- Email: [email protected]
Systems design of a two-echelon steel industry supply chain
internationaljournalof
production economics ELSEVIER
Int. J. Production Economics 45 (1996) 121L130
Systems design of a two-echelon steel industry supply chain K. Hafeeza,*,
M. Griffithsb,
J. Griffithsc,
M.M. Naimd
“Shefield Hallam University, School of Engineering, City Campus, Porid Street, Shefield blJniversity of Glamorgan, Pontypridd, UK ‘Allied Steel and Wire, Car&r, UK ‘School of Engineering, University of Wales College of Card$
SI 1 WB, UK
UK
Abstract Supply chains are a complex combination of “man” and “machine” and usually difficult to design. In this paper we argue that systems engineering can be used as an effective tool for this purpose as it comprehensively takes into account
intricacies associated with modelling the attitudinal, organisational and technological issues. We describe the analysis and modelling of a two-echelon steel industry supply chain that services the construction industry using an integrated system dynamics framework as an example to good total system design. Key features of the framework are outlined and implementation details are provided. One objective of the design is to move more rapidly towards a minimum reasonable inventory (MRI) scenario in the presence of capacity constraints, breakdowns and material supply leadtime bottlenecks. Simulation results are presented showing how the developed model may be viewed as a “Management Information System” to investigate various business strategies. Keywords: Supply chain management;
Systems dynamics conceptual modelling; Inventory planning
1. Introduction Conventionally, “real world” supply chains have formed over time as the result of vague associations between individual business units. The absence of a compatible operating strategy results in the poor dynamic behaviour displayed by the majority of supply chains. Such criticism applies to both inventory swings and demand amplification. These features greatly affect the “bottom line” in a variety of damaging ways. For instance, on-costs are incurred in production,
* Corresponding author.
warehousing and distribution, factory experiences alternating periods of over-capacity followed by under-capacity, financial losses are reflected due to obsolescence and the late arrivals of new products in the marketplace [l]. One major problem usually faced in modelling supply chains is that they are a complex combination of “man” and “machine”. Our experience shows that systems engineering can be used for the analysis and design of supply chains as it comprehensively takes into account intricacies associated with modelling the attitudinal, organisational and technological issues [2]. Systems engineering makes extensive use of both “soft systems” as well as “hard systems” tools, such as structured
0925-5273/96/$15.00 Copyright 0 1996 Elsevier Science B.V. All rights reserved SSD10925-5273(96)00052-7
Hafeez rt al. /ht. J. Production Economics 45 (1996) 121-130
Information
Source
Knowledge
Source
Written Documents
Content Analysis
lndivlduals
Queslionaire Interview Log Book
Group of People
Brainstorming Workshops
Investigative
Methods
Flow Charting “Fish Bone” diagram Pareto Analysis
Numerical
Methcds
Information
Flow Analysis
Pmductlon
Control Analysis
lime
Series Analysis
Constramed
Fig. 1. Integrated design.
system dynamics
framework
for supply chain
interviews, input-output analysis, process flow charts, information flow and production control analyses, influence and block diagrams, control theory and computer simulations [3]. A mathematical model of a two-echelon steel industry supply chain is built using the described tools. Simulations are performed based on real data in order to obtain optimum values for parameters corresponding to the conceptual model of the supply chain. The purpose is to obtain a better understanding of the behaviour of different parts of the supply chain. Various economic/operational scenarios are tested to obtain the minimum reasonable inventory (MRI) [4] and to gauge the effect on the manufacturing and customer service level.
2. Integrated systems dynamics framework Fig. 1 illustrate the key features of our integrated systems dynamics framework which is designed
Fig. 2. Typical approaches mation processing.
Curve Fitting
used for data collection
and infor-
specifically to model supply chain using a holistic approach. The framework is described at length elsewhere [3] however a brief description is provided here for convenience. An important aspect of our methodology is to decompose a supply chain into distinct (preferably naturally existing) autonomous business units. This helps to simplify the designing task as we develop conceptual and mathematical models for each unit and analyse them individually. Aggregation of these models to represent a complete supply chain then follows. The design process is of an iterative in nature (shown as feedback loop in Fig. 1) requiring continual feedback of preliminary results and information to the respective management via meetings, reports and presentations for the verification purposes. Essentially the framework consists of two overlapping phases, namely, qualitative and quantitative. The qualitative phase is related to acquiring sufficient intuitive and conceptual knowledge to
123
Fig. 3. Steel reinforcement
understand the structure and operation of a supply chain, whereas, the quantitative phase is associated with the development and analysis of mathematical and simulation model. Modelling becomes more qualitative as we progress from top down to the operations level. The qualitative phase is commonly applicable irrespective of the nature of supply chain under investigation. The main stages involved in this phase are system input-output analysis, conceptual modelling, and block diagram formulation. Fig. 2 lists a number of industrial engineering tools used for information collection and processing at the qualitative stage. The environment in which qualitative models are to be built and the theoretical analysis to be carried out can be controversial [5]. We have found three techniques namely, control theory, computer simulation and statistical analysis useful when setting up the mathematical equations representing the functioning of a supply chain. However, these techniques have their strengths and weaknesses for a particular application. In general, two factors should be taken into account when choosing between these techniques: (i) the degree of complexity involved in setting up a mathematical model of the particular supply chain and; (ii) the amount of operating and other field data which could be made available for the purpose of statistical analysis. In the present study we have used a
industry
supply
chain
combination of computer simulations coupled with statistical analysis for the purpose of qualitative model building.
3. A Steel industry supply chain The supply chain investigated is the supply of reinforced steel bars to the construction industry. Fig. 3 shows the material flow diagram of the supply chain. The end customer in the chain is a project management team which comprises client, consultant and contractors. The stockholders buy and stock large quantities. The behaviour of the stockholding echelon causes rapid changes in the demand inflicted on the other members of the chain. The steel market is speculative and it is believed that the demand for construction products depends not only on the customer/contractor but also on other factors such as steel price, interest rates and delivery times. The reinforcement industry is recently been changing its order policy resulting in more unpredictable behaviour. The reason is that stockholder target levels have been lowered due to economic changes affecting their business environment. As there is a natural tendency to minimise “buffer” or “safety” inventory in a recession the risk of running out of stock increases.
124
COMPANY FORWARD
a (MASTER
ROLLING
FiLf)
PROGRAMME 16W
W
W COMPANY STOCK
COMPANY 8 BILLET CONTRACT ON A
9 BILLET SHEET
COh -
SCHEDULE
COMPANY
Key
’
D - Daily;
Fig. 4. Information
COMPANY A BILLET STOCK
A
W - Weekly;
M - Monthly:
C * Continuousiy
flow model of supply chain.
The sub-system under study (shown in Fig. 3) consists of three separately controlled business units: Companies A, B and C. The management structure of the business is decentralised, however, the three businesses are highly dependent on one another. Company A is a foundry transforming scrap metal into billets. Companies B and C produces steel products out of steel billets. Company A is sole supplier to Company B and the main supplier (approx. 50%) to Company C. External suppliers make up the shortfalls in raw material for Company C. Company A’s business is essentially capacity planned based on the philosophy to keep its plant working for as much time as possible regardless of the orders placed upon it. The reason is two fold: (i) all the finished goods is sold internally to Companies B and C, and (ii) this allows Company A to minimise the overhead cost as it is spread over a large volume of finished goods. A detailed study of the information flows was undertaken which highlighted the planning and control procedures relevant to each business unit. Fig. 4 gives an illustration of the information flow diagram relevant to Companies A and B. A key is
attached underneath each process of the information model to show how frequently information is updated between various processes. Of particular importance is the rolling programme of Company B comprising the planned rolling dates and production volume for each product over the following 16 weeks (see Fig. 4). As a result the rolling programme acts like a queue of orders with the new orders being placed near the end. The total number of orders in this queue plus any orders not yet delivered from stock constitute the forward orders which are translated subsequently to produce the forward rolling programme of company B. Forward orders are normally used by management as the performance measure of company B. In conjunction with material and information flow analyses, we have found the use of input-output analysis (IOA) to be a powerful tool [6]. IOA takes account of laid down operating procedures, functional definitions, field data, activity sampling and job descriptions. With the aid of IOA those flows of info~ation relevant to the productionscheduling system were mapped for each company. Fig. 5 shows the IO block diagram of Company A’s production-scheduling system. The planning
1% Hufeez
et ai. jIr~t. J. Production
Economics
f /
45 (I9963 121-130
125
Sa’esorder -----L&...
COMPANY A z Billet Delivery \
Fig. 6. High-level
Fig. 5. Input-Output
analysis
of Company
A.
methods used by the production scheduling managers were inv~stigatcd by means of interviewing and observing them at work.
conceptual
model of supply
chain.
middle and top management. Producing the right conceptual model is a major step in the systems engineering approach towards constructing a block diagram which is a powerful aid in formulating mathematical and simulation models.
5. Quantitative model 4. Conceptual model The investigations described form the basis for the development of a conceptual model [7] of the production-scheduling system. As an example, Fig. 6 shows the conceptual model applicable to Company A (where variable “Mill’s” is used for both Company B as well as C). The model highlights the main variables having a dominant impact on the functioning/performance of the business, and the cause and effect relationships resulting from their interactions. (Similar models were constructed for each business unit.) The conceptual diagram is normally verified by gathering the opinions of the relevant persons involved. Usually, we have found conceptual model to be a good tool for communicating with
The first step towards the model building is to transform the conceptual model into a block diagram. A block diagram representation of Company A’s production control system is given in Fig. 7. In this format, the flows of information and materials are represented via various paths. Production, ordering and other physicaliadministrative operations are represented using blocks. The main blocks and flows of the Company A production control diagram shown in Fig. 7 are: Mill’s billet orders: these are the orders placed by Companies B and C on company A. Order proceskg delay: is the time difference between when the billets orders are received from company B or C, and processed for the production planning purpose.
126
K. Hafeer
et al. /ht.
Fig. 7. A block diagram
J. Production
representation
Desired billet level: this is the level of the billet stock Company A aims to maintain at all times. Under normal circumstances the company wants to hold MRI. Billet level recovery timelj&tor: this is the fraction of the Company A’s billet stock error to be recovered in one time period. Planning: this accounts for the day-to-day delays and alterations in production targets made by the Company A Planners as they develop and update the production plan. Production: represents the behaviour of the complete manufacturing process when a demand is placed upon it. Casting schedule deluy: represent the delay between a billet order being placed upon the steel plant and the despatch of the product to the Mill. The delay is caused by waiting for the next casting of the specific billet to occur, when it is not in the stock. Add to stock: this converts the difference between billet production and billet despatch rates into a change in the billet stock error. (Billet stock error is the difference between desired and actual stock levels.)
Economics
of production
45 (1996)
121-130
control
of Company
A.
The diagram shown however only provides a first approximation of the production-scheduling system. This portrays an aggregate level view as it does not take into account the scheduling complexity for each product mix, or the changeover times between products. Block diagrams representing each business unit were developed with the aid of corresponding conceptual models. The corresponding simulation models were verified by relevant personnel and via mathematical cross-checks and validated against field data [8]. These were all then interlinked to form a complete model of the internal supply chain which was used subsequently for dynamic analysis purposes.
6. Dynamic
analysis
“Real-world” dynamic analysis of any business is extremely difficult to perform due to financial, production or time constraints. Perhaps the best output of our supply chain modelling approach is that the models may be used as a means to analyse the dynamic performance of the supply chain. The
K Hafeez
et ul. /ht.
J. Production
Economics
127
45 (1996) 121-130
Supply lead time = 1 month
vl $j
120
-
-
Company
100
‘.
-
60
.’
Company A Production
60
f
_ -/
i i :
Company
B F. Goods
6 Billet
Stock 3
40
200
{’
.~
‘,
*.’ 0
\1.
fl: 5
10
1 ::::,
,,:,:: 15
20
25
30
:::: 35
,,,;,,40
45
50
Time (months)
Fig. 8. Impact
of supply
lead time (1 month)
designs are verified and validated and then subject to extensive dynamic analysis. For example, simple test inputs (step, impulse, periodic, etc.) may reveal an envelope of the supply chain’s behaviour. Sensitivity analysis may then be undertaken in order to reveal how vulnerable the supply chain is to changes in control procedures. It is at this point that simulation analysis comes into its own as scenarios may be readily changed. A structured approach to exploiting the supply chain model [3] will include tuning existing model parameters (that is, optimise the system under prevailing conditions), structural re-engineering (which involves changing the current decision rules, and information material flows), and “what-if?” experimentation (which requires the testing of different business scenarios) (see Fig 1). Due to the high level of integration of the three business units, it is the latter approach that has provided the most insight by gauging the impact of some interdependent scenarios. For example, a large proportion of scrap to Company A is transported by rail wagons. If a full, partial or complete rail strike should occur some percentage of scrap supply loss will result. It was of interest to quantify the consequent loss in Company A’s production, and analyse the impact of this on Companies B and C in terms of percentage volume loss of steel billets to percentage volume lost in despatchlsales over a certain period of time. Another likely scenario was to evaluate the impact of break downs/strikes at Companies
on Company
B finished goods.
B and C on the demand/production policy of Company A. A “real world” situation was also simulated by analysing the consequences of a transformer failure supplying power to the electric arc furnaces of Company A used to melt scrap into molten steel. Clearly a transformer failure would result in cutback in Company A’s production for a given length of time (see Fig. 8). Consequently, Company A (which is the sole raw material supplier to company B) can only supply a percentage of the billet requirements of Company B. In order to meet the end-customer demand Company B has to order its outstanding requirements of billets from external supplier(s). The ordering procedure and raw material supply can take a lead time in the range of two weeks to two months. Fig. 8 illustrates the impact of the supply lead time scenario on the end-customer demand of Company B. (All numerical quantities are normalised for confidentiality reasons). As mentioned earlier Company B is essentially a “make-to-stock” business and satisfy all customer orders from its finished goods (“F. Goods” curve) stock. A depletion of F. Goods stock means low customer service level and/or lost sales. Simulation shows that if the external raw material supply lead time is one month, Company B’s customer demand cannot be met for at least 5 months (see figure). Even when customer demand is finally met, the impact of supply lead time is an unwanted upswing in the Company B’s billet stock level. (The F. Goods and
K. Hafeez
128
et al. JInt. J. Production
Supply
Economics
lead time
= 2 months -
120 100
45 (1996) 121-130
Company
.’
B F. Goods
Company A Production -+-+-I-
Company Stock
B Billet
\ -
0
5
10
20
15
25
30
35
40
45
B finished
goods.
50
Time (months)
Fig. 9. Impact
of supply
lead time (2 months)
Billet Stock curves for Company B have the same level until month 10). An extreme worst-case scenario was also tested, i.e. what if the supply lead time is increased by 2 months? As shown in Fig. 9 this portrays totally unacceptable circumstances as the customer demand is not met for at least ten months and the upswing in the billet stocks is raised to over two times its normal value. Simulations have shown that billet stock level is a major issue of concern to Company B. In this particular situation transformer repair time is difficult to predict accurately. Therefore, there is a likely chance that due to supply lead time the billets from external suppliers would arrive at a time when Company A has already recovered to its full capacity. Consequently, Company B would face a massive billet stock surplus resulting from simultaneous billet supply by Company A and emergency external supplies and should cater for this in its inventory control strategies.
7. Benchmarking performance
steel industry supply chain
Johansson et al. [9] have presented a useful measure which could be utilised to benchmark supply chain performance given by Performance
index = [quality x customer service level]/[cost x lead time].
on Company
Essentially, each of these factors may be viewed as either a market qualifier or market winner. However, their relative importance would vary according to market sector, cultural or demographic reasons. It is interesting to know that two of these factors namely, lead time and customer service levels are critically time dependent. Lead time is frequently identified as a major cause for supply chain poor dynamic behaviour which results in detrimental consequences for each business within the chain [lo]. A number of steps for achieving time compression in supply chains in order to improve supply chain performance are discussed at length by Towill [ 111. As part of the study a survey was conducted with the end-customer of the investigated company in order to ascertain the relative importance of key competitive criteria related to order-winners and market qualifiers down the supply chain. In total, seven performance factors (including those mentioned in the performance index) were used for this purpose as shown in Fig. 10. The results of the survey (illustrated in Fig. 10 and Table 1) supported the company’s view that lowest cost was the order winning criterion in the market sector. The other factors i.e. delivery on time, service, delivery speed, quality and product range scored into market qualljier category. However, product “unavailability” or “stock-out” was ascertained as the main reason for losing customer orders/market share.
K. Hgfeez
L
2
3
1
5
6
et al. /ht.
7
I
J. Production
9
45 (1996)
Table 1 The status
121-130
of competitive
criteria
129
for supply
chain
Order-winner
Market-qualifier
Order-losing-sensitive
cost
Delivery Service Delivery
Product
on time
Q
availability
speed Quality Product range
High sewta
Economies
Leve
Short Delivel Time
Wide Produc Range
Degree of Importance Fig. 10. Relative
8.
importance
of key competitive
criteria.
Inventory planning for Company A
“Inventory planning and control attempts to balance the advantages against the disadvantages of holding stock” [12]. The simulation analyses in line with the survey results suggest that the stock-out situation due to supply lead time would be detrimental for Company B through losing its market share as dissatisfied customers vote with their feet and place their orders elsewhere. Under this circumstance, a solution adopted in many industrial environments is to increase the inventory level across the chain. The simulation results demonstrate that holding a higher level of inventory has many advantages, such as, satisfying customer demand with a perceived zero lead time. However, inventory holding means binding working capital and space, and in most cases it would affect the overhead cost and thereby increase the total cost. This is an important issue in a competitive environment like the steel construction industry where lowest cost is quoted as order winning criterion. Our survey also supported the view that an order placed on Company B is to be satisfied from the existing stock, therefore, the major issue for meet-
ing customer demand is to have the right product mix in stock. While the raw material supplier delay is an important issue, of bigger concern to Company A is the amount of time it takes to repair the transformer. There are two options for management to choose from: (i) to accept the breakdown risk and “insure” against it; or (ii) Assuming it takes a fixed time to repair/replace a transformer increase the level of MRI accordingly. Option (i) is often the case in practice, however, the only way to avoid such a scenario is to keep a spare transformer to replace the damaged one as quickly as possible. Here the simulation exercise provides an aid to management to determine the amount of MRI that can readily be held to ensure 100% customer satisfaction.
9. Conclusions As an example to good total system design, we have described the analysis and modelling of a steel industry supply chain using an integrated systems dynamics framework. A number of industrial engineering tools have been implemented at various stages of modelling. Simulation results have been presented showing how the developed model may be viewed as a “Management Information System” to test against the overall business objectives, and compare various re-engineering strategies. More specifically, simulation has been used to quantify the impact of a “real world” breakdown scenario on the customer service level of Company B, and find ways to move more rapidly towards MRI in the presence of capacity constraints, breakdowns and material supply lead time bottlenecks. The established framework is generalised in nature and
130
K. Hafeez et al. JInt. J. Production Economics 45 (1996) 121-130
may be exploited by other supply chain designers/ practitioners to find its effectiveness in various market sectors.
Acknowledgements
The authors wish to thank the Design and Integrated Production Group of the EPSRC for sponsoring the research work presented in this paper. Our special thanks are also due to Professor D.R. Towill FEng., who led the Industrial Systems Dynamics Research Group (ISDG) at Cardiff, and to the Company and all people of various business units who contributed towards our findings. We would also like to thank the referees whose remarks were found very useful in preparing the revised version of this paper.
References [1] Towill, D.R., 1991. Engineering Change; or is it change engineering? A personal perspective. IEE Management and Design Proc. A, 138: 1 l-21.
121Checkland,
P.. 1981. Systems Thinking, Systems Practice. Wiley, UK. ]31 Naim, M.M. and Towill, D.R., 1993. Establishing a framework for effective logistic management. The Int. J. Logist. Mgmt., 5: 81-88. L., 1992. Many steps towards [41 Grunwald, H.J. and Fort& zero inventory. Eur. J. Oper. Res., 59: 359-369. I51 Edghill, J. and Towill. D.R.. 1989. The use of system dynamics in manufacturing systems engineering. Trans. Int. Measurement Control. 11: 2088216. system. Int. [61 Parnaby, J., 1979. Concept of a manufacturing J. Prod. Res., 17: 1233135. E.F., 1990. System Enquiry: A System [71 Wolstenholme, Dynamics Approach. Wiley, Chichester, UK. 181Griffiths, M.G., Hafeez, K. and Naim, M.M.. 1993. Use of statistical techniques in the dynamic modelling of an industrial supply chain. Proc. 30th MATADOR Int. Conf.. Manchester, UK, pp. 4133420. H.J., McHugh, P.. Pendlebury, A.J. and [91 Johansson, 1993. Business Process ReWheeler III, W.A., Engineering. Wiley, Chichester, UK. [lOI Stalk, Jr, G.H. and Hout, T.M., 1990. Competing Against Time; How Time-based Competition is Reshaping Global Markets. Free Press, New York, 1990. 1111Towill, D.R.. 1995, Time compression & supply chain dynamics In: G. Brace (Ed.), Logistics International. Stirling Publications Ltd., pp 43-47. 112:Bonney, M.C., 1994. Trends in inventory management, Int. J. Prod. Econom., 35: 1077114.
production economics ELSEVIER
Int. J. Production Economics 45 (1996) 121L130
Systems design of a two-echelon steel industry supply chain K. Hafeeza,*,
M. Griffithsb,
J. Griffithsc,
M.M. Naimd
“Shefield Hallam University, School of Engineering, City Campus, Porid Street, Shefield blJniversity of Glamorgan, Pontypridd, UK ‘Allied Steel and Wire, Car&r, UK ‘School of Engineering, University of Wales College of Card$
SI 1 WB, UK
UK
Abstract Supply chains are a complex combination of “man” and “machine” and usually difficult to design. In this paper we argue that systems engineering can be used as an effective tool for this purpose as it comprehensively takes into account
intricacies associated with modelling the attitudinal, organisational and technological issues. We describe the analysis and modelling of a two-echelon steel industry supply chain that services the construction industry using an integrated system dynamics framework as an example to good total system design. Key features of the framework are outlined and implementation details are provided. One objective of the design is to move more rapidly towards a minimum reasonable inventory (MRI) scenario in the presence of capacity constraints, breakdowns and material supply leadtime bottlenecks. Simulation results are presented showing how the developed model may be viewed as a “Management Information System” to investigate various business strategies. Keywords: Supply chain management;
Systems dynamics conceptual modelling; Inventory planning
1. Introduction Conventionally, “real world” supply chains have formed over time as the result of vague associations between individual business units. The absence of a compatible operating strategy results in the poor dynamic behaviour displayed by the majority of supply chains. Such criticism applies to both inventory swings and demand amplification. These features greatly affect the “bottom line” in a variety of damaging ways. For instance, on-costs are incurred in production,
* Corresponding author.
warehousing and distribution, factory experiences alternating periods of over-capacity followed by under-capacity, financial losses are reflected due to obsolescence and the late arrivals of new products in the marketplace [l]. One major problem usually faced in modelling supply chains is that they are a complex combination of “man” and “machine”. Our experience shows that systems engineering can be used for the analysis and design of supply chains as it comprehensively takes into account intricacies associated with modelling the attitudinal, organisational and technological issues [2]. Systems engineering makes extensive use of both “soft systems” as well as “hard systems” tools, such as structured
0925-5273/96/$15.00 Copyright 0 1996 Elsevier Science B.V. All rights reserved SSD10925-5273(96)00052-7
Hafeez rt al. /ht. J. Production Economics 45 (1996) 121-130
Information
Source
Knowledge
Source
Written Documents
Content Analysis
lndivlduals
Queslionaire Interview Log Book
Group of People
Brainstorming Workshops
Investigative
Methods
Flow Charting “Fish Bone” diagram Pareto Analysis
Numerical
Methcds
Information
Flow Analysis
Pmductlon
Control Analysis
lime
Series Analysis
Constramed
Fig. 1. Integrated design.
system dynamics
framework
for supply chain
interviews, input-output analysis, process flow charts, information flow and production control analyses, influence and block diagrams, control theory and computer simulations [3]. A mathematical model of a two-echelon steel industry supply chain is built using the described tools. Simulations are performed based on real data in order to obtain optimum values for parameters corresponding to the conceptual model of the supply chain. The purpose is to obtain a better understanding of the behaviour of different parts of the supply chain. Various economic/operational scenarios are tested to obtain the minimum reasonable inventory (MRI) [4] and to gauge the effect on the manufacturing and customer service level.
2. Integrated systems dynamics framework Fig. 1 illustrate the key features of our integrated systems dynamics framework which is designed
Fig. 2. Typical approaches mation processing.
Curve Fitting
used for data collection
and infor-
specifically to model supply chain using a holistic approach. The framework is described at length elsewhere [3] however a brief description is provided here for convenience. An important aspect of our methodology is to decompose a supply chain into distinct (preferably naturally existing) autonomous business units. This helps to simplify the designing task as we develop conceptual and mathematical models for each unit and analyse them individually. Aggregation of these models to represent a complete supply chain then follows. The design process is of an iterative in nature (shown as feedback loop in Fig. 1) requiring continual feedback of preliminary results and information to the respective management via meetings, reports and presentations for the verification purposes. Essentially the framework consists of two overlapping phases, namely, qualitative and quantitative. The qualitative phase is related to acquiring sufficient intuitive and conceptual knowledge to
123
Fig. 3. Steel reinforcement
understand the structure and operation of a supply chain, whereas, the quantitative phase is associated with the development and analysis of mathematical and simulation model. Modelling becomes more qualitative as we progress from top down to the operations level. The qualitative phase is commonly applicable irrespective of the nature of supply chain under investigation. The main stages involved in this phase are system input-output analysis, conceptual modelling, and block diagram formulation. Fig. 2 lists a number of industrial engineering tools used for information collection and processing at the qualitative stage. The environment in which qualitative models are to be built and the theoretical analysis to be carried out can be controversial [5]. We have found three techniques namely, control theory, computer simulation and statistical analysis useful when setting up the mathematical equations representing the functioning of a supply chain. However, these techniques have their strengths and weaknesses for a particular application. In general, two factors should be taken into account when choosing between these techniques: (i) the degree of complexity involved in setting up a mathematical model of the particular supply chain and; (ii) the amount of operating and other field data which could be made available for the purpose of statistical analysis. In the present study we have used a
industry
supply
chain
combination of computer simulations coupled with statistical analysis for the purpose of qualitative model building.
3. A Steel industry supply chain The supply chain investigated is the supply of reinforced steel bars to the construction industry. Fig. 3 shows the material flow diagram of the supply chain. The end customer in the chain is a project management team which comprises client, consultant and contractors. The stockholders buy and stock large quantities. The behaviour of the stockholding echelon causes rapid changes in the demand inflicted on the other members of the chain. The steel market is speculative and it is believed that the demand for construction products depends not only on the customer/contractor but also on other factors such as steel price, interest rates and delivery times. The reinforcement industry is recently been changing its order policy resulting in more unpredictable behaviour. The reason is that stockholder target levels have been lowered due to economic changes affecting their business environment. As there is a natural tendency to minimise “buffer” or “safety” inventory in a recession the risk of running out of stock increases.
124
COMPANY FORWARD
a (MASTER
ROLLING
FiLf)
PROGRAMME 16W
W
W COMPANY STOCK
COMPANY 8 BILLET CONTRACT ON A
9 BILLET SHEET
COh -
SCHEDULE
COMPANY
Key
’
D - Daily;
Fig. 4. Information
COMPANY A BILLET STOCK
A
W - Weekly;
M - Monthly:
C * Continuousiy
flow model of supply chain.
The sub-system under study (shown in Fig. 3) consists of three separately controlled business units: Companies A, B and C. The management structure of the business is decentralised, however, the three businesses are highly dependent on one another. Company A is a foundry transforming scrap metal into billets. Companies B and C produces steel products out of steel billets. Company A is sole supplier to Company B and the main supplier (approx. 50%) to Company C. External suppliers make up the shortfalls in raw material for Company C. Company A’s business is essentially capacity planned based on the philosophy to keep its plant working for as much time as possible regardless of the orders placed upon it. The reason is two fold: (i) all the finished goods is sold internally to Companies B and C, and (ii) this allows Company A to minimise the overhead cost as it is spread over a large volume of finished goods. A detailed study of the information flows was undertaken which highlighted the planning and control procedures relevant to each business unit. Fig. 4 gives an illustration of the information flow diagram relevant to Companies A and B. A key is
attached underneath each process of the information model to show how frequently information is updated between various processes. Of particular importance is the rolling programme of Company B comprising the planned rolling dates and production volume for each product over the following 16 weeks (see Fig. 4). As a result the rolling programme acts like a queue of orders with the new orders being placed near the end. The total number of orders in this queue plus any orders not yet delivered from stock constitute the forward orders which are translated subsequently to produce the forward rolling programme of company B. Forward orders are normally used by management as the performance measure of company B. In conjunction with material and information flow analyses, we have found the use of input-output analysis (IOA) to be a powerful tool [6]. IOA takes account of laid down operating procedures, functional definitions, field data, activity sampling and job descriptions. With the aid of IOA those flows of info~ation relevant to the productionscheduling system were mapped for each company. Fig. 5 shows the IO block diagram of Company A’s production-scheduling system. The planning
1% Hufeez
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COMPANY A z Billet Delivery \
Fig. 6. High-level
Fig. 5. Input-Output
analysis
of Company
A.
methods used by the production scheduling managers were inv~stigatcd by means of interviewing and observing them at work.
conceptual
model of supply
chain.
middle and top management. Producing the right conceptual model is a major step in the systems engineering approach towards constructing a block diagram which is a powerful aid in formulating mathematical and simulation models.
5. Quantitative model 4. Conceptual model The investigations described form the basis for the development of a conceptual model [7] of the production-scheduling system. As an example, Fig. 6 shows the conceptual model applicable to Company A (where variable “Mill’s” is used for both Company B as well as C). The model highlights the main variables having a dominant impact on the functioning/performance of the business, and the cause and effect relationships resulting from their interactions. (Similar models were constructed for each business unit.) The conceptual diagram is normally verified by gathering the opinions of the relevant persons involved. Usually, we have found conceptual model to be a good tool for communicating with
The first step towards the model building is to transform the conceptual model into a block diagram. A block diagram representation of Company A’s production control system is given in Fig. 7. In this format, the flows of information and materials are represented via various paths. Production, ordering and other physicaliadministrative operations are represented using blocks. The main blocks and flows of the Company A production control diagram shown in Fig. 7 are: Mill’s billet orders: these are the orders placed by Companies B and C on company A. Order proceskg delay: is the time difference between when the billets orders are received from company B or C, and processed for the production planning purpose.
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Fig. 7. A block diagram
J. Production
representation
Desired billet level: this is the level of the billet stock Company A aims to maintain at all times. Under normal circumstances the company wants to hold MRI. Billet level recovery timelj&tor: this is the fraction of the Company A’s billet stock error to be recovered in one time period. Planning: this accounts for the day-to-day delays and alterations in production targets made by the Company A Planners as they develop and update the production plan. Production: represents the behaviour of the complete manufacturing process when a demand is placed upon it. Casting schedule deluy: represent the delay between a billet order being placed upon the steel plant and the despatch of the product to the Mill. The delay is caused by waiting for the next casting of the specific billet to occur, when it is not in the stock. Add to stock: this converts the difference between billet production and billet despatch rates into a change in the billet stock error. (Billet stock error is the difference between desired and actual stock levels.)
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control
of Company
A.
The diagram shown however only provides a first approximation of the production-scheduling system. This portrays an aggregate level view as it does not take into account the scheduling complexity for each product mix, or the changeover times between products. Block diagrams representing each business unit were developed with the aid of corresponding conceptual models. The corresponding simulation models were verified by relevant personnel and via mathematical cross-checks and validated against field data [8]. These were all then interlinked to form a complete model of the internal supply chain which was used subsequently for dynamic analysis purposes.
6. Dynamic
analysis
“Real-world” dynamic analysis of any business is extremely difficult to perform due to financial, production or time constraints. Perhaps the best output of our supply chain modelling approach is that the models may be used as a means to analyse the dynamic performance of the supply chain. The
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Fig. 8. Impact
of supply
lead time (1 month)
designs are verified and validated and then subject to extensive dynamic analysis. For example, simple test inputs (step, impulse, periodic, etc.) may reveal an envelope of the supply chain’s behaviour. Sensitivity analysis may then be undertaken in order to reveal how vulnerable the supply chain is to changes in control procedures. It is at this point that simulation analysis comes into its own as scenarios may be readily changed. A structured approach to exploiting the supply chain model [3] will include tuning existing model parameters (that is, optimise the system under prevailing conditions), structural re-engineering (which involves changing the current decision rules, and information material flows), and “what-if?” experimentation (which requires the testing of different business scenarios) (see Fig 1). Due to the high level of integration of the three business units, it is the latter approach that has provided the most insight by gauging the impact of some interdependent scenarios. For example, a large proportion of scrap to Company A is transported by rail wagons. If a full, partial or complete rail strike should occur some percentage of scrap supply loss will result. It was of interest to quantify the consequent loss in Company A’s production, and analyse the impact of this on Companies B and C in terms of percentage volume loss of steel billets to percentage volume lost in despatchlsales over a certain period of time. Another likely scenario was to evaluate the impact of break downs/strikes at Companies
on Company
B finished goods.
B and C on the demand/production policy of Company A. A “real world” situation was also simulated by analysing the consequences of a transformer failure supplying power to the electric arc furnaces of Company A used to melt scrap into molten steel. Clearly a transformer failure would result in cutback in Company A’s production for a given length of time (see Fig. 8). Consequently, Company A (which is the sole raw material supplier to company B) can only supply a percentage of the billet requirements of Company B. In order to meet the end-customer demand Company B has to order its outstanding requirements of billets from external supplier(s). The ordering procedure and raw material supply can take a lead time in the range of two weeks to two months. Fig. 8 illustrates the impact of the supply lead time scenario on the end-customer demand of Company B. (All numerical quantities are normalised for confidentiality reasons). As mentioned earlier Company B is essentially a “make-to-stock” business and satisfy all customer orders from its finished goods (“F. Goods” curve) stock. A depletion of F. Goods stock means low customer service level and/or lost sales. Simulation shows that if the external raw material supply lead time is one month, Company B’s customer demand cannot be met for at least 5 months (see figure). Even when customer demand is finally met, the impact of supply lead time is an unwanted upswing in the Company B’s billet stock level. (The F. Goods and
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20
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Fig. 9. Impact
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Billet Stock curves for Company B have the same level until month 10). An extreme worst-case scenario was also tested, i.e. what if the supply lead time is increased by 2 months? As shown in Fig. 9 this portrays totally unacceptable circumstances as the customer demand is not met for at least ten months and the upswing in the billet stocks is raised to over two times its normal value. Simulations have shown that billet stock level is a major issue of concern to Company B. In this particular situation transformer repair time is difficult to predict accurately. Therefore, there is a likely chance that due to supply lead time the billets from external suppliers would arrive at a time when Company A has already recovered to its full capacity. Consequently, Company B would face a massive billet stock surplus resulting from simultaneous billet supply by Company A and emergency external supplies and should cater for this in its inventory control strategies.
7. Benchmarking performance
steel industry supply chain
Johansson et al. [9] have presented a useful measure which could be utilised to benchmark supply chain performance given by Performance
index = [quality x customer service level]/[cost x lead time].
on Company
Essentially, each of these factors may be viewed as either a market qualifier or market winner. However, their relative importance would vary according to market sector, cultural or demographic reasons. It is interesting to know that two of these factors namely, lead time and customer service levels are critically time dependent. Lead time is frequently identified as a major cause for supply chain poor dynamic behaviour which results in detrimental consequences for each business within the chain [lo]. A number of steps for achieving time compression in supply chains in order to improve supply chain performance are discussed at length by Towill [ 111. As part of the study a survey was conducted with the end-customer of the investigated company in order to ascertain the relative importance of key competitive criteria related to order-winners and market qualifiers down the supply chain. In total, seven performance factors (including those mentioned in the performance index) were used for this purpose as shown in Fig. 10. The results of the survey (illustrated in Fig. 10 and Table 1) supported the company’s view that lowest cost was the order winning criterion in the market sector. The other factors i.e. delivery on time, service, delivery speed, quality and product range scored into market qualljier category. However, product “unavailability” or “stock-out” was ascertained as the main reason for losing customer orders/market share.
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Table 1 The status
121-130
of competitive
criteria
129
for supply
chain
Order-winner
Market-qualifier
Order-losing-sensitive
cost
Delivery Service Delivery
Product
on time
Q
availability
speed Quality Product range
High sewta
Economies
Leve
Short Delivel Time
Wide Produc Range
Degree of Importance Fig. 10. Relative
8.
importance
of key competitive
criteria.
Inventory planning for Company A
“Inventory planning and control attempts to balance the advantages against the disadvantages of holding stock” [12]. The simulation analyses in line with the survey results suggest that the stock-out situation due to supply lead time would be detrimental for Company B through losing its market share as dissatisfied customers vote with their feet and place their orders elsewhere. Under this circumstance, a solution adopted in many industrial environments is to increase the inventory level across the chain. The simulation results demonstrate that holding a higher level of inventory has many advantages, such as, satisfying customer demand with a perceived zero lead time. However, inventory holding means binding working capital and space, and in most cases it would affect the overhead cost and thereby increase the total cost. This is an important issue in a competitive environment like the steel construction industry where lowest cost is quoted as order winning criterion. Our survey also supported the view that an order placed on Company B is to be satisfied from the existing stock, therefore, the major issue for meet-
ing customer demand is to have the right product mix in stock. While the raw material supplier delay is an important issue, of bigger concern to Company A is the amount of time it takes to repair the transformer. There are two options for management to choose from: (i) to accept the breakdown risk and “insure” against it; or (ii) Assuming it takes a fixed time to repair/replace a transformer increase the level of MRI accordingly. Option (i) is often the case in practice, however, the only way to avoid such a scenario is to keep a spare transformer to replace the damaged one as quickly as possible. Here the simulation exercise provides an aid to management to determine the amount of MRI that can readily be held to ensure 100% customer satisfaction.
9. Conclusions As an example to good total system design, we have described the analysis and modelling of a steel industry supply chain using an integrated systems dynamics framework. A number of industrial engineering tools have been implemented at various stages of modelling. Simulation results have been presented showing how the developed model may be viewed as a “Management Information System” to test against the overall business objectives, and compare various re-engineering strategies. More specifically, simulation has been used to quantify the impact of a “real world” breakdown scenario on the customer service level of Company B, and find ways to move more rapidly towards MRI in the presence of capacity constraints, breakdowns and material supply lead time bottlenecks. The established framework is generalised in nature and
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may be exploited by other supply chain designers/ practitioners to find its effectiveness in various market sectors.
Acknowledgements
The authors wish to thank the Design and Integrated Production Group of the EPSRC for sponsoring the research work presented in this paper. Our special thanks are also due to Professor D.R. Towill FEng., who led the Industrial Systems Dynamics Research Group (ISDG) at Cardiff, and to the Company and all people of various business units who contributed towards our findings. We would also like to thank the referees whose remarks were found very useful in preparing the revised version of this paper.
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P.. 1981. Systems Thinking, Systems Practice. Wiley, UK. ]31 Naim, M.M. and Towill, D.R., 1993. Establishing a framework for effective logistic management. The Int. J. Logist. Mgmt., 5: 81-88. L., 1992. Many steps towards [41 Grunwald, H.J. and Fort& zero inventory. Eur. J. Oper. Res., 59: 359-369. I51 Edghill, J. and Towill. D.R.. 1989. The use of system dynamics in manufacturing systems engineering. Trans. Int. Measurement Control. 11: 2088216. system. Int. [61 Parnaby, J., 1979. Concept of a manufacturing J. Prod. Res., 17: 1233135. E.F., 1990. System Enquiry: A System [71 Wolstenholme, Dynamics Approach. Wiley, Chichester, UK. 181Griffiths, M.G., Hafeez, K. and Naim, M.M.. 1993. Use of statistical techniques in the dynamic modelling of an industrial supply chain. Proc. 30th MATADOR Int. Conf.. Manchester, UK, pp. 4133420. H.J., McHugh, P.. Pendlebury, A.J. and [91 Johansson, 1993. Business Process ReWheeler III, W.A., Engineering. Wiley, Chichester, UK. [lOI Stalk, Jr, G.H. and Hout, T.M., 1990. Competing Against Time; How Time-based Competition is Reshaping Global Markets. Free Press, New York, 1990. 1111Towill, D.R.. 1995, Time compression & supply chain dynamics In: G. Brace (Ed.), Logistics International. Stirling Publications Ltd., pp 43-47. 112:Bonney, M.C., 1994. Trends in inventory management, Int. J. Prod. Econom., 35: 1077114.