BUS606: Operations and Supply Chain Management

Markets for engineer-to-order (ETO) manufacturers that were stable in the past are now dynamic and uncertain in which prices have reduced over the last decades. At the moment, many ETO manufacturers, such as companies in the metallurgical sector, working for specific needs and end-segments such as the steel industry, are under pressure; the current overcapacity is destroying prices and profitability and many producers have closed facilities, have made layoffs and have gone in bankruptcy in recent years. As an example by the end of 2015, U.S. steel producers were utilizing less than 65% of their capacity, and were forced to lay off 12,000 employees in this year. In 2007–2011 capacity utilization was about 80% and supply and demand was in a balanced state, but since 2012 the overcapacity challenge was becoming obvious, with 74.8% in 2014, less than 70% in 2015 and with a recovery in 2018 due to a reduction of capacity since 2015 with 75.7% capacity utilization with 2233.7 million tons of capacity available and 1690.1 of produced crude steel. The overcapacity created was driven by new investment from old market leaders as well as new emerging market companies in the last decades like in China, which now has two-thirds of world steel overcapacity.

On the other hand, in respond to that, ETO companies are continuously seeking for ways or methods how to reduce costs and lead times as well as increasing their external flexibility. In the literature there is limited research related to supply chain management in the low-volume engineer to order (ETO) sector, in contrast to the extensive literature on the high-volume sector, particularly automotive and electronics. The limited research that has been undertaken in the low volume ETO sector has focused on production control, information systems, manufacturing systems and the co-ordination of marketing and manufacturing.

In this global market situation, current producers need to work on their technological and organizational advantage. On the one side, the steel industry has a low frequency of innovation compared to other industries with process technologies have been used for decades. Until the recession in 1973, advanced countries such as US, Japan and European countries were increasing their steel production. Due to the recession, steel demand decreased and US and Japanese steel firms were forced to export their technology. Therefore, the first potential unique selling proposition (USP), the technological advantage, hardly exists anymore for US, Japanese, or European countries, as many companies worldwide have access and have invested in new equipment in recent decades and years, mainly in developing countries such as Korea, Taiwan, Brazil, and China.

For ETO companies, the question arises of what options remain to improve their competitive situation and to shape a long-term and profitable business model. In addition, additional cost savings programs will not be enough to achieve sustainable returns. For this reason, ETO producers need to be focus on the second potential USP, the internal process optimization, such as digitization and optimization of business processes enabling a better service level to end-customers.

Research Questions and Goals

On the basis of the previous paragraphs, this paper aims to provide a methodology for the improvement of internal processes in regard to production planning and control by optimizing internal parameters, work-in-progress (WIP), and production lead times, as well as end-customer service level, and order-to-delivery (OTD). The final goal of this optimization would be to increase the company's competitiveness securing its long-term existence. Based on these objectives the following research questions can be declared:

• Which are current challenges of ETO manufacturers?
• Which is the current common production planning strategy being used in ETO industries?
• How do other production planning strategies can be applied to the ETO sector?
• What is the potential benefit of this change?
• In order to achieve these goals, this paper presents the following structure:

1. Introduction: definition of challenge, objectives, and simulation.
   • Literature review of:
   • Engineer-to-order typology and characteristics;
   • Organizational model for the order management process;
   • Production planning and control: push versus pull;
   • Capacity planning and maintenance;
   • Theory of constraints (TOC) and drum-buffer-rope (DBR);
   • Agent-based simulation.
2. Typical initial situation and development of pull production control for ETO manufacturers.
3. Applying the model for production control to the metallurgical industry through simulation.
4. Simulation results and implementation.
5. Conclusions.
   

The main expected outcome of the research paper is that a pull control approach using drum-buffer-rope (DBR) approach will lead to an improvement of OTD and to a reduction of lead times and WIP stocks.

Methodology Used

The research methodology for an ETO company consists of the steps shown in Figure 1:

Figure 1. Methodology used: steps.

Based on the previously described methodology two different models are simulated, a classical push production control model versus a pull production control. The main objectives are: to generate knowledge of the supply chain, development and validation of improvements using what would happen if of analysis and quantification of the benefits of decision support at the level of strategic decision making. One recent modeling method is agent-based simulation and it has no standard language. The structure of an agent based model is created using graphical editors depending on the software. The behavior of agents is specified in many different ways. Frequently, the agent has a notion of state, and its actions and reactions depend on its state. For the research work AnyLogic was used and the models presented production orders as agents with their own characteristics. As a consequence the research was qualitative in the conceptualization and quantitative for the simulation models and data used. In Figure 2 it can be shown the steps followed to perform the simulation of the two models:

Figure 2. Simulation methodology used.