Dear friends,
Recently, a client approached me for streamlining demand for their product. The brief about the client is given below.
Client buys imported Palmolive oil. However, it is in crude form. Client refines it and sells it to the traders. Traders sell the refined oil to the end users who are retail customers, shopkeepers, caterers, hoteliers etc. The problems with the client is that his customers i.e. traders do not have any uniformity in their demand pattern. Sometimes all of them place their demand occasionally he loses his sale. On the contrary, sometimes nobody places demand and capacity remains idle.
To avoid cost of stock-out, he is unable to store the inventory of refined oil. The price of the Palmolive oil at commodity exchange is fluctuating. Should the oil is bought at higher price and later if the prices fall then there is risk of incurring losses. Corollary of this is that oil could be bought at lower price and it can be sold at higher price also. In such case, sale could give higher profits. However, client is risk averse and does not want to deal with the fluctuations in the oil prices.
The additional restrictions are because of total lead time. Total import cycle consist of time required for import, time for process and time for delivery to the client.
In addition to the use of traditional mathematical tools like moving average, weighted moving average, regression analysis, can you suggest some other solution?
Thanks,
Dinesh Divekar
Recently, a client approached me for streamlining demand for their product. The brief about the client is given below.
Client buys imported Palmolive oil. However, it is in crude form. Client refines it and sells it to the traders. Traders sell the refined oil to the end users who are retail customers, shopkeepers, caterers, hoteliers etc. The problems with the client is that his customers i.e. traders do not have any uniformity in their demand pattern. Sometimes all of them place their demand occasionally he loses his sale. On the contrary, sometimes nobody places demand and capacity remains idle.
To avoid cost of stock-out, he is unable to store the inventory of refined oil. The price of the Palmolive oil at commodity exchange is fluctuating. Should the oil is bought at higher price and later if the prices fall then there is risk of incurring losses. Corollary of this is that oil could be bought at lower price and it can be sold at higher price also. In such case, sale could give higher profits. However, client is risk averse and does not want to deal with the fluctuations in the oil prices.
The additional restrictions are because of total lead time. Total import cycle consist of time required for import, time for process and time for delivery to the client.
In addition to the use of traditional mathematical tools like moving average, weighted moving average, regression analysis, can you suggest some other solution?
Thanks,
Dinesh Divekar