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Predicting the demand for products in different life cycles helps to avoid wastage associated with excessive costs. Currently, StarTech’s demand forecasting process does not differ throughout its lifecycle. I will argue that further guidance can help the company maintain sales growth without increasing investment in inventory.
First of all, demand forecasting should vary for the products in the different life cycles. It is known that demand increases less sharply at the maturity stage compared to demand at the growth stage. Such analysis allows more precisely to predict the required supply and reduce the costs associated with surplus goods’ manufacturing and storage.
The second recommendation touches upon anchor product relationships. It is suggested that besides taking into account the stages of the life cycle, the demand forecast should also consider that growing demand for one product might influence sales for supplementary.
Let me provide an example: the growing demand for portable batteries might lead to a higher need for specific cables. Simultaneously, the appearance of wireless batteries that charge devices via Bluetooth connection would lead to lower demand for traditional portable batteries and wires. Consequently, demand forecasting needs to be restructured to account for changes in demand for other products that require cables to use.
As we wrap up, for supply chain improvement, the Internet of Things and Machine Learning usage are highly recommended to StarTech. The technological solution allows for processing vast amounts of data and providing more accurate predictions.
In this essay, I wanted to introduce the recommendations that will help the company keep its sales growth. The advice to implement different demand forecasting processes for products in different life cycles, thus, seems to be an effective strategy that would reduce high transportation costs and investments in inventory.
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