Search results

This paper aims to automate the supply chain operations reference (SCOR) model as an enabler for process‐oriented supply chain business intelligence.

The hypothesis is the following: SCOR model automation is possible using data that is directly extracted from integrated enterprise systems. To test the hypothesis, an alignment product that allows the SCOR model to be automated with information that is directly extracted from the Oracle E‐Business Suite was developed.

In order to achieve the full benefits from the SCOR model, effective business process management and the SCOR key performance indicators (KPIs) must be implemented and used. Unless data collection to support KPI construction is automated, it is difficult to institutionalize the SCOR model as a measurement and benchmarking framework. We have demonstrated that automated support for KPIs is feasible and achievable.

The E‐Business Suite is a single enterprise solution, but we assert that the same procedures could be followed with other enterprise solutions or even applied in a legacy system environment.

The developed solution described in the paper can immediately be applied to the design, development, and deployment of corporate performance management systems.

Case studies examining long‐term ERP system success/failure are scarce. However, there is a clear need for critical knowledge in this field. This paper aims to address this issue through providing evidence from a case study completed in an after‐sales division of a multinational corporation.

Data were collected from ERP users in two phases: during the so‐called shakedown phase and two years after system implementation. Data gathering was done by means of a questionnaire based on the DeLone and McLean model.

Overall, user evaluations were more negative in the shakedown phase. However, problems were still identified two years after system implementation. For example, the quality of user support was considered to have worsened during this time. Evaluations also differed depending on the user's respective business process and user type. For instance, data integration was generally identified as a positive issue, but at the same time others considered it a downside of the new system.

The research results are based on data from one organization, which naturally limits the generalizability of the findings.

Although the study relies on case‐based evidence, it underlines the value of using user evaluations in identifying problem areas during the ERP implementation process. Results elaborate on the effects that ERP system inflexibility and integration may have in a complex business environment.

The study is seminal in its field as evidence covers multiple points in time. The results are valuable for improving the quality of ERP evaluation in companies as well as for developing research in this curriculum.

This paper reflects on the growing trend of engaging management consultancies in implementing operations management innovations in the public sector. Whilst the differences between public and private sector operations have been documented, there is a dearth of material detailing the impact of public sector engagements on the consultancies themselves and the operations management products and services they develop. Drawing on qualitative data, the paper aims to identify both the impact of operations management in the public sector and the impact of this engagement on the consultancies that are involved.

This paper draws on rich, qualitative data from six large management consultancies, amounting to over 48 interviews. An inductive methodology sought to identify both how consultancies have adapted their operations management products and services, and why.

The paper finds that the different context of the public sector provides consultants with considerable challenges when implementing operations management projects. The research shows that public services are often hampered by different cultures, structures, and managerial knowledge and investment patterns. Such constraints have an impact on both the projects being implemented and the relationship between consultants and clients.

There are few studies that consider the implementation of operations management in the public sector and fewer still which examine the impact of public sector engagement on the products that consultancies develop. This paper aims to develop understanding in both. At a more theoretical level, the paper contributes to considering operations management through knowledge management literature in seeking to understand how consumers of management knowledge influence its producers.

This paper aims to expand the knowledge about Lean Six Sigma (LSS) implementation in the public sector. By analyzing an LSS improvement initiative in a German municipality, examples of success, barriers and challenges are discussed. A comparison with literature regarding the production and service sectors unfolds similarities and differences.

The paper applies the action research method. Especially for the broad field of project management, methods focusing on actual experience from practice have been recommended by many researchers.

Implementations of LSS in the public sector seem to be particularly challenging and lengthy. Change and communication management have proved to be the most important aspects to successful acceptance, cooperation and improvement sustainability. In the analyzed cases, the needed volume of data could often not be procured. The applied Six Sigma methodology primarily included the DMAIC project phases as well as selected standard instruments. In contrast, the lean elements of LSS achieved more results and were appreciated by project team members.

The LSS application in this paper provides insights into practical implementation experience in a municipality, as well as lessons learned. Until now, most research addressed the single application of lean, continuous improvement or Six Sigma. This paper represents the first academic report of a LSS program in a German municipality and underlines the need for scientific support of those initiatives in further municipalities worldwide.

To develop a methodology to augment enterprise resource planning (ERP) systems with the discrete event simulation's inherent ability to handle the uncertainties.

The ERP system still contains and uses the material requirements planning (MRP) logic as its central planning function. As a result, the ERP system inherits a number of shortcomings associated with the MRP system, including unrealistic lead‐time determination. The developed methodology employs bi‐directional feedback between the non‐stochastic ERP system and the discrete event simulation model until a set of converged lead times is determined.

An example of determining realistic production lead‐time data in the ERP system is presented to illustrate how such a marriage can be achieved.

The research demonstrates that the limited planning functionality of the ERP system can be complemented by external system such as discrete event simulation models. The specific steps developed for this research can be adopted for other enhancements in different but comparable situations.

The organizations who have been using the discrete event simulation in their planning and decision‐making processes can integrate their simulation models and the ERP system following the steps presented in this paper. The ideas in this paper can be used to look for automatic data collection process to update or build the simulation models.

The ERP implementation is a significant investment for any corporation. Once the ERP implementation is completed successfully, the corporations must look for ways to maximally return on their investment. The research results may be used to enhance the implemented ERP systems or to fully utilize the capabilities in a corporation.

This paper aims to study the main requirements of traceability and examine how the technology of radio frequency identification (RFID) technology can address these requirements. It further seeks to outline both an information data model and a system architecture that will make traceability feasible and easily deployable across a supply chain.

The design research approach is followed, associating traceability requirements to a proposed system design.

The technological approach used has great implications in relation to the cost associated with a traceability system and the ease of its deployment.

Validation of the proposed information data model and system architecture is required through practical deployment in different settings.

The paper provides practitioners with insight on how RFID technology can meet traceability requirements and what technological approach is more appropriate.

Food quality has become an important issue in the last decade. However, achieving end‐to‐end traceability across the supply chain is currently quite a challenge from a technical, a co‐ordination and a cost perspective. The paper contributes by suggesting a specific technological approach, exploiting the new possibilities provided by RFID technology, to address these issues.

The purpose of this paper is to provide industrial managers with insight into the real‐time progress of running processes. The authors formulated a periodic performance prediction algorithm for use in a proposed novel approach to real‐time business process monitoring.

In the course of process executions, the final performance is predicted probabilistically based on partial information. Imputation method is used to generate probable progresses of ongoing process and Support Vector Machine classifies the performances of them. These procedures are periodically iterated along with the real‐time progress in order to describe the ongoing status.

The proposed approach can describe the ongoing status as the probability that the process will be executed continually and terminated as the identical result. Furthermore, before the actual occurrence, a proactive warning can be provided for implicit notification of eventualities if the probability of occurrence of the given outcome exceeds the threshold.

The performance of the proactive warning strategy was evaluated only for accuracy and proactiveness. However, the process will be improved by additionally considering opportunity costs and benefits from actual termination types and their warning errors.

Whereas the conventional monitoring approaches only classify the already occurred result of a terminated instance deterministically, the proposed approach predicts the possible results of an ongoing instance probabilistically over entire monitoring periods. As such, the proposed approach can provide the real‐time indicator describing the current capability of ongoing process.