THE PROPOSAL OF PRODUCTION PLANNING AND CONTROL SYSTEM APPLICABLE BY SUPPLY CHAIN INTEGRATION THROUGH AGENT-BASED SOLUTIONS

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1 THE PROPOSAL OF PRODUCTION PLANNING AND CONTROL SYSTEM APPLICABLE BY SUPPLY CHAIN INTEGRATION THROUGH AGENT-BASED SOLUTIONS P. Golinska 1, N.Brehm 2, M. Fertsch 1, J. Marx Gómez 2, J. Oleskow 1, P. Pawlewski 1 1 Faculty of Computing and Management, Poznan University of Technology, Poznan, Poland 2 Department of Business Informatics, Oldenburg University, Oldenburg, Germany Abstract The changing business environment in which manufacturers are acting creates the need for more effective production processes planning and control methods that are able to deal with uncertainties inherent in internal processes and external deliveries. The aim of the paper is to introduce the method for production planning and control applicable in conditions of supply chain (SC) able to overcome the limitation of standard MRP/ERP systems in changing environment. Conceptual framework for the method involves the hybrid solutions combining the advantages of MRP simple logic and theory of constrains (TOC) ability to synchronize all production and material flow in supply chain. Authors discuss how application of TOC buffers monitoring procedures can help to improve the control of synchronized production and material flow in supply chain. Finally paper presents application of elaborated method for creation of an agentbased system that integrate the parties involved in supply chain. Keywords: production planning and control, theory of constrains, agent-based systems, supply chain 1 INTRODUCTION Supply chain is a flexible and cooperative business network of suppliers, manufacturer and distributors through which raw material are acquired, transformed within value adding (VA) processes and delivered as final goods to customers. In many modern supply chains the power has been shifted from manufacturer to consumer, so the ability to fulfill customer orders at required time horizon and cost effective determines the supply chain overall performance. The supply chain performance is defined in following paper as ability to fulfill clients orders on-time, so the perspective of product delivery process (PDP) is taken in consideration by all assumptions. The SC performance will be measured as a ratio regarding customers orders delivered due-to-date to overall customers orders being executed in supply chain in defined period, so SC performance equals to 1 means that all products delivery processes very executed in accordance with clients requirements. In order to optimize the performance, supply chain functions especially production planning must operate in an integrated manner. In number of industries where high value products (like automobiles, high-tech equipment) are made on basis of make-to-order strategy the manufacturer has sufficient power over the suppliers and others parties involved in supply chain (SC) and is able to act as a leader. In following paper we refer by all assumptions to such a situation. In order increase the SC performance the production processes planning and control methods have to be able to deal with uncertainties inherent in internal processes and external deliveries. Uncertainty can be defined as the conditions within supply chain when probability of particular events/disturbances appearance cannot be counted. There is already a variety of information systems to facilitate the flow of materials, information and funds from MRP (Materials Requirements Planning) and ERP (Enterprise resource planning), to newly developed SCM systems. The comprahensive research regarding potentail of mentioned above systems in SC application area can be found in [1]. The production planning and control based mainly on MRP/ERP allows integrating functional areas within enterprise at the operational level, however main limitations of such systems is intra-enterprise focus, simultaneously most of existing Supply Chain Management (SCM) systems provided analytical tools for advanced planning but lack the integration with MRP/ERP system. There is a need to introduce the method for production planning and control supported by IT solution applicable in conditions of SCM able to overcome the limitation of standard MRP/ERP systems in changing environment. In following paper in Section 2 authors will discussed main problems that appear by integration of supply chain in PDP planning and control processes. Section 3 enhances the issues related to distributed planning process, where authors proposed algorithm for hierarchical production planning by multi-agent system. Authors also present how application of TOC buffers monitoring procedures can help to improve the control of synchronized production and material flow in supply chain. The elaborated by authors concept of an agentbased system for integration of production planning in supply chain is presented in Section 4. Final conclusions and further research steps are stated in Section 5. 2 PRODUCTION PLANNING AND CONTROL (PPC) IN SUPPLY CHAIN 2.1 Integration of material flow in Supply Chain-main problems The benchmark for integration of material flow in supply chain is the concept of seamless supply chain. The seamless supply chain [2] is a state of total integration in which players think and act as one. Due to the uncertainty inherent in supply chain the seamless supply chain is nowadays mainly a theoretical concept however, a number of companies take a challenge to reach this goal in the nearest future. To

2 approach the seamless supply chain the following sources of uncertainty must be reduced [3]: process uncertainty, supply uncertainty, demand uncertainty, distribution uncertainty, control uncertainty. The Figure 1 presents the diagram of uncertainty problem referring four main area of uncertainty: demand side, supply side, process side and control side. It shows the simplified Product Delivery Process (PDP) where manufacturer response to customers demands by replenishing the inventories of raw materials and subelements from suppliers. Referring to propose by Masson- Jones and Towill [4] generic approach to uncertainty problem in PDP we apply the following framework to reduce uncertainty in supply chain Fig. 1. The reduction of uncertainty is achieved by understanding and tackling the roots of uncertainty in any of mentioned area and especially by analyses of flows at any of pointed interferences. Suppliers (supply side) Material flow Information flow Control system Manufacturer (VA-processes) Distributors (demand side) Area to be monitored/improved Clients (demand side) Interfaces to be monitored/reengineered Figure 1: Diagram of the uncertainty problem and potential for its reduction (modified from 3) Production in defined supply chain is executed on maketo-order strategy and due the technology requirements orders are placed in advance by defined time window (for example at least 4 weeks in advance) effect of demand uncertainty in production planning horizon can be ignored. Regarding supply side uncertainty, VA processes uncertainty and control uncertainty SC performance can be improved by lead-times reduction regarding both deliveries lead times and technological processes leads times. In order to achieve this goal better platform for information exchange is required in order to identify potential problems in material flow regarding supplies availability and material quality in area of making production plans and in area of control when disturbances appear which requires preplanning of initial production plans and schedules. 2.2 Role of Master Production Schedule in PPC Planning is the process of selecting and sequencing activities such that they achieve one or more goals and satisfy a set of domain constraints [5]. Scheduling can be defined as a process of selecting among alternative plans and assigning resources and times to the set of activities in the plan. Schedules should reflect the temporal relationships between activities and the capacity limitations of a set of shared resources. In following research authors refers to production planning strategy based on the Planed Order Release schedule and MRP concept where Master Production Schedule is the main planning and control schedule. It states what kind of end products should be produced and it helps preliminary verify whether customers orders can be fulfilled due-todate. MPS is main driver and information source for further material requirements planning and accompanying calls or supplies and allows making details production schedules for production system resources in planning horizon [6]. Due uncertainty inherent in SC in manufacturing systems things rarely go as expected. Researches examined a variety of buffering or dampening techniques to minimize the effect of uncertainty in manufacturing systems based on MRP logic. Comprehensive literature review can be found in Guide and Shiverasta [7]. In situation when the dampening or buffering techniques are not efficient the modification of Master Production Schedule is needed. The high replanning frequency in order to overcome the uncertainty induces the system nervousness. The system nervousness can be defined as state of a system when a minor change in MPS creates significant changes in material requirements planning (MRP) [8]. The syndrome of system nervousness can be described as following: 1. Frequent changes in Master Production Schedule (MPS) result in due-date changes in open orders, quantity and timing for planned order of end products, 2. Mentioned changes are translated into gross requirements changes for components and timing of their delivery, 3. Unexpected changes in MPS effect that materials needed for a particular order may not be available The availability of materials is often limited due the fact that suppliers have similar bottlenecks and schedules variations transmitted from sub-tier suppliers. In supply chain where manufacturer acts as a leader the manufacturing system nervousness has negative impact on overall SC performance, so it can be assumed that changes in MPS in ERP system should be reduced. 2.3 Problem formulation The research problems can be formulated as following : How to make initial MPS that is as feasible as possible and that optimized the SC performance How to limit the number of replanning activities How to be reactive to disturbances in materials flow How to provide planners with accurate information about material resources available which often lead to bloated inventory and inaccurately promised delivery dates to customers In manufacturing environment the initial MPS has to be changed often due the fact that it is not feasible. The manufacturer can fulfill customer orders on time only when by elaboration of MPS he negotiates with other supply chain participants the due-dates for raw materials and components deliveries and distribution of finals goods. It can be assumed that the reduction of manufacturer s ERP system nervousness can be achieved by increasing the cooperation and information exchange between supply chain participants at moment of initial MPS preparation and further by MPS execution.

3 3 METHOD FOR PRODUCTION PLANNING AND CONTROL APPLICABLE IN CONDITIONS OF SUPPLY CHAIN (SC) 3.1 Synchronization approach The synchronized production planning and control requires the constant flows of information, materials, and funds across multiple functional areas both within and between chain members. Due the fact that MPS is the main driver and information source for further material requirements planning and details production schedules for production system resources it can be assumed that in supply chain where exist a single manufacturer and a single 1-tier supplier/limited number of 1-tier suppliers and the manufacturer has sufficient power over the others parties involved in supply chain a global MPS can be elaborated. Taking in consideration following the distributed planning can be described as a situation where in centralized process of preparing plans the distributed sub plans for each supply chain s participant are elaborated [9]. The sub-plans for supplier side and distribution side are dependent on manufacturer capacities, so it can be treated as main constrain in planning process. Authors decided to refer to Theory of Constrains [10] by elaboration distributed planning algorithm due the fact that entities in supply chain are bounded in long term cooperation and manufacturing side can be perceived as a global (in given planning horizon) constrain. In TOC all processes are set to the process of most constrained conditions namely bottleneck. TOC is used as a management technique for identification and exploitation of constrained condition that infected the goal achievement of the system. In MRP/ERP systems all processes match to the speed of the production of final goods. In TOC all processes are set to the process of most constrained conditions namely bottleneck. So it can be assumed that the improvement of productivity of all processes at the same speed pace as the improvement of constrained conditions can be expanded to the entire supply chain. The TOC is applied for: Definition of chain goal Definition of goals measures Identification of global constraints Definition for rules for coordination between global and local constraints management Definition of rules for management of material flow through the constrained conditions Synchronized manufacturing (SM) Drum-Buffer-Rope (DBR) (Shragenheim and Ronan 1990) drum exploits the constraint buffers are intended as protection from disturbances rope is a mechanism design to force all the parts of the system to work at pace set by drum Synchronization approach Seamless supply chain (SSC) Figure 2: Synchronization approach The SSC is defined by zero defects in material flow (Towill 1997) there is: no raw material stock, while raw materials arrive at the last moment no WIP due extreme flexibility and speed no finish goods because products are in exact order sequence and of good quality. Authors decided to refer to TOC control mechanism Drum-Buffer-Rope (DBR) [11]. This concept can be applied by elaboration distributed planning algorithm due the fact that DBR allows to synchronize the use of resources without having to actively control each resource. The purpose of drum is to exploit the constraint of the closed loop supply chain. Buffers are time windows protecting the Supply Chain global constrain and bounded to it the critical path of PDP. 3.2 Algorithm for distributed production planning The planning process can be presented as following algorithm: 1. Definition of chain goal and set of performance indicators 2. Generate a initial MPS for manufacturer taking in consideration customer orders assign to planning horizon plan and capacities constrains 3. Negotiate the initial plan with supply and distribution side and find a plan with lower number of constrains among them ( so called feasible MPS for supply chain) 4. Decompose the feasible MPS for subplans for supply, manufacturer and distribution side) 5. Insert synchronization among subplans based on TOC concept for time buffers and Drum-Buffer- Line concept where manufacturer sub-mps is giving pace for supplies and distribution planning activities 6. Allocate subplans to agents using task-passing mechanism, if failure come back to previous step or generate new global MPS (step 2) 7. Initiate plan execution and monitoring when plans are executed in TOC green buffer no additional replanning needed when plans are executed in yellow TOC buffer the replanning at local level, if plans are executed in TOC red buffer go to step 2. The DBR buffers refers to time needed for deliveries of materials and component. The size of buffer in stable manufacturing system is as three times the average lead time to the constraint form the raw material release point [11]. In SC where uncertainty in process and deliveries lead-times appears the buffer can be counted as defined as the multiplied minimum cumulative processing time for the individual part [12] BS i=multi j=1pt ij Where BS i is the size of buffer for part i=(1,2,3,..,n), PT ij is the minimum processing time for operation j on part i. MULTI is a constant multiplier. The mentioned above buffer is divided in three area: green (no action needed), yellow( prepare for actions) and red (react). The size of each part of buffer can be equal, but it is recommended to revise the performance of buffer s green, yellow and read zone on regular basis in order to tune their size to statistical fluctuations appearing in material flows in particular manufacturing environment. Authors propose application of the elaborated method for creation of the proposal of an agent-based system that integrate the parties involved in supply chain. 4 CONCEPT OF AGENT-BASED SYSTEM Recently there is a number of researches regarding the application of an agent-based systems for production purpose [13, 14, 9]. Most of them are dedicated for project-oriented unit production. In following paper authors refers to high stabilized production systems where on assembly lines customized similar products (for

4 example cars) are manufactured. Agent-based system is defined in following paper as a multi-agent system that s acts as a support tool to bigger Information System and utilized the databases of main system (ERP system). Multi-agent system is a collection of heterogeneous, encapsulated applications (agents) that participate in the decision making process [15] gents communicate, collaborate and negotiate in order to meet their own design objective but also a goal that is shared within the community. In following paper authors apply Jennings and Wooldridge s definition: "an agent is a computer system situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives. Agents have the following characteristic [15]: reactive, pro-activeness, social ability. 4.1 System model The following assumptions have been made: Between time of MPS preparation and MPS execution within production system the disturbances will occur The exchange of information allows to improve both activities planning and replanning While optimizing the performance function all have access to the same information (no information asymmetry) The manufacturer has sufficient channel power over the suppliers and others parties involved in supply chain. The planning problem will be described at three layers reflecting to: 1. supply chain perspective so called global planning; 2. the entity level where global plan is divided to sub-plans which are executed by each company and being transform for individual production schedule at company level and local re-planning activities takes place (the entities represent supply side, manufacturer side and distribution side of supply chain) 3. intra-company sub-layer where production control activities are executed and information about disturbances are gathered and passed to upper levels. information systems within each company at global supply chain level. At particular company level system will monitor MPS execution by existing productions management systems and will be responsible for replanning if needed, in order to meet global supply chain goal, namely on time delivery of ordered by customer s products with required quality and price. The architecture of proposed tool (AIPPLUSC) is based on the assumption that system will support the MPS creation in ERP system and will be plug in to ERP system database by for example java connector. Figure 1 illustrates structure and the amount of agents which can be found in each layer. The graphical user interface agent creates a graphical user interface (GUI) for the interaction of the MAS to production manager (direct users). The GUI-Agent is able to initialize and sent behaviour parameters and messages to the Master Planning Agent (MP-Agent). The MP-Agent will be exactly one time in the system because the data from all the managing agents (M-Agent) is fused at this agent to generate re-planning schedules for the production, distributors or supplier. The MP-Agent is responsible for control of the logic of all agents and creates the plans for the M-Agent following the algorithm presented in previous section. M-Agents are initialized by the MP-Agent they are responsible for translation of the global plan into detail schedules. The agent is allowed to prepare the number of alternative(contingency) local plans as long as there are not conflicting with global MPS. The local replanning activities are allowed as long as their don t influence the global MPS. When replanning activities affects the global MPS (red buffer) it has to be passed to MP-Agent. The CI- Agent is responsible for control of plans execution within one company based on given performance indicators. It reports to M-Agent in upper layer whether production plans are executed according to given MPS or not and asses how big is the delay, if any (green, yellow or red buffer flag). The number of Ci-Agent in the AIPPLUSC can vary and this fact is shown by the *.Improvement of cooperation and information exchange for MPS creation will achieved by cooperation of following agents, which reflects to entities involved in supply chain: MP-Agent (master planning)- is responsible for creation of global supply chain s Master Production Schedules on basis of negotiations with logistic agent, production agent and distribution agent, if crisis appears on basis of information received from production agent it changes MPS and inform logistic agent about it; M-Agent Logistic - responsible for coordinating plans and suppliers, to achieve the best possible results in terms of the goals of the supply chain, including on-time delivery, it combines functions of inventory management and purchasing; Figure 3: AIPLUSC model The proposed model is based of assumption that agentbased system will act as glue integration existing

5 Figure 4: System architecture M-Agent Production - is responsible for scheduling and rescheduling activities in the manufacturer factory, exploring hypothetical what-if scenarios for potential orders, and generating schedules that are sent to ERP system for execution. It dynamically manages the availability of resources so that the schedule can be executed. M-Agent Distribution- responsible for the assignment and scheduling of transportation resources to satisfy on-time fulfillment of customers orders. It communicates with production agent in order to update it schedules. 4.2 System logic-buffer control mechanism The buffer can be described as time window in PDP. Main responsible for monitoring of system buffer are Ciagents, that check every defined period of time the size of time buffer on bases of data for PPC system for example ERP. According to achieved results it reports to upper level (appropriate M-Agent) the following status: green (everything ok), yellow (there is a risk of delay) or red (alarm). The buffer control logic is following : 1. when green- it sends a message to layer 2 (Magent) and don t do anything until next check period; 2. when it is yellow Ci-agent reports to its own M- Agent due is not allow to contact M-agent at other company. The M-agent begins the reactive replanning procedure according to schema presented in Figure 5. The schema can be simplified as: When there is the contingency plans plan allowing the local replanning without changes of due-dates to other sub-plans in production of planning process it make the replanning. (i.e. M- agent Logistic has a problem with it plans but for example by working in Saturday it can be achieved to provide materials for next week on time for manufacturer) When there is contingency plan but there is a risk of delay it negotiate with next M-agent When it is no contingency plan it informs MP-Agent, who makes replanning (according to algorithm described in Section 3.2) 3. when red- it sends a message to alarm the M- agent. M-agent send back message to recalculate the buffer. When the answer from Ciagent is confirming (again red) then M-Agent reports it to MP-agent, who sends the message to M-agent how big is the delay (time is main characteristics in this approach due performance is measure by ability to deliver product to customer on-time). On bases of the answer start replanning procedure (algorithm)

6 Figure 5: Buffer control mechanism- yellow instance 5 CONCLUSIONS In following paper authors present the concept of an agent based system for integrated production planning in supply chain AIPPLUSC, the multi-agents distributed system that reflects the needs for distributed decision making in multientities environment. The system design requirements refer to increase of integration of information and material flow between entities that are involved in network. The issues related to distributed planning process were discussed and a new algorithm for hierarchical production planning by multi-agent system based on concept of centralized planning for distributed plans was proposed. Perspectives for further research should reflect to elaboration of communication algorithm among agent and structure of standardized input and output data structure for all companies involved in supply chain. The issue related to increasing trust and safety for information exchange between entities involved in supply chain should be examined. 6 ACKNOWLEDGMENTS The following paper is reporting the work-in-progress status of research project financially supported by DAAD in framework of the 5 months research grant for Paulina Golinska at Department of Business Informatics at Carl von Ossietzky University of Oldenburg. 7 REFERENCES [1] Oleskow J., Fertsch M., Golinska P., The new perspective of supply chain integration through agentbased systems, Proceeding of ITEE 2005, Magdeburg, Germany, , vol.1. [2] Towill, D.R., The Seamless Supply Chain, International Journal of Technology Management, vol. 13, no. 1, 1997, pp [3] Towill, D.R., Childerhouse P. and Disney, S.M., Integrating the Automotive Supply Chain: Where are we Now? International Journal of Physical Distribution and Logistics Management, vol. 32, no. 2, pp , [4] Mason-Jones R., Towill D.R. Shrinking the supply chain uncertainty circle, ION, Control, Vol.24, 1998, pp [5] Shen, W. and Norrie, D.H., An Agent-Based Approach for Dynamic Manufacturing Scheduling, Working Notes of the Agent-Based Manufacturing Workshop, Minneapolis, MN, ( ( [6] Viera G.E., Favetto F., Understanding the complexity of Master Production Scheduling Optimization, Proceeding of the 18 th ICPR, Salerno, Italy, [7] Guide V.D.R., Shiverasta R., A review of techniques for buffering against uncertainty with MRP systems Production, Planning and Control vol. 11, 2000, pp [8] Ho, C.J., and Carter, P.L., 1996, An investigation of alternative dampening procedures to cope with MRP system nervousness, International Journal of Production Research, vol. 34, pp

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