Northco Case Analysis Essay Sample
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Northco Case Analysis Essay Sample
Northco case describes the issues faced by an industry with high demand variability and a strong seasonality trend. The main issue highlighted in the case is building up left over inventory after the sales season is over. This worrying trend has been growing for past 3 years, in spite of being managed by experienced team, having a strong history of improving seasonal cash flows of all the acquired companies in the past.
Northco was the first acquisition in school uniform industry for OCI and was posing slightly different challenges and the operational efficiency hadn’t improved over past 4 years. Even though the business is seasonal and there is a possibility of the left over stock being used in the next season, left of inventory holding is very serious issue for Northco’s existence. This can be further explained by the fact that left inventory is exposed to compounded effect of high cost of working capital which is close to 11% and high uncertainty of demand, meaning the possibility of the left over stock getting sold in next season is very uncertain. This makes the leftover inventory holding a very expensive affair.
Below is a list of possible reasons highlighted in the case, which in various measures contribute to the building up of inventory.
High Forecast error
fluctuating demand ,
schhols changing fabric &
design on short notice
Lack of systems to track
w/h inventory in real time
Basic products also available at
only 25% confirmed
customer orders by
High product variety 12000
SKU with over 5000 SKU for
School Uniform subject to
Michael’s high inventory level issues cannot be tackled by just implementing an information system. The solution would probably be possible if multiple nodes of the whole process are improved together.
Forecasting error: As per the understanding of the case the forecasting error is a problem faced by entire school uniform industry and is not specific to Northco. Forecast could be improved if following can be implemented.
a) The level of aggregation in SKU’s could be increased. Some form of standardization could be introduced in manufacturing process, as to have more of base products and less of customization. The fact that forecast accuracy is very high for fabrics could help this justification
b) The period for fitting trials at schools should be increased if possible from March to April to January to April. A discount scheme for parents confirming orders in Jan, Feb and slightly lesser discount for parents confirming orders in March and April would improve order intake and also provide confirmed data for forecasting. As is indicated in the case that forecast improved considerably after fitting season, using an extended fitting season with some discounts will definitely help in forecasting and also improve cash flows. The cash flow would improve due to reduction in capital cost as more funds will be generated by extending the trial period and a bigger part of the loan could be paid off or the loan amount could be reduced.
Supplier Relationship: The other Area which could assist in reducing inventories is the supplier relationship. At present holding raw material supplies of up to 5 seasons by Northco has to change. It has to be clearly evaluated the benefit order discount with the cost of inventory holding. Even if Northco has to pay a higher price it should refrain from holding such high inventories of raw material and go for only optimal order quantity, keeping the forecasted demand as reference.
Manufacturing: There is insufficient data to analyze if a bigger plant in the vicinity of skilled labor source would be feasible or not, but if all the urgent and low quantities, which have to be produced during peak season can be outsourced and the production at Maine factory is only used for optimal batch size, it would lead to considerable reduction in overtime and setup costs.
Information System: Further investment into warehouse management systems, where critical information about the real time inventory during peak seasons or at any time of the cycle, is integrated with demand planning would definitely make the forecasting more responsive and fact based. Effective use of this integration can lead to significant reduction in variations between supply and demand.
Conclusion: The conclusion is that Michael should first focus on low investment initiatives like standardizing the base product, increasing fitting periods, negotiating with suppliers for optimal inventory, outsourcing low batch sizes production etc. to help improve the forecasting error. In long term capital intensive initiatives like putting up a mega plant in vicinity of skilled labors and warehouse management systems. The gains by implementing short initiatives would further provide insight for long term action plan. Michael should refrain from buying another plant.