The NETSIM Concept and Global Optimization

Dr. Alptekin Erkollar1 and MBM Birgit J. Oberer 1Department of Business Organisation and Business Informatics

University of Applied Sciences Wiener Neustadt, Austria

Abstract. Different factors influence the planning and the production flow as well. Generally, these factors can be considered in different ways. Nevertheless, it is not possible to do a correct and complete planning and to consider

all different alternatives.

Therefore it will be the aim to analyze possible procedures during the planning, to evaluate possible measures and their results and to prepare optima measures. When there should be considered that the task has to consider additional influence factors on the Supply Chain level the complexity of the task increases considerable.

Today's planning systems are not fully prepared for these demands because of the relatively fixed nature of their planning data structures (local planning nets) that hardly may be integrated into an overall planning structure. Moreover, these systems rely upon deterministic input parameters and fixed work sequences, which does not correspond to the situation in dynamic environments. On the other hand supply chain management offers a large potential for the enterprises to reduce cost and improve customer service performance

Apart from the Supply Chain Management Concept planning systems require much more then in the ERP approach; integration as well as adaptability of the planning systems, especially integration of internet and intranet and multiagent based distributed applications.

Beside different technical difficulties for the online data update or the synchronization of planning there are missing modeling approaches which can be integrated in practice.

In this research paper we will analyze the possibilities in the area of combinatoric and distributed planning based on distributed databases for the support of the production planning. Further, we will introduce a new web based alternative for modeling, called NETSIM and we will present its applicableness by means of a Prototype. This NETSIM (Network Simulation) concept, fits into the dynamic scenario of dynamic environments as well as it provides substantial enhancements to traditional planning (Deterministic or stochastic basis). A very important aspect for NETSIM is that all scenario parameter can be considered and if its needed can be changed.

Additional will be analyzed apart from problems of combinatoric there the applicability of deterministic and stochastic planning. A very important aspect in this contribution is an universal interface description for using with all standard software systems, e.g. SAP, BAAN, Navision and Peoplesoft. Using this universal interface can be used the NETSIM concept with all state-of-the-art (ERP Enterprise Resource Planning) systems.

1. INTRODUCTION The paper deals with the integration of network analysis and discrete simulation for distributed production planning and control (DPPC) on the basis of expended availability of production resources. Present standard software PPC system modules support, as a rule, network planning techniques. This is due to the nature of production processes as well as to the fact that process data (work breakdown structure, duration of work processes, down times, quantities etc.) are easy to collect and to maintain in practice [1], [4], [5]. However, this approach does not meet the needs of today’s producing enterprises, especially those of SME’s (small and medium sized enterprises), which have to cope with mostly heterogeneous and variable order mixes and which are used to adapt their processing sequences and lines in a more or less ad-hoc manner. Clearly, simulation is a mean for treating such aspects so that a combination of network analysis and simulation seems to be a viable approach to flexible and optimizing production planning.

This approach can be used in all alone SME (Small and Medium Sized Enterprise) in the area of planning. With the support of simulation can be visualized a better solution for the planning alternatives. For this reason it can be used the parameter optimization. Additional the stochastical approach delivered better combination of parameter and easy understanding of production.

For the SCM (Supply Chain Management) approach is the integration of planning data and synchronization of production information are important task for the success of chain. The planning becomes much more complex because there have to be considered more enterprises simultaneously and the target vector has to contain more optimization alternatives. Therefore, it is the problem of modeling and the integration ability of the model components have to be considered in common. On the other hand is the complexity of model a important factor for the understanding, building and analysis of plan (Figure 1).

Precision

Information capability

Acceptance

Model complexity

PrecisionInformation capabilityAcceptance

Figure 1: Evaluation criteria for the model quality, [Er98].

The network simulation concept (NETSIM) follows this idea [2], [3]. It proposes • Identity and determine important components and

activities in production (consider targets of local and global networks)

• Model single processes with the NETSIM components and connections (activities Fig. 2)

• take the NETSIM network as a basis for a simulation model,

• replace the constant parameters of a network by suitable distributions, thus reflecting the in-determinism of influence factors in manufacturing,

• extend the network by alternative process sequences and the corresponding decision points.

• extend the combinatorial connections between the activities with the attributes (no only directions)

Figures 2 and 3 visualize this extension on the local area.

Using this concept the master production schedule, we call it manufacturing net, does not only reflect temporal and quantity aspects of work units and their sequence but also the performance-influencing factors as well as alternative production sequences [1], [3].

Duration

Capacity

Figure 2: Definition of an activity in

conventional networks

Tools

DurationCapacity

(Distribution)

Preparation(Distribution) (Distribution)

Machine(Distribution)

Employee(Distribution)

Material(Distribution)

Figure 3: Extended specification of activity in NETSIM

The manufacturing net can directly be used as a simulation model, since it includes all data that are necessary for simulation [3].

In a first prototype this concept has been implemented using the simulation tool ARENA. For that purpose a template for process units has been developed which offers all components as proposed by NETSIM (see figure 3 and 4). Thus, a given network, drawn from an PPC application, may be easily converted into an ARENA simulation model. The prototype enables to using of process time, preparation time, capacity, availability of tool, machine, worker and material as (stochastic) variable. The parameters can be updated during the simulation running.

The newly developed module allows the usage of different simulation tools.

Figure 4: NETSIM screenshot showing work unit

variables From experiences in practice it turned out that a planner (user) would find it convenient to be able to manipulate NETSIM parameters during a running simulation experiment. Such experiments serve, to a large extent, for the visualization of what happens in the context of a given scenario. The planner, being expert of the given production environment, will be supported by that visualization and draw from it ideas where to change influence parameters for gaining better performance. Being able to do this online allows him to see and evaluate immediately the consequences of his considerations.

For an effective local use, a Planning system and the NETSIM tool should be integrated. Conventional information systems as available today do not provide such an integration. This might be the reason for the fact that actually SME’s rarely do exploit the opportunities of computerized modeling, simulation and optimization. Especially for the SCM approach its needed for the import of recommended data to using the existing software system. Therefore, we actually work at the development of a prototype system which allows the coupling and exchange of data between existing PPC systems and NETSIM. For the SCM

integration was an WEB Client integrated in NETSIM application.

2. EXTENSION OF MANUFACTURING NETWORK

Today’s Planning systems mainly use network analysis as a planning mechanism. Thus, temporal data like transportation time, machine preparation time, processing time, fault time etc. are modeled by input values that enter the calculation as fixed entities, although they are, in reality, subjected to variations [4], [8]. Network analysis tries to cope with that restriction by calculating the values each from an optimistic value, a pessimistic value and from the expectation. For reasons of simplicity, however, often only average values are considered. Thus, what actually happens in reality and the danger emerging from deviations are considered only insufficiently so that bottlenecks and delays are not always transparent to the planner [7]. Even if the actual processing time of a production activity is a value within known bounds, the use of a constant value may result into an erroneous total production time because of possible variations in reality. Since such errors only become obvious when the resp. production finishes, they may lead to losses of material, time, money etc. [2], [3], and it can be improved with new data.

The following alternatives exist to avoid such losses:

• Prevention of unexpected situations during the

production process.

• Introducing means that allow to preview and thus to take into consideration possible deviations as far as possible during the planning phase and planing of possible steps and their impact.

The first alternative is not realistic whereas the second might be implemented using stochastic calculation or simulation. Within this paper will be presented, how such a simulation based approach could look like. For that purpose in section 2 will be presented a short overview on the actual state of the art in network analysis and production planning, in section 3 will be discussed, how limitations of this approach might be avoided using, in addition, a simulation approach.

3. NETWORK ANALYSIS Network analysis is used today for the planning and for the control of complex projects and operations in planning. There are different methods the first of which were developed and used within the fifties: Critical Path Method (CPM) USA 1956, Metra Potential Method (MPM) France 1957, Program Evaluation and Review Technique (PERT) USA 1956. These methods have been modified for a variety of aspects, the basic idea and calculation methods, however, still are the same [2], [3]. For the Modeling of systems was used only the activities without respect

for the another factors and the model has presented only the relations between different tasks. The approaches may be classified as deterministic and stochastic ones:

Deterministic methods assume that each defined activity is executed for each entity. The processing time of each activity and the minimum/maximum time delays between activities are supposed to be known and used as input values. Examples are CPM and MPM.

Stochastic methods may be divided into

• Methods that assume deterministic processes (each defined activity is executed) and stochastic processing times (and delays). An example for that approach is PERT that uses beta-distributions for stochastic value (although this does not always correspond to the reality).

• Methods that assume each process to be executed with a certain probability. Examples are Graphical Evaluation and Review Technique (GERT) and Generalized Activity Networks (GAN). In practice, however, production sequences are deterministic or, at least, based on deterministic decisions on production alternatives. Therefore, methods like GERT and GAN are not considered within Production planning and thus, not further discussed within this paper.

• The basis of planning was (and is) only a fix Graph (without any options and conditions). This cannot presented an alternative sequence for the production (e.g. breakdown a machine or a project partner). Therefore it can be not easy analyzed a bottleneck and their solution.

Stochastic methods require extensive mathematical analyses. For this reason, in practice mostly deterministic methods are used. In particular, nearly all software systems for production planning use graphical modeling like MPM. The reasons for that are, in our opinion, the following:

• NETSIM uses a network where the nodes represent activities and where the edges represent (causal) relationships between these activities. For production activities are the key aspects. They are to be considered in both phases, planning and control. In contrast to that, events are of minor importance. Thus, users may understand and construct this kind of networks intuitively [5], [8]. The events can be defined and integrated into NETSIM model as decision point for the choice of alternative production flow.

• Planning and checking of the whole production process is relatively simple if it is done on the level of activities.

• Getting the necessary data (duration, delays etc.) for activities is not very difficult. One may, e.g., derive these data from preceding periods (data of the past) or use estimated values, e.g., on the basis of the optimistic, the pessimistic and the average value.

NETSIM network analysis may be performed in two steps, structure planning and time planning. Structure planning This step may be subdivided into the following three phases:

Phase 1: Decomposition of the production process

into activities and determination of the sequence of these activities. The result of this phase is a set of activity lists, each of them representing the production process of a particular product.

Phase 2: Representing the activity lists by a

network. Phase 3: Modeling of the resources and of other

important factors for each activity; determination of the temporal relationships between activities and resources including possible/necessary delays (e.g. drying times in chemical processes).

Time planning

Time planning again may be subdivided into three phases:

Phase 1: Estimation of the processing times of the

particular activities. Phase 2: Calculation of the earliest and latest

possible beginning and ending time for each activity.

Phase 3: Validation of the results of the previous phases.

Time planning (or timing) in NETSIM aims at the determination • of the earliest and latest possible beginning and

ending times of process activities, • of the total production time and • of time reserves (buffer times). This is based on the estimation of the processing times of the particular activities and their time distances. Deterministic methods assume one estimated value for each entity. These values may result either from recordings made, within the past, for similar production processes, or from subjective estimates.

Stochastic time planning

For a more realistic planning one should take into account that the processing time of an activity mostly is not a constant value. Again, the sequence of activities may be deterministic, the particular time values, however, are stochastic. PERT follows this approach by modeling each processing time as a probabilistic value. The calculations are made using these values and are based on the following assumptions: (1) All processing times are normally distributed. (2) The earliest possible start time of the successor

activity of several predecessor activities corresponds to the maximum expectation of the ending times of these predecessors.

(3) The variance of the start time of the successor of several predecessors corresponds to the maximum of the variances of the ending times of these predecessors.

Thus, the total production time can be determined as follows:

Step 1: Replace the random processing times by

their expectations and determine the longest (critical) path of the network.

Step 2: Calculate the total of the variances of the

processing times of all activities on that path.

The result is a normal distribution for the total production time given by its expectation and its variance. The PERT assumptions (2) and (3) may lead to an underestimation of that total production time. In contrast to that the variance tends to be overestimated. The processing time t of an activity is assumed to have a lower bound a and an upper bound b, where a represents the ‘optimistic estimate’ and b the ‘pessimistic estimate’. In general it is not very difficult to estimate these two values. However, for determining the distributions, PERT in addition suggests to estimate the mean m. Consequently we may calculate the expectation of processing time t as

µ = (a + 4m + b) / 6

and its variance as σ2 = [(b-a)/6]2.

Today, network analysis is used in practice either manually or by the aid of a PPC software. Since most frequently PPC-software supports MPM, the following limitations are intrinsic to them: • constant PERT-like processing times, • no consideration of deviations of processing times, • no consideration of additional factors, • no consideration of multiple processing units for

the same activity, • week visualization, • no support for what - if studies.

Resource

Machine 80 %

Employee80 %

Material80 %

Tool 80 %

1 2 3 4 5 6 7 8 time

Figure 5: Availability of resources Simulation support for network analysis

The before mentioned limitations might be mastered using, in addition, simulation during the planning process. Simulation works with distributions for the input values, so that, at a sufficient level of granularity, the simulation results make visible the deviations that are possible in reality. Observations of earlier periods may furnish the basis for the definition of the distributions for the input values. If one follows this approach, the influences, activities may have on others, and resulting alterations of the critical activities can be stated and considered. Moreover, additional input values like availability of tools, materials, workers, machines etc. may be considered (see figure 3, 4 and 5). Finally, using the simulation model, the entire production process may be visualized. This can support the user to find out bottlenecks, that emerge during the production process. Clearly, several simulation experiments will have several different results. These may be used

• on the activity level: to calculate the minimum, the

maximum and the expectation value for each activity and to accomplish with these values the calculation of the whole network.

• on the total production process level: to receive the final results (such as the earliest and latest beginning/ending times) without calculating the network explicitly.

Both aspects represent extensions to the classic network analysis. Another advantage of the simulation approach is the fact, that it allows to use a different distribution (type) for each input value, if necessary. This leads to more realistic results. 4. Construction of the simulation model In a first step, for all processes (activities) of the network their stochastic duration/distribution has to be determined. The network then is harmonized with the production or factory layout (grouping of activities

w.r.t. their executing instances) and is extended by models of entrance and starting points of the system.

Secondly, all resources of the processes must be inserted together with their stochastic distribution. Similarly, the transport of materials and the transport mechanisms are to be modeled, again including their stochastic or deterministic duration.

There are two ways to do this (see fig. 4): • by modeling transportation as process; in this case

additional nodes have to be inserted into the network.

by modeling transport times as edge parameters. Thirdly all decision-driven forks of the production process have to be mapped into the simulation model. Its feasible, for dealing with complexity and for the study of detailed local behavior, the simulation model may be divided into submodels

By these steps a static model of the system in question will be built on the conceptual level. The implementation for the computerized simulation with the NETSIM has to be done automatically with the Interface on the ASCII basis using one of the simulation tools on the market.

The next step then will be the re-importation of simulation results into the PPC or planning system.

5. Conclusion The understanding of different alternative and choice of best alternative is not Trivially. E.g., simulation needs distributions for each input value, the determination of which is a non trivial task in practice. Furthermore, the construction of the simulation model and the execution of simulation experiments needs some efforts. These are compensated, however, to a certain extent by the fact, that the network analysis model can be used as a basis for the simulation model. In addition, simulation systems that are actually on the market offer, e.g., easy to handle graphic user interfaces for the model construction, input analyzers, distribution generators, visualization tools etc.

In practice one has to weigh if these additional efforts are justified to the increase of precision in planning. E.g., if the processes perform with rather small oscillations (e.g. CNC’s, fully automated activities etc.), conventional network analysis would do the job. Against to that, if large variations in processing times of activities are observed (e.g. production processes on a lower automation level) simulation will lead to more trustworthy planning. • For that purpose the NETSIM prototype has been

extended to a version allowing for on-line parameter manipulation (NETSIM-Active). For the integration of different enterprises via WEB was developed a web application (see Figure 6). NETSimWEB can be used for the embedding of

local nets from partner of supply chains for the global optimization of virtual enterprise. For the embedding of local nets can be used different connection types, i.e. AND, OR, XOR. Different products can be defined with the alternative production flows (manufacturing net). For each manufacturing net must be defined a special structure.

The integration of Planning (Production Planning and Control) systems, WEB, simulation and optimization tools allow substantial enhancements of the planning process and more realistic planning results within a producing enterprise. This extends to the integrated planning in supply chain and ERP partner of the today and future, which need for their success even more correct and reliable results. The member companies of virtual cooperatives may use different PPC systems. Consequently, a platform has to be implemented which allows the integration of arbitrary PPC systems. Therefore, a reference model is needed for an unified description of the different systems’ interfaces. A subsequent work will be driven into this direction as well as into the integration of existing optimization tools.

Another task will be the analysis and research concerning automatic means for a kind of integrating simulation models that goes beyond the above described coupling, similarly to the integration of database schemata. Actual results on integrating dynamic models (e.g. state charts) may be used as a starting point for that purpose. The Last version of NETSIM is full functionally with the standard software systems for the successfully using of SCM and ERP. The necessary Client and Server can be used any where and any partner in SCM for the integration of local planning and for the achieve of global optimization – depending on internet access.

Figure 6: Entry point of the NETSimWEB Client

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