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Forecast accuracy improvements at a fast moving consumer goods company : How to improve the Raws and Packs material requirements forecast to reduce procurement losses

Weierink, N (2017) Forecast accuracy improvements at a fast moving consumer goods company : How to improve the Raws and Packs material requirements forecast to reduce procurement losses.

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Abstract:The company produces many grocery items at several production facilities in Europe. These products are made in external factories and internal company factories. To produce the finished goods items, many raw and packaging materials are required. We executed this research to improve the forecast accuracy of the mid- and long term material requirements forecast. This means that we looked at a 3-12 months forecast horizon, on a European product number aggregation level, and with time buckets of one month. The research focusses on all the materials that are supplied to seven company owned factories in Europe. The company needs a high forecast accuracy as materials are bought on contract basis. When contracted volumes are higher than the actual requirements, it can result in forced buys, additional holding cost, and write-off costs. When contracted volumes are smaller than the actual requirements, buyers need to find additional amounts at the spot markets. As prices for specific ingredients fluctuate heavily, this results in additional cost or lost sales if additional volumes cannot be found. The current mid- and long term accuracy is 59.6% while the targeted accuracy is 70%. The problem statement that this report addresses is how to improve the raws and packs material requirements forecast accuracy of the forecast used at procurement. Based on different steps in the process from material requirements and lot-sizing rules the Vendor forecast is generated. By combining the Vendor forecast with the actual requirements the forecast accuracy can be calculated. We identified that the current forecast generating process is set up in a way that activities can be started before their predecessors are finished. Further, the analysis showed that the material resource planning system used in SAP is not a primary source of the low accuracy. The low accuracy seems to be driven by incomplete and incorrect information that is provided as input. To find the settings and parameters that are important in the process of generating the raws and packs material requirements forecast, we analyzed literature. In literature we found that there are components of the MRP system that can have a large impact on the forecast accuracy. We studied the demand forecasting process, Bill of Materials usage, freezing method, lot-sizing rules, lead times, safety stocks and planning horizon. The research has shown that there are different factors that drive the low forecast accuracy. The biggest driver is that the planning horizon of the finished good promotional demand forecast is shorter than the material requirements forecast horizon. This lead to a situation where only around 80% of the requirements were forecasted. Further, high minimal order quantities for the different ingredients and the impact of the current crop planning process are sources of the low forecast accuracy. We identified item specific errors with lead times and incremental order quantities. Further we redesigned the total process that results in the raws and packs material requirements forecast. A tradeoff between the costs and the forecast accuracy improvement percentage, of different solutions proposed, has been made. This resulted in a list of solutions that need to be implemented and solutions that will result in a higher accuracy but do not outweigh the investment, do not bring enough improvements, have negative side effect, or are not feasible to implement. Solutions are proposed to improve different parts of the process like: the SAP material N.Weierink 3 requirements generation process, the process to create the Vendor forecast for procurement, and solutions to decrease short term system nervousness and improve accuracy. Different solutions are proposed that can improve the forecast accuracy to 75.2% if they are implemented on top of each other.  Current forecast 59.8%  Adding the promotional forecast + 10% 69.8%  Changing the crop process + 2% 71.8%  New Product Development Process + 1.4% 73.2%  Tomato paste delivery performance + 0.8% 74.0%  Beans lead time + 0.6% 74.6%  Forecast generating process + 0.6% 75.2% At the end the main conclusions of the research are:  Settings and parameters that have a large impact on the raws and packs forecast accuracy are Bill of Materials, safety stocks, safety lead time/ planned lead time, Lot-sizing rules, planning horizon and the frozen period.  The forecast horizons of different processes are not aligned. All processes required to generate the forecast should have a horizon equal or longer than the final Raws and packs material requirements/Vendor forecast.  Crop production runs have a large impact on the production plan as they are crop driven, instead of demand driven.  The current process is sensitive for errors as new activities can start before their predecessor has finished  For several raw and packaging materials, parameters like the Minimal order quantity, incremental order quantity or lead time are not correctly entered in the system or need to be changed as they create Lumpy demand patterns which are harder to forecast.  The current forecast accuracy measurement has a backward looking focus. Instead of proactively identifying mismatches between contracted and expected requirements. To solve these problems we recommend the company to make the following improvements:  The promotional demand forecast planning horizon should be changed in a rolling horizon of at least 1 year.  The crop production planning process needs to be improved, as crop production batches need to be re-planned and tracked if harvests are delayed.  New product developments should trigger material requirements before production starts  The tomato paste transportation companies performance should be measured and tracked to push them to improve their delivery performance.  The lead time of beans is not correct in SAP and should be changed to reduce the phasing inaccuracy.  The process around the material requirements forecast generation needs to be changed to make sure activities are finished before the next activity starts N.Weierink 4  MOQs that cover more than 3 months of requirements should be reduced by renegotiation, as they are not allowed in the company policy and because they create lumpy demand patterns, which are harder to forecast  Further, some small parameter changes need to be made to improve the accuracy for a specific group of ingredients as the system is not forecasting based on the correct parameters. At the end of the research some recommendations are already implemented, the most important ones are the promotional forecast, forecast generating process, and lead time/rounding value changes. The MOQ reduction has been started and the tomato paste supplier is tracked. Based on the current status a forecast accuracy of 71.0% has been reached and the processes started can improve another 2.8%. The last 1.4% is related to the new product development process that needs a longer implementation time as many departments need to be involved and the company needs to decide if they want to reach 75.2%.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Industrial Engineering and Management MSc (60029)
Link to this item:http://purl.utwente.nl/essays/72254
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