Hajnalka Vaagen

A Multidisciplinary Framework for Robust Planning and Decision-Making in Dynamically Changing Engineering Construction Projects

This paper proposes a multidisciplinary framework for robust planning and decision-making in dynamically changing engineering construction projects. The aim is to facilitate ’optimal’ levels and ’trade-offs’ between the major factors affecting decision-making throughout the project phases, to manage design changes and other disturbances, and to generate the maximum possible value. Offshore shipbuilding case analysis is applied to refine the model and to illustrate its value in decision-making.

The value of numerical models in quick response assortment planning

In agile supply chains, dependencies in demand for products (in particular correlations) as well as substitution among products, vary substantially, and, due to uncertainty in market acceptance, a substantial share of the portfolio item demands follow bimodal distributions. Typically, advanced heuristics and major simplifying assumptions on these dependencies are needed to reduce the complexity to an appropriate level for analytical solutions of models. By applying a single-period stochastic model to the multi-item substitutable newsvendor problem, we demonstrate that simplifying assumptions on distributions and dependencies can lead to rather poor solutions, and as a consequence, numerical models – despite their obvious inability to produce general data-independent results – have an important role to play in assortment planning. By using a brand name sportswear assortment problem, we show that even when technology and supply chain flexibility allows for continuous information and production updates, the underlying distributional and dependency assumptions used in the planning models are crucial. We note, though, that the value of substitution is high and compensates, to some extent for the lack of information. We have found that the expected profit can drop by as much as 30% when simplifications are applied.

A Mockup Stochastic Program to Study the Impact of Design Uncertainty on ETO Shipbuilding Planning

A major driver of planning complexity in dynamically changing ETO shipbuilding is design uncertainty far into the design planning and production processes. This leads to uncertainty in task and project completion time, and complex dependencies and correlations driven by the uncertainty in multiple task parameters. The problem is difficult to be solved exactly, and decision-making is largely based on experience and gut feeling, with subsequent behavioral challenges. We build a mockup stochastic program to draw attention to- and analyze the complexity of formulating and solving the engineering design planning problem. We demonstrate how design uncertainty is affecting the planning complexity and solutions.

The impact of design uncertainty in engineer-to-order project planning

A major driver of planning complexity in engineer-to-order (ETO) projects is design uncertainty far into the engineering and production processes. This leads to uncertainty in technical information and will typically lead to a revision of parts of the project network itself. Hence, this uncertainty is different from standard task completion uncertainty. We build a stochastic program to draw attention to, and analyse, the engineering-design planning problem, and in particular, to understand what role design flexibility plays in hedging against such uncertainty. The purpose is not to devise a general stochastic dynamic model to be used in practice, but to demonstrate by the use of small model instances how design flexibility actually adds value to a project and what, exactly, it is that produces this value. This will help us understand better where and when to develop flexibility and buffers, even when not actually solving stochastic models.

The Value of Information in Quick Response Supply Chains: An Assortment Planning View

Many see timely accurate information availability as the key of successful customer-behaviour-reactive, wait-and-see planning in agile environments. Because of the extensive use of lean retailing, these quick response (QR) strategies require substantially reduced lead times across the supply chain, with the competitive advantage of enabling constant new product supply. However, successful market attempts are rapidly copied by the competition, leading to an accelerating spiral of variety-pricing games, with the inevitable result of often trivial product differentiation, reduced quality and pressure on prices. The environmental, psychological and operational effects of this spiral are severe. This work discusses the operational aspects from an assortment planning point of view, referring to the products to be included in the portfolio, as well as their inventory levels. The aim is to shift focus from timely information availability across the supply chain and wait-and-see planning, to the actual information needed to make substantial and potentially important changes, and to information available at the time when important decisions are to be taken. A proposed decision support framework - with corresponding tool set of estimation and optimization methods - helps evaluating this, by measuring the value and risk of different assortment strategies, and decisions taken at different information levels. The estimation and optimization models presented here are consistent in their use of subjective knowledge and in emphasizing the importance of attribute-based assortment planning in contemporary QR supply chains.