Forecasting demand is a crucial issue for driving\ud efficient operations management plans. This is especially\ud the case in the fashion industry, where demand\ud uncertainty, lack of historical data and seasonal trends\ud usually coexist. Many approaches to this issue have been\ud proposed in the literature over the past few decades. In\ud this paper, forecasting methods are compared with the\ud aim of linking approaches to the market features

The aim of this work is to propose a solution approach for a capacitated lot sizing and scheduling real problem with parallel machines and shared buffers, arising in a packaging company producing yoghurt. The problem has been formulated as a hybrid Continuous Set-up and Capacitated Lot Sizing Problem (CSLP-CLSP). A new effective two stage optimisation heuristic based on the decomposition of the problem into a lot sizing problem and a scheduling problem has been developed. An assignment of mixture to buffers is made in the first stage, and therefore the corresponding orders are scheduled on the production lines by performing a local search. Computational tests have been performed on the real data provided by the company. The heuristic exhibits near-optimal solutions, all obtained in a very short computational time.

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This paper aims to demonstrate the positive effect of a Lean Management (LM) approach to increasing efficiency, even in a company which is subject to critical market issues. The pharmaceutical industry is a well-known example of a crisis-affected context and companies have directed attention to Lean Management for a long time, but they are stable in increasing effectiveness. This research uses a case study to move the attention to efficiency as an attractive LM goal

"If you can not measure it, you can not improve it."(Lord Kelvin) It is a common opinion that productivity improvement is nowadays the biggest challenge for companies in order to remain competitive in a global market [1, 2]. A well-known way of measuring the effectiveness is the Overall Equipment Efficiency (OEE) index. It has been firstly developed by the Japan Institute for Plant Maintenance (JIPM) and it is widely used in many industries. Moreover it is the backbone of methodologies for quality improvement as TQM and Lean Production. The strength of the OEE index is in making losses more transparent and in highlighting areas of improvement. OEE is often seen as a catalyst for change and it is easy to understand as a lot of articles and discussion have been generated about this topic over the last years. The aim of this chapter is to answer to general questions as what to measure? how to measure? and how to use the measurements? in order to optimize the factory performance. The goal is to show as OEE is a good base for optimizing the factory performance. Moreover OEE’s evolutions are the perfect response even in advanced frameworks. This chapter begins with an explanation of the difference between efficiency, effectiveness and productivity as well as with a formal definition for the components of effectiveness. Mathematical formulas for calculating OEE are provided too. After the introduction to the fundamental of OEE, some interesting issues concerning the way to implement the index are investigated. Starting with the question that in calculating OEE you have to take into consideration machines as operating in a linked and complex environment. So we analyze almost a model for the OEE calculation that lets a wider approach to the performance of the whole factory. The second issue concerns with monitoring the factory performance through OEE. It implies that information for decision-making have to be guaranteed real-time. It is possible only through automated systems for calculating OEE and through the capability to collect a large amount of data. So we propose an examination of the main automated OEE systems from the simplest to high-level systems integrated into ERP software. Even data collection strategies are screened for rigorous measurement of OEE. The last issue deals with how OEE has evolved into tools like TEEP, PEE, OFE, OPE and OAE in order to fit with different requirements. At the end of the chapter, industrial examples of OEE application are presented and the results are discussed