How to Analyze and Improve the Accuracy of Veterinary Inventory Demand Forecasting

Transcription

1 How to Analyze and Improve the Accuracy of Veterinary Inventory Demand Forecasting Jorge L. Colón, DVM, MBA Author s address: Jorge L. Colón, DVM, PLLC, PO Box 11631, Lexington, KY 40576; jorgecolondvm@me.com AAEP. 1. Introduction The business-savvy veterinary practitioner recognizes that controlling costs associated with inventory is essential to the lean process management techniques necessary to improve gross profit margins, which ultimately lead toward increased practice profitability and value creation. Holding costs associated with holding too much inventory are a drain to cash resources due to tied-up capital and associated opportunity costs, together with the unavoidable factors of inventory shrinkage, expiration, and obsolescence. At the same time, shortage costs generated by the lack of adequate inventory required for the provision of services affect the profitability of the practice through the loss of revenue. This can be manifested through the loss of service provision, or even worse, the loss of a client s customer lifetime value to the practice. Inventory demand forecasting is the process of predicting demand for future service events and the corresponding inventory required for the facilitation and provision of the predicted demand for services. By nature, forecasting is affected by the practice s external environment and is critically influenced by those factors that are out of the control of the practice such as economic, political, and industry conditions. In addition, forecasting can be greatly influenced by internal decisions associated with demand generation through the use of advertising for services during slow time periods, creating financial incentives for employees, or by temporary reduction in client pricing. Whereas predicting the future inventory demand created by the normal running course of the business presents a difficult task, controllable and uncontrollable influence factors will affect the predictability of demand for future inventory even further. Business owners need the tools necessary to better predict the demand for future inventory that will ultimately decrease the associated costs of goods. 2. Forecasting Analysis The first step in developing a system for demand forecasting is the analysis of previous forecasting performance through the measurement of past forecasting accuracy. Though actual methods of forecasting accuracy such as error, mean error, and mean absolute error tell us by how many units the forecast has been wrong, these methods are not measured relative to the total volume of demand. Knowing the quantity of units the forecast is off by provides little help when the information is analyzed outside the context of total demand volume; a NOTES AAEP PROCEEDINGS Vol

2 Table 1. Example Calculation of PE and MAPE Forecast for Actual Error (Forecast ) PE (Error/), % Cumulative Sum of Absolute PE, % MAPE, % Quarter Quarter Quarter Quarter practitioner would be doing a great job being 10 units off in forecasting when the total volume is 1000, but would be doing poorly if the total volume was 25. In addition, unit error would fail to provide a proper point of reference for comparing different volume-sized inventory items. Measurements of forecasting accuracy relative to a perfect forecast, on the other hand, describe the percent by which the forecast is wrong and are therefore best suited to the goals of the lean inventory management process. These methods are based on the measurement of the percent by which the forecasting technique is off in each period (percent error; PE) and on the measurement of the forecasting technique performance over multiple periods (mean absolute percent error; MAPE) (see Appendix). MAPE, specifically, will tell us how well we are doing over time in terms of error. As seen in the example presented in Table 1, the PE for each period is the percentage by which the forecast was over- or underestimated compared with actual sales or usage. The running MAPE is based on the sum of the absolute value of all the periods PEs divided by the number of periods. The example in Table 1 shows that after four periods, the forecasting technique has cumulatively been off by 21.03% ( ), which translates to a MAPE of 5.26% (21.03/4). What this means is that, on average, the forecast is off by 5.26% each quarter through the total time period measured. A percent-based error measurement allows for performance comparison among different volumesized inventory items. By better understanding the effectiveness the forecasting technique has had over time, the practitioner can better adjust and improve the forecasting technique for future demand to achieve increased accuracy. An Excel spreadsheet calculator for PE and MAPE is provided for download at 3. Forecasting Techniques There are qualitative forecasting techniques that rely on intuition, experience and judgment, and quantitative forecasting techniques that rely on analysis of historical sale patterns through time. Although the young practice will have to rely on qualitative techniques that, by definition, should incur a higher degree of forecasting error, the seasoned practice can incorporate quantitative techniques based on the practice s own historical data. The ability to adapt the forecast based on observed seasonal changes and trend in sales patterns will allow for an improved forecasting accuracy. Notwithstanding the importance of data, qualitative techniques based on insights, experience, and knowledge of external factors should be factored into the forecast to further improve the forecasting accuracy. Qualitative techniques will not do a good job on their own, but they will substantially improve the effectiveness of the quantitative techniques which, by themselves, cannot foresee changes in the external environment. Simple Exponential Smoothing Exponential smoothing is a forecasting technique that places a percentage weight on the last period s sales and the rest of the percentage weight on the last period s forecast; a period being described as the unit of time being analyzed (e.g., if forecasting quarterly demand, last period refers to the previous quarter; if forecasting monthly demand, last period refers to the previous month). Exponential smoothing is described by the formula F t [ Y t 1 ] [(1 ) F t 1 ] (see Appendix) and indicates that this period s forecast (F t ) is dependent on the last period s actual sales (Y t 1 ) and forecast (F t 1 ). This enables the forecast to account for all of the older data s forecasts that were developed from all of the older data s sales. In simple exponential smoothing, a value called alpha ( ) sets the weight assigned to the last period s sales (Y t 1 ). The weight not applied to the last period s sales is therefore applied to the last period s forecast (through 1 ). The higher the alpha, the higher the weight of this period s forecast (F t ) that is placed on the last period s sales (Y t 1 ) and the lower the weight placed on the last period s forecast (F t 1 ), which contains the historical data. The result of a high alpha is that a large weight is placed on the last period s actual demand; therefore, the forecast is more reactive to the latest changes in demand. As seen in Table 2, when alpha is 1, the forecast for this period is identical to the sales in the last period (forecast for quarter 2 same as sales for quarter 1). The result of a high alpha is that the future forecast becomes more responsive to the last period s sales and does not take into account the previous history. If the demand changes are trending, then a higher alpha will provide a better weighted average for forecasting because the forecast changes as it reacts Vol. 61 AAEP PROCEEDINGS

3 Table 2. Resulting MAPE of Exponential Smoothing Forecasts at Different Values for Alpha When Are Trending Previous Quarter Quarter Quarter Quarter Quarter Resulting MAPE 4.5% 5.2% 8.5% 10.2% to the changes in trending sales. Table 2 shows that, when sales are trending, a higher alpha provides a better forecast, as measured by the resulting MAPE than a lower alpha. A low alpha, in contrast, places more weight on the historical data and the forecast is less responsive to recent changes in demand. As alpha reaches 0, the future forecast does not take into strong account the recent demand history; in fact, when alpha is 0, the forecast is identical to the last period s forecast. As shown in Table 2, a small alpha fails to properly respond in a timely fashion to trend changes and therefore results in a higher MAPE. Table 3 shows that when sales are random, the reactivity of a high alpha will lead to a lower forecasting accuracy created by the bouncing reaction to changes in sales, whereas a lower alpha would dampen the effect of randomness by not being reactive; therefore resulting in a lower MAPE. Exponential Smoothing for Seasonality Most veterinary practitioners are faced with a repeating pattern of sales increases and decreases occurring within a 1-year period; a concept termed seasonality. In cases where there is seasonality in the demand pattern, a quantitative analysis of historical sales patterns must be performed so that a seasonality adjustment can be made to the exponential smoothing forecasting technique. After mathematically de-seasonalizing the data, the smoothing forecasting technique can be applied to the new data. Once a forecast is developed for a future period, the data can then be re-seasonalized. Exponential Smoothing for Seasonality and Trend The veterinary practice that is experiencing not only seasonality, but also a trending change, needs an exponential smoothing quantitative forecasting technique that incorporates adjustments for trend and seasonality through statistics to improve inventory demand forecasting accuracy. The explanation of the mathematical calculation of exponential smoothing for trend and seasonality goes beyond the scope of this article but the calculation of such forecasts can be performed within the previously mentioned Excel spreadsheet that can be downloaded at 4. The Need for Improved Forecasting Accuracy Excessive spending related to overestimation of demand or to loss of revenue through the shortage costs related to underestimating demand result in a negative cash flow that reduces operational efficiency. By definition, an increase in inventory investment is seen as an increase in working capital requirements that must be satisfied through tying up of existing cash reserves or through increased capital investments from outside sources of cash. A concurrent increase in cost of goods, or potential loss of revenues, leads to decreased margins that translate into decreased profits. The combination of reduced profits and increased capital investments leads to a reduction in returns and a reduction in shareholder value. In contrast, an improved accuracy in demand forecasting leads to an increase in profits through a reduction in total annual inventory costs. Better Table 3. Resulting MAPE of Exponential Smoothing Forecasts at Different Values for Alpha When Are Random Previous Quarter Quarter Quarter Quarter Quarter Resulting MAPE 10.7% 9.4% 6.4% 6.0% AAEP PROCEEDINGS Vol

4 Table 4. Inventory Cost Savings Over a 5-Month by Improving Forecast Accuracy Demand Forecast at a PE of 16.7% Demand Forecast at a PE of 8.3% Demand Forecast at a PE of 8.3% Using EOQ Demand forecast Quantity per order (as calculated by EOQ) Purchase cost $28, $26, $26, Holding cost $ $ $ Order cost $50.00 $50.00 $ Total actual inventory cost (for the 5-mo period) $28, $26, $26, Savings over the original 16.7% PE forecast $2, (7.68%) $2, (8.31%) Abbreviation: EOQ, economic order quantity. Actual sales 120 units; per unit cost with tax $201.40; yearly holding rate 35%; per order cost $10; ordered equal amount per month for 5 months except when using EOQ. forecasting increases revenue by reducing shortage costs and decreases cost of goods sold by decreasing the holding costs associated with excess inventory. At the same time, the reduced inventory capital investment reduces working capital requirements, which reduces the practice s cash flow toward inventory. More importantly, the increase in gross profit margin and decrease in working capital requirements lead to an increase in profitability and shareholder value. 5. Translating Improved Forecasting Accuracy Into Cost Savings As previously described by the author, 1 the total actual inventory cost is comprised of the purchase cost, holding cost, and order cost. As defined by the formulas presented in the Appendix, the forecasted demand is a key component of the calculations. The example that follows in Table 4 presents a reallife situation where minimization of PE and MAPE led to a better demand forecast and, in turn, a reduced cost of inventory. In addition, the example presents how going one step further within the process of lean inventory management through the calculation of the economic order quantity further enhanced the cost savings that was originally started by the improved demand forecast. The example in Table 4 clearly indicates how improving the forecasting accuracy led to a savings of 8.3% on just this one inventory item. The generated savings go directly into increasing the bottom line by increasing gross profit margin and increasing profits net of taxes, a scenario that directly translates into increased shareholder value. 6. Measuring the Efforts The efforts toward not having too much inventory on hand can easily be measured through the observed change in inventory turnover (see Appendix); a reduced amount of average inventory on hand (the formula s denominator), thanks to better forecasting, resulting in an increase in inventory turns. Attention should be paid, however, to the concurrent efforts at reducing cost of goods sold (COGS) (through the use of economic order quantity analysis or through better pricing attainment, for example) as the reduction in the formula s numerator (COGS) will artificially reduce the calculated inventory turns; to the point that an equal percentage reduction in COGS and in average inventory on hand will result in an unchanged calculation of inventory turns. For this reason, measurement of inventory turns should be made with knowledge of changes in COGS given that the costs of the inventory will affect not only the total cost of goods, but also the cost of the inventory on hand. A second method of measuring inventory turns that is only based on demand and order quantity takes away the effects created by unit cost on the previously described turnover calculation and gives a more precise evaluation of inventory usage. Re-arrangement of this formula provides the useful measure of days of supply, which will also allow the practitioner to monitor the results of improved forecasting. Efforts toward avoiding shortage costs associated with too little inventory on hand cannot necessarily be measured through total revenues other than having personal knowledge of having lost a client s business to a competitor. The effort to go lean through calculating the economic order quantity should be accompanied by the calculation of a reorder point (see Appendix), which will trigger an order and therefore avoid running out of inventory that allows the provision of service. Although measuring shortage costs cannot be done other than through gut feeling, implementation of a reorder point will prevent the shortage costs because the inventory will be reordered before running out. 7. Conclusion To properly analyze forecasting accuracy, the veterinary practitioner must use simple statistics measurements that compare with a perfect forecast. PE and MAPE represent a percentage-based analysis of how far off the forecast was from the actual demand, and provide a continuous measurement to evaluate accuracy of estimation over time. Changes in the forecasting technique to reduce PE and MAPE will lead to Vol. 61 AAEP PROCEEDINGS

5 improved forecasting accuracy that leads to improved demand management. Improved demand management will allow the veterinarian to better manage cash flow associated with invested capital, to take more precise advantage of distributor s specials and promotions, and to improve operational efficiency through implementation of lean processes. Improvement of inventory demand forecasting will also have a significant positive effect on the ability to increase profits while decreasing working capital investments to the extent that a return on investment into inventory cost saving is usually worthwhile based on the resulting improvement to shareholder value. In all, the effect of improving the accuracy of the inventory demand forecasting process will translate into increased gross profit margins, which directly increase THE BUSINESS OF PRACTICE: profits net of taxes, and thereby create an increase in the value of the practice. Acknowledgments Declaration of Ethics The Author declares that he has adhered to the Principles of Veterinary Medical Ethics of the AVMA. Conflict of Interest The Author declares no conflicts of interest. Reference 1. Colón JL. How to implement a lean inventory management system for a solo equine practitioner, in Proceedings. Am Assoc Equine Pract 2014;60: Appendix: Formulas Name Formula Percent error PE (forecast sales) / sales Mean absolute percent error MAPE PE /N (Sum of the absolute value of all percent errors divided by the number of instances) Simple exponential smoothing forecast F t Y t 1 (1 ) F t 1 (Where F t is this period s forecast, Y t 1 is the last period s sales, and F t 1 is the last period s forecast) Total actual inventory cost TAIC PC HC OC Purchase cost PC (demand) (cost tax) Holding cost HC (order quantity / 2) (holding rate) (cost tax) Order cost OC (demand / order quantity) (per order cost) Economic order quantity EOQ square root of (2 demand per order cost) / ((holding rate) (cost tax)) Inventory turnover Inv. TO cost of goods sold / average inventory Or Inv. TO (annual demand) / (order quantity / 2) Days of supply D of S (order quantity / 2) / (annual demand) (No. of days open for business during year) Reorder point ROP (annual demand) / (No. of days open for business during year) lead time (where lead time is the time period between order placement and order receipt) AAEP PROCEEDINGS Vol