Erland Hejn Nielsen

Discussing feedback system thinking in relation to scenario evaluation in a balanced scorecard setup

Since the emergence of the Balanced Scorecard (BSC) at the beginning of the 1990s, literature has intensively discussed the problems of the cause-and-effect relationships, the time-delay elements between measures and perspectives and the concepts of feedback loops. This article focuses on the use of the System Dynamics Modelling approach to deal with these three problems, an approach first suggested by Forrester back in 1958. The wide scope of application of BSC as a decision-supporting instrument continues to spur the interest in BSC. We have used an actual case as inspiration on which our model construction and our simulation scenarios are based. Our findings suggest that the idea of linking BSC and System Dynamics together based on properties of the systems dynamic nature seem to be both a natural and a relevant development. Furthermore, our simulations demonstrate the ability to: (1) provide a scheme for the translation of the strategy into operational terms and at the same time (2) offer possibilities for the decision maker to be able to evaluate the plan–do–review results based on the feedbacks, specifically in a risk evaluating context.

System Dynamic Modelling for a Balanced Scorecard: With a Special Emphasis on Skills, Customer Base, and WIP

Purpose - The purpose of this paper is to analyse some dynamic consequences of the theoretical foundations of the balanced scorecard (BSC) with the aid of a simulation approach based on part of a real BSC. Design/methodology/approach - The model includes five perspectives and a number of financial and non-financial measures. All indicators are defined and related to a number of assumed cause-and-effect relationships. Time lags are included to stipulate one of the most important characteristic of BSC. We use the model to simulate different strategies or scenarios over time. Through three different scenarios we also demonstrate the effects of different variables on the profit or RoCE (Return on Capital Employed). Findings - The results show that minimal changes in the three variables: skills, customer base and work in process may influence profit in different directions. Research limitations/implications - Our analytical model is based on assumed associations hypothesized from literature as well as from our study of a case. In this type of model, the sensitivity of our results with respect to the assumptions should be analysed in subsequent studies. In addition, our model should be extended to cover a complete BSC of an actual company. Practical implications - The model may be used as the first step in putting numbers on an integrated BSC model. The model is our first attempt to study an analytical version of BSC and to make inferences concerning optimal or rational relationships between different indicators and perspectives. The credibility or benefit of the model is insight into the finality of a model but it can also be used as a teaching exercise of BSC. It is easy to extend our model to more realistic circumstances, by including more measures or change time lags. Using the assumed cause-and-effect relationships between financial and non-financial performance measures, attention should also be paid to the definitions and the number of indicators. Originality/value - A large number of case studies and surveys are now present in BSC literature. However, there is a lack of more theoretical and analytical modelling of the BSC. Our paper just throws a little light on this modelling approach.

The Balanced Scorecard and the Strategic Learning Process: A System Dynamics Modeling Approach

The main purpose of this paper is to improve on the conceptual as well as the methodological aspects of BSC as a quantitative model by combining elements from traditional balanced scorecard (BSC) thinking with the Systems Thinking. This is done by combining short and long term aspects of measurements. The result is then used to build and construct a balanced scorecard model for strategic learning with the specific aim to maintain satisfied customers and motivated employees. Strategic planning, operational execution, feedback, and learning are some of the most important key features of any performance measurement model. This paper aims to address not only the conceptual domain related to BSC, that is, learning and system dynamics causality and feedback, but also the methodological domain concept of precision solved by differential equations. Our results show how a potential move from a static strategic vision map to a linked and dynamic understanding may be not fully realistic but very useful for learning purposes. The new knowledge obtained from the learning feedbacks fertilizes both decision discussion and decision-making and what may be required in order to move to the next level of BSC and system dynamics integration.

How does variability in input load relate to the probability of critically delayed delivery in a simple multipart re-entrant flow-line problem?

Aspects of the computation of tail-probabilities by simulation in the context of a generic job/flow-shop model are discussed. The job/flow-shop model consists of structural elements such as bottlenecks, re-entrance as well as a mixture of these two fundamental types of production complexity and is operated as a multi-part production set-up. Standard simulation methodology relies heavily on the Central Limit Theorem (CLT), but as powerful as this statistical concept might be, it has its pitfalls. As is shown, it can be quite deceptive and consequently harmful. Given a certain production batch size, the discussion will focus on the estimation of the probability of critically delayed delivery beyond a specified threshold and try to establish a relation to certain parameters that can be linked to the degree of regularity of the arrival stream of parts to the job/flow-shop. This last aspect relates remotely to the Lean Thinking philosophy that praises the smooth and uninterrupted production flow as being beneficial to the overall operation of productive plants in general, and the findings will also be linked to this discussion.

A Note on the Simple Exponential Smooth Non-optimal Predictor, the Order-up-to Policy and How to Set a Proper Bullwhip Effect

The literature concerning the bullwhip effect is mostly focused on determining expressions for the theoretical bullwhip measure given specific theoretical system setups, whereas it must also be of interest to deal with the problem of how in fact to make a proper choice as to a sensible bullwhip level. Such a management approach to the bullwhip phenomenon has to be of quite some importance, as the bullwhip effect on the one side definitely is a system malfunction, but on the other also an effect the size of which common intuition tells us should be possible to control. The control is based on a decision as to what variation in demand should be locally absorbed and what variation should be passed on upstream. This paper will focus on design aspects of a bullwhip control policy in order to decide on sensible trade-offs between the bullwhip level and the local inventory variability.