【正文】 cess workshops167。 Interviewed 5 Executives of L’Or233。L’Or233。al Supply Chain DiagnosticMarch 18, 2023Presentation Agenda I. Project Scope and ActivitiesII. Overview of Our Supply Chain FindingsI. ProcessesII. Inventory ReviewIII. Maturity ProfilesIV. Organization/ People and Culture V. TechnologyIII. Supply Chain RoadmapIV. Discussion of Next Steps1Our Project ApproachOver the past three weeks, we have:167。al Canada167。 Collected and analyzed data167。 Compared L’Or233。 Identified key business issues and areas of improvement167。ry MayerSalesDominique De Celles Isabelle Genest AnneMarie NelsonDion GemmityCustomer ServiceEli BengioPatricia RaposoLogistics ProcurementVal233。Naity JacelInformation technologyPierre Journel Marcelyne CraftFinance Martin Desch234。 The forecast process is mostly manual, inconsistent across brands and divisions (due to different levels of maturity) as well as both time and resource consuming167。 No one department “owns the forecast”167。 Too much time is spent on manual processes, data cleanup and generating reports, leaving insufficient time to analyze data and manage exceptions167。al Canada’s brand forecasts are not all aggregated up into one forecast limiting visibility and data sharing 167。al Canada does not have simulation or “what if” capabilities167。al ’s requirements167。 Spend time forecasting products that give the highest return. Not all product lines benefit from increased attention. Focus on high margin, low volume, big ticket items, high profile items and items with plements to maximize profitability167。 Implementing a Forecasting tool will enable the automatic generation of a baseline forecast based on sales history and additional variables. Most packages allow users to select a statistical model, enable the system to determine the model with the greatest fit or employ a posite model 167。 Products should be reviewed to determine which ones are critical or high margin to plan in a Forecasting tool, while noncritical or stagnant products may be planned in SAP R/3167。 Coordinate data collection for all available sources and house it in a central repository such as SAP Business Warehouse6Demand Planning – Issues and OpportunitiesIssues OpportunityPeople167。 L’Or233。 Statistical proficiency and system expertise should be balanced with industry, pany and product knowledge 167。 Create incentives to improve the accuracy of forecasts:167。 Establish pensation structure167。 L’Or233。 While the generation of the forecast is a collaborative effort between Sales, Marketing, Finance and Logistics, the involved parties do not share the same view of the forecast’s importance nor appreciate the impact of their input (or lack thereof) on other departments’ ability to execute their functions downstream167。 Sales’ strategy to push products at the end of the month and year by decreasing prices, throws off the forecast167。al Canada realizes that even if they implement a forecasting tool, without the proper processes or resources in place, forecast accuracy will not likely improve 167。al Canada experiences a high turnover among sales and marketing people resulting in the loss of vital customer and product information167。 Compensation reevaluation has begun to align individual goals and metrics with anizational goals with respect to forecast accuracy167。al Canada has made great strides in improving their forecasting process, the accuracy has not improved significantly leading to feelings of frustration among employees7Demand Planning – Issues and OpportunitiesIssues OpportunityProduct Launches 167。al Canada has access to other countries launches as well as to like products’ sales history, there is no tool or process in place to effectively incorporate this data into their forecasts167。al Canada is unable to accurately assess the cannibalization effect of one product on another167。 There is currently no means to accurately cleanse promotional uplifts from sales history 167。 Promotions involving giftware can lead to imbalances in demand, making it difficult to predict true demand for these products167。 Product allocation has not been deployed effectively and has even led to undesirable forecasting behaviours Product Lifecycle Management167。 “Like Modeling” whereby sales history of a similar product is used to forecast a new product167。 The separation of the promotional uplift from baseline forecast generation167。 Sufficient time must be spent cleaning data such as SKU changes, data errors, duplication, onetime events, stockouts, promotions, outliers etc… to enable forecasting models to separate business growth from seasonality and other deviationsProduct Allocation167。al should develop and implement formal policies for allocating products (. based on customer forecasts) 167。 Incorporate customer forecasts which are deemed as reliable into baseline forecasts and challenge their numbers (manage by exception)167。al should provide a website to allow easy access for their Customers to enter their forecasts and to post L’Or233。 Determine the appropriate level to forecast . SKU/customer location for DSD, SKU/Customer DC for warehouse shipments…167。 Order date should be used as the point for establishing demand since it gives the most accurate information on when demand is initiated. Otherwise stockouts, lost sales, opportunity costs and lost customers may be masked167。 Insufficient inventory (stock outs)167。 Safety stock167。 Inventory carrying costs167。 Although some of L’Or233。al does not possess the infrastructure or processes to effectively use the data167。al Canada must first focus on improving it’s own processes before extending their processes and data out to share with Customers and SuppliersForecast Accuracy 167。 Only 40% to 50% of items achieve the target of 177。 SKU ranking has been erroneousImpacts of Forecast