ArticlePDF Available

Abstract and Figures

Stock cover is presented by a key performance indicator calculating the number of days of forecasted consumption which the current stock level can face. The identified problem in production companies, when calculating the “Stock Cover” indicator companies opt for one of three ways, among which there are large deviations in planning frequency and launching the orders for procurement or/and production, as well as a wide aberration in the volume of customer orders; which directly affects costs and competitiveness of enterprises. The idea of the authors of this study was to make “Stock cover” indicator and “Balanced Stock Cover” to be more adequate, and at the same time more efficient and effective in the operational management of a company
Content may be subject to copyright.
41
Nikola Atanasov1, Zoran Rakićević2, Danica Lečić-Cvetković3, Jasmina Omerbegović-Bijelović4
1,2,3,4 University of Belgrade, Faculty of Organizational Sciences
An Approach to Stock Cover Indicator
Adequacy
UDC: 657.474.51; 005.216.1
DOI: 10.7595/management.fon.2014.0026
SPIN´ 13, Belgrade, 05-06. November 2013.
1. Introduction
Modern customers expect an increasing number of types and quality improvement of products and services
(delivery, for example), as well as low prices. Such business conditions dictate the constant development of
products/services, the improvement of processes of production, supply and distribution (and maintaining of
stocks of raw materials and (semi) products), as well as the improvement of the management process (and
planning) – by using new methods and techniques of enterprise management. The goal that the authors of
this paper have is to present one of the possible ways to improve (increase adequacy) performance indica-
tors of the production/distribution enterprise and to apply it as a new approach to inventory management.
By applying the new approach in practice, the improvement of the methodology for determining (calculat-
ing) the value of performance indicators in inventory management of production and/or distribution enter-
prise, is verified. The authors also pointed out the functional connectivity of strategy/ methodology for
determining /calculating the value of performance indicators inventory management with “market charac-
teristics” (level of development) and “components of time series” of realized sales. These market charac-
teristics and components of time series directly influence the importance of certain types of stock cover
indicators in the vector that represents the complex indicator of balanced stock cover - which is shown in
the work of Atanasov et al. (2013).
2. Inventory management based on key performance indicators
The main task of inventory management is the coordination in the realization of common stock policies -
adopted by different members of the supply chain; while the common goal is balancing of material/goods
flows in achieving customers’ demands satisfaction and minimization of belonging costs (Giannoccaro &
Pontrandolfo, 2002). The key performance indicator (KPI) in inventory management, which is considered and
used in this paper is the “Stock Cover” (StC); it determines the priorities for replenishment of certain prod-
ucts/goods, and hence for the timing of production/or purchase orders launching (Omerbegović-Bijelović,
2006). StC, as the chosen KPI, cannot be considered independently of other KPIs in a production enterprise
Management 2014/73
Stock cover is presented by a key performance indicator calculating the number of days of forecasted con-
sumption which the current stock level can face. The identified problem in production companies, when cal-
culating the “Stock Cover” indicator companies opt for one of three ways, among which there are large
deviations in planning frequency and launching the orders for procurement or/and production, as well as a
wide aberration in the volume of customer orders; which directly affects costs and competitiveness of enter-
prises. The idea of the authors of this study was to make “Stock cover” indicator and “Balanced Stock Cover”
to be more adequate, and at the same time more efficient and effective in the operational management of a
company.
Keywords: key performance indicators, inventory management, stock cover indicator, balanced stock cover
indicator, adequacy of indicators
(also in the chains in which it operates); therefore, it is necessary to realize the impact of certain decisions
on the overall business and other KPIs used. According to Lin & Chen (2005), despite the use of KPI in in-
ventory management, production enterprises have a defined (among others) KPI that measures the level of
customer satisfaction - Customer service level (CSL) which represents a valuable indicator to those who
monitor the quality of management (Omerbegović-Bijelović, 1998) to decide whether the established con-
cept of inventory management achieves desired results. The basic concept of determining customer satis-
faction and appropriate ways to measure that satisfaction are listed in Meyr (2009), while further research
on the application of indicator CSL - with selective satisfaction of customers’ orders with limited stocks is pre-
sented in the paper authored by Lečić-Cvetkovic et al. (2010). Babarogić et al. (2012) pointed out the pos-
sibilities for maximizing the customer satisfaction in case of a manufacturing enterprise that operates in
conditions of limited production capacities and limited stocks of final products.
Inventory management cannot be considered separately from demand forecasting. In demand forecasting,
techniques of time series analysis can help. (Time series is a collection of data which describes the move-
ment of the value of some variable in a given period of time.) The time series has four components (Heizer
& Render, 2011):
Trend - gradually ascending or descending trend of data movement over time;
Seasonality - regular movements in time series (up or down), which are related to events that repeat;
these patterns of data behaviour can be repeated after a certain number of hours, days, weeks,
months or quarters;
Cycles - data patterns that occur every few years; there are business cycles (life cycles of products
and enterprises), economic cycles, etc;
Random variations - deviations from the rules in data patterns, which result in random or unusual sit-
uations that do not follow the visible pattern and that are difficult to predict.
By performing a detailed analysis of trends, seasonality, cycles and variations for each product, while keep-
ing in mind the development level of the market in which the enterprise operates, it is possible to generate
recommendations for determining the significance of components of the “Balanced stock cover” vector,
and, thus, for the right selection of inventory management strategy.
Table 1: Demand in relation to the market characteristics and components of the time series
Speaking of the model of supply chain and inventory management applied approach which is created in ac-
cordance with the market needs to be supplied, Hugos (2003) presents a simplified model of market cate-
gorization. In this model, the combinations of supply and demand are considered by classifying market into
four types:
1. Developing market is characterized by low and erratic supply and demand, with potential growth in
the future; the ability of utilization of the growth potential and development in these markets is based
on the efficient management of small quantities of products in stock and fast responses to demand
changes;
42
2014/73Management


  




 !









"
 !










!#
 !








!$
 !









2. Growing market is characterized by demand which exceeds supply, and the supply which is vari-
able, hence a successful business in this market directly depends on the provision of high quality cus-
tomer service (through timely shipments and deliveries of the entire quantity of ordered products); in
contrast to low costs of sales, inventory costs rise - in order to increase the level of stocks, which pro-
vides high quality of customer service;
3. In a stable market, supply and demand are high and relatively predictable, so that the relationship be-
tween supply and demand is balanced in the long run; this market needs the supply chain manage-
ment to focus on inventory optimization (by defining the min-max corridor), while maintaining a high
level of customer service;
4. The basic characteristic of the supply chain in a mature market is that the supply fully meets the de-
mand - which is stable or slightly decreasing (due to a large number of competitors and a wide range
of supply); in this market, flexibility in responding to changes in demand (in order to achieve a high
level of customer service quality) represents the challenge in supply chain management; stocks in an
obsolete market are needed to be maintained at a minimum level - to compensate for very high costs
of promotion and attracting customers from this market.
The market characteristics and components of the time series represent two important factors that define the
demand for a particular product, which is presented in Table 1. The above mentioned market characteris-
tics and time series components directly affect the strategy of inventory management and prioritization in the
selection of an appropriate type of indicator StC in inventory management.
3. Stock coverage indicators
The Stock cover, expressed through the StC indicator, represents the time period in which the available
stocks will be able to satisfy the estimated demand (Omerbegović-Bijelović, 2006). The StC indicator enables
the monitoring of stocks increase or decrease in the warehouse, as well as ordering of new stocks. In par-
ticular, the basic form of the StC indicator is calculated on the basis of stocks that are available in the en-
terprise and that can meet the current demand for products:
(1)
The authors of this paper have identified three different approaches to measuring StC (three types of StC):
a) Based on historical sales (Historical Sales):
(2)
b) Based on sales plan (Sales Plan):
(3)
c) Based on revised sales plan (Last Estimation):
(4)
As observation from different perspectives can provide different values of StC (which becomes the vector
StC = (StChs, StCsp, StCle)), the authors propose measuring the balance of the above mentioned three in-
dicators - components of the vector StC = (StChs, StCsp, StCle). Details of the measurement of balance of
three stated indicators and components of the vector BStC are presented in Atanasov et al. (2013). In the
  

  

  

  

43
Management 2014/73
same paper, the vector Balance indicator StC (BStC) whose value depends on the indicators StChs, StCsp
and StCle, is also defined.
In order to improve the application mode of the Balanced stock cover indicator (presented in the paper “An
approach to Lean inventory management by balanced stock cover”, presented at the conference Lean-
Tech’13), the authors propose further recommendations for the identification of significance coefficients for
each of the elements that constitute the BStC vector. In the identification of significance coefficients for
StChs, StCsp and StCle in a complex BStC vector it is also recommended to respect the following two cri-
teria: 1) “Characteristics of the market in which the enterprise operates,” and 2) “Identified time series in de-
mand of the observed product”. In order to transform such decision into the inventory management policy,
the authors propose a comparative analysis of: a) Sales data (identification of trends, seasonality, cycles and
variations) and b) Characteristics of the market in which the enterprise operates.
4. Case study
The concept of inventory management, by the application of the Balance indicator StC (BStC); it is applied
to a real case from the practice of the enterprise whose core business is the distribution of consumer goods
in the Serbian market. The planning horizon is one month. By applying Excel spreadsheet tables, a system
for calculating the StC according to three different approaches to StC measurement (“historical sales”, “sales
plan” and “last estimation”) was created, and the same was done for the Balance indicator StC - for four prod-
ucts (Tab. 2).
Table 2: The system of calculation of StC indicator and a balance indicator BStC
(source: Atanasov et al. (2013))
The target value of the StC indicator (defined by the enterprise management) is 1,5 [month], so that the
ideal point of balance is positioned in M (1,5; 1,5; 1,5). For product A, the value BStCA = 2,5 is obtained;
for product B the value BStCB = 2,3 is obtained; for product C the value of BStCC = 3,5 and for product D
the value BStCD D = 1,2. In order to increase the adequacy of the Balance indicator, the authors (in this
phase of the topic research) propose further development of the methodology (recommendations) for de-
termination of the priorities among StC types in the complex balanced indicator BStC.
For a more precise analysis of the “Stock cover” it is possible to use the technique of analysis of product
sales time series. By observing graphics in Fig. 1 (which represents the monthly amount/volume of sales of
products A and B), behaviour patterns of the curve (different trends of growth and decline and seasonality)
that can be useful in predicting future demand and stock levels can be identified.
44
2014/73Management






















     !"  # 
$%$
&

    !"  #  !  '  '"
 #"" '  ' " !" 
 ""   !'(   
$%$
&
 #" '  !(! !"  '" ' ! '
 " # !  "!  
    (( "!  
' '    ((  
 !    '"  
$%$
&
 "#!  # !  "  "!  ( '  # "
) "!(  '' !' " !"
) '    #  
)' !" '!  !!   
$%$
&
) ' '! '' " " "  '" '' ! '
          
Product A – it has a declining trend in demand with moderate seasonality of demand, which is typical
of an obsolete market (Fig. 1). When planning replenishment of stocks, it can be assumed that demand fore-
casting is based on historical data, so that the indicator StChs has a major importance in a complex indi-
cator BStC. In other words, target stocks should strive to value between 2.5 (BStC) and 1.4 (StChs) weeks
of stock cover.
Figure 1: The dynamics of demand for products A and B
For Product B - besides a slight increase in overall demand, expressed seasonality of demand that oc-
curs during the market placement of promotional products can be identified (Fig. 1). At the moment when
a promotional product is launched in retail (eg. 20 [%] extra products or more favourable price), sales of the
main product fall to a minimum and are close to zero. These characteristics suggest the possibility that this
is a market which is in transition from a developing to a growing market. In this case, promotional activi-
ties for each product are defined by the sales plan, so that the indicator StCsp has the greatest significance;
target stocks should strive to the value of 2,3 weeks of stock cover (in this case BStC = StCsp). If the im-
plementation of the promotional activities plan is being continuously monitored, and is in compliance with
the sales trend, the revised sales plan has higher accuracy than fixed sales plan, so, in this case, the indi-
cator StCle has a greater importance; or, in other words, target stocks should strive to the value between
2.3 (BStC) and 3.4 (StCle) weeks of stock cover.
Product C - in addition to the basic product, it includes a group of promotional products (Fig. 2). A decline
in sales of the main product, in periods when promotional product is introduced to the market, can be no-
ticed; while, by observing the total amounts, balanced demand is identified. By launching different promo-
tional products, the enterprise makes efforts to maintain sales at the previous level, which indicates the
characteristics of the market which is in transition from a stable to an obsolete market. In this case, there
are a number of promotional products, whose launching is planned in advance, and which directly affect
the demand for basic products and other promotional products. It is necessary to harmonize replenishment
with the sales plan that contains all the necessary data, or, differently put, target stocks should strive to value
between 3.5 (BStC) and 2.6 (StCsp) weeks of stock cover.
Figure 2: The dynamics of demand for products C and D
45
Management 2014/73
Product D - example of a product in which promotional products (products “D promo” and “D promo 2”)
indicate an expressed seasonality of demand and a decline in an overall demand, which points out to
an obsolete market. If the total sales of product D are observed, a negative sales trend can be noted,
which brings into question the accuracy of the sales plan (a highly unlikely probability that a sales plan fore-
sees sales declining), so business is planned based on the revised sales plan. In this case, the indicator
StCle has a greater importance; or, in other words, target stocks should strive to value between 1.2 (BStC)
and 1.4 (StCle) weeks of stock cover.
REFERENCES
[1] Atanasov, N., Lečić-Cvetković, D., Rakićević, Z., & Omerbegović-Bijelović, J. (2013). An approach to
Lean inventory management by balanced stock cover, Proceedings of LeanTech’13, Belgrade, Serbia
(in printing).
[2] Babarogić, S., Makajić-Nikolić, D., Lečić-Cvetković, D., & Atanasov, N. (2012). Multi-period Customer
Service Level Maximization under Limited Production Capacity, Int. J. Comput Commun, 7(5), 798-806.
[3] Giannoccaro, I., & Pontrandolfo, P. (2002). Inventory management in supply chains: a reinforcement
learning approach, Int. J. Production Economics, 78, 153-161.
[4] Heizer, J., & Render, B. (2011). Operations management, 10th edition, Prentice Hall.
[5] Hugos, M. (2003). The Essentials of Supply Chain Management, John Wiley & Sons, Inc., USA.
[6] Lečić-Cvetković, D., Atanasov, N., & Babarogić, S. (2010). An Algorithm for Customer Order Fulfilment
in a Make-to-Stock Manufacturing System, Int. J. Comput Commun, 5(5), 983-791.
[7] Lečić-Cvetković, D., Atanasov, N., & Omerbegović-Bijelović, J. (2012). Improvement of Supply Chain
Management by Bullwhip Effect Reduction, Proceedings of Sym-Org ’12, Zlatibor, Serbia, 226-234.
[8] Lin, J., & Chen, J.H. (2005). Enhance order promising with ATP allocation planning considering mate-
rial and capacity constraints, Journal of the Chinese Institute of Industrial Engineers, 22(4).
[9] Meyr, H. (2009). Customer Segmentation, Allocation Planning and Order Promising in Make-to-Stock
Production, OR SPECTRUM, 31(1), 229-256.
[10] Omerbegović-Bijelović, J. (1998). Metamanagement and quality of management (in Serbian:
Metaupravljanje i kvalitet upravljanja) - monograph, Zadužbina Andrejević, Belgrade.
[11] Omerbegović-Bijelović, J. (2006). Planning and preparation of production and service provision (in Ser-
bian: Planiranje i priprema proizvodnje i pružanja usluga), FOS, Belgrade.
Receieved: May 2014.
Accepted: October 2014.
46
2014/73Management
Conslusion
Different perspectives of available data observation (realized sales, planned sales, market characteristics, etc.) can gen-
erate incomplete criteria for decision making in the inventory management. In order to prevent such situation, the authors
of this paper have proposed the improved concept of inventory management by applying the balanced indicator StC. In
order to further identify the adequacy of stock cover indicators, the authors have proposed a basic concept of determin-
ing the importance of some StC indicators in the BStC vector. The identification of the importance of different types of StC
derives from the characteristics of the market in which the enterprise operates and also from the identified time series in
the observed product’s demand. The authors have also identified opportunities for further improvement of the presented
concept of inventory management.
47
Management 2014/73
Nikola Atanasov
University of Belgrade,Faculty of Organizational Sciences
atanasovn@fon.bg.ac.rs
Nikola Atanasov, MSc, received his MSc degree in Operations Management from the
Faculty of Organizational Sciences in Belgrade in 2012. Currently he is working on his
PhD thesis named: “A model for an appropriate set of performance indicators selection
for production management”. His current research interests are in the general area of
Supply Chain and Production Management. He is the author of more than 30 papers
from field of his interest.
Zoran Rakićević
University of Belgrade,Faculty of Organizational Sciences
zrakicevic@fon.bg.ac.rs
Zoran Rakićević, M.Sc., works as a teaching assistant at the Faculty of Organizational
Sciences (FOS), Department for Operations management (Chair of Production and
Service Management). He completed two master studies: Entrepreneurship and
management of SMEs (in 2012) and Operational Research and Computational Statistics
(in 2013), and is currently enrolled in PhD studies at the FOS. His research and teaching
area of interest includes: Planning of Production and Servicing, Operations
management and Entrepreneurship and Management of SMEs.
Danica Lečić-Cvetković
University of Belgrade,Faculty of Organizational Sciences
danica@fon.bg.ac.rs
Danica Lečić-Cvetković, PhD, is an associate professor of Production and Services
Management and e-manufacturing at the Department for Operations Management, at
the Faculty of Organizational Sciences of the University of Belgrade. Her research
interests and fields are related to Operations management, Production and services
management, e-manufacture. This author has published more than 70 papers in
journals and conference proceedings, published in international and national journals
and conferences.
Jasmina Omerbegović-Bijelović
University of Belgrade,Faculty of Organizational Sciences,
omeja@fon.bg.ac.rs
Jasmina Omerbegović-Bijelović, PhD, is a full time professor and Head of Chair of
Production and Service Management (Department for Operations management, Faculty
of Organizational Sciences, University of Belgrade). Her main areas of interest are:
Planning (of production, services and new business venture), Entrepreneurship and
Management of small and medium-sized enterprises, Resources management,
Servicing management, Tools for Quality improvement and Problem solving. She is an
author of the Metamanagement and Metacybernetic system concepts and has
published numerous papers in the fields mentioned above.
About the Author
... This value must be between the minimum and the maximum number of adequate performance indicators for considered performance. The control domain is defined by three constraints (3)(4)(5). ...
Article
Full-text available
Research Question: This paper presents a simulation model aimed at improving total adequacy calculation for performance indicators of the first phase of production management. Motivation: Defining a set of adequate performance indicators that are specific and highly important for the observed planning phase has a significant impact on the effectiveness of the production process management. The result of the developed model is the value of selected performance indicators adequacy. Consequently, it can be concluded whether defined performance indicators are essential for the observed phase or other performance indicators should be selected. The monitoring of defined performance indicators should provide complete information about the observed phase and improvement of the planning phase management. Idea: The main idea of this paper is to develop a user-friendly simulation model, in accordance with the basic principles of spreadsheet engineering, for improving the selection of adequate performance indicators. This model is intended for use in Small and Medium Enterprises (SMEs) for the improvement of production management. Data: The data used in the paper are grounded on the scientific articles, reviews and empirical studies, related to the performance and performance indicators in production management. The set of most adequate performance indicators based on a relevant literature review and used in the model, represents performance indicators for the planning phase that are the most suitable for SMEs. Tools: The simulation model is developed in a spreadsheet environment. The use of spreadsheet applications enables simple data entry, processing, editing, analyzing and output reports compiling. Findings: Simulation results show the overall efficiency of the developed model in the determination of adequate performance indicators. They influence overall adequacy of performance indicators on the observed phase. Contribution: The main contribution of this paper lies in the model developed to maximize the adequacy of the performance indicators, suitable for use in the production management of SMEs.
Article
Full-text available
Research Question: Optimal financing of the raw material inventories in the copper processing industry is perceived as a problem of choosing the financing sources and determining the purchase dynamics. Motivation: The company can realize the financing of raw material inventories from multiple sources under various conditions. The company efforts should be aimed at reducing the total costs that can occur in the process of purchase. Each company should simultaneously strive to satisfy the demand, but also to avoid keeping the excess of cash assets in inventories. Idea: The core idea of this paper is to evaluate the optimal financing of raw material inventories by the usage of the mathematical model that refers to the determination of financing sources, from which the required assets should be borrowed. Data: For the purpose of the case study example, the data used in the paper are approximations of information from the company and metal stock exchange. Tools: Excel was used to predict demand, while GLPK programme (GNU Linear Programming Kit) was used for the optimization of the defined model. Findings: The paper defines an optimization problem for determination of the financial sources, optimal periods, and the number of assets that will be used from these sources to secure continuity of the production process with minimum purchase costs. The paper also formulates a mathematical model of this problem and then illustrates it on the example of the real-life company for copper processing. Contribution: The results of this study show that such analysis gives the decision-makers a better insight into the possible scenarios while the final decision depends on their assessment, flexibility, attitude towards risk, need for security, etc.
Conference Paper
Full-text available
Stock cover refers to the length of time that available inventory will last if forecasted consumption occurs. It is represented by the key performance indicator calculating the number of days of forecasted consumption which the current stock level can face. The identified problem is that production companies in the fast changing environment are facing an increasing pressure to reduce working capital in order to manage planning and replenishment of a growing variety of products and to deliver a certain level of customer satisfaction. In practice, when calculating the "Stock Cover" indicator companies opt for one of three ways, among which there are large deviations in planning frequency and launching the orders for procurement or/and production, as well as a wide aberration in the volume of customer orders (hence stock); which directly affects costs and competitiveness of enterprises. Lean management of inventories assumes keeping a stock in defined corridors, triggering replenishment at the right time and continuously reducing working capital. The idea of the authors of this study was: make "Stock Cover" indicator more efficient and effective in the operational management of production and/or distribution companies using the "Balanced Stock Cover". The goal of this paper is to recognize the possibilities to apply "Stock Cover” as a key performance indicator set in a balanced way to support the decision making process in inventory replenishment.
Book
Full-text available
Disaster response supply chains face most of the same challenges as commercial supply chains, and they also face two additional challenges: 1. High Levels of Unpredictability - commercial supply chains are based on planning and predictability, but because disasters are not planned, disaster response supply chains must operate in highly unpredictable environments. 2. Ad Hoc Organization - disaster response supply chains do not exist before a disaster occurs, so they are assembled quickly when needed, and ways must be found to coordinate these supply chains where there are often no clear lines of authority among the many different participants involved. (4) (PDF) Essentials of Supply Chain Management. Available from: https://www.researchgate.net/publication/323558264_Essentials_of_Supply_Chain_Management [accessed Dec 08 2021].
Article
Full-text available
This paper will focus on a make-to-stock multi-period order fulfilment system with random orders from different classes of customers under limited production circumstances. For this purpose a heuristic algorithm has been developed aimed at maximizing the customer service level in any cycle and in the entire multi-period. In this paper, in order to validate the results obtained with this algorithm, a mixed integer programming model was developed that is based on the same assumptions as the algorithm. The model takes into account the priorities of customer groups and the balanced customer service level within the same group. The presented approaches are applied to a real example of Fast Moving Consumer Goods. Their comparison was carried out in several scenarios.
Article
Full-text available
p>In the competitive environment, many manufacturers are increasingly focusing on designing the systems that help them to manage variable demand and supply situations. Dynamic allocation of demands is very important in case of customer order allocations. Order promising and allocation can be based on the simple sequence that enables a manufacturing company to receive orders unless there are some other priority orders. Manufacturing company can also manage allocations of supply to key customers and channels, thereby ensuring that they can meet contractual agreements and service levels in the priority that yields better profit. This paper will focus on a Maketo- Stock order fulfillment system facing random demand with random orders from different classes of customers. Available-to-promise (ATP) calculating from master production schedule (MPS) exhibits availability of finished goods that can be used to support customer order allocation. This order allocation system is adapted in MTS (make-to-stock) production model and all orders are treated according to maximization of customer service policy. It allows incoming purchase orders as well as existing inventory on hand to be selected and allocated to customer sale orders and back orders. The system then automatically allocates the available stock to the selected sales orders. We developed an integrated system for allocation of inventory in anticipation of customer service of high priority customers and for order promising in real-time. Our research exhibits three distinct features: (1) We explicitly classified customers in groups based on target customer service level; (2) We defined higher level of customer selection directly defined according to company strategy to develop small and medium customers; (3) We considered backorders that manufacturing company has to fulfill in order to maximize overall customer service for certain customers.</p
Article
Available-to-promise (ATP) calculating from master production schedule (MPS) exhibit availability of finished goods that can be used to support customer order promising. This order promising mechanism is adapted in MTS (make-to-stock) production model and all orders are treated the same on first-come-first-served policy. However, increasingly mass customization results in production model gradually transfers from MTS to ATO (assembly-to-order) or MTO (make-to-order) in order to fulfill the requests from customers such as customer's preference materials or specifications for the ordered products. In ATO or MTO model, the manufacturing resource such as materials and capacity after order penetration point should be checked and allocated for order promising. Moreover, mass customization also drives the trend of customer demand to segmentation and prioritization according to product profit, sales growth potential, contracts or the relationships with customers. Therefore, this research proposes one order promising mechanism that applies mixed integer linear programming (MILP) model to prioritize allocating manufacturing resource for high profit products or important customers and to consider material and capacity constraints after order penetration point. Furthermore, this order promising mechanism takes thin film transistor liquid crystal display (TFT-LCD) manufacturing as illustration for these material and capacity constraints after order penetration point.
Article
Modern advanced planning systems offer the technical prerequisites for an allocation of “available-to-promise” (ATP) quantities—i.e. not yet reserved stock and planned production quantities—to different customer segments and for a real time promising of incoming customer orders (ATP consumption) respecting allocated quota. The basic idea of ATP allocation is to increase revenues by means of customer segmentation, as it has successfully been practiced in the airline industry. However, as far as manufacturing industries and make-to-stock production are concerned, it is unclear, whether, when, why and how much benefits actually arise. Using practical data of the lighting industry as an example, this paper reveals such potential benefits. Furthermore, it shows how the current practice of rule-based allocation and consumption can be improved by means of up-to-date demand information and changed customer segmentation. Deterministic linear programming models for ATP allocation and ATP consumption are proposed. Their application is tested in simulation runs using the lighting data. The results are compared with conventional real time order promising with(out) customer segmentation and with batch assignment of customer orders. This research shows that—also in make-to-stock manufacturing industries—customer segmentation can indeed improve profits substantially if customer heterogeneity is high enough and reliable information about ATP supply and customer demand is available. Surprisingly, the choice of an appropriate number of priority classes appears more important than the selection of the ATP consumption policy or the clustering method to be applied.
Article
A major issue in supply chain inventory management is the coordination of inventory policies adopted by different supply chain actors, such as suppliers, manufacturers, distributors, so as to smooth material flow and minimize costs while responsively meeting customer demand. This paper presents an approach to manage inventory decisions at all stages of the supply chain in an integrated manner. It allows an inventory order policy to be determined, which is aimed at optimizing the performance of the whole supply chain. The approach consists of three techniques: (i) Markov decision processes (MDP) and (ii) an artificial intelligent algorithm to solve MDPs, which is based on (iii) simulation modeling. In particular, the inventory problem is modeled as an MDP and a reinforcement learning (RL) algorithm is used to determine a near optimal inventory policy under an average reward criterion. RL is a simulation-based stochastic technique that proves very efficient particularly when the MDP size is large.
Improvement of Supply Chain Management by Bullwhip Effect Reduction
  • D Lečić-Cvetković
  • N Atanasov
  • J Omerbegović-Bijelović
Lečić-Cvetković, D., Atanasov, N., & Omerbegović-Bijelović, J. (2012). Improvement of Supply Chain Management by Bullwhip Effect Reduction, Proceedings of Sym-Org '12, Zlatibor, Serbia, 226-234.
Metamanagement and quality of management (in Serbian: Metaupravljanje i kvalitet upravljanja) -monograph, Zadužbina Andrejević
  • J Omerbegović-Bijelović
Omerbegović-Bijelović, J. (1998). Metamanagement and quality of management (in Serbian: Metaupravljanje i kvalitet upravljanja) -monograph, Zadužbina Andrejević, Belgrade.
Planning and preparation of production and service provision (in Serbian: Planiranje i priprema proizvodnje i pružanja usluga), FOS
  • J Omerbegović-Bijelović
Omerbegović-Bijelović, J. (2006). Planning and preparation of production and service provision (in Serbian: Planiranje i priprema proizvodnje i pružanja usluga), FOS, Belgrade. Receieved: May 2014. Accepted: October 2014. Management 2014/73