacity requirementsplanning (CRP), distribution resource planning (DRP) and other modules. MRPIIfacilitates the development of a detailed production schedule that accounts formachine and labor capacity. An MRPII output is a final labor and machine schedule.Data about the cost of production including machine and labor time, materials aswell as final production numbers is provided from the MRPII system to accountingand finance. MRP II takes into account the real production capacity and thusovercomes one of the most critical shortcomings of MRP. It also has integratedclosed-loop feedback

Critical drawbacks remain: No integrated solution: Even though MRP II has integrated closed-loop

feedback, the various modules and layers make calculations andcommunication quite inefficient. The large number of modules generates atremendous communication overhead.

No shop floor connectivity: Just like MRP the MRP II approach is an ERP layerapplication and unable to exchange relevant data in real time with shopfloor information systems.

Rules-based (heuristic) approach: MRP II uses rules-based algorithms forplanning and, therefore, produces the inefficient schedules just like MRP.

2.3.3 Advanced Planning and Scheduling (APS)

As seen before, traditional MRP and MRP II systems utilize a step-by-step procedureto allocate material and production capacity and plans them separately. Many MRPsystems do not consider limited material availability or capacity constraints. Suchapproaches often result in unfeasible schedules which cannot be executed on theshop floor.

Unlike previous systems, APS simultaneously plans and schedules production in anintegrated approach which is based on available materials, labor and plantcapacity.

APS solutions appeared to be a tool for the factory director to achieve optimalplant performance. But soon it became obvious that APS could not deliver on itspromise. Often, schedules turned out to be unfeasible on the shop floor becauseAPS plans at an aggregated level using various assumptions regarding data andefficiency factors.

No shop floor connectivity: Although APS is an integrated planning system,it is located at the ERP layer like MRP and MRP II. Therefore, it has noaccess to shop floor data. ERP requires different data than the shop floor,


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which forces APS to use simplifications and to plan at an aggregated level(resource classes instead of single resources). Furthermore, a shop floorplanning and scheduling tool needs to react in real time to disruptiveevents. This would require the ERP system to be updated in real time with ahuge amount of data and constraints. The ERP systems were neither made

for such real time updates nor were they made to handle shop floor specificdata.

Rule-based (heuristic) approach: like the MRP approaches, APS uses rules-based instead of analytical algorithms. This makes the approach fast, butthe results are still inefficient schedules.

APS proved to be a good solution for material flow planning above the factorylevel, but was not sufficient for detailed production scheduling on the shop floor.

2.3.4 Detailed Production Scheduling (DPS)

To give planning and scheduling software access to shop floor data, the gapbetween ERP and shop floor level needed to be closed. Detailed ProductionScheduling (DPS) is based at the MES level. Executable production schedules forthe shop floor can be derived from ERP production targets originating from thebusiness level. Whereas APS and MRP schedules tell when a certain quantity ofproducts need to be finished, MES level scheduling details how to split these intoproduction orders and individual resource duty rosters. The resulting productionplans can be executed on the shop floor.

Detailed production scheduling makes it possible to model each part of a factory

and the production process instead of planning at an aggregate level. Furthermore,MES relevant data and constraints ensure that the plans are executable on theshop floor. For the first time, it became possible to replace manual scheduling bythe factory planners on single resource level.

Even if most of the drawbacks of the ERP planning and scheduling tools wereovercome, DPS kept one major problem of these tools: Rule-based (heuristic) approach: To deliver fast results, especially when

reacting to real time events, DPS solutions use rules for schedulingcalculations. They work in a limited solution space and act like a manualplanner would do. Even if DPS is fast, it only can explore local optima within

the solution space of its rules, thus ignoring vast areas of the solutions spacenot covered by its rules. Furthermore, rules-based approaches are notrobust under changes in factory layout, process changes, or new productvariants. Often DPS systems are limited to a restricted number ofconstraints in order to keep complexity low and to increase calculationspeed.

While MES-based DPS are capable of some of the necessary planning and schedulingtasks; it falls short in allowing plants to reach their full performance potential.What factories now need is a planning and scheduling solution which enables them


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to utilize the advantages of the scheduling optimization levers in order to gaincompetitive advantages and operational excellence.

3. iFRP Goes Beyond State of the Art

3.1 iFRP Intelligent Scheduling

Detailed production scheduling closed the gap between ERP and the shop floor, butit was unable to utilize the scheduling optimization levers. The results were notsufficient to solve efficiency and speed issues necessary to reach desired plantperformance.

The reason is the rules-based approach of the DPS solutions which is the majorproblem of all discussed scheduling approaches. They are not robust under

changes, limited in their optimization capabilities and have problems withvariations in the production process. Factories are under continuous change andrules-based solutions tend to lag behind. It takes time until sufficient knowledgehas been developed and integrated into the rule set. During that timeframeperformance is poor.

The patented iFRP algorithm uses an analytical solution instead of rules. Eachtime a new production schedule is calculated, the whole factory with all itsparameters and constraints is calculated again. Therefore, iFRP calculates a trueoptimum within the solution. In addition, iFRP deals with changes and events inreal time without need for reconfiguration or reprogramming. This is possible due

to the highly efficient calculation scheme of the algorithm, making planning andscheduling no longer dependent on rules. iFRP does not aggregate or reduce thepossible solution space.

iFRP is robust and can operate in rapidly changing environments (which mostfactories are) and delivers real-time results. This also is important for quickreaction to disruptions of the production process, like machine breakdowns ordelays in material delivery.

Comparison of existing planning and scheduling solutions among relevant dimensions for utilizing

scheduling optimization levers

MRP MRP II APS DPS iFRP

MES centric

Analytical

solution

Real timecapacity

Integrated

planning


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3.2 Intelligent Scheduling How iFRP Works

The following example shows the benefit of iFRP compared to traditional, rules-based approaches. Rules-based solutions use a set of heuristics which cover the

most common scheduling scenarios for the given production process.

Rules-based approaches perform poorly when changes occur in the productionprocess. If a machine is changed/added or new product variants are included,rules-based solutions have two possibilities, 1) Using the old heuristics for the newsituation which decreases output quality or 2) adapting the rules. The latter optionresults in a customization effort for the solution and takes time to develop,meanwhile the production process may yet change again. Also, the heuristics aredeveloped from the experiences of factory staff. These experiences also take timeto develop and program into the set of rules.

iFRP on the other hand does not use heuristics. It creates a mathematical modelof the production process and solves this problem in an analytical fashion. Itconsiders many possible schedules within this model and chooses the best onebased on optimization criteria. This feature makes iFRP robust under changes inthe factory layout or the production process. These changes result in a slightlydifferent model which can be calculated in the same way. There is no need toupdate any rules.

The following scenarios will further explain the scheduling capabilities of iFRP

3.2.1 Scenario description

Scenario 1: Suppose the demand environment for a particular productrequires three red products and three green products to be manufacturedduring the next production cycle. The products are produced on the sameproduction line. The color is applied by a coloring device, which must becleaned for 30 minutes before a new color can be applied. It is mostoptimal to produce all of the items of one color first (say reds first), do thechange-over for the coloring device, and then produce the products of thesecond color. This production sequence has one downtime of 30 minutes toclean the coloring device

Scenario 2: The Scenario 1 situation can be made more complicated. Letsassume that the products must be transported to a different factory inspecial transportation crates, which are limited in supply and depend on theshape of the product. Further, assume that two products of the red varietyand one of the green variety are of shape A and the remaining products areof shape B. Further, assume that all of the crates required for shippingshape B products are currently in use. The previous production schedule forScenario 1 (first reds, then greens) is no longer feasible. The third redproduct cannot be placed into a transportation crate because no crates for


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this shape are currently available. In this situation, production schedulingmust take crate availability (a factory restriction) into account.

3.2.2 Conventional Solutions

The production planning problem is commonly solved by systems that possessknowledge about factory restrictions in the form of rules. These rules representknow-how which factory employees have developed over time while performingfactory operations. In Scenario 1, one such rule would state: Produce same colorsin sequence. In Scenario 2, the production rules would state: Produce same colorsas long as transportation crates are available. The problem with such rule-basedapproaches becomes apparent when the factory restrictions change.

3.2.3 Example: Changing Factory Restrictions

In the two Scenarios just considered, the situation may now be changed to upgrade

the coloring equipment of the production line with an additional stand-bycoloring device. This second coloring device can be used while the first device isbeing cleaned. More specifically, when a color change occurs, the second devicecan be rotated into the production line immediately while the first device movesout of it to be cleaned. The change-over time for color changes is thus reduced tozero. The introduction of the second stand-by coloring device totally changesthe optimization problem. Any rules intended to optimize productivity byminimizing color change-over times have become invalid. The entire rule-basedoptimization scheme must be redesigned.

3.2.4 iFRP

Intelligent Scheduling

As demonstrated in the scenarios above, rules-based systems are fragile underchanges in factory restrictions. Furthermore, rule-based optimization strategiesare only as good as the rules system, i.e., the knowledge of individuals (oftentimes not experts) who derived the rules.

The iFRP approach to the production planning is not rules-based. It involves acomputer-based system and method that simulates the factory shop floor andcreates a virtual factory. This virtual factory is designed to provide the ability tosimulate and assess any theoretically possible production sequence. By simulating

many different sequences, an optimal sequence can be established based on userdefined optimization criteria. iFRP is robust under changes in the factoryrestrictions. Each time there are changes, they are factored into a new virtualfactory; and the optimization is determined to provide solutions for productionplanning.

3.2.5 Advantages over current approaches

Currently, factory staff solves the production planning problem either manuallyor with the help of rules-based software systems. The manual process involves a


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factory employee who considers the current demand, the current state of thefactory, and previous experiences gained during the operation of the factory toguess an optimal production sequence. The rules-based software systemcodifies the experiences of factory staff into rules and uses these rules to planproduction. Therefore, a rules-based system solves the problem in the same way

factory staff would solve the problem manually. Using rules-based systems mayspeed up the process. iFRP has a number of advantages over the currently usedmethods for solving the production planning problem.

How these advantages participate in utilizing the efficiency of the schedulingoptimization levers can be seen in the diagram below:

1. Speed: iFRP is very fast especially when compared to the manual process. Theproblem is typically solved in a few minutes, while manual solutions may takehours. The speed advantage depends on the complexity of the particularproduction environment.

2. Robustness: The algorithm does not depend on rules derived from a particularsetup of a given factory. As demonstrated by means of example, any such rulesbecome invalid if the factory setup changes. iFRP does not suffer from thisdrawback

3. Unbiased Solution: The number of theoretically possible production sequencescan be extremely large because they depend on the number of different products

which are to be produced during a given production cycle. Existing solutions do notconsider the entire spectrum of possible solutions, but are biased by experiencederived rules. The quality of the output (the optimal production sequence)depends on the quality of the rules and, ultimately, on the competence of thefactory staff who developed the rules. iFRP considers the entire spectrum ofpossible solutions and calculates the optimal solution analytically unbiased byexperience derived rules.

Bottlenecks and real time

reaction

Reduction of cycle time

Green manufacturing

Cost reduction

Scheduling optimization

leversSpeed

Attributes of iFRP which addresses relevant optimization lever

RobustnessUnbiased

solution

Flexible

optimization

Analytic

solution

iFRP's unique attributes enable it to maximize the scheduling optimization levers.


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4. Flexible Optimization: The optimization criteria are built into rules-basedsystems. Once a rules-based system is defined to optimize according to a certaincriterion for example highest productivity then the optimization criterioncannot be changed. Within iFRP, the planning and optimization aspects aredecoupled. The optimization criteria are input parameters for the productionplanning algorithm and can be easily changed or combined.

5. Analytic Solution: iFRP allows for easy changes to the Virtual Factory.Factory staff can plan changes in the factory by simulating these changes first inthe Virtual Factory and analyzing the impact of these changes. This helps factorystaff in the decision making process.

iFRP operates in the whole solution space, not only at known local optima

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E.G. COST

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iFRP CONSIDERS THE ENTIRE SOLUTION SPACE

MES: suggestedsolution by rules

engine

iFRP calculated optimum

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iFRP CONSIDERS THE ENTIRE SOLUTION SPACE

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4 SummaryPlanning and scheduling enables control of efficiency levers which directlyinfluence factory and company performance. Reduction of inventories, increasedthroughput and reduction of cost help to reach sustainable competitiveadvantages. Planning and scheduling is not a local task; but integrated in a

company-wide planning process contributing to the company's overall success.

To utilize these gains, the planning and scheduling solution must do more than justdeliver an executable schedule. It needs to deliver an optimal solution with regardto the desired goals while considering all types of constraints including dynamicand event driven bottlenecks. Rules-based systems and approaches to productionplanning are out of touch with the every day challenges on the factory floor andsimply cannot deliver optimal results. iFRP is an effective real-time planning toolcapable of controlling all optimization levers and providing savings by unlockinghidden capacity potentials in the production process providing a quick return on

investment.


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About iFactory Inc.

iFactory was founded in 2007 in the United States.The company offers a real time planning and scheduling solution for manufacturing

processes. The iFRP Suite achieves operational excellence, predictability, andtransparency in the entire manufacturing process through a unique and patent-protected algorithm.

iFactory has offices in the USA, Germany and Brazil.

by iFactory Inc., 2010, all rights reserved. iFRP is a registered trademark of iFactoryInc. All used names of products and companies are properties of their relevant owners.The functionality of the described products is under frequent development and can changeat any time. Shown examples have exemplary character only.

iFactory and iFRP contacts in The United States and Europe

Michelle SullivanPhone: 503-719-2194Fax: 270-916-6291

[email protected]

Claudio de LyraPhone: +49-941-29066-170Fax: +49-941-89975-779

[email protected]

iFactory Inc.1690 19th Street

West Linn, OR 97068USA

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