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Before we venture into the answer demanded, one can observe from Exhibit 5 is that the quantity demanded is almost never equal to the quantity supplied. This is true in case of both the Bangalore Warehouse as well as Delhi ASO. From this observation, one can guess that the commitment level on part of the retailers is quite low (ordering higher and accepting lower units of mattresses, assuming the sales value for Delhi ASO implies the sales made to the retailers). Also, there is a lot of stock pushing on to the downstream members when the situation doesn’t warrant such an action.

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Sometimes, these anomalies are quite high and this doesn’t augur well for the integration of a supply chain. Another observation that can be made is that the truckloads that are arriving at each region aren’t in multiples of 160 or 320 as that would have optimized the transportation costs. When there isn’t much of a pattern in the dispatching (as compared to the indents), why not send dispatches in multiples of 160 or 320 whenever possible. As regards to the question here, there is one thing that doesn’t make sense. It can be seen that the Delhi ASO accounts for nearly 30% of Kurlon’s national sales and the figure of 10% is a little misleading and inappropriate.

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With respect to configuration X at both the locations, one can observe that the anomalies (quantity supplied minus quantity demanded) are quite high and sometimes too huge for any rational logic for their presence (even the fact that X is a facts moving configuration cannot in some cases explain the huge difference between the two figures). These anomalies in turn reflect onto the supply chain as either “lost sales” or/and “inventories beyond requirement” which makes the supply chain highly inefficient.

In addition, these anomalies seem to be much worse at the Delhi ASO as they are mostly, and on the aggregate level, negative which means lost sales for the company which doesn’t augur well for Kurlon’s image as well as its revenues. What makes the implication worse is that the anomalies (negative) were mostly caused not due to non-availability of stock but due to some other reason (can be verified by the Opening stock figures for the next week whenever the anomalies were negative).

After answering the second question, we can see that the random component is too high for the indents (in case of the Bangalore warehouse) which means that the demand is too uncertain to predict and it wouldn’t be worthwhile for trying to arrive at an accurate prediction of quantity demanded from the warehouse With respect to configuration Y, the comparatively low figures (with respect to X) are reflective upon the fact that Y is a slow moving configuration.

Typically, slow moving SKUs are the ones that sell in low volumes but accrue higher margins for the retailers. But, here too the saga of anomalies continues and in fact, the situation is worse than that In X. Out of 26 weeks, we find that 17 of the anomalies are negative which doesn’t augur well for the company as this means that the retailer is willing to let go of the higher margins which is possible only because the demand for Y (Kurlon) is decreasing and the retailer is finding much more difficult to sell it and doesn’t think that the higher margin is worth the sale effort.

Critique Order Processing and inventory levels at Delhi ASO and Bangalore Warehouse for Configuration X and Y. There seems to be a complete mismatch between the quantity intended and quantity dispatched. Thus, the forecasting system is to be improved. The company is carrying huge inventory because of improper forecasting.

At the CENTRAL WAREHOUSE IN BANGALORE  For CONFIGURATION X The average Inventory is 860 per week, While the Average Dispatches are 600 per week. Thus, on an average the company holds 1.43 times its sales. This results in huge carrying cost. Also, the intends as a percentage of dispatch vary upto 250%. One of the reasons of this could be that since the performance of the area sales officer is not measured by the anticipated orders, there is no incentive for him to forecast the demand in a better manner. The other reason could be that the dispatch department might be sending material even though it might not be required.

Similarly, for CONFIGURATION Y Configuration Y being a slow moving item is made to order, thus we assume a difference of 4 weeks between quantity intended and quantity dispatched. Here, again we see a large difference in quantity sought and dispatched. Whereas this should have been less given the fact that it is “made to order”.

At the DELHI AREA SALES OFFICE For CONFIGURATION X The average inventory is 736 per week, while the average sales are 218 per week. The inventory held is 3.376 times the sales, which is a whooping figure. The sale office at Delhi holds almost 85% of the total dispatches while it accounts for just 10% of its sales. Thus, the inefficiency seems to have crept in at this level of the supply chain. Also, during week 9 to 11, the intend is zero, hence no replishment of item, which resulted in stock out in week 12. The Delhi sales office does not seem to have any safety stock. But in general, it orders in such high quantity that it is keeping very high stocks. This is to curtail any possibility of stock out.

It is suggested that the sales office finds a minimum safety stock and also define the maximum inventory it will hold. Also, the company would do well to change the distribution pattern, i.e. the distribution centre should just act as a transit point and all the orders by the retailers should be directly send to the central warehouse. By doing so the company would give away with the policy of holding inventory at two places. A detailed study needs to be carried out for the benefits and drawbacks of the system.

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