Inventory Management and Demand Prediction System
for Reagents and Consumables
1
Tzu-Chuen Lu,
2Shih-Chieh Lai,
3Chun-Ya Tseng
*1, First Author, Corresponding Author
Department of Information Management,
Chaoyang University of Technology, Taichung 41349, Taiwan, R.O.C., [email protected]
2,Department of Information Management,Chaoyang University of Technology,
Taichung 41349, Taiwan, R.O.C., [email protected]
3,
Department of Information Management,Chaoyang University of Technology,
Taichung 41349, Taiwan, R.O.C., [email protected]
Abstract
The medical examination is a crucial step among medical behaviours. To guarantee the quality of examination, inventory management of reagents and consumables is also a requiring groundwork, besides keeping the proper function of examining works. However, it is difficult for the staff to effectively manage reagents of various categories and complex packages. The laboratory we cooperated has no any forecasting and decision support system; purchasing is totally based on personal experience of purchasers. When newcomer is in charge of purchasing, the exceeding purchasing results in hoard and overdue reagents or the omission of the fluctuating usage of specific reagents results in deficiency and standstill of the work which are commonly situations appeared. Thus we are combing the forecasting model developed by scholars Liao Yueh Hsiang et al. to calculate the demand of each reagent.
Keywords:
Inventory management, Forecasting model, Medical laboratory, Decision support1.
Introduction
The purpose of medical examination is to help people finding the possible causes of diseases and then to assist doctors making judgment for cure. Thus medical examination is a crucial step among medical behaviors. To guarantee the quality of examination, inventory management of reagents and consumables is also a requiring groundwork, besides keeping the proper function of examining works [9, 13, 13]. However, it is difficult for the staff to effectively manage reagents of various categories and complex packages.
The medical laboratory we have cooperated in this study is a large laboratory which integrated department of laboratory medicine from five hospitals. Its main services include body fluids examination, blood examination, biochemistry examination, drug concentration examination, serum immunology examination, virus examination, allergen examination and so on. However, examination works of a single hospital are already time-consuming and exhausting by dealing with the consumptions of reagents and consumables which are numerous and jumbled, not to mention the integration of five hospitals. So far, the laboratory still has its reagents and consumables managed by human labor. When ordering, the staff has to check reagents of inventory shortage first and then sends orders to suppliers manually. After receiving orders, suppliers make forms of acceptance certificate and usage by Excel files, and then write the information of expiration date, batch number, reagent number, et cetera on the packages.
The management by human labor is time-consuming, exhausting and mistakable, and the examination works cannot be carried out effectively because of the impossibility to track the inventory or current usage conditions instantaneously. Furthermore, the tension of human labor also results in dilemma of management. [3]
(7) Various packages of reagents and consumables.
Aiming at these problems, we try to set up an online management system of inventory, which helps the laboratory to replace its human labor by information system that proves the complicated works such as ordering, examining, importation, exportation and checking. In additional, a demand forecasting of reagents and consumables is also another task of this study. According to the historical consumptions, we set up a model for demand forecasting which provides proper suggestions when the staff considers the ordering amount, and then helps the normal functioning of the examination work [1, 3, 11, 12].
The establishment of this management system of inventory not only helps the inventory management of the laboratory, but also looks forward to a further help which decides effective order timing and a reasonable order amount of reagents and consumables, thus the ordering will not count on the personal experience of purchasers anymore [10].
Consequently, a demand forecasting of reagents and consumables is also another task of this study. According to the historical consumptions, we set up a model for demand forecasting which provides proper suggestions when the staff considers the ordering amount, and then helps the normal functioning of the examination work [4, 5, 6, 7].
In this study, we refer to an expert system for demand prediction proposed in 2010 [11], using the historical usage data collected from hospitals to make a forecast of usage. Reagents are divided into six types which are periodic type, increment type, plateau type, trial type, decrement type and unstable type. Reagent of periodic type showed a regular fluctuated curve in the relationship of usage and time, the calculation used weighted average method. Reagents of increment type had its usage positively related to time while that of decrement type negatively related to time, the calculation used second exponential smoothing method. The usage forecast of plateau type reagent was calculated by ratio method. Reagent of trial type usually fluctuated irregularly, so the usage forecast was calculated by monthly average usage multiplied days. Reagent of unstable type had its usage changeful, so the usage forecast was calculated by day average usage multiplied days. By using the forecasting model, we set up a demand forecasting system which generated suggestions for ordering amount.
2.
Literature Reviews
2.1.
Inventory management
Chang et al. in 2007 [2] had pointed out that inventory management of department of laboratory medicine mainly relies on human labor, which is time-consuming and exhausting. Each laboratory develops its own way to manage without clear and definite regulation rules. Thus negligence of safety inventory quantity, expiration date and time shortage for acceptance certification become common situations, which then result in urgent purchasing, budget crisis and even standstill of examination works. Therefore they proposed a management system of medical reagents and consumables which replaced human labor by an information system, which can increase working efficiency and accelerate the output of examination report.
In 2008, Kao and Liu [8] also proposed a system. The design of former systems based on one specific hospital structure. However, they could not satisfy the need of a laboratory which integrated numerous hospitals, or numerous work groups. In addition to inventory management, the laboratory also requires a system with functions of ordering, audit and checking.
Therefore in this study, we proposed a reagents and consumables management information system which is able to manage ordering, audit, acceptance certification, importation, exportation, inventory, inventory checking, abnormal exportation, refund and so on.
2.2.
Forecasting system
Li and Chang proposed a multi-model interacted decision supporting system [10] to assist the management of drug inventory. The predictions of drug demand and drug baseline are divided into three stages:
(1) Using dynamic parameter weighted average method or time series analysis of quantitative analysis to calculate the best baseline quantity.
(2) Adopting the selected inventory supply system, called the (s, S) model, to calculate the quantity of ordering.
(3) Using quantitative and qualitative analysis to make better decision.
This system can effectively increase the turnover rate of drug and help controlling drug inventory, but it seems inadequate to apply one forecasting model to different kinds of drug.
In 2010, Liao et al. proposed an expert system for the demand prediction of pharmaceutical drugs [11]. They applied regression analysis and Pearson correlation coefficient to obtain regression coefficient and related coefficients, and applied consumption curve analysis to distinguish six types of drug consumption which were periodic type, increment type, plateau type, trial type, decrement type and unstable type. Once the consumption type of a drug is confirmed, they used different forecasting models to analysis the rules of decision tree in order to forecast the demand. They used different forecasting models to predict the demand of different drugs. In this study, we try to test the feasibility of Liao’s forecasting model, so the model is applied to predict the drug demand of the laboratory and hope to provide a useful reference to the ordering of reagents and consumables.
3.
The Proposed Scheme
The methods and procedures of this study illustrate in Fig. 1, and the interpretations of steps are as follows:
Figure 1. The procedures of this study
3.1.
Establishment of an online reagents and consumables management information
system
(2) Ordering audit
The purchase order should be audited by the supervisor, director, warehouse and manager in sequence. After audit, the system will automatically send the purchase orders to the suppliers to complete the order procedure.
(3) Acceptance certification management
Upon the arrival of the reagents and consumables, the first-line group members of laboratory will inspect first, they should inspect the actual delivered quantity and shelf life of the goods, generate and utilize the acceptance certificate to examine the packages, delivery conditions, storage method of the reagents and consumables as well as the quality control test to find out if there are any abnormal issues with the reagents and consumables.
(4) Inventory management
The inventory of reagents and consumables in this system can be enquired with purchasing orders or the groups of the hospital. The staff can log in “Inventory management” or “Inventory overview” to check the storage. “Inventory management” is for enquiries based on order. (5) Exportation management
When export reagents and consumables, the inspector utilizes barcode scanner to read the barcode of the reagents and consumables or input the barcode number manually, the system will automatically bring out the details of the reagents and consumables such as description, batch number, shelf life, inventory and so on. After verifying the information, input the quantity of the reagents and consumable used and send out, the system will deduct the inventory automatically.
(6) Re-importation
Re-importation of remnants. Return of defective goods.
Checking lists all inventories of reagents in various groups of each hospital. (7) Provides various reports
3.2.
Demand forecasting and decision support model
We developed the demand forecasting model suitable for reagents and consumables based on the smart design of the Expert System for the Demand Forecasting of Pharmaceutical Drugs proposed by Liao Yueh Hsiang and other scholars in 2010. Liao Yueh Hsiang and other scholars divided the drugs into periodic type, increment and decrement type, plateau type, trial type, and unstable type, each type adopts different forecasting formula to predict. Periodic type uses weighted average method, increment and decrement type adopts linear quadratic exponential smoothing method, plateau type is predicted with proportion, trial type adopts the average consuming volume in several months as average daily consumption volume, unstable type changes significantly, so the formula of average daily consumption volume times the days of forecasting is adopted. Table 1 indicates the regression coefficient, correlation coefficient and consumption curve relationship of different types of drugs.
Table 1. Types of reagent consumption [11] consumption pattern regression coefficient correlation coefficient consumption curve
Periodic Type Close to zero <0.1
Increment Type Significant positive >0.5
Plateau Type Significant positive 0.3~0.5
Trial Type Positive or close to
zero <0.5
Decrement Type Significant negative >0.5
Unstable Type Close to zero <0.2
This study applied the same strategy to different types of reagents in order to seek suitable forecasting formulas for analysis of consumption volume.
The consuming information of reagents and consumables in this study was provided by laboratories and collected from Jan.2008 to Oct.2010. The information includes the name of the reagents, the month and year of consumption, the groups consuming the reagents, the reference numbers of reagents, the volume consumed, finally, according to the month of consumption, identify the season for seasonal forecasting.
The implementation procedures of forecasting model are illustrated in Fig. 2.
Data collection and preprocessing
Classification of consumption pattern
Decided to predictive model
Construct prediction system
Experimental results and conclusions
(2) Classification of consumption pattern: According to the classification method of Table 1, the system calculates the regression coefficient and correlation coefficient of various reagents to determine the types of the reagents.
(3) Decided to predictive model: According to different types, the system establishes different forecasting models. The forecasting models utilized in this study for predicting order volumes are increment type, decrement type and plateau type. The demand forecasting models of various types of reagents are introduced as below.
a. Increment type and decrement type
The consuming volumes of these two types of reagents are in direct (or inverse) proportion to time, it means that for increment type, the consuming volume of the reagent is increased with time. On the contrary, for decrement type, the consuming volume of the reagent is decreased with time. Time and volume are significantly involved. This type of reagents can be predicted with Linear Quadratic Exponential Smoothing Method [11], the formula is as below:
2 1 2 1 1 . t t t S
S
S (1) In which, 2 t S and 2 1 tS are the values of the Quadratic Exponential Smoothing during the period of t and t-1, α is the smoothing coefficient. If 1
t
S and 2 t
S and are known, the forecasting model is:
ˆ .
t T t
Y
b T(2)
Among which, T is the advanced forecasting time.
1 2 1 2 2 , . 1 t t t t t t S S b S S (3) b. Plateau typeThe forecasting formula for plateau type is:
. 16 2 1 1 16 1 w K S w K S SAVG K t t K i i
(4)In which, w1 is the proportion during previous period of 16-K, while w2 is the proportion of previous period of K.
4.
Experimental Result
This study has developed “Online laboratory reagents and consumables management information system” and applied in practice successfully. Because the usage of increment type of reagents increases with time, its consumption curve is trending upward and shows increasing trend. Its correlation coefficient is a positive number, which means the time is directly proportional to the consumption volume. But the decrement type is opposite to the increment type. Its consumption curve shows downward trend. Its correlation coefficient is a negative number, which means the time is inversely proportional to the consumption volume. Also, because the absolute value of the correlation coefficient of its consumption curve is greater than 0.5, it indicates that time is related to consumption at moderate-to-high levels.
No. Group Product Name Average Type
1 Serum - Bakeman Estradio1 7 Increment (RC:0.09, CC:0.54) 2 Serum - Yapei HBSAg Reagent 1 Increment (RC:0.11, CC:0.72) 3 Serum - Yapei Free T4 Reagent 0 Increment (RC:0.09, CC:0.50) 4 Special SALM Typhi-H 8 Increment (RC:0.20, CC:0.60) 5 Special SALM Typhi-A 8 Increment (RC:0.20, CC:0.60) 6 Special SALM Typhi-B 8 Increment (RC:0.20, CC:0.60)
7 Special Protus OX19 8 Increment (RC:0.20, CC:0.59)
8 Special Protus OXK 8 Increment (RC:0.20, CC:0.60)
9 Special Protus Ox2 8 Increment (RC:0.20, CC:0.60)
10 Special SALM Typhi-O 8 Increment (RC:0.20, CC:0.60) 11 Special Serodia NYCO II 5 Increment (RC:0.11, CC:0.73) 12 Special Washing Solution 1 Increment (RC:0.07, CC:0.57)
13 Special Immuno CAP
Development 1 Increment (RC:0.07, CC:0.57) 14 Special EliA dsDNA Well 2 Increment (RC:0.07, CC:0.59)
15 Special EliaA IgG
Conjugate,96 test 3 Increment (RC:0.09, CC:0.70)
16 Serum - Bakeman
Bio-Rad Lyohocheck Tumor Markers Level1
2 Increment (RC:0.11, CC:0.64)
Figure 3. Increment Reagents
Quadratic Exponential Smoothing Method is utilized to forecast the consumption volume for the increment and decrement types of reagents. This method is to use historical data to calculate the weighted average value, in which the smoothing coefficient α is used to adjust the smoothing level of data, the value ranges from 0.1~0.9, a higher value of α indicates a larger fluctuation, on the contrary, a lower value of α indicates a smaller fluctuation. Under usual conditions, if the fluctuation of the forecasted object is not large, we can assume that α is 0.3. However, in our experiment, if the values of smoothing coefficients are fixed, the forecasting volumes of the reagents can not reflect the actual consumption. This is due to various types of reagents in laboratory. Each type of reagent is consumed at different time with different consumption style. So we try the smoothing coefficient from 0.1 to 0.9 respectively in order to find the most suitable value of α. The most important fact is that the medical laboratory has peak seasons and off seasons too, for example, when a new school term begins, the schools will arrange the physical examination for all students, or the government and companies arrange annual physical examination for the employees, during this time, usage of reagents increases dramatically. Therefore, in our experiment, we included demand forecasting aiming at seasonal consumption volume. We estimated the consumption of the next summer based on that of this summer. This is also one of the programs
For plateau type of reagent, the consumption curved line is almost horizontal without a trend of upward or downward, which indicates that time is not significantly related to usage. It means that they are not directly or inversely proportional to each other. The correlation coefficient of the reagent of plateau type is between 0.3 to 0.5.
Regard to reagents of plateau type, we adopted different K and weight (w1, w2) to perform the test in order to predict the consumption volumes in different periods and estimate the error quantity. Table 2 is the forecasting result of Cylindrical test tube, when the value of the advanced forecasting time is 1, K = 15, w1 = 0.9, w2 = 0.1, the value of forecast error is 5, this is the smallest error value when K = 15.
Table 2. The experimental result of demand forecasting of Cylindrical test tube
Item: Cylindrical test tube 16-K K w1 w2
Predict error amount periods：1 1 15 0.9 0.1 5 1 15 0.8 0.2 5 1 15 0.7 0.3 6 1 15 0.6 0.4 7 1 15 0.5 0.5 7 1 15 0.4 0.6 8 1 15 0.3 0.7 8 1 15 0.2 0.8 9 1 15 0.1 0.9 10 2 14 0.9 0.1 10 2 14 0.8 0.2 10 2 14 0.7 0.3 10 2 14 0.6 0.4 10 2 14 0.5 0.5 10 2 14 0.4 0.6 10 2 14 0.3 0.7 10
Tables 3 is the experimental results of all reagents.
Table 3. The experimental result of reagents in different hospitals
(a) T Hospital (b)TP hospital
(c) FY Hospital (d)NT hospital
When we forecast the demand of reagents of plateau type, we adopt proportion method. The purpose is the same as forecasting the demand of other reagents which is to find the smallest forecast error. The proportion method is to sum up the total consumption volume in the past several periods, then times the different weight value from 0.1 to 0.9 respectively to find out the smallest forecast error. Take an example of Cylindrical test tube, we utilize the historical data in half of the longest 32 periods, that is 16 periods
to forecast this consumable, sum of the total order quantity in earlier 10 periods times weight value 0.7, then plus the result of the order quantity in the late 6 periods times weight value 0.3, which result in the value of the forecasted consumption volume.
The experimental result shows that the consumption volume of Cylindrical test tube in earlier 12 periods times weight value 0.1, then plus the result of the consumption volume in late 4 periods times 0.9, which achieves the smallest forecast error. The result also indicates that the consumption volume in late periods has more impact on the forecasting result than that in earlier periods. However, other reagents such as B group (antibiotic) in CH hospital, the consumption volume of earlier 14 periods times weight value 0.8, then plus the result of the consumption volume in late 2 periods times weight value 0.2, which achieves the smallest forecast error. It indicates that the consumption volume in earlier periods had more impact on the forecasting result. According to our observation, each reagent in different hospitals and different groups shows different consumption time and style. Table 4 is the experimental results.
Table 4. The experimental result of reagents of different hospitals
(a) T Hospital (b)FY hospital
(c) NT Hospital (d)CH hospital
5.
Conclusion and future research
Before the introduction of the computerized operation system, the central laboratory managed most of the inventory manually. After the integration of five hospitals, the enormous amount of work made the manpower shortage more obvious. We anticipate that after the introduction of computerized operation, this inventory management system for reagents and consumables is benefit for the central laboratory to manage the groups, track orders, find out the proper time to place orders and evaluate suppliers, thus the inventory management is no longer an obstacle for medical examination, the work efficiency is enhanced and the goals of time-saving and labor-saving are achieved.
In the respect of inventory forecasting, in the past, after the group member found out the shortage, they started to purchase. When making the decision regard to order quantity, the group members estimated the order quantity based on past experience and reported to the supervisor, direct, warehouse and so on. However, due to no way to find out the existing inventory, so when the administrators audited the orders, they could only audit according to the quantity that the group member requested which easily resulted in shortage or excess. We anticipate that the demand can be analyzed and forecasted according to different hospitals and groups with the historical consuming record in the system and the group members can be assisted when they make decisions regarding suitable order quantity.
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