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The shift of traditional mass producing industries towards mass customisation practices is nowadays evident. However, if not implemented properly, mass customisation can lead to disturbances in material flow and severe reduction in productivity. This paper discusses the design and development of a Cloud-based production planning and control system for discrete manufacturing environments, referred to as i-MRP. The proposed approach takes into consideration capacity constraints, lot sizing and priority control in a 'bucket-less' manufacturing environment. The i-MRP system offers simultaneous shop scheduling and material planning, where material and capacity constraints are considered together in a continuous time environment. A number of feasible alternative shop schedules and material plan combinations are formed and are evaluated on the Cloud platform where the i-MRP engine is hosted. The Cloud platform enables mobility, since it is device and location independent, as well as it minimises the cost of IT infrastructure ownership, which is especially important for SMEs. The performance of the i-MRP system has been studied in an SME from the textile sector, using real production data. The system demonstrates high performance in cases of short production times, high value inventory and frequent, small deliveries by suppliers. The i-MRP can be easily integrated with legacy IT systems as an interfaced functional module under the Software as a Service (SaaS) architecture.

This paper presents a hybrid backwards-scheduling method, referred to as HBS, which mainly addresses discrete manufacturing environments. It operates under the framework of hierarchical finite capacity shop-floor modelling and discrete event simulation. HBS applies a set of transformation relations in order to convert a finite capacity forwards scheduling method (FS) that can employ different assignment policies to their backward counterparts. These policies include both single criterion conventional dispatching rules, as well as an adjustable multiple-criteria decision making technique that can take into consideration a number of different conflicting criteria, such as flowtime, tardiness and manufacturing cost. Performance of the HBS method was studied through a set of simulation experiments in a typical textile industry and was evaluated through a number of relevant performance indicators.

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