Case Study on Inventory Management Improvement

Abstract

Inventory management is a challenging problem area in supply chain management. Companies need to have inventories in warehouses in order to fulfil customer demand, meanwhile these inventories have holding costs and this is frozen fund that can be lost. Therefore, the task of inventory management is to find the quantity of inventories that will fulfil the demand, avoiding overstocks. This paper presents a case study for the assembling company on inventory management. It is proposed to use inventory management in order to decrease stock levels and to apply an agent system for automation of inventory management processes.

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Information Technology and Management Science doi: 10.1515/itms-2015-0014
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Case Study on Inventory Management Improvement
Darya Plinere1, Arkady Borisov2
1, 2 Riga Technical University
Abstract – Inventory management is a challenging problem
area in supply chain management. Companies need to have
inventories in warehouses in order to fulfil customer demand,
meanwhile these inventories have holding costs and this is frozen
fund that can be lost. Therefore, the task of inventory
management is to find the quantity of inventories that will fulfil
the demand, avoiding overstocks. This paper presents a case
study for the assembling company on inventory management. It
is proposed to use inventory management in order to decrease
stock levels and to apply an agent system for automation of
inventory management processes.
Keywords – ABC classification, demand forecasting methods,
inventory management, replenishment policies.
I. INTRODUCTION
Inventory is the stock of any item or resource used in an
organisation [1]. There are three types of manufacturing
inventories: raw materials, work in progress and finished
goods (Fig. 1).
Fig. 1. Types of manufacturing inventories.
The author [2] mentions several reasons why it is needed to
have inventories:
•To meet anticipated demand;
•To smooth production requirements;
•To protect against stock-outs;
•To take advantage of order cycles;
•To hedge against price increases or to take advantage of
quantity discounts;
•To permit operations;
•To decouple components of the production-distribution
system.
Otherwise, it will lead to production delays, shortages
and/or dissatisfied customers [3]. The paradox of inventory
management is that having inventory is needed, but it is not
desirable to have inventory due to inventory keeping costs.
This situation makes inventory management a challenging
problem area in supply chain management. This paper
presents a continuation of the research [3], [4] adding new
experiments and forecasting algorithms on the same analysing
data.
This paper is organised as follows: at first, the task is
presented, after that the existing situation is analysed, then the
solution is proposed, after that the experiments are shown and,
finally, conclusions are presented.
II. TASK DEFINITION
Inventory management is not the novelty, but still not every
company uses it in order to reduce inventory costs. The
inventory management task is to find out how much and when
to order:
•Objective: To keep enough inventory to meet customer
demand,
•Purpose: To determine the amount of inventory to keep
in stock – how much to order and when to order.
The task of the research takes place within the company,
which deals with assembling of microchips from raw materials
and selling them to customers. Therefore, there are raw
materials and finished goods warehouses with
inventories (Fig. 2).
Fig. 2. Assembling company’s inventories.
The authors [5] state that only 8 % of the companies have
the trained personnel for inventory management. Companies
are used to have big safety stock in order to fulfil the
demand [3].
The task of this research is to analyse existing inventory
management situation for finished goods inventories, to
propose the improvement on it and to compare the proposed
results with real demand data.
III. DATA ANALYSIS
The company’s data on sales, inventories in warehouses
were analysed for the period of 2014. The data analysis of
previous year’s microchip quantity fluctuation revealed that
there were items in stock with no sale in 2014. The results for
these items were the following: 16.69 % of total inventories
Raw materials
Work in
progress
Work in
progress
Finished goods
Work in
progress
Production Suppliers Assembling company Customers
Crystals
Packages
…
Crystals
Packages
…
Microchips
…
Company A
Company B
…
Raw material
warehouse Finished goods
warehouse
Microchips
Crystals
Packages
...
DE GRUYTER
OPEN
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(at the end of 2014) in warehouse did not have any movement
that year, 3.95 % of total inventories reduced their quantity
due to expiration of time, 5.13 % of total inventories having
no sale for the year of 2014 increased their quantity due to
production of new ones.
In addition, there were some items, whose assembled
quantity was higher than the sold one, meanwhile having big
amount of inventory in stock (Fig. 3). There were also items,
whose inventory level was high, meanwhile company
assembled new ones, therefore inventory level was higher than
the quantity of annual sales at the end of 2014 (Fig. 3).
Fig. 3. Annual operations of two microchips.
It was also detected that an inventory level was too high for
items, whose quantity on monthly sales was less than their
safety stock (Fig. 4).
Fig. 4. Inventory levels of two microchips.
In addition, it was noticed that an inventory level for one
item fell down to zero, which indicated out-of-stock situation
(Fig. 4). Therefore, inventory management was highly
recommended for this company.
IV. THE PROPOSED SOLUTION
In order to improve the existing situation of company’s
inventory control, it was proposed, firstly, to use inventory
management for inventory control, and secondly, to apply
agent system for inventory management [3], [4].
Effective inventory management consists of ABC
classification, demand forecasting algorithms and
replenishment policies [3]. Meanwhile, an agent system can
provide automatization of inventory management and timely
react to demand deviation from the forecasted demand, by
making corrections on replenishment policies.
The proposed system can be used in two modes: completely
autonomous mode, when an agent performs all of the
inventory management operations: ABC classification, future
demand forecasting, replenishment policy definition and
assembling order making, or it works as a decision support
system for a human inventory manager performing all the
mentioned activities except ordering by providing the
achieved results to an inventory manager and he decides
whether to accept or not these recommendations.
A. ABC Classification
ABC classification (or ABC analysis) is a basic supply
chain technique, often carried out by inventory
controllers/materials managers, and the starting point in
inventory control. This classification allows assigning
priorities to management time and financial resources. The
ABC analysis is based on the Pareto analysis, which says
that 20 % of the items contribute to 80 % of sales [6]. It
implies that a small portion of items in inventory contribute to
maximum sales (Table I). Typically less than 20 % of items
classified as class A contribute to as much as 80 % of the
revenue. Class B items do the next 15 % (80 %–95 %)
contribution to revenue. Items classified as class C generate
the last 5 % revenue.
TABLE I
ABC CLASSIFICATION
Number of items
Number of annual sales
revenue
Class A items
About 20 %
About 80 %
Class B items
About 30 %
About 15 %
Class C items
About 50 %
About 5 %
ABC classification usually categorises company’s products
into three classes in order to assign priorities in inventory
control [7]:
•Class A items are the most critical ones. These items
require tight inventory controls, frequent review of
demand forecasts and usage rates, highly accurate part
data and frequent cycle counts to verify perpetual
inventory balance accuracy;
•Class B items are of lesser criticality. These items require
nominal inventory controls, occasional reviews of
demand forecasts and usage rates, reasonably accurate
part data and less frequent but regular cycle counting;
•Class C items have the least impact in terms of
warehouse activity and financials and therefore require
minimum inventory controls.
The inventory management starting point is the definition of
class A items – microchips that represent the top 80 % of total
annual revenue; class B items are the next 15 % and class C
0
5000
10000
15000
20000
25000
mchipcode20 mchipcode71
Quantity, pcs
Microchips
Sold Assembled Stock on the end of the year
0
1000
2000
3000
4000
5000
6000
7000
1 2 3 4 5 6 7 8 9 10 11 12
Quantity, pcs
Months
mchipcode40 mchipcode38
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items are the last 5 %. Please, refer to [8], [9], [10] in order to
better understand calculations of ABC classification.
The results of ABC classification for the analysed company
by total annual revenue is presented in Table II.
TABLE II
FRAGMENT OF COMPANY’S MICROCHIP CLASSIFICATION
Microchips
ABC classification
1
mchipcode56
B
2
mchipcode71
A
3
mchipcode139
C
4
mchipcode49
C
5
mchipcode133
C
6
mchipcode33
A
7
mchipcode264
C
8
mchipcode471
C
9
mchipcode473
C
10
mchipcode38
C
11
mchipcode39
C
12
mchipcode40
A
13
mchipcode96
C
14
mchipcode620
B
15
mchipcode674
C
For class C items with low (or zero) demand volume it is
proposed to use make-to-order production strategy [11].
B. Demand Forecasting Methods
Demand forecasting is used to determine the quantity of
goods or services that will be purchased by customers in the
near future. Demand forecasting methods fall under these
categories:
Qualitative forecasting;
Quantitative forecasting.
Qualitative forecasting methods are typically used when
historical data are limited, unavailable, or not currently
relevant to perform a quantitative method for forecasting.
Forecast depends on skills and experience of forecaster(s) and
available information. This is a subjective method used and is
based upon how customers and experts think or feel a product
will sell [12], [13]. Many new businesses use this method
when writing business plans and projecting first year sales
[13]. Four qualitative models are as follows [12]:
Jury of executive opinion;
Sales force composite;
Delphi method;
Consumer market survey.
Quantitative forecasting methods take numbers or quantities
sold in the past to forecast how much will be sold in the near
future. Usually this forecast provides quantities for the next
sales year. Some examples of quantitative forecasting methods
include last period demand, multiplicative seasonal indices,
and simple and weighted moving averages. Each of these
methods use past data in different types of mathematical
formulas to determine how many products or services will be
sold at the same times in the future that is being
predicted [13].
Here, the following quantitative forecasting methods are
used in order to predict future demand using historical data on
demand for the period of 2014:
Naïve forecasting method;
Simple moving average forecast;
Weighted moving average forecast;
Exponential smoothing method;
Single moving average.
The forecasting accuracy can be measured using forecasting
errors defined as the difference between actual demand
quantity and the forecasted demand. Several measures of
forecasting accuracy are as follows: Mean Absolute
Deviation (MAD), Mean Absolute Percentage Error (MAPE),
Mean Squared Error (MSE), Running Sum of Forecast
Errors (RSFE) – indicates bias in the forecasts or the tendency
of a forecast to be consistently higher or lower than actual
demand. Tracking signal – determines if forecast is within
acceptable control limits. If the tracking signal falls outside
the pre-set control limits, there is a bias problem with the
forecasting method and an evaluation of the way forecasts are
generated is warranted [12]. More detailed explanation of
forecasting methods and accuracy measures is presented
in [3], [12].
Fig. 5. The real demand and forecasted demand for one microchip.
The inventory level graph with forecasting results for one
class A microchip is shown in Fig. 5. The calculations of
forecasting accuracy measures have given the appropriate
forecasting algorithm for this kind of microchip. The above-
mentioned forecasting methods have been applied to all
company’s microchips.
C. Replenishment Policies
An inventory system provides the organisational structure
and the operating policies for maintaining and controlling
goods to be stocked. The system is responsible for ordering
and receiving of goods: timing the order placement and
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
12345678
Quantity, pcs
Months
naïve
weighted moving average
Single moving average
exponential smoothing a = 0.3
simple average forecasting method
exponential smoothing a = 0.2
weighted moving average II
Real Demand
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keeping track of what has been ordered, how much, and from
whom [14].
There are a single-period and multi-period inventory
systems [15]:
•In a single-period inventory system, the items unsold at
the end of the period are not carried over to the next
period (for example, newspapers). The unsold items,
however, may have some salvage values.
•In a multi-period inventory system, all the items unsold
at the end of one period are available in the next period.
Here, the talk is about a multi-period inventory system.
There are two types of multi-period inventory systems: fixed-
order quantity models and fixed-time period models [14]. A
fixed-order quantity model initiates an order when the
specified reorder level is reached. This model requires
continual monitoring of inventories. In contrast, in the fixed-
time period model placing orders is available only at the end
of a predetermined time period [14].
Fixed-order quantity models attempt to determine the
reorder point, R, at which an order, Q, will be placed and the
quantity of Q. An order Q is placed when the inventory level
(currently in stock and on order) reaches the reorder point R.
Inventory position is defined as follows: on-hand plus on-
order minus backordered quantities [14].
In a fixed-time period model, inventory is counted only at
particular times, such as every week or every month. Counting
inventory and placing orders periodically are desirable in
situations such as when buyers want to combine orders to save
transportation costs. Fixed-time period models generate order
quantities that vary from period to period depending on the
usage rates. These generally require a higher level of safety
stock than a fixed-order quantity system [14]. Safety stock can
be defined as the amount of inventory carried in addition to
the expected demand. Safety stock must protect against stock-
outs during the review period and also during the lead time
from order placement to order receipt.
Some concluding remarks are the following:
•If demand is constant, reorder point is the same as the
demand during the lead time.
•If demand is uncertain, reorder point is usually set above
the expected demand during the lead time.
•Reorder point = Expected demand + Safety stock [15].
Having forecasted demand, it is possible to calculate safety
stock and reorder points for every microchip (see Table III).
Please, refer to [14], [15] for more details in calculations of
safety stock and reorder points.
TABLE III
FRAGMENT OF RESULTS OF FORECASTS, SAFETY STOCK AND REORDER
POINTS FOR MICROCHIPS
Microchips
Forecasted
Demand
Safety Stock
Reorder
Point
mchipcode33
1688
2081
3769
mchipcode40
12249
9096
21345
mchipcode20
1508
1603
3111
mchipcode102
537
5927
6464
mchipcode465
3625
6363
9988
V. EXPERIMENT 1
Having calculations on future demand and replenishment
policies, the proposed inventory management result check on
real data is of interest. The real data is the demand data on
first 5 months of 2015.
The idea of this experiment is that the proposed quantities
of inventories with replenishment policy results are compared
with real demand, and the quantities of inventories are
compared with company’s inventories (Fig. 6).
The experiment has shown the following results: inventory
level has decreased (Fig. 6), real data average inventory level
is 20860 pcs, the proposed inventory management system’s
average inventory level is 11705. It was not an out-of-stock
situation in both cases.
Fig. 6. Comparison of the inventory management system with real data.
0
5000
10000
15000
20000
25000
30000
35000
40000
01.01.2015
08.01.2015
15.01.2015
22.01.2015
29.01.2015
05.02.2015
12.02.2015
19.02.2015
26.02.2015
05.03.2015
12.03.2015
19.03.2015
26.03.2015
02.04.2015
09.04.2015
16.04.2015
23.04.2015
30.04.2015
07.05.2015
14.05.2015
21.05.2015
Quantity, pcs
Date
real data as of 2015
stock level using
inventory
management
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The experiment for another microchip has shown that
safety stock has been used during the lead time because of
unpredictably high demand – having average monthly
demand of 9800 pcs, that month demand is 17789 pcs. This
demand will be taken into account in further safety stock
calculations. Company’s average inventory level based on
real data is 6964 pcs, average inventory level of the
proposed inventory management system is 5955 pcs.
VI. EXPERIMENT 2
An improvement of company’s inventory management
situation is twofold: inventory management process
application and agent system realisation [3], [4]. Agent
system can provide the following benefits to the proposed
system:
It can learn from the past inventory, forecasting and
replenishment histories;
It can change demand forecasting techniques,
inventory control constants and replenishment policies
if needed;
It can ensure monitoring and control of large amount
of SKUs;
It can provide autonomy and pro-activeness.
This paper presents part of the ongoing research on
AEMAS (Assembling Enterprise Multi-Agent System);
therefore, it is related to the inventory management
agent [3], [4]. One of the functions of inventory
management agent is to make decisions on when and how
many microchips to assemble. It has the information of the
possible minimum reserves – safety stock, the future
demand forecasting algorithm and the production capacity.
Inventory management agent has the following behaviours:
ABC classification algorithm, future demand forecasting
algorithms and replenishment policies in order to avoid out-
of-stock situations, meanwhile decreasing the inventory
levels and their holding costs.
Fig. 7. The idea of agent system application for inventory management.
Excel file is provided as input data for agent system’s
ABC classification algorithm, then the forecasting methods
are applied to microchips and forecasting errors are
calculated, according to achieved results the best
forecasting method is chosen for every microchip that is
intended to be used subsequently. Replenishment algorithm
uses forecasting results, calculates safety stock and reorder
points. Meeting real demand, the agent-based inventory
management system compares it with the forecasted
demand and makes modifications in future orders to
assemble if needed. This inventory management system can
be fully automated or work as a decision support system for
an inventory manager [3]. The output of a fully automated
system is an agent decision, alternatively, if the system
works as a decision support system, then a manager decides
whether to agree or not with the proposed system
recommendations.
Fig. 8. Comparison of the inventory management agent system with real data.
ABC
classification Forecasting
algorithms
Replenishment
policies
Inventory
management
Agent system Inventory Management Agent
Microchips Forecasted
Demand Safety
Stock Reorder
Point
mchipcode33 1688 2081 3769
mchipcode40 12249 9096 21345
mchipcode20 1508 1603 3111
ABC
classification
Forecasting
algorithms
Replenishment
policies
Forecasted demand
Replenishment
Demand
Demand comparison
Replenishment policies
modification if needed
0
5000
10000
15000
20000
25000
30000
35000
40000
01.01.2015
08.01.2015
15.01.2015
22.01.2015
29.01.2015
05.02.2015
12.02.2015
19.02.2015
26.02.2015
05.03.2015
12.03.2015
19.03.2015
26.03.2015
02.04.2015
09.04.2015
16.04.2015
23.04.2015
30.04.2015
07.05.2015
14.05.2015
21.05.2015
Quantity, pcs
Date
real data as of 2015
stock level using agent
system for inventory
management
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The idea of the second experiment is that meeting the real
demand it is possible to change replenishment
policies (Fig. 7). Again, the comparison of inventory levels’
quantities is presented (Fig. 8).
The experiment has shown the following results: for the
first type of microchips the inventory level has decreased
compared with company’s real inventory level. Company’s
average inventory level is 20860 pcs; the average inventory
level proposed by the agent system is 11461 pcs.
Another microchip type has the following results:
company’s average inventory level is 6964 pcs; the average
inventory level proposed by the agent system is 5405 pcs.
At the end of May 2015, inventory levels had the following
results: the lowest inventory level was typical of the agent-
based inventory management system (due to timely reaction to
demand comparison), the highest one was shown by
company’s data. Agent-based inventory management system
showed the best results in comparison with simple inventory
management application and real data. The difference between
the results provided by the agent system and that of the
inventory management (Fig. 6, Fig. 8) was not so
considerable, as it was between real data and agent-based
inventory management system. This could be explained by the
following: at the end of 2014 the demand had an increasing
trend, but in February it started to decrease; therefore, the
agent system took into account this demand shift.
VII. CONCLUSION AND FUTURE WORK
Inventory management is essential to every company,
having inventories. Companies need to have stock, but in such
amount to avoid out-of-stock and overstock situations.
Inventory management can improve company’s inventory
control existing situation and decrease costs of the company.
Agent system, in turn, proposes the automation of this
process, it can support several forecasting methods and it
reacts to changes in the environment.
In this paper, the existing inventory management situation
is analysed, twofold improvement is proposed – to use
inventory management with the aim to decrease company’s
inventory level and holding costs by avoiding overstocks and
to apply the agent system in order to automate the inventory
management processes and to timely react to demand
deviations from the forecasted demand by making corrections
in replenishment policies.
According to experiments, it can be concluded that timely
reaction to changes in the environment can propose better
results. This can be done by a human or decision support
system comparing the forecasted demand with real and
making corrections in orders, or this can be done by an agent
as it is proposed here.
The next step of the present research will be the application
of achieved results of demand forecasts, safety stock and
reorder points into simulation software in order to achieve
more accurate results.
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Darya Plinere received her Mg. sc. ing. from Riga Technical University in
2006. She is a Research Senior Assistant at the Faculty of Computer Science
and Information Technology, Riga Technical University. Her research
interests include multi-agent systems, ontology engineering and supply chain
management.
E-mail: darja.plinere@rtu.lv
Arkady Borisov received his Doctoral degree in Technical Cybernetics from
Riga Polytechnic Institute in 1970 and Dr. habil. sc. comp. degree in
Technical Cybernetics from Taganrog State Radio Engineering University in
1986. He is a Professor of Computer Science at the Faculty of Computer
Science and Information Technology, Riga Technical University. His research
interests include fuzzy sets, fuzzy logic, computational intelligence and
bioinformatics. He has 235 publications in the field. He is a member of IFSA
European Fuzzy System Working Group, Russian Fuzzy System and Soft
Computing Association, honorary member of the Scientific Board, member of
the Scientific Advisory Board of the Fuzzy Initiative Nordrhein-Westfalen
(Dortmund, Germany).
Contact information: 1 Kalku Str., Riga, LV-1658.
E-mail: arkadijs.borisovs@cs.rtu.lv
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