Oleg Gusikhin

Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain

Firms are exposed to a variety of low-probability, high-impact risks that can disrupt their operations and supply chains. These risks are difficult to predict and quantify; therefore, they are difficult to manage. As a result, managers may suboptimally deploy countermeasures, leaving their firms exposed to some risks, while wasting resources to mitigate other risks that would not cause significant damage. In a three-year research engagement with Ford Motor Company, we addressed this practical need by developing a novel risk-exposure model that assesses the impact of a disruption originating anywhere in a firms supply chain. Our approach defers the need for a company to estimate the probability associated with any specific disruption risk until after it has learned the effect such a disruption will have on its operations. As a result, the company can make more informed decisions about where to focus its limited risk-management resources. We demonstrate how Ford applied this model to identify previously unrecognized risk exposures, evaluate predisruption risk-mitigation actions, and develop optimal postdisruption contingency plans, including circumstances in which the duration of the disruption is unknown.

Least in-sequence probability heuristic for mixed-volume production lines

The paper focuses on the sequencing aspects of a stochastic hybrid flexible assembly system (FAS) operating in a build-to-order environment. In such a system, although the flow of parts is unidirectional, parallel paths can exist for accommodating different types of parts produced and potential rework of the parts that fail inspection at a given production stage. As a result, the original sequential order of parts can become distorted, resulting in an exit demand sequence which is at variance with the input sequence. To compensate for such sequence disturbances, an adequately sized buffer is installed at the exit end of the FAS. From a practical viewpoint, the study is relevant to the sequencing of upstream operations in an automotive assembly plant functioning in an in-line vehicle sequencing mode. An important feature of the FAS considered in this study is that the demand sequence of part types is known and fixed for a given period of time. Further, the different part types that constitute the demand sequence can have different frequencies of occurrence in a range specified from low to high. We exploit this property of the demand sequence in the development of the least in-sequence probability (LISP) algorithm. The development of LISP is based on the trade-off of pulling low-volume parts ahead in the input sequence while delaying the high-volume parts. We propose the use of the heuristic as a means to achieve both of the following: (a) to improve customer service levels in terms of the number of in-sequence parts output from the system, given a fixed size for the re-sequencing buffers; and (b) to reduce re-sequencing buffer sizes given target levels of customer service.

Intelligent Vehicle Systems:Applications and New Trends

Most people usually do not consider the car sitting in their drivewayto be on the leading edge of new technology. However, for most people, the personal automobile has now become their initial exposure to new intelligent computational technologies such as fuzzy logic, neural networks, adaptive computing, voice recognition and others. In this chapter we will discuss the various intelligent vehicle systems that are now being deployed into motor vehicles. These intelligent system applications impact every facet of the driver experience and improve both vehicle safety and performance. We will also describe recent developments in autonomous vehicle design and demonstrate that this type of technology is not that far away from deployment. Other applications of intelligent system design apply to adapting the vehicle to the driver’s preferences and helping the driver stay aware. The automobile industry is very competitive and there are many other new advances in vehicle technology that cannot be discussed yet. However, this chapter provides an introduction into those technologies that have already been announced or deployed and shows how the automobile has evolved from a basic transportation device into an advanced vehicle with a host of on-board computational technologies.

Application of intelligent methods to automotive assembly planning

The automotive manufacturing process is one of the most complex processes in industry today. The rapid changes in the marketplace and introduction of new technologies require that the underlying manufacturing processes keep pace. The use of intelligent systems for this environment is quickly becoming a necessity. In this paper, we describe how some of these intelligent methods are used at Ford Motor Company in the automotive assembly planning domain. The following applications of intelligent systems will be described: knowledge-based labor management, ergonomics analysis, and workcell layout optimization. We will also discuss how natural language processing can assist in understanding unstructured textual data. All of these examples show how computational intelligence can be successfully applied in an industrial setting.

Ford Motor Company Implements Integrated Planning and Scheduling in a Complex Automotive Manufacturing Environment

Ford Motor Company has designed, developed, and implemented a collective system of decision support tools, supply chain visualization methods, and optimization techniques to aid its planning and scheduling in a complex automotive manufacturing environment. Although originally developed particularly for stamping plants, the system can also be applied in other manufacturing environments where setup times play a dominant role. By enabling substantial improvements in workplace planning and production scheduling, the implementation of this system has resulted in significant reductions in premium freight charges, overtime wages, and inventory costs.

A hybridization of mathematical programming and dominance-driven enumeration for solving shift-selection and task-sequencing problems

A common problem in production planning is to sequence a series of tasks so as to meet demand while satisfying operational constraints. This problem can be challenging to solve in its own right. It becomes even more challenging when higher-level decisions are also taken into account. For example, determining which shifts to operate clearly impacts how tasks are then scheduled; additionally, reducing the number of shifts that must be operated can have great cost benefits. Integrating the shift-selection and task-sequencing decisions can greatly impact tractability, however, traditional mathematical programming approaches often failing to converge in reasonable run times. Instead, we develop an approach that embeds mathematical programming, as a mechanism for solving simpler feasibility problems, within a larger search-based algorithm that leverages dominance to achieve substantial pruning. In this paper, we introduce the Shift-Selection and Task Sequencing problem (SS-TS), develop the Test-and-Prune algorithm (T&P), and present computational experiments based on a real-world problem in automotive stamping to demonstrate its effectiveness. In particular, we are able to solve to provable optimality, in very short run times, a number of problem instances that could not be solved through traditional integer programming methods.

A hybridization of Mathematical Programming and search techniques for integrated operation and workforce planning

Decision-support tools are essential to assist planners when scheduling operations in complex systems. It is critical that real-world operational and workforce details be taken into account when constructing models of the system, so as to ensure the feasibility and implementability of the final solution. However, the level of detail can greatly impact the tractability of the model - for example, increased modeling complexity can decrease the performance of mathematical programming (MP) techniques. Furthermore, high levels of detail are often important for ensuring feasibility but may not dramatically impact system cost. This research is motivated by our efforts to develop decision-support tools that simultaneously create workforce and detailed operational plans. The focus of this paper is to solve the first phase of an integrated workforce and operation plan, that is, to determine the minimum amount of time workers are required to meet demand. In our initial experience, we were able to model the real-world problem with sufficient accuracy, but found that MP approaches to finding optimal solutions to this model were inefficient. In this paper, we describe how using MP to find feasible solutions, then embedding these feasibility problems in a search-based algorithm, allowed us to find high-quality solutions. We present the general model and algorithm and then an automotive supply chain example to illustrate this technique.

Context-aware service composition in cyber physical human system for transportation safety

The paper addresses the problem of increasing transportation safety due to usage of new possibilities provided by modern technologies. The proposed approach extends such systems as ERA-GLONASS and eCall via service network composition enabling not only transmitting additional information but also information fusion for defining required emergency means as well as planning for a whole emergency response operation. The main idea of the approach is to model the cyber physical human system components by sets of services representing them. The services are provided with the capability of self-contextualisation to autonomously adapt their behaviours to the context of the car-driver system. The approach is illustrated via an accident emergency situation response scenario.

Context-driven on-board information support: Smart space-based architecture

Equipping vehicles with complicated information systems does not only introduce infotainment features but also makes it possible to implement new ideas to provide richer driving experience. The paper presents an approach integrating such concepts as smart space, collaborative recommendation system and context-based decision support. The key idea of the proposed approach is to implement context-based service fusion supported. This would provide a new, previously unavailable level of personalized on-board information support via generating solutions that take into account drivers preferences, information from various services as well as previous choices of users with similar interests. An application of these ideas is illustrated by an example of decision support taking into account available coupons and special offers. The system consists of several services that find, extract and process potentially useful information and provide it in a user-friendly vehicle interface. The smart space technology is used for providing interaction possibilities and interoperability of these services.

Context-Based Service Fusion for Personalized On-Board Information Support

Current in-vehicle information systems make it possible to benefit from integration of new ideas to provide richer driving experience. The paper presents a concept, main supporting technologies and an illustrative case study for improved on-board information system. The key idea of the proposed approach is to implement context-based service fusion supported by a negotiation model. This would provide a new, previously unavailable level of personalized on-board information support via finding compromise decisions taking into account proposals of various services and driver preferences.