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1),(0,f.jsxs)("div",{className:l.compDetails,children:[(0,f.jsx)("div",{className:l.compPublisher,children:e.publisherData.name}),(0,f.jsx)("div",{className:l.compBookTitle,children:(0,f.jsx)("button",{onClick:()=>{a&&a(e._id)},className:l.compButton,children:e.title})}),(0,f.jsx)("div",{className:l.compAuthors,children:e.authors.join(", ")})]})]},e._id))})]})]})]})}),(0,f.jsx)("hr",{}),(0,f.jsxs)("div",{className:"gridLayout gridColumns2 gridGapLarge",children:[(0,f.jsx)("div",{className:"asterismCard",children:(0,f.jsx)(p.InventoryHistory,{id:t._id,maxDisplay:25})}),(0,f.jsxs)("div",{className:"asterismCard",children:[(0,f.jsx)("h2",{children:"Methodology"}),(0,f.jsx)("h3",{children:"Forecasting"}),(0,f.jsx)("p",{children:"We use a combination of historical sales data, inventory levels, and seasonal market trends to forecast demand for each product. When we don't have a lot of data, we do a rough estimate based on the last 90 days of sales. 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This approach allows us to leverage the strengths of each method and provide a more accurate forecast by weighting the predictions based on their historical performance."}),(0,f.jsx)("p",{children:"We then apply a buffer to the forecast to account for potential spikes in demand, which is calculated as a percentage of the forecasted demand. This buffer helps us to ensure that we have enough inventory to meet unexpected increases in sales."}),(0,f.jsx)("p",{children:"Finally, we round the order quantity to the nearest multiple of 5 for easy ordering, and we adjust the order quantity based on current inventory levels and any open backorders for the product."}),(0,f.jsx)("h4",{children:"What To Do When There's Not Much Data"}),(0,f.jsx)("p",{children:"When we don't have enough data to generate a reliable forecast, we use a simple average of the last 90 days of sales data, adjusting for seasonality. This helps us to provide a rough estimate of future demand based on the most recent sales trends."}),(0,f.jsx)("p",{children:"If we have absolutely no data, the best approach is to use a conservative estimate based on similar products or categories. Start by looking at other titles by the same author, or the most recent titles by the same publisher."}),(0,f.jsx)("h3",{children:"Other Factors"}),(0,f.jsx)("p",{children:"In addition to historical sales data and inventory levels, we consider several other factors to help us predict ordering quantities."}),(0,f.jsx)("h4",{children:"Seasonality"}),(0,f.jsx)("p",{children:"With our steady sales history being limited, we use industry-wide trends based on publicly available data to add a seasonality component to our forecasts. 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This buffer is calculated as a percentage of the forecasted demand, and it helps us to ensure that we have enough inventory to meet unexpected increases in sales."}),(0,f.jsx)("p",{children:"For products before their publication date, we use a 50% buffer to account for early demand. For products within two months of their publication date, we use a 20% buffer. For all other products, we use a 10% buffer."})]})]})]})]})}

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