Reducing the delivery lead time in a food distribution SME through the implementation of six sigma methodology

Purpose – Six sigma is a systematic data driven approach to reduce the defect and improve the quality in any type of business. The purpose of this paper is to present the findings from the application of six sigma in a food service “small to medium sized enterprise” (SME) in a lean environment to reduce the waste in this field. Design/methodology/approach – A simplified version of six sigma is adopted through the application of appropriate statistical tools in order to focus on customer's requirements to identify the defect, the cause of the defect and improve the delivery process by implementing the optimum solution. Findings – The result suggests that modification in layout utilization reduced the number of causes of defect by 40 percent resulting in jumping from 1.44 sigma level to 2.09 Sigma level which is substantial improvement in SME. Research limitations/implications – Simplicity of six sigma is important to enabling any SME to identify the problem and minimize its cause through a systematic approach. Practical implications – Integrating of supply chain objectives with any quality initiatives such as lean and six sigma has a substantial effect on achieving to the targets. Originality/value – This paper represents a potential area in which six sigma methodology along side the lean management can promote supply chain management objectives for a food distribution SME

How do top- and bottom-performing companies differ in using business analytics?

Purpose Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA. Design/methodology/approach Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies. Findings Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment. Practical implications Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities. Originality/value This study provides useful management insights into the effective use of BA for improving organizational performance

A review of data visualization: opportunities in manufacturing sequence management.

Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application

An integrated aerospace requirement setting and risk analysis tool for life cycle cost reduction and system design improvement

In the early conceptual stage of the service orientated model, decisions regarding the design of a new technical product are largely influenced by Service Requirements. Those decisions, therefore, have to merge both technical and business aspects to obtain desired product reliability and reduced Whole Life Cost (WLC). It is, therefore, critical at that phase to define the risk of potential noncompliance of Service Requirements in order to ensure the right design choices; as these decisions have a large impact on the overall product and service development. This paper presents outcome of research project to investigate different approaches used by companies to analyse Service Requirements to achieve reduced Life Cycle Cost (LCC). Analysis using Weibull distribution and Monte Carlo principle have been proposed here; based on the conducted literature review these are considered as the most widely used techniques in product reliability studies. Based on those techniques, a methodology and its software tool for risk evaluation of failure to deliver a new product against Service Requirements are presented in this paper. This is part of the on-going research project which, apart from analysing the gap between the current Service Requirements achievements and the design targets for a new aircraft engine, it also facilitates an optimisation of those requirements at the minimum risk of nonconformity

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed