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Slow Moving and Dead Stock: Some Alternative Solutions

The change in the paradigm of inventory from
valuable assets to worthless assets forces a company
to vary its business process innovation. Companies
that have not implemented a just-in-time system
have to prepare process innovation that focused on
minimizing inventory and avoiding deadstock (Pinçe
& Dekker 2011, Van Jaarsveld & Dekker 2011).
Previous studies showed that the main reason for
slow-moving and dead stock is the low or rare
demands for goods (Mobarakeh et al. 2017,
Petropoulos et al. 2019), excessive stocks (Escalona
et al. 2019, Matsebatlela & Mpofu 2015), seasonal
product (Panda et al. 2008), design errors that make
these goods unsold (Fan & Zhou 2018), and the lack
of follow-up initiatives for stocks with low turnover
(Chuang & Zhao 2019). Slow-moving and dead
stocks certainly affect the newness or the
obsolescence of the products and the huge carrying
cost of stock (Fan & Zhou 2018, He & Wang 2019).
Thus, slow-moving and dead stocks should be
anticipated, and when they occur, these stocks
should be wisely handled as not to impact the
company’s finance negatively.
Manufacturing companies have three types of
inventories, namely, raw materials, work in process
and finished good inventory. Each type of
inventories requires appropriate inventory
management (Chuang & Zhao 2019; Lee 2005,
Rozhkov & Ivanov 2018, Tsourveloudis et al. 2000).
The study focuses on finished goods inventory since
finished goods inventories are the largest portion in
PT. ―SK‖ as one of the largest ceramic tile
companies in East Java. The uniqueness of the
stocks that PT. ―SK‖ has is that even though there
are few quantities of stocks per design type, it owns
millions of designs and colors. This study, thus, is
aimed at exploring inventory management of the
company in efficiently minimizing costs of slow-
moving and dead stocks.
The study contributes significantly to business
people in a creative way in overcoming dead stock.
The contribution is also to provide insights to social
responsibility activists that quite a few company
resources are without added value for the company.
However, these resources can be used to help
marginal communities. In addition to that, the study
can have positive impacts on other researchers to
explore the solutions of slow-moving items and
deadstock further more creatively.
Slow Moving and Dead Stock: Some Alternative Solutions
N.K. Sugiono & R.S. Alimbudiono
University of Surabaya, Surabaya, Indonesia
ABSTRACT: Slow-moving and dead stocks have been a classic problem of ceramic tile industries. Different
varieties of ceramic tiles have the potentials of remaining as dead stock. Thus, this study is aimed at exploring
some preventive alternatives and solutions in overcoming slow-moving and dead stock. Quite many compa-
nies experience similar problems, and companies take creative solutions. Semi-structured interviews, non-
participant observation, and documentation analysis are data collection methods used in this study. This study
showed that the demand forecast is a strategy to avert dead stock. In addition, the solution for the past dead
stock is that additional services should be provided to the customers by initiating sales of ceramics with beau-
tiful patterns and being committed to social responsibility activities through giving away the dead stock of
ceramics to factory laborers or marginal communities who are renovating their houses.
Keywords: Slow-moving inventory, dead stock, ceramic.
Advances in Economics, Business and Management Research, volume 115
17th International Symposium on Management (INSYMA 2020)
Copyright © 2020 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license ( 330
The study starts with an introduction related to
the background, the objective, and the significance
of the study. The second part of the paper focuses on
the literature review, and then, the paper continues
to in-depth research methodology process. The next
sections are about findings and their analysis closed
with the conclusion and limitations and prospects of
future studies.
Inventory has been an essential asset for a
company and has been used as a bumper over
uncertainty of market demands (Chuang & Zhao
2019, Nemtajela & Mbohwa 2017), unreliability of
material supply (Van Jaarsveld & Dekker 2011),
price protection (Gavirneni 2006), quality discount,
and lower ordering cost (Cárdenas-Barrón et al.
2018, He & Wang 2019). In contrast, since stocks
contained non-value-added costs, it should be
minimized (Tayyab & Sarkar 2016). Thus, the
company should be able to balance the needs and the
costs of inventory.
Slow-moving inventory is stocked, which is
considered very slow in its circulation and
distribution volumes (Dolgui & Pashkevich 2006,
Pinçe & Dekker 2011, Snyder et al. 2012).
Deadstock is an unsold stock and stored in a
warehouse for a long time (Snyder 2002). The
stipulation of slow-moving inventory dan dead stock
criteria is different for different companies because
these criteria are based on the managerial judgment
that is influenced by the type and the characteristics
of a business. Inventory that remains unsold beyond
six months is categorized as slow-moving inventory,
and inventory above one year is categorized as
deadstock (Goh & Lim 2014).
Slow-moving and dead stocks contain quite high
non-value-added costs, among which are warehouse
cost, maintenance cost to preserve the quality of the
stock, repair cost, and opportunity cost. This high
non-value-added cost can be avoided using various
approaches in forecasting demands more accurately
(Chuang & Zhao 2019, Dolgui & Pashkevich 2006).
In the meantime, to overcome slow-moving and
dead stock, sales promotion can be intensified using
the price reduction, premiums, bonus product, lotter-
ies, and coupons, sample, and lump-sum sales or do-
The study is applied research with an analytical
approach (Silverman 2000). The research problem of
the study is how slow-moving and dead stocks are
solved to streamline the cost of the finished goods
inventory at a ceramic tile company. The scope of
this study is on the finished goods inventory. It is
based on the consideration that the composition of
the finished goods is approximately 70% of the total
stock. The subject of the study is one of the ceramic
tile companies in East Java, named PT. “SK”. The
selection is based on the vulnerability of the product
regarding slow-moving and dead stock issues. The
data were collected using interviews, documentation
of the process, and observation of the sales activities
and production plan of the company.
Semi-structured interviews were conducted with
sales manager, production manager, accounting and
finance manager, warehouse division head, and
PPIC division head. The interview questions were
related to demand forecasting, sales forecasting
process, aspects considered in forecasting, demands,
production planning, logistics, inventory costs,
carrying costs, and other inventoriable costs along
with activities conducted to overcome slow-moving
and dead stock. The interviews were conducted 15
times with the duration of each interview between
45 minutes and 3 hours.
The non-participant observation was done by
observing the process of the flow of receipts of
goods from production division, the process of
structuring and releasing goods, scheduling plans,
and production and marketing plans, along with
meetings regarding the handling process of slow-
moving and dead stock. The observation was
conducted seven times, with the total time spent 15
hours. The document analysis was applied to
inventory stock cards, product costing, inventory
cost, carrying cost, production schedules, and sales
After the data had been collected, data processing
and analysis is done. The triangulation method was
used to test the validity and reliability of the data
(Silverman 2000). The data processing was started
with the classification of the inventory for every
type of ceramic tile. Afterward, the stock card was
analyzed and classified into slow-moving and dead
stock. Then, it was continued with the observation
and the measurement of inventory storage space, in-
ventory cost analysis, and minutes of sales and pro-
duction meetings. Next, an aggregate analysis of the
data was also conducted. At the final stage, discus-
sions related to alternative current and future solu-
tions to the problems were carried out by the com-
Slow-moving inventory is categorized as a
warehouse stock of 6 to 12 months old (Dolgui &
Advances in Economics, Business and Management Research, volume 115
Pashkevich 2006, Pinçe & Dekker 2011).
Meanwhile, deadstock is a warehouse stock of more
than one year old with no mutation at all (Snyder et
al. 2012). Slow-moving inventory, which is not
immediately addressed, have a chance of being dead
stock, and this stock will be soon obsolete (Goh &
Lim 2014). For the ceramic industry, stock inventory
has been a classic problem. The head of the
production mentioned that slow-moving inventory
potential indicators could be traced from stock cards
at the warehouse where the stock leaving the
warehouse divided by the available stock is less than
Slow-moving inventory and dead stock in PT.
"SK" are caused by internal factors, such as (1) error
of the warehouse that lacks discipline in updating
stock cards, (2) mistake in estimating sales demand.
This is due to the lack of careful marketing and less
attention to external and internal factors that will
affect future sales demand and impact on making the
sales forecast to be less precise, (3) IT mastery
limitations that result in the company not using
computerized system and concepts that can help
improve the accuracy of demand forecasting, and (4)
employee's capability limitations that result in a lack
of understanding of quality standards and policies,
ceramic quality targets and the worst of all, the
existence of an overproduction tolerance by the
management to prevent stockout. The management
believes that in-demand fluctuation should be
anticipated using sufficient inventory. Moreover,
there are several other reasons for the production
bottlenecks, such as broken machines, exhausted
materials, and manufacturing company's labor
problems that are responsible for the company's
problems with stocks. All these situations are in line
with the previous studies confirming the numerous
internal factors responsible for slow-moving
inventory and dead stock (Fan & Zhou 2018, He &
Wang 2019, Matsebatlela & Mpofu 2015).
Aside from internal factors, external factors also
trigger the occurrence of deadstock, among which
are the development of the design trends and colors
requiring the company's quick responses. The rapid
development of the design trends and colors results
in shorter product life cycles. The shorter the life
cycle is, the more rapid the planning, scheduling,
and production execution (Nemtajela & Mbohwa
2017, Snyder 2002). Based on the production
planning schedule, it can be seen that new types of
ceramic tiles are produced monthly. Adaptive and
responsive actions are required to boost customer
satisfaction and company sales. However, this
condition does not apply to classic design, for
example, white ceramic tiles. White ceramic tiles are
considered as classic ceramics since it is needed at
all times.
Slow-moving inventory and dead stock may incur
higher costs of inventories, especially on its
transporting cost (Goh & Lim 2014, Lee 2005).
Based on the company's records, 39 ceramic types
are slow-moving inventories with storage duration
between six and twelve months. Seventeen types of
dead stock with storage duration over 12 months and
some others with storage duration of about four
years have also been detected.
Table 1. Slow Moving and Dead Stock
Quantity (in box)
Slow Moving Inventory
15,967 boxes
Dead stock
3,335 boxes
The more the quantities of slow-moving items
and dead stock are, the more the holding costs the
company has to spend. In addition to the cost of
money that depends on the cost of the product, the
embedded cost in the stock relates to warehouse ex-
penses, repacking, fumigation, and opportunity cost.
Warehouse rent expense is calculated by measuring
the stock storage area, multiplying it by rent expense
per square meter. Fumigation expense is the cost of
spraying pests, bugs, and termites since they destroy
box packing and pallets. The spraying is conducted
every six months. Repacking expenses for the cost
of the boxes for inner and outer boxes should also be
considered when some stocks are defected, corrupt,
or damaged. Opportunity cost is money embedded in
inventory that should have been spent on other in-
vestments to gain a higher return. Opportunity cost
should also be considered when the capital invested
in slow-moving stocks is going with very slow turn-
over, even if the available stocks have turned into
dead stocks, and the invested capital has no more
turnover. The summary of slow-moving inventory
and dead stock expenses is indicated in Table 2 and
Table 3.
Of all these meticulous calculations, it is obvious
that there are huge costs spent by the company, and
these costs are periodic in nature, except for the
product-related costs. Product costs are imposed
when those products are totally unusable and/or de-
fected. The rental costs of the warehouse, the fumi-
gation, and the packing and opportunity costs borne
by the company are avoidable as long as the compa-
ny management abides by good practices of demand
forecasting. The proper forecast of the demand may
result in the improvement of the accounting system,
the quality of human resources, and the support of
information technology. PT. "SK" has been using an
accounting service and bought accounting software
Advances in Economics, Business and Management Research, volume 115
for their accounting and financial processes. The ac-
counting system is revisited annually.
Table 2. Slow-Moving Inventory Cost
Slow-Moving Inventory
Total Cost
warehouse rent
Rp 7,361,400
Fumigation cost
Rp 6,765,000
Packing cost
Rp 44,657,133
Product cost
(when unsold)
Rp 624,154,000
Rp 45,429,490
Table 3. Dead Stocks’ Cost
Dead Stock
Total Cost
warehouse rent
Rp 1,662,100
Fumigation cost
Rp 1,530,000
Packing cost
Rp 11,349,586
Product cost
(when unsold)
Total Cost = Rp
Rp 33,483,517
The demand forecasting process precisely
requires three essential stages, namely, design
issues, specification issues, and evaluation issues
(Boylan et al. 2014). The first stage is the design
issues. At this stage, the logistics manager and the
sales manager define the process of and the demand
forecasting objectives. The main reason for the sales
forecast is to be able to predict future customers'
demands. The results of the sales forecast are
communicated to the production division in order to
plan production to minimize slow-moving inventory.
The second stage is the specification issues using
the application of the judgmental method. At this
stage, the sales manager requests recapitulations of
stock cards weekly and monthly from the
warehouse. These data are combined with market
research to interpret consumer behaviors. Besides,
every week, the sales division of PT. "SK" holds
regular meetings. During these meetings, the
salesmen are asked to inform the company regarding
their observations regarding the customers and the
competitors. The sales division also searches for
information about trends and designs in the future by
seeing the trends and the designs of ceramic tiles
abroad. From these meetings, customers from
outside Java prefer strikingly bright designs, while
those from Java prefer classic designs and colors.
The purchases from customers outside Java result
in preventing dead stock because these consumers
tend to buy PT. "SK" stocks as a whole. However,
the distance between customers' warehouses and the
company's warehouse is the primary concern. Thus,
in every launch of a new ceramic tile type, the
salesmen from the company will offer designs
within or outside Java. When the consumers outside
Java make new orders, the management will not be
reluctant to produce the order, including calculating
the proportion of poor product tolerance. The
company's customers outside Java do not even mind
if PT. "SK" delivers more goods beyond their orders.
This condition is very different from when the
company handles customers in Java. PT. "SK" often
makes decisions to produce order-based ceramic
tiles for customers from Java without allowance for
bad stocks. This is because customers from Java are
not going to accept excess of goods, especially when
the company overproduces the tiles.
The last stage is evaluation issues, where the
management makes a judgment of the demand fore-
cast. This demand forecast then is socialized to all
the departments in the company, especially the pro-
duction department, purchasing department, and fi-
nance department. The socialization is aimed at
communicating and coordinating among depart-
ments. The readiness of the production capacity, the
adequacy of the material supply, and the readiness of
the cash flow play essential roles in the success of
the sales of PT. "SK". The revision in the drafting of
the demands should be integrated. However, PT.
"SK" should not ignore the slow-moving inventory
and dead stocks that have been occurring and will
possibly occur in the future even in the smaller
Slow-moving inventory and dead stock, which
has been happening are handled in the corporate
management using very creative approaches. One of
the approaches is made by preparing designs of
floors and walls by mixing various types of ceramic
tile designs. Providing the designs of the images that
match the area of a room is additional customer ser-
vice. This method is considered to be highly useful
to terminate slow-moving inventory. Approximately
60% of the slow-moving inventory has been sold
out. The cost-benefit analysis of the design cost is
implemented, and a designer's salary is more inex-
pensive than the cost of slow-moving items and dead
stock. The CEO also stated that post evaluation,
there is a potential to establish a ceramics design di-
vision to solve slow-moving items and deadstock;
and to provide better services to consumers.
Advances in Economics, Business and Management Research, volume 115
Dead stocks that have not been sold, after 12-
month storage, have partly been on sale, and the rest
of them have been given away in corporate social re-
sponsibility programs. PT. "SK", through its general
affairs department, opens an opportunity to factory
laborers or marginal communities to come and get
ceramic tiles for free with requirements that the tiles
must be used for their house renovation and not to
be resold. The last thing to be done on dead stocks is
to donate them to marginal communities with the
agreement to do good words of mouth. This is
aligned with the statement from the director of PT.
"SK" stating that social responsibility should be
based on the win-win concept. This means that the
donations should benefit both marginal communities
and the company because the receivers of the ceram-
ic tile donations are going to spread the donations on
both the social media of the receivers and the com-
pany. This will also positively impact the company.
The results of this study showed that slow-moving
inventory and dead stocks occur because of the
inaccuracy in demand forecast resulting in
inaccuracy in production planning or forecast.
Furthermore, the limited capabilities of human
resources and information technology significantly
impact on the business. As a consequence, the
company has to be accountable for high inventory
costs. The initiative to reduce slow-moving and dead
stocks is taken by improving the current system,
human resource capabilities, and information
technology. Another creative solution can be in the
form of giving additional services to customers and
creating win-win social responsibility programs for
the company and its customers.
The study focuses on exploring and explaining
possible solutions that the company may implement
in solving dead stock problems. The limitation of the
study is due to research subjects confined to the
slow-moving and dead stocks of the finished
products only. In addition, the cost of inventory
calculation is based on the four types of costs
traceable in accounting records. Thus, future
research may be expanded to an in-depth discussion
of all types of inventories and the cost of inventory
traceability by considering both the direct and
indirect costs of a product.
Boylan, J.A. Syntetos, & Karakostas, G. 2014. Classication
for Forecasting and Stock Control : A Case Study. Journal
of the Operational Research Society 59(4): 473-481.
Cárdenas-Barrón, L.E. Shaikh, A.A. Tiwari, S. & Treviño-
Garza, G. 2018. An Eoq Inventory Model with Nonlinear
Stock Dependent Holding Cost, Nonlinear Stock
Dependent Demand and Trade Credit. Computers &
Industrial Engineering 5: 105557.
Chuang, C.H. & Zhao, Y. 2019. Demand Stimulation in
Finished-Goods Inventory Management: Empirical
Evidence from General Motors Dealerships. International
Journal of Production Economics 208: 208-220.
Dolgui, A. & Pashkevich, M. 2006. Demand Forecasting for
Multiple Slow-Moving Items with Low Consumption and
Short Requests History. International Journal of
Production Economics 39(3): 161-166.
Escalona, P. Angulo, A. J.W. Stegmaier, R. & Kauak I. 2019.
On the Effect of Two Popular Service-Level Measures on
the Design of a Critical Level Policy for Fast-Moving
Items. Computers & Operations Research 107: 107-126.
Fan, D. & Zhou, Y. 2018. Operational Safety: The Hidden Cost
of Supply-Demand Mismatch in Fashion and Textiles
Related Manufacturers. International Journal of Production
Economics 198: 70-78.
Gavirneni, S. 2006. Price Fluctuations, Information Sharing,
and Supply Chain Performance. European Journal of
Operational Research 174(3): 1651-1663.
Goh, S.H. & Lim, B.L. 2014. Centralizing Slow-Moving Items
in a Retail Network a Case Study. International
Conference on Industrial Engineering and Operations
Management; Proc. Of 2014, Bali, 7-9 Januari 2014.
Indonesia: IEOM Society.
He, H. & Wang. S. 2019. Cost-Benefit Associations in
Consumer Inventory Problem with Uncertain Benefit.
Journal of Retailing and Consumer Services 51: 271-284.
Lee, W. 2005. A Joint Economic Lot Size Model for Raw
Material Ordering, Manufacturing Setup, and Finished
Goods Delivering. Omega 33(2): 163-174.
Matsebatlela, M.G. & Mpofu, K. 2015. Inventory Management
Framework to Minimize Supply and Demand Mismatch on
a Manufacturing Organization. IFAC-PapersOnLine 48(3):
Mobarakeh, N.A. Shahzad, M.K. Baboli, A. & Tonadre, R.
2017. Improved Forecasts for Uncertain and Unpredictable
Spare Parts Demand in Business Aircraft’s with Bootstrap
Method. IFAC-PapersOnLine 50(1): 15241-15246.
Nemtajela, N. & Mbohwa, C. 2017. Relationship between
Inventory Management and Uncertain Demand for Fast
Moving Consumer Goods Organisations. Procedia
Manufacturing 8: 699-706.
Panda, S. Senapati, S. & Basu, M. 2008. Optimal
Replenishment Policy for Perishable Seasonal Products in a
Season with Ramp-Type Time Dependent Demand.
Computers & Industrial Engineering 54(2): 301-314.
Petropoulos, F. Wang, X. & Disney, S.M. 2019. The Inventory
Performance of Forecasting Methods: Evidence from the
M3 Competition Data. International Journal of Forecasting
35(1): 251-265.
Pinçe, Ç. & Dekker, R. 2011. An Inventory Model for Slow
Moving Items Subject to Obsolescence. European Journal
of Operational Research 213(1): 83-95.
Advances in Economics, Business and Management Research, volume 115
Rozhkov, M. & Ivanov, D. 2018. Contingency Production-
Inventory Control Policy for Capacity Disruptions in the
Retail Supply Chain with Perishable Products. IFAC-
PapersOnLine 51(11): 1448-1452.
Silverman, D. 2000. Doing Qualitative Research: A Practical
Handbook. New York: Sage Publication.
Snyder. 2002. Forecasting Sales of Slow and Fast Moving
Inventories. European Journal of Operational Research,
140(3): 684-699.
Snyder, J.K. Ord. & Beaumont, A. 2012. Forecasting the
Intermittent Demand for Slow-Moving Inventories: A
Modelling Approach. International Journal of Forecasting,
28(2): 485-496.
Tayyab, M. & Sarkar B. 2016. Optimal Batch Quantity in a
Cleaner Multi-Stage Lean Production System with Random
Defective Rate. Journal of Cleaner Production 139: 922-
Tsourveloudis, N.C. Dretoulakis, E. & Ioannidis, S. 2000.
Fuzzy Work-in-Process Inventory Control of Unreliable
Manufacturing Systems. Information Sciences 127(1): 69-
Van Jaarsveld, W. & Dekker, R. 2011. Estimating Obsoles-
cence Risk from Demand Data to Enhance Inventory Con-
trola Case Study. International Journal of Production
Economics 133(1): 423-431.
Advances in Economics, Business and Management Research, volume 115
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Automobile dealerships are key players in the automobile industry. Their inventory policies are crucial to automobile supply chain management, yet they are largely unexplored empirically. Using data from General Motors dealerships, this paper proposes two simultaneous equation modeling (SEM) systems to examine the interactive and simultaneous effects of sales demand, order quantity and inventory level in dealerships. Our empirical results indicate that high demand leads to high inventory levels (sales effect), while high inventory levels stimulate sales demand (demand stimulation effect) in a dynamic and uncertain environment. We also find that inventory cannot stimulate demand infinitely. This study is among the first empirical investigations to experiment with a SEM system applied to finished-goods inventory management in the automobile supply chain. Together, our methodology and results provide several avenues for developing finished-goods inventory policies in business management.
Inventory management is a focus for operation management scholars and operations managers. Previous literature mainly investigates the relations between firm's inventory and financial performance. However, the link between firm's inventory and non-financial performance (e.g., social outcome) is missing. This study takes a fresh perspective to examine the impacts of supply-demand mismatch on firm's safety performance. Based on a sample set from fashion and textiles related manufacturers, the analyses suggest that supply-demand mismatch (measured by inventory volatility) can lead to a higher likelihood of safety incidents. The impact is more salient when the firms are operating in complex (labour intensive) and tightly coupled (high production capacity utilization) environments. This study provides significant contributions to the inventory management literature, occupational health and safety management literature and operational managers.
The supply chain performance depends on accurate demand forecasting. This becomes more critical when it comes to non-contract spare parts service supply chains. This is because of the fact that customers are not obliged to place an order for the required spare parts to its Original Equipment Manufacturer (OEM) due to the availability of multiple suppliers. The business aircraft spare parts supply chains are the ones most affected by this phenomenon because their travel pattern and usage is totally unpredictable in comparison with passenger airline carriers. These highly uncertain and unpredictable demands and subsequent inaccurate forecasts have severe financial consequences. It is also computationally expensive to predict demand forecast for each part due to huge number of spare parts in business aircraft’s supply chain. Hence, in this paper the objective is to investigate forecasting methods, their variants and artificial intelligence (AI) methods, developed for irregular demands, to propose best method variant that is capable of accurately forecasting not only uncertain but unpredictable demand e.g. business aircraft’s spare parts supply chain. We retained Boot Strapping (BS) method as the most suitable base method for uncertain and unpredictable demand forecasting. This is because of its inherent ability to reduce error due to resampling with replacement. The point and interval (existing), and sliding window (proposed) BS methods are implemented in Matlab and results of demand forecasts are compared with the forecasts generated from benchmarked existing forecasting methods as: Croston, Croston variants (SBJ, SNB, TSB), moving average (MA), single exponential smoothening (SES) and Commercial (proprietary black box) methods. The data used in this study is collected from Dassault Aviation. The results demonstrate that proposed sliding windows BS variant with ‘Mean’ function outperformed 75% of the spare parts with significant financial gains in terms of inventory holding and shortage costs.