Quantitative Management Practice - International Textile Company - Case Study Example

TABLE OF CONTENTS PROBLEM STATEMENT 1 Objective 2 Shipping Costs 2 Fabric Demand 3 Capacity Constrains 4 Overtime Production Cost 4 PROBLEM FORMULATION 5 Objective function 5 Decision Variables 6 Constraints 6 SOLUTIONS 6 Cotton 6 Polyester 7 Silk 7 Deviations 8 Is strategic plan to produce polyester in Nigerian Mill viable? 8 Impact of changes in demand 9 In case cotton customer makes purchase 10 In case the polyester customer cuts off 11 Overtime production analysis 12 Silk 12 Cotton 13 Silk 13 Cost Cutting Suggestions 15 Conclusion 16 INTRODUCTION International Textile Company (ITC) Ltd is a Hong Kong-based firm that does textile business. The company distributes its products worldwide which contain two or more fabrics (cotton, polyester and or silk). The international trade has a lot of logistics regarding transportation of raw materials and finished products. Therefore, it is imperative for the management to develop a transportation model that optimises the cost, satisfaction and revenue. The current transportation cost accounts for less than 10% of total expense which is relatively low compared to other cost centres and thus negligible. Therefore, the transportation sector has experienced a lot of inefficiencies which drains millions out of the company’s revenue. For instance, the company has been transporting fabric products from Mexico to Nigeria instead of sourcing them from Venezuela which is relatively short and thus reducing transportation cost. The report provides optimal transportation model for the company to reduce transportation cost and timely delivery by matching the demand and supply requirements. The strategy will save the company on transportation cost and thus improve its profitability. The industry is facing huge competition and thus efficiency improvement would offer a competitive edge for ITC Ltd. PROBLEM STATEMENT The company is experiencing two major problems in its current transpiration model and thus requires a solution that improves plant efficiency and cut on cost. Therefore, the concerns revolve within mismatch between the production capacity and demand (off and peak seasons). Also, high transportation cost of fabric to distributors and raw material to plants due to long distance i.e. Venezuela to get supply from Nigerian plant instead of Mexico which is much far compared to Venezuela. Objective The company seeks to reduce the transportation cost to its plants by reducing the distance between plants, raw material and fabric distributors i.e. Manila to source its textile from Honk Kong. Also, the matching of production capacity and demand is imperative to ensure that the operation cost is minimised and reduce shortages. Therefore, the company requires a scheduling model to conform above objective to save on cost and improve its efficiency. Shipping Costs The company statistic shows that the transportation cost accounts to 10% of the expenses and thus draws little concern from management. Therefore, the current shipping model requires revamping to remove inefficient routes and introduce new routes. The pie chart above ranks the current shipping cost from the mill to distributors. It is evident that Honk Kong and Tokyo contributes a considerable proportion of cost (24%) while New York ranks last with 4% of the total cost. The high transportation cost might signify inefficiency or high fabric demand and thus adopting new scheduling model would provide a clue in determining probable contributor to high cost. Fabric Demand The company supplies cotton, polyester and silk fabrics to eight distribution centres where it is then sold globally. The company has a responsibility in ensuring that the demand for fabric in its distribution centres is satisfied by reducing shortage cost. Additionally, mills are not required to supply excess fabric since it increases storage related costs. Therefore, it is imperative to know what the market requires to avoid ordering excess or less fabric. The bar graph below shows the fabric demand during March period; The graph shows that polyester has high demand in all distribution centers with London being the highest. The silk demand is considerably low but relatively high in Tokyo. The cotton fabric demand comes second except Rome and Tokyo which ranks last. Capacity Constrains The production capacity depends on various factors which are not limited to demand for the output. These factors include human resource, capital (physical facilities) and raw materials used in making the fabric. The production below the capacity leads to high fixed cost while overstretching capacity leads to substandard products, machine breakdown and high employee turnover or strikes. The graph below shows production capacity of the mills in bolts of each fabric; The mills are concentrating on producing polyester due to high demand in the market except Nigeria which process only cotton. The Korean and Hong Kong are the only mills that have the capacity to produce the three fabrics. Overtime Production Cost The company can service demand requirement that exceeds the normal production capacity. The overtime production requires machines and human capital to operate more than they are required and thus extra work should come at a cost. The following graph shows the overtime production per bolt of fabric for the five mills. PROBLEM FORMULATION The problem formulation is crucial in determining the decision variables, objectives function and constraints during the creation of a more efficient transportation scheduling process for the ICT Ltd. The stage involves conceptulisation of the theoretical problem into a mathematically solvable problem. Objective function The ICT Ltd seeks to minimise the transportation cost for cotton, silk and polyester in the month of March by reducing the distance of the shipping route. Therefore, the objective function seeks to minimise the shipping or transportation cost (Dantzig, 2016). Decision Variables The objective function is to minimise the transportation cost subject to the specified constraints on the decision variables. Therefore, decision variables in ICT objective function are bolts of fabric that are required to be shipped at a minimum cost (Dantzig, 2016). Constraints As noted above, constrain act as a limiting factor and makes the objective function relevant. The constraints are numerical tied to the decision variables. The constrains limits the production volumes and shipping of each fabric to distribution centres (Kuhn, 2014). SOLUTIONS The quality of the optimisation problem depends on the problem formulation stage. Therefore, imperative to consider that the problem is well defined before solving the objective function. Taking that into consideration, the following shows the analysis of the transportation cost minimisation each fabric as per the five mills. Cotton The optimal shipment cost for cotton is $15,400 and the demand is below the total capacity of the mills by 30%. Hong Kong and Nigeria mills are having low demand compared to its ability to produce cotton bolts. Polyester The optimal shipment cost for polyester is $43,000 and demand is more than production capacity by 4.34%. Therefore, mills would opt to consider overtime production to satisfy the demand and maximise profits. The Rome distribution centre required extra 500bolts of polyester which was not shipped. Silk The optimal shipment cost for silk is $5,000 and the demand is more than production capacity of 1,500bolts. The mills should go for overtime to satisfy the demand of 450bolts for London, Mexico City and Rome distribution centres. The management of transportation regarding units and routes enables the company to determine the centres that require more or fewer bolts and the nearest mill. Therefore, reducing the cost associated with storage, transportation and shortages. Deviations Is strategic plan to produce polyester in Nigerian Mill viable? The decision to produce or not depends on the current capability and cost of enabling the mill to produce the polyester. Also, the management should consider the cost of producing the polyester and market demand. The costly production and lack of market demand would result in losses on invested capital. The Nigerian Mill can produce polyester fabrics in March at a one-time cost of $2,000 and thus important to determine its cost-effectiveness. The table below breaks down the impact of Nigerian mill producing polyester in March; The production of polyester fabric in Nigeria Mill improved the production capacity and satisfied the 500bolt demand that was short. Additionally, the company has an extra production capacity of 500bolts in the case of unexpected demand arise. The table below shows cost comparisons showing when Nigeria is milling polyester and without; Polyester Introduction Milling without Polyester Shipping cost $ 45,500.00 $ 43,000.00 One-time $ 2,000.00 $ - Total cost $ 47,500.00 $ 43,000.00 Units Shipped 12,000 11500 Cost per Bolt $ 3.96 $ 3.74 Cost change (%) 5.9% The milling of polyester resulted in increasing in shipped bolt by 500units and thus satisfying the shortfall demand. The production increased shipping cost by $4,500 or 5.9% as shown above. The increased in cost would reduce in the next month since the increase is associated with a one-time expense of $2,000. The ability to meet the previously unsatisfied demand of 500bolts improves customer loyalty since the company can meet their demand needs. Also, the milling provides additional capacity in case of breakdown of one of the mills or unexpected demand in the market. Therefore, Nigerian mill should produce polyester since the company will benefit more compared to the one-time cost incurred. Impact of changes in demand The company anticipation of changes in demand for polyester and cotton in the month of March requires close monitoring to avoid under or over production of fabric. The management anticipates that Manila and Mexico would get a new customer and thus increase in demand for cotton by 10%. Also, New York would lose one customer whose impact causes a reduction in polyester demand by 10%. The table below shows a simplified analysis of the impact caused by optimal schedule plan and shipping cost; In case cotton customer makes purchase The incoming cotton customer will improve the demand for cotton fabric since the mills were operating at 70% capacity. The new production capacity stands at 72.4%. The increase in production will then reduce other associated production cost such as fixed expenses but increase shipping cost. The breakdown of the shipping cost is as shown in the table below; New Cotton Customer Original Demand Shipping cost $ 16,010.00 $ 15,400.00 Units Shipped 5,070 4,900 Cost per Bolt $ 3.16 $ 3.14 Cost change (%) 0.4% It is evident that the shipping cost increased by 0.4% which is relatively low compared to non-shipping related expenses such as salaries, machine servicing and other cost incurred during production. Therefore, the company should seek to increase cotton demand to improve to match production capacity with demand. In case the polyester customer cuts off The customer cut off would reduce the demand by 450bolts of polyester and reduce the gap of production capacity by 50bolts. The table below shows further analysis shows changes in shipping cost structure; Polyester Customer Cut’s off Original Demand Shipping cost $ 44,350.00 $ 43,000.00 Units Shipped 11,500 11,500 Cost per Bolt $ 3.86 $ 3.74 Cost change (%) 3.1% It is evident the unit shipped will remain constant since the demand is more than production capacity. The unsatisfied demand will reduce by 450 which are shipped to Rome distribution centre. The shipping cost will increase since the cost of shipping to Rome is high compared to New York. The change will reduce the profit margin due to increase in cost by 3.1% of the total shipping cost. The impact of the change in demand is relatively low since the increase in demand did not overstretch the mill's production capacity. The increase is not alarming since it is compensated by the reduction of fixed cost per unit. On the other hand, polyester customer cut off led to the rescheduling of polyester to Rome though the transportation cost increased by a margin of 3.1%. Overtime production analysis The overtime has its associated cost and thus requires separate analysis to determine the mills that will undertake the overtime production. The management estimates that the company loses $20 per bolt of polyester, $10 per bolt of cotton and $50 per bolt of silk for underproduction. The analysis that incorporates overtime production; Silk Objective to minimize Normal Cost $ 5,000.00 Overtime Cost $ 13,850.00 Total Cost $ 18,850.00 The overtime production results in overtime cost of $13,850 and normal cost of $5,000. The overtime production enables the company to meet unsatisfied demand of 450 silk bolts and the cost associated to its production and profit maximisation is as shown below; Mill Not Shipped Cost London Mexico City Rome Total Costs Hong Kong $ 33.00 $ 35.00 $ 34.00 $ 15,150.00 Korea $ 30.00 $ 33.00 $ 31.00 $ 13,850.00 The Hong Kong and Korea mill will be engaged in overtime production and supply to London, Mexico City and Rome distributors. The analysis below shows the profit maximisation as a result of overtime in production of cotton bolts; Profit Bolts Not shipped Total Total profit $50 450 $ 22,500.00 Total overtime cost $ 13,850.00 Maximisation of the profit $ 8,650.00 The company increases its profitability by $8,650 and satisfy the 450bolt demand. Therefore, improves customers’ experience and loyalty. Therefore, the company should carry on with overtime production. Cotton The company mills production capacity stands at 70% of the full capacity and thus not necessary to do overtime production. Silk Objective to minimise Normal Cost $ 43,000.00 Overtime Cost $ 6,000.00 Total cost $ 49,000.00 The company has production overtime cost of $6,000 and normal cost of $43,000. The overtime takes care of the 50bolt of polyester to satisfy the demand. The table below shows overtime cost for ‘not shipped cost’ for Bahamas, Hong Kong and Korea; Not Shipped Cost Mill Normal Cost Overtime Cost Total Cost Bahamas $ 4.00 $ 10.00 $ 7,000.00 Hong Kong $ 9.00 $ 12.00 $ 10,500.00 Korea $ 9.00 $ 8.00 $ 8,500.00 Nigeria $ - $ - $ - Venezuela $ 6.00 $ 6.00 $ 6,000.00 The countries engaged in overtime production include Bahamas, Hong Kong and Korea. The Hong Kong market has a high overtime cost while Venezuela has the lowest cost. The table below shows profit maximisation for silk production; Profit Bolts Not shipped Total Total profit $ 20.00 500 $ 10,000.00 Total overtime cost $ 6,000.00 Total maximizing of the profit $ 4,000.00 The company can increase its profitability by $4,000 for overtime production in three mills (Bahamas, Hong Kong and Korea). According to the analysis above, we can find out that the Korea Mill will produce the overtime silk 450bolt and supply it to Chicago (100bolt) and Tokyo (350bolt). The company will increase its profits by $11,650. Cost Cutting Suggestions The ICT Company requires consideration of some cost-cutting measures that would reduce the transportation cost and increase profitability level as per the analysis above. First, the company would reduce production in Nigerian Mill and increase the production capacity of Bahamas and Venezuela since its transportation cost is relatively low compared to Nigerian Mill. Secondly, the company should consider building a mill in the US since it is one of the biggest markets for its fabrics. The cost would greatly reduce the company profitability but improve with time due to a reduction in transport cost. Thirdly, the company can employ a third party to help in transportation since during off-peak period the third party can combine with other products. The strategy would reduce transportation cost greatly since the company will not be engaged in shipping activity and the cost is fixed in all seasons (Olsson and Woxenius, 2014; Higginson and Bookbinder, 2015). Lastly, the company should consider reorganise shipping routes to reduce the distance from mills to the distribution centres. Conclusion In conclusion, we can find out that rescheduling the shipping routes improves the profitability of the company greatly. The 10% proportion of shipping cost in company expense is not negligible and thus requires management attention. The optimisation analysis eases the management decision making in choosing the routes, overtime production and implementing the strategy to produce polyester in Nigerian Mill. Reference: Dantzig, G., 2016. Linear programming and extensions. Princeton university press. Higginson, J.K. and Bookbinder, J.H., 2015. Policy recommendations for a shipment-consolidation program. Joshi, R., 2013. Optimization techniques for transportation problems of three variables. IOSR Journal of Mathematics, 9(1), pp.46-50. Kuhn, H.W., 2014. Nonlinear programming: a historical view. In Traces and Emergence of Nonlinear Programming (pp. 393-414). Springer Basel. Olsson, J. and Woxenius, J., 2014. Localisation of freight consolidation centres serving small road hauliers in a wider urban area: barriers for more efficient freight deliveries in Gothenburg. Journal of Transport Geography, 34, pp.25-33. Trappey, A.J., Trappey, C.V., Dai, D.W., Chang, S.W. and Lee, W.T., 2014, May. 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