Multi-echelon inventory control in supply chain ...

Technical Journal, University of Engineering and Technology (UET) Taxila, Pakistan

Vol. 20(SI) No.II(S)-2015

Multi-echelon inventory control in supply chain management (SCM) K. Naeem1*, M. Mahmood1, S. Maqsood1, M. Ullah1, I. Hussain1 1

Department of Industrial Engineering, University of Engineering and Technology, Peshawar, Pakistan *Corresponding Author: [email protected]

are to integrate all the activities starting from supplier to the customer so that optimized inventory level is obtained which result in holding cost minimization. Multi-echelon SC is of main focus which is to balance the effectiveness of the central inventories with the responsiveness of distributed inventories so as to provide high system performance without excessive investment in inventory. Inventory is the 40-60% investment of the organization and efficient utilization of such huge percent of investment really makes the difference (adexa.com). The proposed approach used to manage the inventory in Olympia Paper Mill is Multi-echelon inventory management. The most suitable and widely used way to tackle with Multi-echelon inventory is to handle it by decomposing it into the single echelon inventory. This decomposing is done for purpose of easy monitoring, managing and controlling performance. The main objective to achieve by applying Multi-echelon is to deliver to the customer with improved service level and optimal inventory along all the echelons. Multi-echelon can be considered as multi-level distribution network where each level is considered as an echelon in a network. In Olympia Paper Mill raw material has to pass through various steps to from Olympus Duplex Board.

Abstract: Inventory is lifeblood in an organization. Multiechelon inventory of Olympia paper mill, Khyber Pakhtonkhwa is optimized by efficient management of the organizational supply chain (SC). A supply chain is designed starting from raw material receiving from supplier up to finished good delivered to the customer. The on-going setup of the selected organization is traditional based on the experience of the current managers while in the recommended procedure, the demand is forecasted, produced and filled using various scientific model. The inventory level at various level is reduced which in turn reduce the cost. Decisions such as safety stock SS, ROP Reorder Point and outsourcing are recommended based on engineering knowledge. The designed SC is modelled in an Expert System tool, Application Manager (AM). With the efficient development of SC and automated tool, the inventory level and associated cost of the company is reduced by 35%. Also lead time of the product to customer is reduced from 3 days to 1 day. Key words: Supply chain management, inventory control, Material Requirement Planning MRP, Multi-echelon, Expert system, Application Manager.

I.

INTRODUCTION

Supply Chain Management (SCM) is the integration of the activities that procure materials and services transform them into intermediate goods and final products, and deliver them to customers (operations management 8th edition). Global competition is between the SC and companies are more emphasizing on the effective SCM. SC costs a major percent of the sales and which demands of a suitable decision making to make jobs or purchase orders from suppliers. SC strategy is also of core importance for planning and controlling the activities related to supplier. SCM is a vast area and it encompasses areas as inventory control, forecasting, customer demand, bill of material BOM, Master production schedule MPS, Material requirement planning MRP, forecasting and expert system for decision making. To design a multi-echelon Supply Chain with optimal inventory level of Olympus Duplex Board(A product of Olympia Paper Mill) Our objectives of working with SC

II.

LITERATURE REVIEW

The proposed model by Fahimnia et al. , which incorporates multi-time periods, multi-products, multiplants, multi-warehouses and multi-end users, considers the real-world variables and constraints. In model, the first echelon consists of multiple production plants and the second echelon includes multiple distribution centres (warehouses).For SC production alternatives at each plant are modelled with certain assumptions to achieve the objective to minimize cost, based on the integration of Aggregate Production Plan and Transportation/Distribution Plan, the paper developed a mixed integer formulation for the optimization of a twoechelon Supply Network (SN) [i]. Different production planning algorithms for each Weaving, Starching, Warp making of textile industry phase are required. YFADI is a

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Technical Journal, University of Engineering and Technology (UET) Taxila, Pakistan decision support system DSS that has been developed for the production planning and scheduling by the Textile Company and it has Openness, Rapid and efficient data interchange and User-friendliness. YFADI has been developed aiming at inventory reduction, increased productivity, improved customer service and control of the business in a textile industrial unit [ii]. Integration of the network in organization and for this integration of the network management of SC is the like a building block for optimization of the links. Merging internet and SC is to call e-SCM. The Internet can have three main impacts on the SC. One is the impact of e-commerce, which refers mainly to how companies can respond to the challenges posed by the Internet on the fulfillment of goods sold through the net. Second impact refers to information sharing, how the Internet can be used as a medium to access and transmit information among supply chain partners. third type of impact of the Internet on SCM refer to it as knowledge sharing to access data analysis and modeling to jointly make a better plans and decision making. The issues being addressed are; impact of internet on SC processes, analysis of e-SCM and review of existing literature [iii]. The SC management activities aim to reduce manufacturing cycle time, delivery lead time and inventory reductions. It starts with supplier who provide raw material and as goods are manufactured sent to whole sales or distribution centers that ships goods in ware houses. At the end, end users buy and use the product. Management of SC is a big deal and efficient integration of all the activities play the vital role. This paper mainly emphasize that going for real test of the integration activities of SC its more strong decision to go for simulation and evaluate the operation performance prior to execution of the plan [iv]. The edges in the lower raw material cost and cheaper cost of lab is no more an edge. The competitive advantage is now on the basis of implementation of SC and logistics management. The research is diversified to 3 areas to explore fundamental issues and factors i.e. Inbound Logistics and SC, Physical Distribution and Transportation and Strategic Logistics and SCM. Five recommendations on basis of research are put forward The development of government and industrial sector policy (These policies set from each sector should facilitate the implementation of SC and LM in the country), The development of information technology (IT) for SC and LM, and its application with reasonable cost and suitable implementation, Strategic alliance research. This includes ways and methods of creating strategic alliance and how to select appropriate partners for building their SC, The importance of SC best practices should be collected and documented. The need of human resource development SC education and training system together with knowledge and resource sharing should be developed for Thai industries [v].


The research is based on extending the Clark and Scarf model of Multi-echelon (the units flow from one echelon to another in sequence, the new units entering the most upstream echelon and the finished goods meet the demand at lower stream echelon). With certain assumptions and the theory of dynamic lead time management decision regarding whether to slow or speed up the movement of units ordered was made possible [vi]. Focusing on lead time and demand variation. The Multiechelon models consider probabilistic demand and deterministic lead times, but in reality lead time can also be probabilistic due to uncertain events in products, inventory and transport. A Multiechelon inventory model is built by using EOQ model, based on demand following Poisson distribution and lead time following Normal distribution. The stated model as compare to multi echelon inventory models concludes that both the demand and lead times are uncertain and more practical. The models for both distribution centre inventory and retailer inventory are build separately and then combined together to obtain distribution model for two echelons. Both models follow economic order quantity formula by which ordering quantity, optimal order quantity and optimal period are obtained [vii]. Simulation technique is applied to the control of inventory and other business process parameters such as order fulfilment, procurement and supply planning. The methodology adopted by the author is discrete event simulation. ARENA software is used to simulate all the activities from supply of raw material to delivery of finished goods. The developed model is passed through some experiments like experiment in the first stage describe relationships; the control parameters are identified and then used to check its effects on the key performance indicators. In the second stage the operating range is identified. The controlled parameters are ABPT (administrative business process time) EPT (executive process time). The first mentioned is the time between the arrival of customer demand to the customer service centre and distribution centre when shipment order is received to it while EPT is the time between the distribution centre when shipment order is received there and when the end item is delivered to the customer [viii]. Research on the Inventory Control and Optimization Algorithms of Supply Chain described the inventory optimization and control in supply chain management. In distributed factories and warehouses, control of inventory is a difficult task An optimizing inventory model is developed for the supply chain distributed in factories and wholesalers. The inventory control conditions are improved and inventory level is reduced by updownstream node of information sharing. The inventory is optimized by connecting suppliers, distributors and retailers and this makes the production of products very fast, value increased and most important on time delivery to the customer at reasonable costs (as the total cost of inventory is equal to the sum of inventory holding cost,

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Technical Journal, University of Engineering and Technology (UET) Taxila, Pakistan shortage cost and transaction cost). Optimization is achieved through reduced inventory, lowest costs, effective communication and the availability of resources [ix]. The integration of supply chain (SC) of a manufacturing firm is essential for optimization of the inventory, effective information flow, reduced lead time and increased customer satisfaction. Though the inventory optimization is based on scientific models, designing a SC model for a company is an art because each company is different from the other. In the undertaken research, a SC framework for a local paper industry is designed, taking into the consideration of both inventory and information flow. Further an expert system using Application Manager (AM) is also developed for the industry to automate their SC activities. The model is presented in the subsequent section. III.


(low inventory effect productivity) and high investments (due to high inventory level). To address the issue, Inventory models like EOQ, POQ, News vender, Base stock q-r etc are used to manage the inventory of products in an optimal way. III.4 Economic order quantity model EOQ is selected among different inventory models because its assumptions are very close to the real conditions of Olympia Paper Mill. The assumptions are; constant demand, fixed lead time, constant setup time, constant purchase price and replenishment of inventory at once. The demand of the stated industry is almost constant (demand varies, but the upper cap of the industry is 20 M ton, so demand per day is always 20 M ton. Demand greater than 20 M ton is outsourced), as the inventory level reaches to zero the new order is immediately replenished. When the order is received and is stored there is a cost (holding cost) associated with it. Similarly the ordering cost is incurred each time by placement of an order. The lead time is fixed i.e. 3 days. EOQ formulae for EOQ and total cost calculation are (2.1) and (2.2) respectively, where A is setup cost, H is holding cost and D is demand.[11] Q ∗= √ (2AD/H) (2.1)

METHODOLOGY

The supply chain management (SCM) model for the company is designed in the following way; Study of the material flow from the supplier, inner house manufacturing, up to customer.  Data collection related to inventory, suppliers, customers, average demand, and lead time from the Olympia Paper Mill  SC Model development using process flow analysis and standard inventory models.  Developing of the proposed model using AM.  Demonstration of the SC model of the paper industry with an example.

TC = A + H

(2.2)

III.5 Economic order production model EOP differs from the EOQ on the basis that unlike the EOQ model in which inventory is replenished instantaneous, the order is received over a period of time. it builds the inventory and deliver items to the customers at the same time. The assumptions for EOP model are same as that for EOQ model [xi]. The optimal quantity in this case is Q ∗ = √ (2AD/H) ∗ √ (P/ P − D) (2.3)

The scientific knowledge used to make the SC model for Olympia Paper Mill is presented below; III.1 Demand Forecasting The demand of end item (Olympus Duplex Board) of Olympia Paper Mill is determined by customer direct orders and/or (in some cases) it is forecasted by using quantitative approach of Moving average. The demand of end item for nine days is the historical demand of each day and the demand for day 10th is forecasted by the stated method.

III.6 3.7 Material requirement planning MRP is a tool that control the production on the shop floor and manage the manufacturing processes, delivery schedule and purchasing activities. The system insures end items/component parts availability for production or delivery to the customer at optimal level of inventory. The stated industry has no such system to help it out in determining their ability to fulfil customer demand in present or future, which products needed to be produced ,in what quantities and control of raw material they purchase each time for production of paper. To address the stated issues and attain other benefits like minimization of inventory level and the holding cost associated, MRP tool is developed.

III.2 Moving Average The future forecasting demand for period using moving average technique is that a sequence of series of previous data is averaged (sum of the data of sequential periods divided by the number of periods taken). The formula for moving average is “Σ demand in previous N periods divided by N”, like F 5 = {D1 + D2 + D3 + D4}. ./ 4 where F 5 is forecast of the period 5 and D1, D2, D3 and D4 represent demand for period 1,2,3,and 4 respectively [x].

III.7 The inputs to mrp The inputs to MRP system are Bill of Material (BOM), On hand inventory (inventory records) and Master Production Schedule (MPS). The inputs to MPS are forecast and demand from the customer. It basically states

III.3 Management of inventory The inventory level is not properly managed then the whole SC is disturbed both in the sense of productivity

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Technical Journal, University of Engineering and Technology (UET) Taxila, Pakistan the amount of product that will be produced and the time in which they will be needed. The time horizon is divided into buckets which may be months, weeks or days. The third input to MRP is the inventory record file which states how much inventory is on hand or ordered to meet the demand.

Chemicals

Aluminum Sulphate

III.8 The mrp outputs The MRP outputs are generated when the inputs are provided and the steps of working (MRP procedure) are applied to them, these outputs are Planned Order Releases, Exception reports and Change notices. Exception report is output of MRP which describe basically the discrepancies between what was expected and what is achieved for example the products are expected to be produced let’s say 20 M tons but is actually produced 18 M tons due to certain reasons. IV.


Rosin

Soap Stone

H2SO4

Brighteners

Methyl Violet

Fig. 3. Raw material B breakdown structure Olympus Duplex Board raw material has ( i) Paper Board costs 3075000 for 100 metric ton (MT) and (ii) chemicals composed costs 182250 for 100 MT addition cost of utilities is 1800000. V.

PROPOSED SC MODEL

The proposed SC Model for Olympia Paper Mill, khyber pakhtonkhwa (KP) is shown in Fig. 4.

PROPOSED MODEL FOR INVENTORY CONTROL MODEL

Olympia Paper Mill mainly manufactures four products and OLYMPUS DUPLEX BOARD is selected for study purpose. The manufacturing setup consists of a single machine which produces different products as the raw material is changed. Raw materials of OLYMPUS Duplex Board are (i) board and (ii) chemical. 1 Ton of duplex board requires 1.45 Ton raw paper and 0.0741 Ton of chemical as shown in Fig.1 . As the OLYMPUS DUPLEX BOARD is a final product ordered by the customer, so it is assigned a code of Level 0. Level 0

Olympus Duplex board Independent-demand

Level 1

Paper Board

Chemicals

Dependent-demand

Fig. 1. Graphical BOM of Olympus Duplex Board Fig. 4. Proposed SC model for the Olympia Paper Mill.

This is the independent demand by the customers. Raw material for the final product is Paper Board and Chemicals as shown in Fig.2 and 3 respectively. The contents used in raw materials are also shown in the Fig. 2 and 3 respectively.

Customer demands are received at the industry through an AM developed interfaced. If the demand can be full filled using the stored finish good inventory (FGI), the orders are full filled right away. Else the demand of the product along with the due dates in the form of MPS is calculated and sent to the production department in the form of MRP releases. The required amount of raw material is analysed for the production of the product and is checked in the raw material inventory (RMI). If sufficient, then it is supplied to manufacturing department otherwise MRP for raw paper and raw chemical is generated and ordered to the supplier 1 and 2 respectively. As the raw material is supplied to the manufacturing plant, production of the final product is carried out. The product

Paper Board

Board Scrap

Copy paper scrap

Fig. 2. Raw material A breakdown structure

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Technical Journal, University of Engineering and Technology (UET) Taxila, Pakistan is finally shipped to the customer as FGI or end item inventory. As this is a tedious job and consumes time, the SC Model is automated using AM. The developed module is verified and validated using an actual demand data of 10 periods. The data was provided by the production department of the industry. VI.

2 3 4

5 6

TOOL DEVELOPMENT USING APPLICATION MANAGER (AM)

The developed SC Model is automated using AM. AM for Windows, shown in Fig. 5, is a professional development tool capable of building sophisticated client/server applications. AM includes a wealth of database access and remote system connectivity features that enable you to develop powerful client/server applications.


Demand of Product Schedule Receipts (SR) On-hand Inventory of Product Lead Time -

Due Dates EOQ ROP

Export Values to Excel Dynamic Graphs

The input to the AM is number of periods (days), demand for the product (M. Ton), schedule receipts (M. Ton), on-hand inventory of the product (M. Ton) available at warehouse and lead time of product (days). The output of AM is planned order releases (M. Ton) of product and raw material, Due Dates (period/day), economic order quantity (M. Ton) of product and raw material, re-order point (M. Ton) of product and raw material, data export to a designated Microsoft Excel files defined in the model, and results presentation to the management using dynamic graphs that updates instantly as the data is exported to Excel. The developed model in AM is explained using the data provided by the industry.

PRODUCT MRP (On-hand Inventory = 30)

TABLE II THE MRP RESULT OF OLYMPUS DUPLEX BOARD (PRODUCT) SR. DEMAND P.O. NO RELEASES

Fig.5. Main window The Input data required by the AM SC Model and the output results it generates is presented in Table I.

1

20

8

2

18

20

3 4 5 6 7 8 9 10

22 22 25 23 24 18 25 22

20 20 20 20 18 20 20 0

First, the MRP for the product is generated and its releases are shown in Table II. This data is entered into the AM module as shown in Fig. 7.

Fig.6. Inventory Management System’s GUI As the model is run, a welcome screen appears, as shown in Fig. 6. As “ok” button is pressed, input parameters are needed to be entered that are given in Table I. TABLE I INPUT DATA AND OUTPUT RESULTS OF AM

Sr. No 1

INPUT PARAMETERS Number of Periods

OUTPUT

Fig. 7. Data input to model It can be seen that in period 3 the demand is more than 20, i.e 22 so Net Requirement becomes 20 and 2 is sent to

Planned Order Releases

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Technical Journal, University of Engineering and Technology (UET) Taxila, Pakistan the supplier as an outsource value. Same is for the other

POR is 8 M. Ton which requires 11.76 M. Ton of raw paper, calculated using the mentioned ratio.

value of demand.

0

2

2

0

2

3 4 5 6 7 8 9 10

2 2 5 3 4 0 5 2

5 3 4 0 5 2 0 0

TABLE V THE MRP OF CHEMICAL SR. DEMAND P.O. NO RELEASES

CHEMICAL MRP (On-hand Inventory = 9)

SUPPLIER MRP

TABLEIII THE MRP RESULT OF SUPPLIER SR. DEMAND P.O. NO RELEASES 1


The extra demand greater than 20 M. Ton is outsourced to the supplier as shown in Table III. The generated product and supplier MRP is shown in Fig.8.

1

0.59

0

2

1.48

0.44

3 4 5 6 7 8 9 10

1.48 1.48 1.48 1.48 1.33 1.48 1.48 0

1.48 1.48 0 0 0 0 0 0

The demand for the chemical is the release of the product with the ratio of 1 = 0.074. The MRP values of Chemical are shown in Table V. The manual MRP results of raw paper and chemical are shown in Table IV and V. The same results are obtained from the AM model shown in Fig. 9. The total releases of Raw. Paper and Chemical are 128.02 M. Ton and 3.41 M. Ton respectively.

Fig. 8.The MRP for product and Supplier generated

RAW. PAPER MRP (On-hand Inventory = 116)

The AM generated MRP results in Figure 8 are consistent with the manual calculation of MRP shown in Table II and Table III respectively. Now to produce the product order, raw paper and chemical are needed. So the demand data of both of the raw material is given in Table. IV and V respectively.

Fig. 9. The generated MRP for raw paper and chemical The economic order quantity (EOQ) and re-order point (ROP) are calculated and shown in Table VI.

TABLE IV THE MRP OF RAW.PAPER SR. DEMAND P.O. NO RELEASES 1

11.76

0

2

29.40

13.36

3 4 5 6 7 8 9 10

29.40 29.40 29.40 29.40 26.46 29.40 29.40 0

29.40 26.46 29.40 29.40 0 0 0 0

TABLE VI EOQ AND ROP VALUES Total Release EOQ (M. (M. Ton) Ton)

ROP Ton)

Raw. Paper

128.02

90.47

72.99

Chemical

3.41

10.52

3.98

Values

(M.

The final results are shown in Fig. 10. First column shows the results of the product. Second column shows the results of the raw.paper. Third column shows the results of chemical. The product results are utilized in the manufacturing shop floor, as how much to produce and what is the deadline. The results of raw.paper and chemicals are the input material required to produce the

The values of Raw Paper are shown in Table IV. The release of the Product MRP is the Demand for the Raw. Paper with the ratio of 1 = 1.45. At period 1 of Table II,

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Technical Journal, University of Engineering and Technology (UET) Taxila, Pakistan required quantity of the product. It is utilized by the inventory department to provide the required material at the right time.

Objectives

Fig.10. Final Output The dynamic graphs for all of the above data are also generated. They aid in better understanding of the inventory status. They change dynamically as the data in the above MRP record is changed. Dynamic graph of product demand is shown in Fig.11. The x-axis is the period started from 1 to 10. They y-axis shows the releases of the product graphed using the data shown in Table II.

Vol. 20(SI) No.II(S)-2015 TABLE VII ACHIEVED OBJECTIVES Current Proposed Model Model

Improvement

Product Inventory (I)

260 M. Ton

169 M. Ton

35% reduced inventory

Raw. Paper I

882 M. Ton

130 M. Ton

85%

Chemical I

45 M. Ton

11 M. Ton

75%

Product Cost

13148850

8528041

35%

Lead Time

3 days

1 day

67%

This is not only in the accurate results but the real beauty of the tool made lies in its Interactive and userfriendly GUI (Graphical User Interface). It provides help to user where ever necessary and one can move back and forth from every step, hence never bounds a user at a single stage. With the aid of AM developed tool, mulit-echelon SC of the Olympia paper is well managed. The production and inventory managers were more confidents to track the accurate amount of inventory processed at each echelon of the supply chain. REFERENCES [i]

[ii]

Fig. 11. The demand of the product

[iii]

VII.

CONCULSION

[iv]

The verification and validation, as presented using an example in methodology, is done by solving twenty data sets into the tool. The manually and automated results are compared with one another and found consistent. Hence the model working is verified and its results are valid results. The achieved objectives are presented in Table VII. The inventory of the product and its associated cost is reducded by 35% while the lead time is reduced by 2 days. This ultimately increases the customer satisfaction and profits of the organization.

[v]

[vi] [vi]

[viii] [ix]

[x] [xi] .

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Fahimnia.B , L.L.a.M.R., , , An integrated model for the optimization of a two-echelon supply network,. journal of Achievements in Materials and Manufacturing Engineering, 2008. volume 31( issue 2). Karacapilidis Nikos, P., Costas, , , Production planning and control in textile industry. 1996. Cristina Giménez, H.R.L., e-Supply Chain Management: review, implications and directions for future research. 2003. Chang Yoon and Makatsoris Harris, supply chain modeling using simulation,. journal of simulation 1996,. vol.2. . Kritchanchai Duangpun, W.T., Somboonwiwat Tuanjai, Koetsinchai Woranut and Athikomrattanakul Pongchai,, Research direction and knowledge management of supply chain and logistics in Thailand Logistics Research Network 2003, . Lawson David and Porteus Evan, Multi-Stage Inventory Management with Expediting. 1998. Bin Zheng, X.D., , , Controlling of Multi - echelon Inventory in Distribution Supply Chain System based on Stochastic Demand. . 2004: p. 284-288. Jain Sanjay, Supply Chain Management Tradeoffs Analysis, Proceedings of the 2004 Winter Simulation Conference. 2004,. Senping Tian and Hanfeng Zhou, Research on the Inventory Control and Optimization Algorithms of Supply Chain. . 2007, . Jay Heizer and Barry render, Operations Management. Prentice hall 2007, : p. 64-67. Spearman, W.J.H.a.M.L.S.J.H.a.M.L., Factory Physics. second ed. 2007: Irwin McGraw-Hill