The objective of the tool presented in this text is the use of a computational system to evaluate the impact of two characteristics of the modes of transport (delivery time and delivery variability) on the dimensioning of the safety stock and on the total logistic cost.
For a given set of product, operation and demand characteristics, the tool will be able to point out which of the transport parameters allows for the greatest reduction in safety stock. The tool will also allow the shipper to determine the mode of transport that minimizes the total logistical cost for this set of business characteristics.
The methodology used is based on the assumption that the modes of transport influence the safety stock and the purchase batch. Therefore, your choice should be based on the total logistical cost and not just focus on the transportation cost.
- ANALYZED TRANSPORT PARAMETERS
According to Ballou, delivery time (average) and variability in delivery time (standard deviation) are occupying the first places in importance for transport performance. Delivery time, or lead-time, is the average time that a shipment takes from origin to destination. Variability, on the other hand, refers to the differences in the duration of the delivery time of shipments that have the same origin and destination, moving in the same modal. Lead-time variability is the main measure of uncertainty in transport performance.
These two parameters are highly relevant for logistical planning and are fundamental for calculating the safety stock. The safety stock is dimensioned to absorb the uncertainties of the logistical process, allowing the company to meet the level of service desired by the market. On the other hand, there is an opportunity cost of capital associated with the safety stock, and the higher the opportunity cost, the lower the return on assets.
It is then evident the importance of reducing these two transport parameters, in order to increase the efficiency of the process, reducing uncertainties and the safety stock. But which parameters should be preferably reduced, the average time or the variability? Which modal should be chosen for a given operation? Answering these questions is at the heart of this tool.
- IMPACTS OF TRANSPORT ON THE STRATEGIC PROFIT MODEL
Choosing a certain mode will define the transport parameters of an operation. As there is a strong interdependence between the functions of logistics, the area of influence of a modal decision is not limited to the transport activity. This decision will also impact other activities in the logistics process.
Therefore, contrary to what is usually done, the decision for a modal should not be based only on the cost of transport, but on the total logistical cost. In this way, the presented methodology is not only attractive for companies with a high representativeness of transport or inventory costs. This methodology presents strong contributions for companies whose total logistic costs are of great importance for the profitability of the business.
For example, in some companies in the retail sector, inventories represent 25% of assets. For these companies, the developed tool will allow the management of these transport parameters, providing a reduction in the safety stock. On the other hand, in some companies of the industrial sector, the stock represents only 4% of the total assets. For these, the tool will indicate a lower cost and performance modal. By using a worse performing modal (in terms of average time and variability), the company will have an increase in inventory. However, as in this case the stock is not very representative, the increase in its cost will be overcome by the economy in transport, resulting in a reduction in the total logistics cost.
For shippers, the tool would be used to determine which modal to use to obtain a lower total logistics cost and which of the transport parameters (average time or uncertainty) should preferably be reduced in order to provide a greater gain in safety stock. For multi-modal logistics service providers, the tool would be useful to arm the sales team, so that they will know which modal will provide the best profitability of the potential client's operation.
Figure 1 below represents the Strategic Profit Model. This model summarizes the calculation of return on assets, demonstrating the importance of several items in the company's profitability. The top half of this model consolidates information from the Income Statement and will be used to calculate the net profit margin. The lower half presents information from the Accounting Balance Sheet and will be used to calculate asset turnover. The return on assets will be obtained by multiplying the net margin by the turnover of assets.
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| Figure 1: Strategic Profit Model |
As inventory is one of the main items in Current Assets and transportation expenses are included in Total Expenses, this model allows the evaluation of the financial impact of logistical decisions. Therefore, the strategic profit model demonstrates how the decision for a certain modal would influence the net margin, the turnover of assets and, finally, the return on assets.
For companies with high inventory levels, a reduction in this item would represent an important reduction in assets, which would thus incur an increase in asset turnover and, consequently, a greater return on assets. In the case of companies with less representative inventories, the analysis of logistical trade-offs can lead to an increase in inventory and a reduction in transportation expenses. The increase in inventory would not have much impact on total assets. However, the reduction of transportation expenses would imply an increase in net income and ultimately a greater return on assets.
- OBJECTIVES OF THE PROPOSAL
The main objective of the tool is to point out the influence of the transport parameters (lead-time and variability) on the safety stock and thus allow the determination of which of the two should be reduced in order to obtain a greater gain. However, for this analysis to be carried out, it is necessary to know which modal should be used. This decision should not be taken based only on the cost of transport or safety stock, but on the total logistical cost.
Therefore, the objectives of the proposal rest on two pillars. The first corresponds to the analysis of the total logistic cost. This analysis is carried out through the parameterization of the characteristics of the product, the operation and the demand necessary to plan the operation in four different modes (road, rail, cabotage and air). Based on the characteristics of the business, the tool calculates, for the four modes, the economic lot, the safety stock, the cycle time, the stock turnover and other characteristics of the logistical process. Based on this information, it will be possible to calculate the total logistical cost of the four options, thus determining the recommended mode.
The second pillar consists of analyzing the characteristics of the operation and the transport parameters of each mode. From this analysis, it will be measured which parameter of each modal has greater relevance in the safety stock, and should therefore be reduced. As this analysis is carried out for all modes, even if the company decides not to accept the recommendation of the first analysis due to aversion to the cost of switching or other restrictions of its logistical process, it will be able to benefit from the second, as it will know in any mode, whether to reduce lead-time or variability.
- CONCEPTUAL BASIS
The first analysis is based on the calculation of the economic purchase lot (LEC), the safety stock (ES) and the total logistics cost (CLT) for each of the modes. For the calculation of these parameters, the criteria established in the inventory management manuals were used. Regarding the economic lot, its sizing aims at the balance point between the cost of keeping stocks and the cost of order processing.
According to Bowersox, the larger the purchase lot, the larger the average inventory and, consequently, the higher the cost of maintaining inventory. However, the larger the purchase order, the fewer orders will be needed in the planned period and, consequently, the lower the total ordering and ordering costs. So, the calculation related to the dimensioning of the LEC aims to find the quantity that minimizes the sum between the cost of maintaining inventories and processing orders. The most efficient way to calculate the LEC is through the following mathematical equation illustrated in Figure 2:
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| Figure 2: Economic Purchase Lot Formula |
The above formula was applied to calculate the lot size for each modal to be used to minimize the total logistics cost. In the formula, the CTR represents the fixed cost associated with 1 fulfillment, regardless of the quantity sent. This cost depends on the modal used. OD represents the average daily demand for the product being transported. The T in turn represents the daily opportunity rate of shareholders' capital and the CAq, the added value of the product served.
The greater these last 2 factors, the greater the impact of inventory on the profitability of the business and the tendency towards lean resupply strategies. Otherwise, the stock will show low representation in the results and the tendency will be for cargo consolidation with the decision for worse performing modes in terms of average delivery time and variability.
As for the safety stock (ES), it is a cushion used to absorb the uncertainties of the logistical process. Assuming that actual demand is symmetrically distributed around forecast, it will be above forecast 50% of the time. This implies at least a 50% chance that there will be a shortage if no safety stock has been built up. This level of service is considered unsatisfactory for the vast majority of markets, which justifies the formation of ES.
The safety stock will be dimensioned based on the desired service level and process uncertainties. This calculation is performed by multiplying the statistical factor k (which guarantees the desired availability) by the combined variability of demand and lead-time (which represents the uncertainties of the process). These calculations were made using the following formulas shown in Figures 3 and 4:
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| Figure 3: Safety stock formula |
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| Figure 4: Combined demand and lead-time variability formula |
According to the model used, the safety stock is calculated by multiplying k and sc. k is the factor that ensures the desired service level is met and sc is the combined variability of demand and lead-time (delivery time). Combined variability is calculated using mean time, average daily demand, standard deviation of lead time, and standard deviation of demand. Of these, the average time and the standard deviation of the delivery time will depend on the mode of transport used.
So, the LEC and ES depend on the modal used. These parameters will influence inventory and transport costs. Therefore, the logistics cost analyzed will be the sum of these two costs. The cost of inventory is calculated by multiplying the average inventory, the cost of acquiring this inventory, the number of days it is carried, and the daily opportunity rate. The carry cost is calculated by the number of shipments for the operation times the total cost to replenish (CTR).
These calculations will allow the indication of the most suitable modal for the operation. The next step will be the determination of the most important transport parameter in the dimensioning of the safety stock. According to Evers , by analyzing the safety stock formula shown above, it is concluded that logistics managers have influence on only 2 safety stock control factors.
These 2 factors are lead-time and standard deviation of lead-time. So, Evers performed an experiment, where he calculated the partial derivatives of the safety stock formula with respect to these 2 factors. Through the development of his study, he arrived at a relationship between the demand variation coefficient and the lead-time standard deviation, which determines which transport parameter has the greatest influence on safety stock control. This relationship was replicated in the tool, for choosing the preferred parameter to be reduced, and is summarized in Table 1 below.
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| Table 1: Summary of decision rules |
After carrying out his experiment, Evers observed the relationship between the coefficient of variation of demand and the standard deviation of the lead-time expressed in Table 1. Therefore, when the coefficient of variation of demand is greater than or equal to the square root of 2 times the lead-time standard deviation, the most representative transport parameter in ES sizing is the delivery time (average lead-time). Otherwise, it is the variability (lead-time standard deviation).
In the first case, the focus of transport managers should be to seek faster response alternatives. However, in the second case, which will have the greatest impact in terms of safety stock, will be transport alternatives with greater reliability in delivery time, that is, lower lead-time standard deviation.
The academic frameworks referring to the inventory management manuals and the aforementioned experiment were consolidated in the tool with an intelligible interface that allows the user to carry out the proposed analyses.
6. JUSTIFICATION OF THE PROPOSAL
This computational tool will allow the manager to choose the most suitable mode of transport for the characteristics of the business, based on the calculation of the economic purchase lot, the safety stock and the total logistical costs. As these two parameters (LEC and ES) are often determined “ad hoc” in companies, an important benefit of the tool already resides in the suggestion of their values.
From the LEC and the ES, the total logistics costs are calculated for each alternative mode, allowing the choice of the most profitable mode. According to the survey “Logistic Positioning of Large Brazilian Companies” carried out by CEL / COPPEAD, samples of companies in the Food, Automotive, Electro-electronic, Pharmaceutical, Petrochemical and Technology sectors indicate that road transport is widely used. However, the analyzes generated through the tool indicate that this modal may not minimize the total logistics costs in all sectors.
To assess the impacts of applying the tool, data from the 6 major Brazilian industrial sectors mentioned above were used for a given route. Table 2 below shows for each sector the percentage reduction benefit of total logistics costs comparing the current modal with the one proposed by the tool.
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| Table 2: Comparison between current modal and modal proposed by the tool |
It is observed that the general practice of the Brazilian market is the majority use of the road modal by the analyzed sectors. The analysis showed that in most cases this is not the most appropriate mode. For example, in the case of the pharmacist, from the point of view of total logistical cost, the recommended modal for the analyzed route would be air, with a cost reduction of around 43% compared to road.
Regarding the reduction of the mean or standard deviation of the transport time, the determination of the parameter with the greatest influence on the ES will allow the company to reduce the opportunity cost of capital. This benefit will be greater the greater the weight of inventory in the company's assets and the greater the investment made in reducing the most influential transport parameter in the calculation of ES.
Table 3 above indicates that the standard deviation of delivery time should be the main target of a transport improvement program, in the vast majority of sectors and modes surveyed. Typically, transport variability improvement programs may target scheduling strategies, while average time reduction programs target quick-response strategies.
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| Table 3: Parameter with the greatest influence on ES by modal and sector |
- TOOL INTERFACES
The tool was structured in an initial menu, shown below, which gives access to four screens. These are: a screen for defining the parameters; a screen for analyzing the total logistic cost; a screen for analyzing the safety stock and a screen that summarizes the data outputs for all modes. In Figure 5, below, the total cost and ES analysis screens are also illustrated.
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| Figure 5: Initial menu |
To use the tool, a detailed parameterization of the operation data in the different modes is necessary. This parameterization will be carried out on the “Parameter Definition” screen (represented in Figure 6). The parameters to be defined are segmented into transport information (total cost of resupply, transport capacity, average delivery time and standard deviation of delivery time) and product information (value density, daily demand, coefficient of variation of the demand and service level). In addition to this information, the capital opportunity rate will also be required.
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| Figure 6: “Parameter Definition” screen |
After setting the parameters, the tool handles the data and the analyzes can be viewed on the screens “Analysis of Total Logistics Cost” and “Analysis of Safety Stock”.
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| Figure 7: “Total Logistics Cost Analysis” screen |
Figure 7 shows the “Total Logistics Cost Analysis” screen. On this screen, the operation costs are calculated for each mode, allowing the identification of the most efficient alternative in terms of total logistical cost. In addition, this screen gives access to operation visualization graphs in the different modes and comparative cost analysis graphs between modes.
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| Figure 8: “Operation Visualization” chart |
Figure 8 presents the visualization graphic of the operation. The 2 main pieces of information on this chart are total stock and safety stock.
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| Figure 9: Chart for “Cost Analysis” |
Figure 9 presents the chart for cost analysis. In the specific figure, the cost of inventory is represented, but graphs were also generated for analysis of the transport cost and the total cost.
The “Safety Stock Analysis” screen, shown in Figure 10, shows the calculation of the relationship between demand variation coefficient and lead-time standard deviation for each modal, pointing out in each case which transport parameter is more relevant in the ES sizing.
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| Figure 10: “Safety Stock Analysis” screen |
- CONCLUSION
Comparing the tool's diagnosis with the reality of the companies, it is seen that on a daily basis, the 6 sectors make massive use of road transport. But when using the tool, this modal was appointed as preferred only for the Automotive sector, despite appearing as the second best modal for all other sectors. This indicates that road transport does not minimize logistical costs for most companies in other sectors. However, as it provides at least reasonable performance when compared to other modes, road transport continues to be widely used.
So, this factor added to the Brazilian tradition in road transport, the “know-how” of transport managers in this modal, their low “know-how” in other modes, the aversion to cost and risk of change, among others justifies preference for road transport. However, the pressure to reduce logistics costs is forcing several Brazilian companies to overcome this paradigm, exposing themselves to the risk of trying to operate in new modes.
At this point, lies the great utility of this tool: allowing logistics managers to visualize the benefits of operating in new modes of transport and, in addition, how to work with these modes to achieve a greater reduction in the safety stock, increasing the return on costs. shareholders.
- BIBLIOGRAPHY
BALLOU, Ronald H., Supply chain management: planning, organization and business logistics. 4th ed. Porto Alegre: Bookman, 2001.
BOWERSOX, Donald J., & CLOSS, David J. Business Logistics: The Supply Chain Integration Process. São Paulo: Atlas, 2001.
EVERS, PT, 1999, “The Effects of Lead Times on Safety Stocks”, Production and Inventory Journal.
CEL, Survey of Logistical Positioning of Large Brazilian Companies available on the website www.ilos.com.br.
