Short-term and stochastic variations can create negative effects on the delivery capability, and companies often adopt their processes accordingly. What is then ignored is the fact that causes for variations are often self-induced and, therefore, possible to affect. Proposed actions in current literature focus mainly on continuous improvement without explicitly emphasizing that certain behaviours, rules and routines have a direct impact on variations affecting the company. In this study, a framework of causes for self-induced variations is established with proposed actions for each cause to reduce the variations. The framework is situated relative to practice for the actions applied at six manufacturing companies. This study sought to increase the understanding of self-induced variations and where companies can apply the proposed framework for an overview of 22 causes and 60 appropriate actions to support the systematic reduction of self-induced variations that can be internally or externally generated. 

The view on safety buffers is fragmented in the current literature; some researchers argue that a safety buffer is only waste, while others see them as prerequisites to absorb variations and secure a competitive delivery capability. This study conceptualises various safety buffer types in terms of materials, capacity and lead time to mitigate the negative effects of short-term stochastic variations in supply and demand. The identified safety buffers are categorised based on a material flow perspective as inbound, process and outbound buffers. In total, seven safety buffer sub-types are identified and investigated in terms of their utilization in four manufacturing companies. The experiences from eleven respondents highlight the utilization purposes in their selection of safety buffers. The empirical investigation also indicates several concerns through four propositions that highlight the significance of decision support, providing a more holistic perspective on different types and sub-types of safety buffers and their application in practice. Finally, a conceptual framework is proposed to facilitate the selection of safety buffers in practice. 

Systematic and stochastic variations, both endogenous and exogenous to companies, are a constant challenge for decision makers struggling to maintain a competitive advantage for the business. In response the decision maker introduces buffers to absorb variations but this does not target the source of the problem. The first step should instead be to focus on how to reduce variations and then to handle the remnant variations. In summary the first step should be to perform variation management and then as the second step buffer management should be applied. The combination of these two subprocesses represent service performance management and within this context is buffer dimensioning a key challenge. Input data, decision maker and process logic are identified as three key aspects of buffer dimensioning which are integrated and resulting in six scenarios. These scenarios unravel different conditions for performing buffer dimensioning and facilitate an awareness of a match or mismatch between current and desired situation. 

Purpose

The purposes of this study were (1) to explore empirical challenges in dimensioning safety buffers and their implications and (2) to organise those challenges into a framework.

Design/methodology/approach

In a multiple-case study following an exploratory, qualitative and empirical approach, 20 semi-structured interviews were conducted in six cases. Representatives of all cases subsequently participated in an interactive workshop, after which a questionnaire was used to assess the impact and presence of each challenge. A cross-case analysis was performed to situate empirical findings within the literature.

Findings

Ten challenges were identified in four areas of dimensioning safety buffers: decision management, responsibilities, methods for dimensioning safety buffers and input data. All challenges had both direct and indirect negative implications for dimensioning safety buffers and were synthesised into a framework.

Research limitations/implications

This study complements the literature on dimensioning safety buffers with qualitative insights into challenges in dimensioning safety buffers and implications in practice.

Practical implications

Practitioners can use the framework to understand and overcome challenges in dimensioning safety buffers and their negative implications.

Originality/value

This study responds to the scarcity of qualitative and empirical studies on dimensioning safety buffers and the absence of any overview of the challenges therein.

The rapidly changing world in which companies operate within sets high requirements on being both responsive and cost efficient. At the same time safety buffers play an important role in covering the variations, that cannot be or are not reduced, to maintain a high delivery capability. The dimensioning of safety stock, safety capacity and safety lead time are often based on experience or intuition in practice, without a systematic way of working to evaluate if the safety buffers are of the right size. This research is building on the core of protecting the flow of relevant information and materials by visibility and clear signals as in Demand Driven Material Requirements Planning (DDMRP). This research investigates how colour-coded zones can be developed and utilised for safety buffers, that together with measuring the share of utilisation—how much of the safety buffer is used—can facilitate evaluation and decisions regarding the size of safety buffers in the dimensioning. How colour-coded zones could be used for safety stock, safety capacity and safety lead time in a similar manner as in DDMRP are proposed, and an example is used to illustrate how the measurements and decision making can be done based on empirical insights. This is sought to open possibilities for facilitated evaluation of safety buffers to better balance the costs associated with maintaining safety buffers with the actual safety they provide. Ultimately this can lead to positive effects such as reduced costs and increased responsiveness.