Wolfgang Garn

Assessing critical success factors for military decision support

This paper outlines the application of case-based reasoning and Bayesian belief networks to critical success factor (CSF) assessment for parsimonious military decision making. An important factor for successful military missions is information superiority (IS). However, IS is not solely about minimising information related needs to avoid information overload and the reduction of bandwidth but it is also concerned with creating information related capabilities that are aligned with achieving operational effects and raising operational tempo. Moreover, good military decision making, should take into account the uncertainty inherent in operational situations. Herein, we illustrate the development and evaluation of a smart decision support system (SDSS) that dynamically identifies and assesses CSFs in military scenarios and as such de-clutters the decision making process. The second contribution of this work is an automated configuration of conditional probability tables from hard data generated from simulations of military operational scenarios using a computer generated forces (CGF) synthetic environment.

To eliminate or absorb supply chain complexity: a conceptual model and case study

Purpose Existing works in the supply chain complexity area have either focused on the overall behavior of multi-firm complex adaptive systems or on listing specific tools and techniques that business units (BUs) can use to manage supply chain complexity but without providing a thorough discussion about when and why they should be deployed. This research aims to address this gap by developing a conceptually sound model, based on the literature, regarding how an individual BU should reduce versus absorb supply chain complexity. Design/methodology/approach This research synthesizes the supply chain complexity and organizational design literature to present a conceptual model of how a BU should respond to supply chain complexity. The authors illustrate the model through a longitudinal case study analysis of a packaged foods manufacturer. Findings Regardless of its type or origin, supply chain complexity can arise because of the strategic business requirements of the BU (strategic) or because of suboptimal business practices (dysfunctional complexity). Consistent with the proposed conceptual model, the illustrative case study showed that a firm must first distinguish between strategic and dysfunctional drivers prior to choosing an organizational response. Furthermore, it was found that efforts to address supply chain complexity can reveal other system weaknesses that lie dormant until the system is stressed. Research limitations/implications The case study provides empirical support for the literature-derived conceptual model. Nevertheless, any findings derived from a single, in-depth case study require further research to produce generalizable results. Practical implications The conceptual model presented here provides a more granular view of supply chain complexity and how an individual BU should respond, than what can be found in the existing literature. The model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response. Originality/value There are no other research works that have synthesized the supply chain complexity and organizational design literature to present a conceptual model of how an individual BU should respond to supply chain complexity. As such, this paper improves the understanding of supply chain complexity effects and provides a basis for future research, as well as guidance for BUs facing complexity challenges.

Agile factorial production for a single manufacturing line with multiple products

Industrial practices and experiences highlight that demand is dynamic and non-stationary. Research however has historically taken the perspective that stochastic demand is stationary therefore limiting its impact for practitioners. Manufacturers require schedules for multiple products that decide the quantity to be produced over a required time span. This work investigated the challenges for production in the framework of a single manufacturing line with multiple products and varying demand. The nature of varying demand of numerous products lends itself naturally to an agile manufacturing approach. We propose a new algorithm that iteratively refines production windows and adds products. This algorithm controls parallel genetic algorithms (pGA) that find production schedules while minimizing costs. The configuration of such a pGA was essential in influencing the quality of results. In particular providing initial solutions was an important factor. Two novel methods are proposed that generate initial solutions by transforming a production schedule into one with refined production windows. The first method is called factorial generation and the second one fractional generation method. A case study compares the two methods and shows that the factorial method outperforms the fractional one in terms of costs.

Profitability in the car industry: New measures for estimating targets and target directions

In this paper we study the profitability of car manufacturers in relation to industry-wide profitability targets such as industry averages. Specifically we are interested in whether firms adjust their profitability in the direction of these targets, whether it is possible to detect any such change, and, if so, what the precise nature is of these changes. This paper introduces several novel methods to assess the trajectory of profitability over time. In doing so we make two contributions to the current body of knowledge regarding the dynamics of profitability. First, we develop a method to identify multiple profitability targets. We define these targets in addition to the commonly used industry average target. Second, we develop new methods to express movements in the profitability space from t to t + j, and define a notion of agreement between one movement and another. We use empirical data from the car industry to study the extent to which actual movements are in alignment with these targets. Here we calculate the three targets that we have previously identified, and contrast them with the actual profitability movements using our new agreement measure. We find that firms tend to move more towards to the new targets we have identified than to the common industry average.