Supply chain executive monitor for controlling and failure management in supply chains.

Laessig, Joerg ; Heinrich, Steffen ; Duerr, Holger 等

Abstract: Suppliers (raw part and raw material plants), manufacturers (producer, assembly plants), dispatcher (dispatching agencies, dispatching centres) and logistics services providers (forwarding agencies, Express services) are integrated in more and more complex and flexible supply chains. In terms of cost efficiency and short reaction times to customer requests, an efficient supply chain organization is the major competition factor on the global market. Existing ERP or PPS systems also integrate functions to the Supply Chain Management. Nevertheless, they still provide an insufficient scope of features and cannot integrate supply chain members with their different systems or systems without SMC functionalities into the planning and organizational processes. Many aspects of the Supply Chain Management do not offer a solution or offer only sub-optimal solutions. There is a great demand for cost-saving solutions of the cross-company controlling and particularly for supply-chain-global early detection of conflicts, as well as intelligent conflict management. In this context, problems occur due to the various interfaces of existing systems and the related high development and implementation costs. The focus of the project Controlling for Intra Logistics--Supply Chain Executive Monitor (SCEM) presented in this paper is the development of a minimal-invasive add-on system for possibly existing ERP-/PPS systems for efficient controlling and dynamic failure management of business processes in supply chains and networks on the basis of a supplier network global dynamic identification number concept.

Key words: Supply Chain Management, Controlling, Failure Management, Logistics

1. The Problem

Today, companies in all sectors have to face global competition for customers and markets. To ensure cost efficiency and service, many of them joined together and established Supply Chains (SC) more intensively and consistently than in the past (Bartsch & Bickenbach, 2001). This poses a number of questions with regard to the monitoring and management of such supply chain systems. A significant reduction of delivery times and delivery costs as well as of operative resources can only be achieved by implementing an efficient supply chain organization. By putting it into practice, response times on failures can be decreased significantly (Mehnert & Durr, 2004).

Members of existing supply chains face problems primarily with the implementation of a holistic monitoring concept for customers that focuses on cost minimization and the adherence to deadlines within the SC (Heinrich et al., 2005). Currently, this process can only be realized locally by each member without considering cross-company optimization potentials (Jacobs, et al. 2000).

Apart from real-time monitoring of processing statuses and customer orders, an efficient and dynamic trouble shooting in case of breakdowns is similarly difficult and important, since every member only feels responsible for his scope of work within the supply chain process (Bodendorf et al., 2000).

[FIGURE 1 OMITTED]

Based on the known deficiencies and available optimization potential, the Chemnitz Institute of Manufacturing and Welding Technology, in cooperation with the iFD AG Chemnitz, decided to contribute to the solution of the problem by developing an integral, minimal-invasive software system for efficient monitoring and early detection of failures as well as failure management for orders in supply chains. This is based on the model of a SC representing a cross-company virtual organizational structure (a network), which, as a holistic production system, produces specific assets for a defined target market and integrates suppliers (e.g. raw materials, semi-finished products, standard parts and market products), manufacturers (producers, assembly plants), dispatchers (mail order, dispatching centers) and logistics services providers (dispatcher, express services) (Fig 1).

2. Current State of Technology

The current state of technology is the local controlling and monitoring of individual parts of supply chains by means of software systems from the domain of ERP, PPS and production control as well as by means of fleet controls for forwarding agencies, which are marketed in various price categories and with different features.

In the domain of inventory management and control, systems with customized interfaces are available to connect to external systems (such as dispatch systems) for different kinds of storage types (goods intake, supply, issue).

The monitoring of processes on the SC's global level is primarily based on methods evaluating post-completion processes. Therefore, an intervention into the process for cross-company trouble shooting is only possible after the evaluation of the monitoring data (not realized in real-time).

For the dynamic requirements of an efficient failure management in cross-company supply chains, such system concepts are not sufficient anymore, because, apart from the static data evaluation, they also lack any integration components for existing production systems, which would make the aggregation of process data and their efficient evaluation possible. Therefore, corrections in the supply chain are only possible at a late stage, which often leads to solutions that can hardly be considered acceptable.

The industry-specific systems that SC members have installed organize their data update in most cases locally. Currently, it is not possible to get any information about the actual state of job order processing from the participating supply chain members, e.g. via shared online databases and dynamic data exchange.

This not only leads to sources of error and potential interferences between the involved companies, but also, as technical opportunities are missing, to a contractive information policy among the supply chain members. This causes problems with regard to unbalanced knowledge distribution and inefficient information flow within the supply chain, so-called information asymmetries (Simchi-Levi et al., 2002). This is also known as the Bullwhip Effect.

Why is the current state of technology insufficient for the solution of the problem?

1. Too much data for controlling and monitoring in the individual systems, missing interfaces with other supply chain members and missing possibilities of standardization of relevant process data make broad data evaluation in the supply chain impossible.

2. In many supply chains, the transfer of local data of the members is not practiced at all, which makes a global review of the actual situation in the supply chain impossible.

3. Even in the case of data exchange between the systems of the supply chain members, a central component initiating appropriate measures in case of failures, is often missing.

2. The Solution

The developed software system is composed of a central component for supply chain controlling in the master cockpit (Fig. 2), that is preferably placed at the final producer (Supply-Chain-Executive-Monitor/M), and the local components constituting composition systems (Supply-Chain-Executive-Monitor/C) for available PPS or ERP systems, which are installed at each supply chain member. Communication between the individual members takes place via Internet by means of SSL-encrypted connections.

[FIGURE 2 OMITTED]

Core of the system are the monitoring and controlling features based on an identification number system, which will be explained in chapter 2.1 (Koch, 1996), (Weber & Dehler, 1999). As soon as the system detects a failure, such as a delay or potential cancellation of delivery, a specification vector for all alternative suppliers or producers is generated, which constitutes the basis for alternative planning in the master cockpit. In case of a manufacturing company, for example, the specification vector comprises necessary machining processes, such as turning or milling, and, in order to process the job at hand, typical technological parameters, such as the minimal dimensions of a work place, the requirements for interface values and the number of necessary axes of the working machines.

Alternative planning determines various possible alternative resources for a certain order and displays some of the options in the query-reply system. Fig. 2 shows an overview of the system. The mode of operation will be explained in more detail later.

2.1 Effective Management based on identification numbers

In order to make controlling transparent, efficient and fast, and to ensure the protection of sensitive data of individual companies involved in the SC, order-related data in the members' stations are summarized by means of identification numbers, enabling the master station to operate with identification numbers only--instead of operating with a large scope of input data. The identification numbers themselves make it possible to look at company-related matters, based on specifications that have been defined in advance, and to look at them from different perspectives.

In the following it will be explained which single data can be summarized in form of identification numbers at the members' stations and how they are used in controlling and for the failure detection process at the master station.

The identification numbers determined are primarily applied for three basic functional areas within the supply chain management:

* Selection of the company that is most suitable for processing the order prior each execution of order based on identification numbers of previous orders.

* Monitoring of processes with the SC based on dynamic identification numbers, whose input parameters are determined in the participating companies during the processing phase of the order and are transferred in real-time to the master cockpit.

* Selection of alternative resources in case of failure. If a failure emerges and a part of an order or a certain operation cannot be processed by one of the project partners or cannot be processed in time, appropriate alternative resources can be identified based on the available identification numbers in the history.

In the following, some of the identification numbers will be exemplified for producers and logistic service providers on the basis of their formation regulation for each of the three functional areas.

2.2 Formation regulation of statistic identification numbers

Static identification numbers are not determined before completion of an order and, therefore, can generally not be taken into account for monitoring. Their application becomes especially important for the allocation of incoming orders by generating a ranking of possible partners on the basis of different identification numbers, depending on the setting of priorities or the order-related preferences.

Order-related static identification numbers are computed directly after the completion of an order, whereas company-related identification numbers have to be determined directly before the application of the respective identification number. In the following, the formation regulation of adherence to delivery dates [LD.sub.SMC] and quality of delivery [LQ.sub.SMC] are exemplified:

[LD.sub.SMC] = [absolute value of [AU.sub.tok]]/[absolute value of [AU.sub.SMC]] (1)

[LD.sub.SMC] = [absolute value of [AU.sub.Qok]]/[absolute value of [AU.sub.SMC]] (2)

[AU.sub.tok] : number of all completed orders in due time of the respective SMC

[AU.sub.SMC] : number of all current orders of the SMC

[AU.sub.Qok] : number of all orders completed along to quality standards of the SMC

Both identification numbers are non-dimensional. They have the target value 1 and the codomain * ?[[omega].sub.1], 0 .

2.3 Formation regulation of dynamic identification numbers

During processing and production, dynamic identification numbers are frequently redetermined in small intervals and have a high informative value about the current state of the job order processing in the SC. They have been developed particularly for monitoring and controlling purposes. For the nomination of parameters, we introduce the following function,

norm(f) = f/[absolute value of f] x (1- 1/[absolute value of f] + 1) (3)

mapping a function f [member of] (- [infinity], [infinity]) in the interval (-1,1). Examples of dynamic identification numbers are the state of process of order [AP.sub.Order] as well as adherence to delivery dates of the working system [AD.sub.WS] :

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)

[P.sub.actual] / [P.sub.t arg et] : number of produced units until now/ of units to be produced

[t.sub.actual] / [t.sub.t arg et] : processing time of respective order/scheduled processing time for order

[WS.sub.actual] : number of current working steps in production process

[TV.sub.i] : scheduling delay of working step i

[PT.sub.i] : scheduled processing time of working step i

Both identification numbers are: non-dimensional, target parameter: 0 and codomain (-1,1).

2.4 Formation regulation of global identification numbers

A special status applies to global identification numbers or top identification numbers. They provide SC-global statements about processes running in the SC and make use of identification numbers of the two previous groups as input parameters.

These identification numbers allow drawing conclusions with regard to the degree of efficiency of the adaptation of single processes within the SC and, therefore, provide valuable qualitative statements.

In the following, global adherence to delivery dates [AD.sub.Glob] as well as the global degree of capacity utilization [CU.sub.Glob] will be exemplified:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)

P: number of participants of the respective supply chain

[s.sub.i] : substitutability of resource i as weighting factor from interval (0,1)

[w.sub.i] : weighting of resource i by the SCM-pilot with a factor from Intervall (0,1)

[CU.sub.i] : degree of capacity utilization of resource i, which is computed as dynamic identification

Both identification numbers are non-dimensional. The target parameter of [AD.sub.Glob] is 0, but the target parameter of [CU.sub.Glob] depends on what is taken into account--the dispatching of a new order or the utilization of own resources. The codomain for [AD.sub.Glob] is (-1,1), for [CU.sub.Glob] [0,1].

2.5 Architecture of Software Solution

The system is composed of one master cockpit (SCEM-Server) and a number of local components for members (SCEM-Client). Depending on the partner, there are different models and algorithms implemented in the SCEM-Client modules to enable overall controlling.

The developed SCEM Client modules can be integrated in pre-existing subsystems of the supply chain members and can be connected to the master cockpit via internet (Fig. 3).

[FIGURE 3 OMITTED]

The master system constitutes the central processing unit for the monitoring of all orders in the supply chain network and the derivation of measures to be taken for trouble shooting or blocking of failures for the individual members and member groups. Provided by the single systems, the master system receives the identification numbers for all orders and members that are in the supply chain and administers them in a database on the central SCEM Server.

If required, the master system provides the members' SCEM clients with new identification numbers and processing models via update modules. Initially, this allows to start with a smaller number of identification numbers to be monitored and to enlarge the model without any software adjustment after the operation time has been extended.

2.6 The Master System

Following the application of identification numbers, the master system has three major features, using different identification numbers:

* Identification of failures by determining the deviation of the dynamic identification numbers, set by the members, from the target parameters (dates, costs) by means of a monitoring module and through visualization in the cockpit. The identification numbers of the interacting modules can be evaluated on the basis of tolerance parameters and limit values that are set. In case significant aberrations are identified, failure management will be activated.

* Trouble Shooting and failure management is performed through the selection of resources according to standard vectors and by initiating a ranking that determines which alternative resources are technologically suitable. This means in detail:

--Generation of a standard vector to look for technologically equivalent resources in the supply chain

--Search for alternative resources and check of temporal availability of located alternative resources

--Output of alternatives for failure compensation as support for the SCEM Manager.

* Process optimization during the dispatch of new orders on the basis of the identification number history. This process consigns those companies that have completed previous orders as all-embracing, on schedule and in the required quality as possible, with the new orders.

Change requests with regard to scheduled order deadlines, process changes, etc., which might become necessary, are reported to the respective partners in the supply chain.

The condensed data of the forwarding agents, the suppliers, the manufacturing companies and the distribution centers is made transparent by means of identification numbers that are visualized on a screen. Further information and concrete, nonvisualized identification numbers can be accessed via the respective menu. In addition, it is possible to directly intervene in the processes of the visualized SC.

3. Important Sub-Aspects of the Solution

3.1 Possibilities of Intervention through Controlling Instruments

The goal of controlling is the determination of important parameters that are significant for the assessment of the reliability of order processing of the supply chain partners. These cannot only be used for the assessment of the company performance with regard to defined controlling criteria for completed orders, but also for prognostic evaluation of processing orders. A cross-company assessment of the whole supply chain is also performed, for which the following major controlling issues are monitored statically or dynamically and evaluated via identification numbers in the SCEM cockpit:

* Adherence to delivery dates and delivery quality

* Stock capacity and degree of capacity utilization

* Likeliness of failures and degree of failure

With the implementation of above aspects into the SCEM cockpit, decision and controlling support is provided for the SC Manager, enabling it to react efficiently to deviations of target states from actual states on the one hand, and to unpredictable situations on the other.

[FIGURE 4 OMITTED]

The following major features have been implemented:

* Visualization of all job orders, allocated to one supply chain with the respective job order status (registered, dispatched, started, completed) in form of a Gantt diagram and with selectable time levels (month, week, day, shift, etc.)

* Color signals provide information about the respective state of process (green process as scheduled, yellow--value of one or more identification number reaches a defined limit value, red--limit value is exceeded, processing does not run as scheduled, failure).

* By clicking the single job order fields, sub-menus provide detailed information about the job order, such as the single actual identification number values and the determined limit values or the necessary job order and resource data for failure management.

3.2 Communication Scheme and Interfaces

Communication between the central master cockpit and the local clients is achieved via web services, which have to be provided by each member. The transmission is done via telegrams for the capsulation of all relevant data.

Two database tables, including all data originating from requests of the master cockpit to the members, as well as data of the clients needed by the cockpit, are used as interfaces with the client's production systems. The input of this data and its evaluation with regard to resource-related matters can be realized manually via a separate client or fully automatically through the implementation of an appropriate algorithm.

If no data is delivered for a scheduled time frame, the resource will be put on "inactive", implying a problem or a lack of cooperation.

3.3. Data Basis and Generation of Identification Numbers

All data that is necessary for the computing, administration and evaluation of identification numbers are administered in the database table of the SCEM database.

The telegrams with the relevant input parameters, which are transmitted by the clients, constitute the basis for the generation of the individual identification numbers. They include all necessary information. With the receipt of a telegram from the SCEM server, the actual data is entered into the respective database table.

After a defined time interval, a Timerthread initiates the periodical generation of identification numbers in the SCEM server, which are necessary for the monitoring of the supply chain. It reads all necessary data of the input parameter table and computes the actual identification numbers based on the presented formula. They are entered into the identification number table and old data from this table is moved to an archive. The allocation of identification numbers to the clients is realized via the Client-ID, which is available for each input parameter and can be used for the identification number presentation. The update in the visualization tool of the SCEM cockpit is also performed periodically, for which the tool reads all current data from the identification number table again and updates the presentation.

3.4 Integration Test and Results

The implemented identification numbers were tested in several test scenarios. They have been tested with regard to their suitability concerning trouble shooting standards, failure management, as well as with regard to process optimization during re-dispatching and alternative dispatching. Firstly, the single identification numbers were computed in basic testing. This becomes clear by taking a look at the delay quota:

[DQ.sub.Joborder] - norm(-SD/[PT.sub.Planned]) (8)

SD: actual scheduling delay of the respective job order

[PT.sub.Planned]: Planned Processing Time of the current job order in the PPS-System

The target value 0 appears if the current job order does not show any delay (see job order 48 ). Positive values of [DQ.sub.Joborder] indicate a faster processing of the job order than originally scheduled, whereas negative values indicate a delay.

The identification number is scaled along the interval (-1,1) and the value shows the degree of deviation from the target value.

4. Summary and Prospect

Within the framework of the presented project, a Supply Chain global Monitoring, an Early Detection of Failures, as well as an overall Failure Management for SCs were actualized.

The core of the new model is the evaluation of actual process data of all network members in real-time and centrally by means of dynamic identification numbers, which are generated in client interfaces within the individual partner stations and are transferred to the central component (the master system) directly afterwards. There, another evaluation is performed considering previous values as well as reference values for the respective identification numbers.

Furthermore, a process optimization approach to the dispatching of new job orders as well as for the search of alternative resources was presented. This could be achieved through the selection of the most appropriate resource for the respective job order based on technological standard vectors, an identification number history with identification numbers of previous job orders, as well as the actual situation on the basis of dynamic identification numbers.

The system presented in this paper does not replace the SCM Manager, but was developed as a decision support, making it possible to react faster and more precise to new situations. Dispatching and rescheduling of job orders are performed or can be monitored but have to be done in a traditional way. More complex systems are theoretically possible, but lead to other problems, such as legal problems with regard to current contracts or the necessity of placing new contracts.

In the future, the features of early detection of failures will be improved by comparing the identification number history by means of a monitoring process that inhabits the functionalities of machinery learning, on a continuing basis with available failure classifications. A monitoring feature of this kind is broader than the monitoring of ranges and limit values of defined parameters or parameter combinations and, therefore, enables a faster and more precise failure detection.

A further goal is to make the whole SCM network more dynamic by creating a flexible company association, which, depending on the requirements, can initially react to new situations spontaneously and produce concrete supply chains afterwards.

The result would then be a cross-company, software-supported generation and controlling of cooperation between different companies. In the future, cooperation of such kind could be built within the frame of national and international production or supply chain networks, in which companies offer their competencies and resources or request for resources (Durr & Mehnert, 2002). Such a system would support companies, e.g. small and medium-sized companies, within such a company association on a long-term basis and without a huge cost budget to develop to a full-range supplier.

5. References

Bartsch, H. & Bickenbach, P. (2001). Supply Chain Management mit SAP APO, Galileo Press, ISBN 3898421112, Bonn

Bodendorf, F.; Butscher, R. & Zimmermann, R. (2001). Agentengestutzte Auftragsuberwachung in Supply Chains, Industrie Management, 17, 6, 25-28, ISSN 1434-1980

Duerr, H. & Mehnert, J. (2002). Description of the Compotence Cell Process Planning, Proceedings of 18 th Edition of CARs & FOF' 2002, ISBN 9729519455, Portugal, July 2002, Porto

Heinrich, S.; Duerr, H.; Haenel, T. & Laessig, J. (2005). An Agent-based Manufacturing Management System for Production and Logistics within CrossCompany Regional and National Production Networks, Journal of Advanced Robotic Systems, 2, 1, 7-14, ISSN 1729-8806

Jacobs, H.-J.; Durr, H. & Heinrich, S. (2000). The Holonic Steering wheel--a new approach to bridge the gap between planning, scheduling and manufacture on CNC machine tools, Proceedings of the 33rd CIRP International Seminar on Manufacturing Systems, Hogskolan, K. T. (Ed.), pp. 187-192, Schweden, June 2000, The Royal Institute of Technology, Stockholm

Koch, U. (1996). Bewertung und Wirtschaftlichkeitsermittlung logistischer Systeme, Gabler, ISBN 3824463318, Hallstadt

Mehnert, J. & Duerr, H. (2004). Process Planning in non-hierarchical Production Networks--New Options for the Development of self-learning Planning Software, Proceedings of COMA'04, Dimitrov, D. (Ed.), ISBN 0797210180, South Africa, February 2004, Global Competitiveness Centre in Engineering Stellenbosch, Stellenbosch

Simchi-Levi, D.; Kaminsky, P. & Simchi-Levi, E. (2002). Designing and Managing the Supply Chain. Concepts, Strategies and Case Studies, McGraw-Hill, 2002, ISBN 00712214046.

Weber, J. & Dehler, M. (1999). Effektives Supply Chain Management auf der Basis von Standardprozessen und Kennzahlen, Verlag Praxiswissen, ISBN 3932775368, Dortmund

Authors' data: Dipl.-Inf. Laessig J.[oerg], Dr.-Ing. Heinrich S.[teffen], Prof. Dr.Ing. habil. Duerr H.[olger], Chemnitz University of Technology, Institute of Manufacturing and Welding Technology, Germany, joerg.laessig@mb.tuchemnitz. de, steffen.heinrich@mb.tu-chemnitz.de, holger.duerr@mb.tu-chemnitz.de

This Publication has to be referred as: Laessig, J.; Heinrich, S. & Duerr, H. (2006). Supply Chain Executive Monitor for Controlling and Failure Management in Supply Chains, Chapter 31 in DAAAM International Scientific Book 2006, B. Katalinic (Ed.), Published by DAAAM International, ISBN 3-901509-47-X, ISSN 1726-9687, Vienna, Austria

DOI: 10.2507/daaam.scibook.2006.31

Tab. 1 Degree of [DQ.sub.Joborder] for different test scenarios

Job name Scheduled Real Scheduled
order starting Starting Completion
 time time time

36 cover I 06.03.200 06.03.200 06.03.200
 6 12:20:00 6 12:00:00 6 13:30:00

39 metal 06.03.200 06.03.200 06.03.200
 6 14:30:00 6 14:30:00 6 17:00:00

43 rolling 07.03.200 06.03.200 07.03.200
 bearing 6 11:00:00 6 11:00:00 6 12:00:00

44 casing 06.03.200 07.03.200 06.03.200
 over 6 12:00:00 6 12:00:00 6 13:20:00

46 spacer 06.03.200 06.03.200 06.03.200
 ring 6 12:48:00 6 14:10:00 6 13:08:00

48 scanning 06.03.200 06.03.200 06.03.200
 head 6 12:00:00 6 12:00:00 6 13:20:00
 complete

Job name Real Value
order Completion
 time

36 cover I 06.03.200 0,222
 6 13:10:00

39 metal 06.03.200 -0,286
 6 18:00:00

43 rolling 06.03.200 0,96
 bearing 6 12:00:00

44 casing 07.03.200 -0,947
 over 6 13:20:00

46 spacer 06.03.200 -0,600
 ring 6 14:30:00

48 scanning 06.03.200 0,0
 head 6 13:20:00
 complete