Kenn Steger-Jensen

Technical and industrial issues of Advanced Planning and Scheduling (APS) systems

The basic functionality of planning and scheduling in Advanced Planning and Scheduling (APS) systems, especially constraint-based planning and optimization, is analyzed and discussed by use of theory and examples including how objectives, decision-variables and penalty factors are handled. The paper concludes that the planning functionality is radically improved compared to MRP and ERP, but stresses how essential it is for a good outcome that the user is familiar with the core APS functionality to enable a careful setup of the many (conflicting) planning parameters. The research presented in this paper is funded by the EU Union via the EmpoSME and ValuePole projects while the APS descriptions and tests are based on Oracles Advanced Planning and Scheduling software.

Issues of mass customisation and supporting IT-solutions

Mass customisation production is a challenge to the existing production management systems. The opportunity to ensure an efficient utilisation of the production system is reduced due to the build to order (BTO) approach which is most often associated with a customisation strategy. Existing software provides little support because they are mostly based on mass production approaches. BTO on the other side has not been subject to the same attention as mass production, and as the problems are slightly different, the techniques of traditional industrial production can only be applied to a limited extent.

Scheduling a single mobile robot for part-feeding tasks of production lines

This study deals with the problem of sequencing feeding tasks of a single mobile robot which is able to provide parts for feeders of machines on production lines. The mobile robot has to be scheduled in order to stoppage from lack of parts in the production line. A method based on the characteristics of feeders and inspired by the (

Aligning supply chain design with manufacturing strategies in developing regions

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Advanced planning and scheduling technology

) inventory system, is thus applied to define time windows for the feeding tasks of the robot. The capacity of the robot is also taken into consideration. The performance criterion is to minimize total traveling time of the robot for a given planning horizon. A genetic algorithm-based heuristics is presented which results in a significant increase in the speed of finding near-optimal solutions. To evaluate the performance of the genetic algorithm-based heuristic, a mixed-integer programming model has been developed for the problem. A case study is implemented at an impeller production line in a real factory and computational experiments are also conducted to demonstrate the effectiveness of the proposed approach.

Buyer-supplier Relationships and Planning Solutions

OVERVIEW: Product development at manufacturing companies is increasingly complex. Linear product development processes, including the traditional Stage-Gate process, cannot support the iterative cycles and external collaboration that characterize todays product development efforts. Hybrid processes combining elements of Agile and Stage-Gate models offer a more flexible alternative to conventional systems. A comparative case study of seven technology-intensive companies shows how combining Stage-Gate models, at the strategic level, with the Agile method Scrum, implemented at the execution level, can offer performance improvements and other advantages over even improved Stage-Gate processes. The key contribution of this study is a generic Agile/Stage-Gate hybrid process based on best practices as identified in the case companies.

Scheduling a Single Mobile Robot Incorporated into Production Environment

Managing supply chains effectively is a complex and challenging task, due to the current business trends of increasing outsourcing of manufacturing activities to developing regions to gain competitive advantage from being close to customers, making available low and/or high skilled labour, participating in local government initiatives, and reducing transportation cost and production delivery lead times. This paper explores how a manufacturing enterprise, when sourcing production facilities to developing regions, chooses manufacturing strategies to reduce uncertainty rather than to gain competitive advantage according to how the enterprise obtains orders from its customers. The paper aligns the design of supply chain with the choice of manufacturing strategy by incorporating some of the most important design dimensions of uncertainties with production operations in developing regions. The result from empirical research of 34 international manufacturing joint ventures (IMJV) located in different developing regions shows that their choice of strategy is limited to manufacturing efficiency design with strong focus on cost advantage. This paper argues that these IMJV must follow contrasting supply chain design according to their focus on either product innovation as the primary objective for competitive advantage or on cost efficiency.

RFID technology to support environmentally sustainable supply chain management

Demand-driven manufacturing is an extremely unstable planning environment compared to forecast-driven manufacturing. This requires preparation and makes knowledge of demand behaviour even more important for planning and control. The basic assumptions of pre-ante allocation based on forecast of independent end-products demand are critical for manufacturing planning and control in general. However, the importance is higher for demand-driven manufacturing than forecast-driven manufacturing. This is due to the sensitivity of demand-driven manufacturing to demand fluctuations, e.g. time and interdependency of demand rates, due to the customer order decoupling point. This paper presents a method to establish time and interdependency of demand rates (the Time- and Interdependent Demand Rate Method), which can improve the planning and control performance as well as the order management performance in a MTO environment. The method is tested on data from two cases. For both cases results and demand planning implications are presented. Use guidelines for the method are also presented along with avenues of further research.