Information Flow in a Multi-echelon Supply Chain-Dr. Kishore Pankan

8/10/2019 Information Flow in a Multi-echelon Supply Chain-Dr. Kishore Pankan

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A novel technique of information flow in a multi-echelon supply

chain comprising strategic partnerships for facilitating just-in-

time inventory replenishment through automated order

processing

Kishor Pankan1

1Procurement Manager

ALHOSN University,

P.O. Box 38772,

Abu Dhabi, UAE

E-mail: [email protected]

G. Pradeep Nair, 2

1Safety and Logistics Manager

Buniya,

Abu Dhabi, UAEE-mail: [email protected]

Abstract

Inventory replenishment is a known SCM challenge area especially under uncertain demand and supply scenarios.

Lack of synchronization of supply and demand information may lead to stock overflows or stock out scenarios

leading to financial losses, and loss of customers and prospects. Strategic partnership and flow of timely, accurate,

and complete information are recognized methods for dealing with demand and supply uncertainties. These

methods help in dealing with extreme inventory replenishment problems during Forrester (Bullwhip) effectscenarios, as well. In absence of these methods, inventory managers allow asynchronous replenishment policies like

beer game and rationing. Past research studies have proved that such asynchronous replenishment policies cause

stock overruns and stock out situations, and amplify the demand fluctuation waveforms caused due to Forrester

effect.

Strategic supplier and buyer partnership is the recognized solution to inventory replenishment problems. However,lack of synchronous information flow and human interventions in information access and order processing may

make such partnerships ineffective. In this research, a novel technique of information flow and automated order

processing is presented and is demonstrated using a replenishment model. The system comprises a framework of

information packets collected from information systems of the suppliers and the manufacturer that is fed into a

decision-making engine. The information packets comprise current (actual) stock information, current demand

information (actual consumption), and current (actual) lead-time information captured from the information systems

of the echelons. The packets are captured at the end of the day following a scheduler. The decision-making systemwill run an algorithm and will automatically release orders in the form of Kanban-like cards and allow them to flow

upstream. The cards will be issued automatically in the ordering queue of the manufacturer and the suppliers,

accessible through computer terminals/mobile phones. The entire system has been demonstrated in MATLAB using

mock testing data.

1. Introduction

A fully synchronised supply chain requires alignment of decision-making across all the echelons with common

objectives (Sahin & Robinson, 2002). Minor oscillations and delays in flow of demand information result in

amplification effects on the production and inventory levels (Forrester effect; also called Bullwhip effect) (Li,

2013). Unsynchronised decision-making in supply chains may result in batching (Potter & Disney, 2006), beer

games (Geary, Disney, & Towill, 2003), rationing (Fransoo & Wouters, 2000), and shortage gaming (Fransoo &

Wouters, 2000). As described by Potter & Disney (2006), Geary, Disney, & Towill (2003), and Fransoo & Wouters(2000), these decisions often cause dysfunctional consequences on supply chain operations due to amplification ofForrester waves resulting in inadequate demand fulfilment or excess inventory. The solution is to implement



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integrated information systems for timely sharing of upstream demand information across all echelons of the supply

chain (Agrawal, Sengupta, & Shanker, 2009; Ahmed et al., 2005).

In this research, a framework of automated order processing and releasing is proposed in such a way that there is no

need for manual interventions for processing demand information and releasing purchase orders. The framework

can be implemented in an information system comprising a centralised decision-making engine. The enginecontinuously tracks consumption rates at all the echelons and calculates quantities and lead times at all the echelons

based on past experiences. Based on the inputs, the engine creates purchase orders in the form of Kanban cardsflowing upstream. The entire framework is proposed in the form of a system design and an algorithm. The next

chapter presents a review of literatures for ensuring theoretical support to the proposed framework.

2. Review of literatures

Horizontal integration in supply chain among all its echelons is viewed as a key performance driver as the

differences and factors and constructs among the echelons can be solved by achieving effective coordination and

optimum process efficiency (Van Der Vaart & Van Donk, 2008). Integrated processes and information among the

echelons help in synergising the attitudes, practices, and patterns such that the effects of misalignments and

misinformation could be reduced (Van Der Vaart & Van Donk, 2008). The suppliers’ inventory policies and their planned service levels directly affect the delivery performance of a manufacturer (Fu & Piplani, 2004). Simatupang

& Sridharan (2004) presented a conceptual framework for supply chain synchronisation, which is redrawn in Figure1. As per this framework, the manufacturer should align its supply chain management processes, information

processing systems, decision-making, and incentives with the suppliers for synchronising supplies with demands

effectively (Simatupang & Sridharan, 2004). This level of alignment requires strategic relationships with suppliers

based on joint business objectives and goals, and shared stakeholders for controlling their operations (Simatupang

& Sridharan, 2004). The incentives, costs, and risks need to be shared between the manufacturer and its suppliers

and there should appropriate mapping of supply chain processes and the information systems running their steps

(Simatupang & Sridharan, 2004).

Figure 1: A conceptual framework for supply-chain synchronisation



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In an enhanced version of the conceptual framework shown in Figure 1, Simatupang & Sridharan (2008: p. 405)

added an additional layer between collaborative performance system and shared SCM processes for facilitating

process redesigns and enhancements based on actual feedback on benefits gained by all associated partners, or

based on newly negotiated incentives agreed by the SCM partners.

As discussed by Sarmah, Acharya, & Goyal (2005), the information about order interval times, demandinformation, and order-to-supply lead-times should be shared among all suppliers and the manufacturer. They

argued that this has been one of the key limitations of supply chain synchronisation modelling because of multiplemanual interventions required for operating the co-ordination procedures. In addition, they also argued that the

information systems of suppliers and manufacturers have been different traditionally with limited integration

enabled. The collaborative planning, forecasting, & replenishment (CPFR) strategies have been identified as the

solution to this problem by many scholars (Bailey & Francis, 2008; Christopher, 2011; Attaran & Attaran, 2008;Holmstrom et al., 2002; Holmstrom et al., 2003; Christopher & Peck, 2004). The CPFR strategies have been

formulated and proposed for meeting the objectives of efficient customer response (ECR) (Holmstrom et al., 2002;

Holmstrom et al., 2003; Christopher & Peck, 2004; Christopher, 2011). CPFR requires mass collaboration between

multiple manufacturers/retail stores and their common suppliers such that the demand information could flow

upstream seamlessly and timely replenishments can be carried out at all the echelons of the supply chain

(Holmstrom et al., 2002; Holmstrom et al., 2003; Christopher & Peck, 2004; Christopher, 2011). In this mode, the

inventory may not pile up at any of the echelons because the replenishment process will be lean (that is, based on

actual demand information only) (Christopher, 2011). In such a scenario, the demand forecasting accuracy at the

last stage downstream is very critical for avoiding initiation of Forrester effect and its dysfunctional consequences(Agrawal, Sengupta, & Shanker 2009; Li, 2013). Li (2013, p. 1900) described that a switching mechanism may be

established between the manufacturer/retailer and its suppliers. At time “t”, when the demand information isreceived by the suppliers, they can make decisions accordingly and when the demand information disappears at

time “t + t1”, the decisions may continue as per the information received at time “t”. The inventory system stability

(dampened Forrester waves) may be improved by setting the switching frequency at an estimated rate, which is less

than the average demand fluctuations recorded in the past period “T”. Another research by Zhang (2005: p. 292-

293) proved that Forrester waves formation can be controlled by delaying the demand information by a multiples of

a small period, which is a constant determined by the demand forecaster.

Ouyang & Daganzo (2006: p. 15-16) presented a scenario in which a chain of suppliers are positioned in the form of

a chain represented by i = 1, 2, 3, - - - - , I +1, I. In their model, the orders flow upstream and the shipments flowdownstream, sequentially. In this scenario, if all suppliers make ordering decisions to their previous echelons, the

chain will be decentralised. Ouyang & Daganzo (2006: p. 19) presented bi-lateral and multi-lateral mechanisms of price negotiations among the suppliers in a de-centralised fashion. They demonstrated damping of Forrester waves

by circulating advance demand information among all the suppliers. This model is further extended by Ouyang

(2007: p. 1108-1110) in which, the effect of demand variation is included as a metric for negotiating orders between

the intermediate echelons on the supply chain. In both the projects, the lead-time is assumed as constant betweenthe echelons. Kelepouris et al . (2008: p. 3659-3660) modified this model by providing a common central warehouse

for all the suppliers such that the inventory and lead-time information can be synchronised. In this model, a safety

stock factor and an exponential smoothing factor are considered for testing their effects on amplification of demand

variability (Forrester effect). The demand “D” is taken as variable with time “t”. They used a metric called “mean

cycle inventory” for controlling the replenishment rate for estimating optimum safety stock to keep the Forrester

waves in control. Samaranayke (2005: p. 49-50) presented a conceptual framework of integrating multiple echelons

of a supply chain in manufacturing environments comprising structural integration. In this concept, the multiple

echelons of the supply chain are considered as parts of a single assembly line extending upstream up to the

manufacturer and finally to the distributors/customers.

In practical supply chains, the current state of multiple variables needs to be compared with the historical

information for making replenishment decisions (Kaipia & Lakervi, 2006). The historical information should not be

very old and the forecasting should not be done for distant futures (Kaipia & Lakervi, 2006). The average and

maximum values of the decision variables in the current state and few previous states can help in more accuratecoordination of inventory replenishment (Gurbuz, Moinzadeh, & Zhou, 2007). If the replenishment needs to be

done just-in-time, the information, goals, SCM processes, decisions, incentives, and resources need to be

synchronised effectively (Cao et al., 2010). If advanced demand information is shared, and the production and

inventory lead times are planned, the orders can be released through Kanbans maintaining a minimum base stock

level (Liberopoulos & Koukoumialos, 2005). Kanbans are automated purchase orders released in an assembly line

in a large-scale multi echelon manufacturing system (Liberopoulos & Koukoumialos, 2005). Kanbans can be usedin supply chains of manufacturing organisations using tightly integrated strategic vendors connected over clearly

visible supply chains (Liberopoulos & Koukoumialos, 2005).





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needs to be placed when M1(Tm1 – 1) inventory is available at E1. Similarly, the order to E3 for M2 needs to be

placed when M2(Tm2 – 1) inventory is available at E2. At En-1, the order to En needs to be placed when Mn-

1[Tm(n-1) – 1] inventory is available at En-1. Hence, the JIT inventory value at which, the order should be placedmay be represented as the following.

JIT inventory at E1 = M1 (Tm1 – 1)



JIT inventory at En-1 = Mn-1[Tm(n-1) – 1]

Thus, the order to En on behalf of En-1 for Mn-1 is placed automatically by the system when an inventory of En-1

= Mn-1[Tm(n-1) – 1] or lesser is available on any day. In this manner, even in worst-case scenario when the safety

stock is also exhausted, it is expected that the inventory will have at least the materials available for consumption on

the next day. In mathematical notation, the order placed to the previous echelon (represented by “On”) could be

calculated as the following:

If I <= Mn-1[Tm(n-1) – 1], then On = Mn-1 Equation (1)

If I > Mn-1[Tm(n-1) – 1], then On = 0 Equation (2)

For testing this decision, a mock dataset is generated in an Excel sheet, with values entered in seven sheets as

presented in Appendix A. The mock data set comprises ten echelons between the last supplier and the manufacturer.

The data set represents results of 25 days. The parameters named “actual consumption” and “actual lead-time” are

inputs, parameters “M”, “Tm”, and “JIT inventory” are intermediate calculated variables, and “orders placed” and“inventory” are output calculated variables.

On the first day, M = actual consumption on day 1, and on the second day, M = higher value of the actual

consumptions on the first and second days. From the third day, M = maximum value of past three values of actual

daily consumption of materials at the respective echelons. The values in the sheet named “M” are shown as M1through M10 representing the maximum value of actual consumption of past three days at E1 through E10,

respectively. Similarly on the first day, Tm = actual lead-time (T1), and on the second day, Tm = higher value of

the actual lead-times on the first and second days (T1 versus T2). From the third day, Tm = maximum value of past

three values of actual lead-times of materials at the respective echelons. The values in the sheet named “Tm” are

shown as Tm1 through Tm10 representing the maximum value of actual lead times of delivery of materials M1

through M10, respectively.

The JIT inventory is calculated by the following formula in the sheet named “JIT_Inventory”:

JIT Inventory = M (Tm – 1)

For the ten echelons in the mock data set,

M = M1, M2, M3, - - - - - - - , M10

Tm = Tm1, Tm2, Tm3, - - - - - - - , Tm10

The actual inventory on the first day is the inventory in hand. On the second day and onwards, the actual inventory

is calculated by the following formula in the sheet named “Inventory”:

I = Inventory on the previous day before the day of order placement – actual consumption (C) + Order placed (O)

For the ten echelons in the mock data set,C = C1, C2, C3, - - - - - - - , C10

O = O1, O2, O3, - - - - - - - , O10

In MATLAB, the sheets with these values may be imported or only the input data sheets can be imported and the

rest calculated in MATLAB itself by importing row-wise (data of each day) separately. The algorithms written in

MATLAB are presented in Figures 3 and 4.



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Figure 3: MATLAB algorithm with the 10 th row of sheets Tm, inventory, and M imported and the rest of data calculated

In Figure 3, the 10th

row of sheets Tm, inventory, and M are imported and the corresponding values of JIT

inventory, and orders placed (Kanbans) are calculated by the algorithm. The output of this algorithm (named as

Order) in MATLAB is presented in Appendix B.

Figure 4: MATLAB algorithm with the 10th

row of sheets Tm, actual consumption, and M imported and the rest of datacalculated



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In Figure 4, the 10th

row of sheets Tm, actual consumption, and M are imported and the corresponding values of JIT

inventory, actual inventory, and orders placed (Kanbans) are calculated by the algorithm. The output of this

algorithm (named as “Order1”) in MATLAB is presented in Appendix B. In both the algorithms, it is assumed thatthe initial inventory on day 1 is equal to the estimated JIT inventory on day 1. In this way, the variations of actual

inventory with respect to the initial projected JIT inventory can be analysed from the output of the two algorithms

(both algorithms generate the same output). The calculated values of “On” for each imported row of data (that is,

for each of the 25 days) are entered in the sheet named “Orders_Placed”. The same algorithm can be configured in

Excel as well by using the If, then commands as formulae. The overall schematic of the system tested using themock data set is shown in Figure 5.

Figure 5: The overall schematic of the system tested using the mock data

The blue coloured arrows indicate inbound information to the decision engine about actual consumption and actual

lead time, the red coloured arrows indicate the automatic orders (Kanbans) placed, and purple arrows indicate theorders placed against the Kanbans.

In the next chapter, the results of tests conducted in MATLAB are presented.

4. Results and discussions

The results presented in this chapter present the plots generated in MATLAB after the algorithms have been

executed. The outcomes of algorithms presented in Figures 3 and 4 are presented in Appendix B (the final orders

placed are identical showing that both algorithms have generated identical results).

The Figure 6 presents the staircase plot of automatic orders placed to all the ten echelons. It may be observed thatthere are seven days when the decision-making engine did not place any order to four of the ten echelons. This is

because the actual inventory was more than the estimated JIT inventory. On the remaining days, the system placedautomatic orders equal to “M” to all the echelons preceding the final echelon.





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The waterfall plots in Figure 8 present a plotting of actual inventory, projected JIT inventory, and orders placed in

all the ten echelons over the period of 25 days. If the plots are smoothened, they will appear as waves with crests

and troughs. The actual inventory level varies in the form of a waveform that may be viewed as being controlled byanother waveform of projected JIT inventory levels. There is no endless piling up of inventories at any of the

echelons. Whenever the inventory levels peak, a hidden force takes them down automatically just like a control

system. Also, in this test of 25 days of mock data the inventory level has never reached zero causing a stock-out.

Figure 8: Waterfall plots: Actual inventory vs Projected JIT inventory vs Orders placed

The order placement waves are steady except some abrupt drops (hidden in the waterfall plot but apparent in the

line plot in Figure 7) when the system decided not to place the Kanban orders. A progression of controlled ordering based on projected JIT inventories resulted in controlled inventory replenishment in such a way that even after 25

days the inventory level from day 1 to day 25 has changed only marginally in most of the echelons. This may be



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viewed as quite close to an ideal JIT scenario in which, the inventory never exhausts and the residual inventory is

steady forming a straight line.

The standard deviations generated in MATLAB for actual consumption, actual lead-time and the actual inventory

levels are presented below:

Standard deviation of Actual consumption

4.45 4.23 5.27 3.22 3.12 3.07 3.71 3.33 3.05 3.59

Standard deviation of Actual Lead-time

0.71 0.71 0.71 1.04 0.73 0.91 0.86 1.04 1.00 0.60

Standard deviation of actual inventory levels

16.75 17.77 17.49 10.84 16.36 15.56 17.18 13.21 19.05 11.37

It may be observed that the actual inventory levels have delinked from actual consumption (demand) and actual

lead-time variations. This is against the usual theory that inventory levels are highly correlated with the demand

variance and lead-time variance (Bailey & Francis, 2008; Christopher, 2011; Attaran & Attaran, 2008). From

MATLAB, the correlation between the standard deviation of actual consumption (demand variance) and thestandard deviation of actual inventory levels is 0.32, and the same between standard deviation of actual lead-times

and the standard deviation of actual inventory levels is – 0.15. These results may be viewed as the outcomes of the

synchronisation effect.

The framework may be extended to a scenario in which, “N” number of materials are consumed at each of the

echelons. In such a scenario, the decision-making engine needs to monitor the actual consumption and lead-times of

all the materials in all the echelons. This scenario is presented in Figure 9.

Figure 9: Modified scenario with multiple materials with their respective lead-times at each of the echelons

In this scenario, the projected JIT inventories at all the echelons may be represented by the following equations.

JIT inventory at E1 = M11 (Tm11 – 1) + M21(Tm21 – 1) + - - - - - + MN1(TmN1 – 1)



JIT inventory at En-1 = M1(n-1)Tm1(n – 2) + M2(n-1)Tm2(n – 1) + - - - - - + MN(n-1)TmN(n – 1)



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The total orders placed to the nth echelon could be calculated as the following:

If I <= M1(n-1)Tm1(n – 2) + M2(n-1)Tm2(n – 1) + - - - - - + MN(n-1)TmN(n – 1),

Then, On = M1(n-1), M2(n-1), - - - - -, MN(n-1)

If I > M1(n-1)Tm1(n – 2) + M2(n-1)Tm2(n – 1) + - - - - - + MN(n-1)TmN(n – 1),

Then, On = 0

5. Conclusions

Supply chain synchronisation has been viewed as the enabler of performance of inventory replenishment and

demand fulfilment. A closely synchronised supply chain requires synchronisation of seven parameters namedinformation, goals, SCM processes, decisions, incentives, and resources. These parameters could be integrated to

form a just-in-time inventory replenishment system maintaining a minimum safety stock. Such a system can be

designed for avoiding stock overruns or stock-out situations in the real world supply chains without compromising

on demand fulfilment performance. It is also recognised that the decision-making for the next state should be based

on the information collected from the present state and a few previous states not going deep into the history of

transactions. Moreover, the maximum values of past demands and lead-times will be more effective than theaverage value when there are fluctuations.

In this research, an inventory replenishment system is designed employing a decision-making criteria based on the

actual demand and lead-time information collected from all the echelons of a supply chain. Based on the

information, the centralised decision-making system shall estimate a projected JIT inventory and make a decision

for releasing orders automatically in the form of Kanban cards. The Kanban cards shall flow upstream while the

supplies shall flow downstream. Using a mock data set of ten echelons over 25 days, the actual change in inventory

levels and the orders placed are calculated in MATLAB following the decision-making algorithm. The results of

testing in MATLAB have reflected that change in inventory levels remained under control in spite of considerable

variations in the actual consumption of materials (demand) and lead-times of delivering materials at their respective

echelons.

It is proposed that this system shall be useful for manufacturing settings with a tightly synchronised supply chainconsidering each supplier as the part of an assembly line. The automated flow of Kanbans based on comparisons of

actual and projected JIT inventory levels shall ensure continuous inventory replenishment with no possibilities of

inventory overflows or stock out scenarios.

References:

Agrawal, S., Sengupta, R. N., Shanker, K. (2009), "Impact of information sharing and lead time on bullwhip effect

and on-hand inventory", European Journal of Operational Research, 192: 576 – 593, Elsevier.

Ahmad, B., Hassanein, I, Krol, K. K., Tsai, H., Kim, L. L., & Li, H. H. (2005), "E-logistics overcomes the BullwhipEffect", International Journal of Operations and Quantitative Management , 11 (4): 281-289.

Attaran, M. & Attaran, S. (2007), "Collaborative Supply Chain Management: The Most Promising Practice for

Building Efficient and Sustainable Supply Chains", Business Process Management Journal , 13 (3): 390-404,Emerald.

Bailey,K. & Francis, M. (2008), "Managing information flows for improved value chain performance", Journal of

Production Economics, 111 (1):2-12, Elsevier.

Cao, M., Vonderembse, M. A., Zhang, Q, & Raghu-Nathan, T. S.(2010), "Supply chain collaboration:

conceptualisation and instrument development", International Journal of Production Research, 48 (22): 6613 –

6635, Taylor & Francis.

Christopher, M. & Peck, H. (2004), "Building the resilient supply chain", International Journal of Logistics

Management , 15 (2): 1-13, Emerald.

Fransoo, J. C., & Wouters, M. J. F. (2000), "Measuring the bullwhip effect in the supply chain", Supply Chain

Management: an International Journal , 5 (2): 78-89, Emerald.





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Appendix A: Mock Test Data for demonstrating the algorithm’s

performance



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Actual Consumption = Actual intra-echelon consumption

C10 C9 C8 C7 C6 C5 C4 C3 C2 C1

35 34 32 43 44 42 34 32 45 3232 31 29 44 43 39 35 36 39 41

33 32 30 41 40 41 37 38 37 40

34 35 34 43 41 38 40 41 42 40

35 36 33 41 42 40 41 42 40 39

38 37 39 41 40 41 42 41 39 40

40 34 37 44 41 40 39 38 39 40

44 42 43 41 42 40 40 41 40 42

41 40 42 44 43 41 39 39 38 40

40 39 41 43 45 44 42 41 43 42

42 41 45 44 42 39 37 36 35 36

37 39 37 38 39 41 44 42 45 43

45 44 42 43 41 43 46 42 41 45

41 36 37 36 37 42 43 45 43 44

44 42 45 43 38 37 39 41 44 47

48 46 46 47 46 45 47 45 46 45

44 39 43 43 46 44 43 41 37 44

46 44 49 45 43 46 45 42 43 44

35 36 37 42 45 43 46 43 45 47

36 37 39 35 36 38 36 35 39 3839 37 38 36 36 32 39 41 44 45

40 42 44 43 45 43 46 41 42 41

45 45 46 43 45 44 41 34 37 37

43 45 43 46 47 43 45 44 43 41

43 36 38 48 43 45 44 41 43 46

Actual_Lead_Time = Actual Intra-echelon lead-time

T10 T9 T8 T7 T6 T5 T4 T3 T2 T15 3 5 4 3 4 5 4 3 4

4 5 4 4 5 4 3 6 5 4

5 4 5 3 4 3 4 5 4 3

4 3 4 5 4 3 4 5 4 3

5 4 5 3 4 5 4 3 3 4

5 5 4 5 4 3 4 5 5 4

5 4 5 4 4 5 4 5 4 5

4 5 4 5 4 3 5 4 4 3

5 4 5 3 4 5 4 3 5 4

4 4 5 4 3 4 5 4 3 4

3 4 3 5 4 3 4 5 4 3



15

T10 T9 T8 T7 T6 T5 T4 T3 T2 T1

4 5 4 5 3 4 5 3 4 3

5 4 5 3 4 5 3 4 5 4

4 4 5 4 3 4 5 4 5 3

4 3 4 3 3 4 5 4 3 4

4 5 4 6 4 5 6 7 5 4

3 4 5 4 3 4 5 4 6 3

4 5 4 5 3 4 6 5 3 4

4 3 5 6 5 4 5 6 5 5

5 4 3 5 4 5 6 4 5 4

4 5 4 3 4 5 5 4 6 4

6 5 6 4 5 6 4 5 6 3

5 5 4 6 5 3 5 6 5 4

4 4 5 6 5 4 6 5 3 4

5 4 5 4 5 6 4 6 5 4

M = Values of M1 to M10 for the echelons E1 through E10, respectively

M10 M9 M8 M7 M6 M5 M4 M3 M2 M1

35 34 32 43 44 42 34 32 45 32

35 34 32 44 44 42 35 36 45 41

35 34 32 44 44 42 37 38 45 41

34 35 34 44 43 41 40 41 42 41

35 36 34 43 42 41 41 42 42 4038 37 39 43 42 41 42 42 42 40

40 37 39 44 42 41 42 42 40 40

44 42 43 44 42 41 42 41 40 42

44 42 43 44 43 41 40 41 40 42

44 42 43 44 45 44 42 41 43 42

42 41 45 44 45 44 42 41 43 42

42 41 45 44 45 44 44 42 45 43

45 44 45 44 42 43 46 42 45 45

45 44 42 43 41 43 46 45 45 45

45 44 45 43 41 43 46 45 44 47

48 46 46 47 46 45 47 45 46 47

48 46 46 47 46 45 47 45 46 47

48 46 49 47 46 46 47 45 46 45

46 44 49 45 46 46 46 43 45 47

46 44 49 45 45 46 46 43 45 47

39 37 39 42 45 43 46 43 45 47

40 42 44 43 45 43 46 41 44 45

45 45 46 43 45 44 46 41 44 45

45 45 46 46 47 44 46 44 43 41

45 45 46 48 47 45 45 44 43 46



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Tm1 = Values of Tm1 to Tm10 for delivery of M1 to M10, respectively

Tm10 Tm9 Tm8 Tm7 Tm6 Tm5 Tm4 Tm3 Tm2 Tm1

5 3 5 4 3 4 5 4 3 45 5 5 4 5 4 5 6 5 4

5 5 5 5 5 4 4 6 5 4

5 4 5 5 4 5 4 5 4 4

5 5 5 5 4 5 4 5 5 4

5 5 5 5 4 5 4 5 5 5

5 5 5 5 4 5 5 5 5 5

5 5 5 5 4 5 5 5 5 5

5 5 5 5 4 5 5 4 5 4

5 4 5 5 4 5 5 5 5 4

4 5 5 5 4 4 5 5 4 4

5 5 5 5 4 5 5 5 5 4

5 5 5 5 4 5 5 4 5 4

5 4 5 4 4 5 5 4 5 4

4 5 5 6 4 5 6 7 5 4

4 5 5 6 4 5 6 7 6 4

4 5 5 6 4 5 6 7 6 4

4 5 5 6 5 4 6 6 6 5

5 5 5 6 5 5 6 6 5 5

5 5 5 6 5 5 6 6 6 56 5 6 5 5 6 6 5 6 4

6 5 6 6 5 6 5 6 6 4

6 5 6 6 5 6 6 6 6 4

5 5 5 6 5 6 6 6 5 4

5 4 5 6 5 6 6 6 5 4

Inventory = Actual Inventory left with each echelon on each day

I10 I9 I8 I7 I6 I5 I4 I3 I2 I1140 68 128 129 88 126 136 96 90 96

140 68 128 129 88 126 136 96 90 96

143 71 131 129 89 129 136 96 96 96

110 73 101 132 93 88 136 96 104 97

110 73 101 133 95 91 96 96 104 98

110 73 102 135 95 92 96 96 106 99

110 73 102 137 97 92 96 97 109 99

110 76 104 137 98 93 99 101 110 99

110 76 104 140 98 94 101 101 110 99

113 78 105 140 98 94 102 103 112 101

117 81 107 141 98 94 102 103 112 101



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I10 I9 I8 I7 I6 I5 I4 I3 I2 I1

117 81 107 141 101 99 107 108 120 107

122 83 115 147 107 102 107 108 120 107

122 83 118 148 108 102 107 108 124 107

126 91 123 155 112 103 110 108 126 108

127 93 123 112 115 109 117 112 126 108

127 93 123 112 115 109 117 112 126 110

131 100 126 116 115 110 121 116 135 113

133 102 126 118 118 110 123 119 138 114

144 110 138 121 119 113 123 119 138 114

154 117 148 131 128 121 133 127 144 123

154 117 149 137 137 132 140 129 145 125

154 117 149 137 137 132 140 129 147 129

154 117 149 137 137 132 145 136 154 137

156 117 152 137 137 133 146 136 154 96

Orders Placed = Orders (Kanbans) placed for materials M1 to M10 to echelons E2 to E11 on each day

O10 O9 O8 O7 O6 O5 O4 O3 O2 O1

35 34 32 43 44 42 34 32 45 32

35 34 32 44 44 42 35 36 45 41

0 34 0 44 44 0 37 38 45 41

34 35 34 44 43 41 0 41 42 41

35 36 34 43 42 41 41 42 42 4038 37 39 43 42 41 42 42 42 40

40 37 39 44 42 41 42 42 40 40

44 42 43 44 42 41 42 41 40 42

44 42 43 44 43 41 40 41 40 42

44 42 43 44 45 44 42 41 43 42

42 41 45 44 45 44 42 41 43 42

42 41 45 44 45 44 44 42 45 43

45 44 45 44 42 43 46 42 45 45

45 44 42 43 41 43 46 45 45 45

45 44 45 0 41 43 46 45 44 47

48 46 46 47 46 45 47 45 46 47

48 46 46 47 46 45 47 45 46 47

48 46 49 47 46 46 47 45 46 45

46 44 49 45 46 46 46 43 45 47

46 44 49 45 45 46 46 43 45 47

39 37 39 42 45 43 46 43 45 47

40 42 44 43 45 43 46 41 44 45

45 45 46 43 45 44 46 41 44 45

45 45 46 46 47 44 46 44 43 0

45 45 46 48 47 45 45 44 43 46



18

Appendix B: MATLAB Test Reports

>> Order

filename = Test_data.xlsx

sheet1 = Inventory

sheet2 = Tm

sheet3 = M

xlRange = A10:J10

num1 = 110 76 104 140 98 94 101 101 110 99

num2 = 5 5 5 5 4 5 5 4 5 4

num3 = 44 42 43 44 43 41 40 41 40 42

UnitM = 1 1 1 1 1 1 1 1 1 1

LT = 4 4 4 4 3 4 4 3 4 3

JIT = 176 168 172 176 129 164 160 123 160 126

O = 44 42 43 44 43 41 40 41 40 42

>> Order1

filename =Test_data.xlsx

sheet1 = Actual_Consumption

sheet2 = Tm

sheet3 = M

xlRange = A10:J10

num1 = 41 40 42 44 43 41 39 39 38 40

num2 = 5 5 5 5 4 5 5 4 5 4

num3 = 44 42 43 44 43 41 40 41 40 42

UnitM = 1 1 1 1 1 1 1 1 1 1

LT = 4 4 4 4 3 4 4 3 4 3

JIT = 176 168 172 176 129 164 160 123 160 126

JITI = 140 68 128 129 88 126 136 96 90 96

I = 143 70 129 129 88 126 137 98 92 98

O = 44 42 43 44 43 41 40 41 40 42

Information Flow in a Multi-echelon Supply Chain-Dr. Kishore Pankan

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