How Can We Tackle Volatile Demand in the Palmolive Oil Market? Seeking Your Insights!

Dear Friends,

Recently, a client approached me to streamline the demand for their product. The brief about the client is given below.

The client purchases imported Palmolive oil in crude form. They refine it and sell it to traders, who then sell the refined oil to end-users such as retail customers, shopkeepers, caterers, and hoteliers. The issue the client faces is that their customers, the traders, do not have uniformity in their demand patterns. Sometimes, all of them place their orders at once, leading to potential sales loss. Conversely, there are times when no orders are placed, leaving the capacity idle.

To avoid the cost of stock-outs, the client is unable to store an inventory of refined oil. The price of Palmolive oil at the commodity exchange fluctuates. Purchasing the oil at a higher price poses the risk of losses if prices fall later. Conversely, buying at a lower price and selling at a higher price could result in increased profits. However, the client is risk-averse and prefers not to deal with fluctuations in oil prices.

Additional restrictions stem from the total lead time. The total import cycle includes the time required for import, processing, and delivery to the client.

Apart from traditional mathematical tools like moving averages, weighted moving averages, and regression analysis, could you suggest other solutions?

Thanks,

Dinesh Divekar

Dear Dinesh,

There is not enough information about how long the firm has been in business and whether it has any records, etc. I came across this HBR article on the web http://prof.usb.ve/nbaquero/03%20-%2...%20-%20HBR.PDF. Kindly see if it's of any use.

To make your demand forecasting accurate, one has to study the factors on which the customers' demand depends. Once you identify those factors, you need to determine to what extent each factor influences the customer's demand. The accuracy of demand enhances when you accurately predict the percentage of influencing factors. To do this, one has to master regression analysis with multiple variables. However, it is much easier said than done.

Thanks for sharing the article from HBR. Though it is more than 21 years old and a lot of changes have happened since then, its utility does not diminish even now.

Thanks,

Dinesh Divekar

Dear Dinesh,

You are right. Though some of the techniques are very old, their utility is still valid. For example, I learned queuing theory 40 years ago, and the same, I was teaching a few years ago. I had not checked the date of publication of the article. Thanks for the information.

This is the typical scenario for all retail products, especially for those that depend predominantly on imports. More inputs would be beneficial, such as the geographic area(s) covered by your client, whether they have a distributor network, and the typical sales volumes per year.

Please note that factors like commodity exchange fluctuations and import lead times are all part of the game in this sector. Solutions need to be devised to address these issues; that's the only way forward in this sector.

However, one way to tackle the lack of demand planning or forecasting from their customers is by having a reasonably good distributor network. This network would work hand-in-hand with traders within their geographic areas, much closer than your client can realistically achieve.

Most companies in similar lines of business adopt this model because demand varies from location to location, except during important festivals or occasions where demand can be comfortably predicted.

A distributor network also helps distribute buffer stocks across many entities rather than just one, such as your client. This spreads out stocking costs and leverages stocks across multiple locations from one stockist to another without the fear of losing business.

With specific issues related to the Palmolein oil sector, your client also needs to keep track of the offtake of other oils and, during festivals, the demand prospects of ghee—basically competitive oils.

Regards,
TS