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Batch sizing in the architectural coatings supply chain: an analysis of the economical feasibility of reducing batch sizes to save inventory

Oude Alink, Pim H. (2012) Batch sizing in the architectural coatings supply chain: an analysis of the economical feasibility of reducing batch sizes to save inventory.

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Abstract:PPG Industries Inc. is a global supplier of coatings (performance, industrial, and architectural coatings) and specialty products (optical materials and specialty materials, glass, and commodity chemicals). The scope of this thesis lies within the Architectural Coatings (AC) Europe, Middle East, and Africa (EMEA) Business Section of the company. More specifically, the central issue of the project lies within the Western European (WE) Supply Organization. AC EMEA WE produces and distributes a wide range of architectural coatings: approximately 40 brands, each comprising of a full range of colors and package volumes. Supplying numerous different unique Stock Keeping Units (SKUs) at a high service level requires a responsive, lean organization; yet to a certain extent, this demand could also be compensated by holding high finished goods inventory. Producing in a responsive manner requires smaller batches, hence high setup costs as well as capacity loss, whereas holding excess stock yields high inventory cost and lays a burden on working capital. The central matter of interest in this thesis is: “Is it economically feasible to reduce inventory in the AC EMEA supply chain by producing in smaller batch sizes?” This research shows that producing smaller batches in order to save inventory is an economically feasible line of action. The batch size optimization process in its full dynamics and complexity is rather cumbersome, yet a simpler model such as the one built for this research’s purposes already yields some useful decision support for determining the appropriate lot size for a specific product at a certain time. Supported by the model, we propose some specific alterations in parts of the 1100-SKU product range of the Uithoorn factory. Currently, a policy of large batches characterizes the production side of the Western European supply chain. Some capacity constraints may arise when altering the batch sizes and increasing setup frequency. PPG AC EMEA employees should bear this in mind, while simultaneously being watchful for opportunities to decrease setup times or (re-)engineer processes in such a way that it enhances the new, responsive production approach. Increasing setups may contradict many people’s intuition. From that perspective, it is not surprising that there is so much literature and discussion on this topic. Literature on batch sizing shows several ways to simplify the rather complex capacity constrained, multi-level, multi-product factory context. Assumptions need to be made on several aspects of the problem and its environment to find a feasible solution. The assumption on the nature of demand is rather momentous; a constantdemand assumption leads to the more intuitive EOQ-models, while deterministic and stochastic dynamic demand premises bring forth economic lot scheduling problems (ELSPs) and stochastic economic lot scheduling problems (SELSPs) respectively. The model constructed for this research combines the knowledge on batch size optimization with real data from the AC EMEA factories. An intuitive EOQ-like approach has been chosen, thereby keeping model complexity relatively low while slightly raising the intricacy of interpreting the model’s results. The model can be used to make the trade-off between increased setup cost and decreased inventory holding cost for any product produced in the VFU factory, yet it is also suitable for other factories if some parameters are adjusted and new data is included. Preferably, batch size trade-offs within the AC EMEA supply chain should be supported by up-to-date optimization calculations, which take into account relevant factors such as current inventories and demand patterns. When analyzing the desired situation as compared to the current supply process, the key discrepancy is the lack of precisely that quantitative decision support – there is no up to date data on the optimality of batch sizes. Therefore, SKUs are produced in sub-optimal quantities (that are generally larger than the optimum). Quite some discussion concerning the effects of batch size alterations is going on within AC EMEA. We have seen that there is no apparent universal rule that states whether small batches or large batches are optimal. On the contrary, each SKU has its own unique properties, leading to a different optimum. Proper decision support on optimal batch sizing will resolve this discord on making small or large batches. Furthermore, this will shift the focus of the discussion towards the way in which the optimal batch sizes should be calculated, ultimately yielding even better batch sizing decision models.
Item Type:Essay (Bachelor)
Clients:
PPG Industries
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Industrial Engineering and Management BSc (56994)
Link to this item:http://purl.utwente.nl/essays/61777
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