Managing the impact of a complex product portfolio on outbound logistics operations

Abstract

The past few years have shown a significant increase in demand for diversity in the global beer market. HEINEKEN, the third largest beer brewer in the world, has therefore embraced a global strategy with a diverse product portfolio in beer and packaging types. This strategy towards diversity is directly affecting the supply chain of global supplier HEINEKEN Netherlands Supply (HNS). The organization has to cope with increasing demand and diversity for over 160 countries as their direct customers. The result of increased diversity is clearly seen in the number of produced Stock Keeping Units (SKU’s) at the Zoeterwoude brewery, which almost doubled from 2012 to 2014. The three main processes within the HNS supply chain are brewing, packaging and outbound logistics. The current supply chain planning is mainly focused on packaging, as this is the most expensive process. However, the growing volume and SKU diversity have resulted in additional challenges within outbound logistics operations. These challenges are perceived as complexity by HNS without identifying clear factors and drivers for this perception. In this research is therefore analysed how diversity influences the perceived complexity at the outbound logistics organization of HNS. The main research question is: What is the impact of an expanding product portfolio on perceived complexity in outbound logistics operations and how can the main drivers for this perception be managed in the future? Complexity in this research is defined as the uncertainty in processes as a result of increased diversity. Based on the commercial HEINEKEN strategy, the diversity in the portfolio for HNS is expected to increase in the future. The research goal is therefore not to challenge product diversity but to determine the key factors that drive uncertainty at HNS based on the diversity. Through extensive literature research and expert interviews at HNS, different types of perceived complexity in a supply chain have been defined. These types are structured in the framework for perceived complexity. Market and customer variety form the input of perceived complexity, factors that cannot be influenced by a production organization like HNS. A qualitative analysis based on the framework has revealed the main drivers for each type of perceived complexity. A quantitative case study analysis at HNS has strengthened the framework by analysing trends for the key factors that drive perceived complexity at the breweries in Den Bosch and Zoeterwoude. The results of both the qualitative and quantitative analysis are summarized below. Product complexity - Uncertainty related to the product portfolio. This is driven by the number of SKU’s that are produced by an organization and the volume related to these SKU’s. At HNS specifically, uncertainty is driven by the diversity in loading types requested by the market. The expanding portfolio has reduced insight in the logistical impact per SKU, increasing uncertainty of the expected volume flows in the warehouses at Zoeterwoude and Den Bosch. Technology complexity – Uncertainty related to available technology, both physical and information related. Physical technology is related to capacities of equipment and warehouse layout. As this information is clearly reported at HNS, no specific uncertainty is related to physical technology in this research. At HNS, uncertainty is mainly driven by missing information in the variety of IT systems, perceived as information technology complexity. A very important driver for perceived complexity is the absence of logistics parameters per SKU that determine the impact of a packaging plan on outbound logistics operations. Process complexity - Uncertainty in the logistics processes resulting from a mismatch between products and technology. HNS is faced with a simultaneous increase of SKU diversity and volume, leading to full warehouses and inefficient use of space. Reactive solutions are currently in place to cope with the high storage volume as no clear outlook of expected volumes is provided. Planning complexity - Uncertainty in logistics planning, driven by the absence of the most important logistics parameters per SKU. The current IT and planning structure at HNS limit the possibility to plan the expected stock and load volumes in the long term. From the case study analysis is concluded that information availability is the most important driver for perceived complexity at HNS. The Zoeterwoude and Den Bosch brewery both face an increase in storage volume of finished SKU’s, while the logistics parameters that determine storage demand are not measured. This limits the translation of the long-term packaging plan towards storage and load planning, thereby increasing uncertainty in expected volumes. It is also concluded that HNS can reduce the uncertainty driven by this missing information, as the logistics parameters are all covering internal processes that are controlled by HNS. The planning organization therefore has a large influence on the perceived complexity; measuring the logistics parameters correctly enables HNS to create more clarity in the long term by planning the expected demand for storage and loading. Managing perceived complexity at HNS is therefore focused on designing a long-term logistics planning model that is based on the packaging plan. A functional model is designed for the Zoeterwoude brewery that determines the impact of the main volume flows on outbound logistics processes. The identified flows are the packaging flow at the brewery and co-fill, re-pack and inter-brewery flows that come into the warehouse from other facilities. The main parameters that determine the logistical impact in the model are the following: - Loading type; determines the type of warehouse handlings and demand for storage - Cross-dock percentage; determines how much of the volume can be loaded directly - Storage time: determines how long a certain SKU has to be stored in the warehouse By coupling the 78-week packaging planning with the logistical parameters per SKU, the logistical impact can be calculated for the same time horizon. The model output is a clear overview of expected stock levels and load volumes per loading type in pallets. During model validation can be concluded that the model can predict the expected storage and load volumes with a bias of ±20%. Conclusions based on the model output can be made when using the packaging plan from May 2015 until December 2016 as input. Based on this plan is foreseen that mainly storage demand for Export will often exceed the storage capacity, thereby indicating the need for external storage. This is mainly caused by the large Conventional Truck volume that cannot be loaded directly with the cross-dock lanes. It is already known within the organization of HNS that stock volume for this specific loading type is a bottleneck but this has never been quantified and presented next to the demanded stock volume for other loading types. The logistics model now provides a long-term outlook of the expected volume on stock, which clearly shows the impact that Conventional Truck loading has on the total Export stock volume. Proactive solutions can now be found for the expected capacity shortage. Different packaging scenarios can be run to evaluate the impact on outbound logistics. Several successful experiments have already been completed with the model, illustrating the added value of the model for HNS. A clear implementation plan is also part of the research, ensuring the sustainability of the model for future use. Based on the research, model design and outputs several recommendations can be made to manage and reduce the perceived complexity at HEINEKEN Netherlands Supply. This research has pointed out that the perception of complexity in outbound logistics is mainly driven by a lack of information and planning. It is therefore highly recommended to place logistics planning next to packaging planning in the organization. The developed model provides a good starting point for strategic planning, proven by its application at Zoeterwoude. First experiments have already been completed to tailor the model to Den Bosch and it is recommended to continue this. Besides implementing the model, it is the author’s strong belief that the lack of interchange ability between the different IT systems limits the possibility to plan ahead, mainly in the outbound logistics processes. It is therefore recommended to set up a project that clearly defines what information different stakeholders need to successfully plan and manage their operations. Besides that, it is very important to secure the right information in a more centralized planning system. This will significantly increase alignment and visibility of information across the HNS supply chain. A final remark can be made to the complexity framework. From this research can be concluded that the framework successfully supports the analysis of perceived complexity in an organization, proven by its applicability to HNS. However, given the time constraints attached to this research, it is not assumed that the framework is complete. Further research should point out if the relationships sketched in the framework are also found at other companies, increasing the robustness of the complexity framework.