Development of an Improved Demand Planning Process - A Case Study at KåKå

Abstract: Background: Operations planning and control has undergone an extensive shift from an individual company focus to a complete supply chain focus which has required a new common approach for supply chain planning and control to evolve. Increasing competition and globalization has created complexity in supply chain planning and integration since it requires a new cross-functional approach. To handle such complexity and difficulties, the cross-functional planning process Sales and Operations Planning (S&OP) can be used. However, to create a successful S&OP process the first step: demand planning that creates the work base for all S&OP activities, must be performed properly. To do so, forecasting activities must provide necessary input to the demand planning phase. This study aims to identify solutions to an improved demand planning phase in order to overcome the difficulties that supply chain planning and integration implies. Purpose: The purpose of this thesis is to improve the demand planning phase of KåKå’s S&OP process to create better conditions for more efficient operations. Research Questions: RQ1: How is the current demand planning process at KåKå designed and how does it perform? RQ2: How can bakery products be categorized, based on characteristics, to simplify the forecasting and demand planning process? RQ3: How can forecast methods be selected to fit different demand patterns? Methodology: A single case study with an abductive research approach, conducted by utilizing both quantitative and qualitative data. Findings: To summarize the findings of this thesis, the main identified issues of KåKå’s demand planning process are related to forecasting and product management. The use of one single forecast method for all products along with inadequate parameters, results in poor forecast accuracy. Also, the lack of clear processes, a large product assortment and product management being performed on an individual SKU level causes difficulties for both forecasting and demand planning. To face these issues, several potential solutions were identified in the analysis. Primarily, products can be categorized for forecasting based on their demand model to be able to allocate appropriate forecast method to each group, where the methods of simpler type resulted in the most robust and accurate forecasts. Secondarily, due to different grouping purposes, products can be categorized for demand planning by utilizing an ABC-XYZ analysis. The analysis should be based on two important demand planning characteristics in order to identify critical categories. Contribution: This thesis has been a complete elaboration between the two authors. Each author has been involved in every part of the process and contributed equally.