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Optimize visibility of final demand in the supply chain

Hausarbeit 2017 31 Seiten

BWL - Beschaffung, Produktion, Logistik

Leseprobe

Table of contents

List of figures

Formula directory

1 Introduction: the bullwhip problem

2 Supply Chain Management (SCM)
2.1 The value-adding process across companies
2.2 The first law of the Supply Chain Dynamic
2.3 Supply Chain Planning (SCP)

3 Demand Planning
3.1 Demand Fulfillment (DF)
3.2 Available to Promise (ATP)
3.2.1 Responsibilities of Demand Fulfillment
3.2.2 Order Promising
3.2.3 Master Planning as the common basis
3.2.4 Assembly-to-Order
3.2.5 Bottleneck Planning
3.2.6 Demand Supply Matching (DSM)

4 Practical example
4.1 Introduction
4.2 Starting situation
4.3 Available-to-Promise in the case Assembly-to-Order
4.4 Possible improvements

5 Conclusion

6 List of references

List of figures

Figure 1: The Supply Chain Planning Matrix.

Figure 2: Master Planning as the common basis of supply processes and order promising

Figure 3: Process steps of an order for ATO production

Figure 4: Production: Final Assembly

Figure 5: Customer: Order Overview

Figure 6: Period 1: Final Assembly – Black

Figure 7: Period 1: Final Assembly – White

Figure 8: Period 2: Final Assembly – Black

Figure 9: Period 2: Final Assembly – White

Figure 10: Possible improvements – Black

Figure 11: Possible improvements – White (Example 1)

Figure 12: Possible improvements – White (Example 2)

Formula directory

(1) Calculation of the planned future inventory

(2) Calculation of the cumulative ATP quantity

(3) Calculation of the ATP quantity

(4) First Responsibility of Demand Fulfillment

(5) Second Responsibility of Demand Fulfillment

(6) Order Promising related on components

1 Introduction: the bullwhip problem

The problem in the simulation game "Supply Chain Simulation Module" was that the different players of a chain (supplier, manufacturer, retailer / wholesaler (customer)) communicated exclusively with orders in order to fulfill the given market demand or the needs of their teammates. Due to delays in deliveries, there were increased orders in period three, which already exceeded the final demand as a whole. This led to a chaotic ordering and / or demand behavior. The uncertainties in the demand forecast initially led to bottlenecks. The bottlenecks led to higher safety stocks, which led to overproduction. This led to a negatively evolving supply chain that affected the company. This phenomenon is also referred to as a bullwhip effect.

In this assignment, a possible cause, which has strengthened the bullwhip effect, is investigated. The role of the author in order management as well as the presentation topic "Information and Funds Flow in Supply Chains" are taken into account. During the execution of the planning game, the author was free to choose within the order management whether he should initiate an internal production or serve the customer demand from the warehouse. This free decision leaves a very high risk of the bullwhip effect taking place. Decisions are made without knowing all possible outcomes which can have a reverberating effect on the entire company. For this reason the assignment is being investigated. It is researched which tools are necessary for the order management to make secure and reliable decisions in the future. In addition, it will be shown how the communication within the company can be improved.

2 Supply Chain Management (SCM)

2.1 The value-adding process across companies

In principle the supply chain works as a network, since it has more than one supplier, service provider or customer. The network concept is used to extend the SCM's approach to the entire value-added network, in relation to multiple customers, service providers and supplier relationships.[1]

The supply chain is oriented at a process-oriented view, which follows the flow of the performance objects. Performance objects are materials and information that flow along the supply chain or within the supply net. For a complete understanding some authors also include funds in this view. The temporal classification of the object flow is oriented at or over the entire product life cycle.[2]

2.2 The first law of the Supply Chain Dynamic

This law implies that even the slightest changes in the consumer behavior of the end customers lead to an overreaction of the demand deficit at the end of the supply chain. This leads to an ever-increasing order fluctuation. This phenomenon has already been studied by Jay Forrester for the Massachusetts Institute of Technology in the 1950s. Daniel Corsten and Christoph Gabriel described this phenomenon as the first law of supply chain dynamics. In the literature the first law is also referred to as a forrester effect or bullwhip effect.[3]

This law occurs in two forms. Form one is the short-term overshoot of the order quantities, which is characterized by an increase in variability. Form two describes the longer-term overshooting of the order quantities. This occurs when a good prognosis of demand is possible by a very smooth course. The failure of the actual requirements leads to an increase or decrease of the error potential over a longer period of time.[4]

Related to the game “Supply Chain Simulation Module” factors which increase the bullwhip effect on the uncertain forecast can be subdivided into local production of demand forecasts, increased safety stocks and no information on actual demand.

In conclusion, the need for communication, transparency and the exchange of planning information between suppliers, manufacturers and service providers within the supply chain is very high. The most important measure is the simultaneous provision of the same information along the entire supply chain in order to allow for predictive planning and a continuous flow of material and information. The basic idea behind the SCM is to transform a poorly regulated system of a value-added network into an optimally regulated system. Inside the SCM, methods and instruments are assigned in a strategic, tactical and operational level. The terms supply chain design (strategic), supply chain planning (tactical) and supply chain execution (operating) have been developed.[5]

2.3 Supply Chain Planning (SCP)

SCP deals with the planning within the entire supply net.[6] As this work is concentrated on the improvement of demand signals, it is confined to the SCP.

With the implementation of an SCP, an efficient use of resources within the entire supply chain is strived for. The aim is to reduce or control the bullwhip effect by means of a high integration of all partners.[7] The implementation of the SCP is carried out by Advanced Planning Systems (APS). APS include an integrated planning of the entire supply chain and the optimization of the planning problems.[8]

illustration not visible in this excerpt

Figure 1: The Supply Chain Planning Matrix (Modified by: Stadtler, H.: Purchasing and Material Requirements Planning, published in: Stadtler, H., Kilger, C., Meyr, H.: Supply Chain Manangement and Advanced Planning – Concepts, Models, Software and Case Studies, Fifth Edition, Heidelberg Berlin, 2015, pp.71 - 94)

Figure 1 shows the Supply Chain Planning Matrix. A distinction is made between medium-term and short-term tasks as well as the respective sub-areas of a supply chain process.[9] APS systems are based on a hierarchical planning concept in which the overall planning task is subdivided into individual partial plans. The individual planning tasks can be understood as modules for which APS solutions can be developed.[10] In the following, Demand Fulfillment is focussed as the main topic of this work.

3 Demand Planning

3.1 Demand Fulfillment (DF)

„The planning process that determines how the actual customer demand is fulfilled is called demand fulfillment.“[11] The Demand Fulfillment (DF) describes an availability check which makes a quick statement about material availability as well as delivery dates. DF is fixed in the short-term planning horizon. The availability check with regard to the fulfillment of customer orders and customer requests can be carried out in three different ways.[12] “Modern Demand Fulfillment solutions based on the planning capabilities of APS employ more sophisticated order promising procedures, in order to improve the on time delivery by generating reliable quotes, to reduce the number of missed business opportunities by searching more effectively for a feasible quote and to increase revenue and profitability by increasing the average sales price.”[13]

Following the first option, the goal is to check customer orders for their feasibility in real-time and to develop realistic delivery dates as part of a one-step and rule-based process. This method is called Available to Promise (ATP). This focusses the feature of reliability in the form of punctuality.[14]

The other ways did not occur during the course of the game and are therefore not further explained in this assignment.

3.2 Available to Promise (ATP)

The availability of resources (products, materials, or capacities) decisively determines the results and quality of the demand fulfillment. Therefore the decisions should be made on the basis of the availability of these resources in the company. The term available to promise (ATP) has become established to describe the available quantities of resources for new orders. This is also formulated as ATP quantity.[15] The calculation is based on material stocks, intermediate stocks or final product stocks as well as future resources. ATP quantities can be divided into cumulative and non-cumulative ATP quantities. Cumulative ATP is the total quantity of a product that is available for new orders in a given period. However the storage time cannot be determined here. Non- cumulative ATP quantities correspond to the free quantity of an entry or stock arriving in a period and may be viewed as a free quantity that is additionally available in a period.[16]

The calculation of an ATP quantity of a final product or material for a planning horizon of t = 0,…, T days is possible with the data:[17]

illustration not visible in this excerpt

The following equation allows the determination of the planned, future inventory It at the end of day t ≥ 0:

illustration not visible in this excerpt

The cumulative ATP quantity (short: ) of a day t is the complete quantity that is available on this day for the confirmation of new orders. This can be determined by:

illustration not visible in this excerpt

With the ATP quantity the still available new quantity of a resource is calculated for one day:

illustration not visible in this excerpt

The allocation of new dates t for an order with the set q is possible if the condition or equivalent .

3.2.1 Responsibilities of Demand Fulfillment

DF deals with two questions:

- How do you react to new incoming customer orders?
- What happens to customer orders already received up to their complete fulfillment?

These questions lead to several tasks of the operational demand fulfillment and can be divided into the following components:

- Incoming orders in the company
- Reaction to order receipts
- Processing and control of orders up to their fulfillment
- Delivery of orders[18]

In order to be able to deal with the topic of this work in a focused manner, the following segment is exclusively concerned with the definition of a delivery date for new incoming customer orders. This task within the scope of the demand fulfillment is called Order Promising.

3.2.2 Order Promising

By setting delivery dates for new orders, order confirmations are also created within order promising. These may include a first expected delivery date ("First Promised Date") as well as the planned delivery quantity. This requires a delivery date. In connection with the delivery date the company must decide whether it accepts or rejects the order in principle.[19] If fulfillment of a customer order is necessary or possible due to availability situations at the time of order promising, a later delivery date is determined and communicated to the customer. As a result the company is not currently in a position to fulfill the contract. If the order is canceled by the customer, the order is not fulfilled.

The quality of the Order Promising is measured by the on-time delivery and the delivery performance. It measures the percentage of the order that was fulfilled as promised. Thus, to achieve a high on-time delivery it is important to generate reliable promises. Hereby, a distinction is made between different models, which are divided into On-Line Order Promising, Batch Order Promising and Hybrid Order Promising.[20] Order Promising is presented below, as it might be an improvement to the simulation game.

In the game "Supply Chain Simulation Module" Order Promising was not carried out. No order confirmations were sent to the customer and the orders were always accepted. It is therefore assumed that the bullwhip effect ensued from this cause.

For this reason the order promising should be included in the game to the full extent. The game could be extended by the calculation scheme of order promising or the ATP to provide decision support. The implementation is discussed in chapter four.

In principle, new incoming orders with the desired quantity q and the desired delivery date d can be accepted if the following condition is fulfilled:

illustration not visible in this excerpt

3.2.3 Master Planning as the common basis

Master Planning is based on forecasts that reflect the ability of the market to create demand. The Master Planning Process has the task to create a plan for the complete supply chain, including production and purchasing decisions. Thus, Master Planning generates a plan for future supply from internal and external sources even beyond the already existing schedules receipts. With the APS-based Demand Fulfillment, the supply information is taken from the master plan to create reliable order quotes. The master plan is the common basis for the supply process and the order promising process which is shown in Figure 2.[21]

illustration not visible in this excerpt

Figure 2: Master Planning as the common basis of supply processes and order promising (Modified by: Stadtler, C., Kilger, C., Meyr, H.: Supply Chain Management and Advanced Planning – Concepts, Models, Software and Case Studies, Fifth Edition, Berlin Heidelberg, 2015, pp. 180)

As ATP is derived from the supply information of the master plan, it is structured according to the level of detail of the master plan. The most important dimensions for structuring ATP are customer, product and time. In principal, ATP can be looked for along all dimensions. If no ATP can be found for an order, the supply chain will not be able to fulfill the order within the allocation planning horizon.[22]

[...]


[1] Cf. Zaeh, M.F., Habicht, C., Neise, P.: Classification and parameterization of bilateral supply chain, Cairo, 2004, pp. 102

[2] Cf. Martens, H.: Planung und Steuerung von Produktion und Recycling in kreislaufwirtschaftlich ausgeprägten Unternehmensnetzwerken. Ein Supply Chain Management orientierter Ansatz, Diss., Hamburg, 2007, pp. 63.

[3] Cf. Corsten, D., Gabriel, C.: Supply Chain Management erfolgreich umsetzen – Grundlagen, Realisierung und Fallstudien, Heidelberg, 2002, pp. 18.

[4] Cl. Alicke, K.: Planung und Betrieb von Logistiknetzwerken – Unternehmensübergreifendes Supply Chain Management, Heidelberg, 2003, pp. 116.

[5] Cl. Wannenwetsch, H.: Vernetztes Supply Chain Management – SCM-Integration über die gesamte Wertschöpfungskette, 2005, pp. 81.

[6] Cl. Blecker, T.: Unternehmung ohne Grenzen – ein modernes Konzept zum erfolgreichen Bestehen im dynamischen Wettbewerb, published in: Gronalt, M. (ed.): Logistikmanagement – Erfahrungsberichte und Konzepte zum (Re-)Design der Wertschöpfungskette, Wiesbaden, 2001, pp. 34.

[7] Cl. Wannenwetsch, H.: Vernetztes Supply Chain Management – SCM-Integration über die gesamte Wertschöpfungskette, 2005, pp. 83.

[8] Cl. Fleischmann, B., Meyr, H., Wagner, M.: Advanced Planning, published in: Stadtler, H., Kilger, C., Meyr, H.: Supply Chain Manangement and Advanced Planning – Concepts, Models, Software and Case Studies, Fifth Edition, Heidelberg Berlin, 2015, pp.74.

[9] Cl. Seifert, D.: Efficient Consumer Response – Supply Chain Management (SCM), Category Management und Collaborative Planning, Forecasting and Replenishment (CPFR) als neue Strategieansätze, Third Edition, Munich and Mering, 2004, pp. 107, According to Ferber, S.: Strategische Kapazitäts- und Investitionsplanung in der globalen Supply Chain eines Automobilherstellers, Diss., Augsburg, 2005, pp. 54, Konrad, G.: Theorie, Anwendbarkeit und strategische Potentiale des Supply Chain Management, Wiesbaden, 2005, pp. 94.

[10] Cl. Rüggeberg, C.: Supply Chain Management als Herausforderung für die Zukunft, Prozessorientierte Materialwirtschaft in KMU, Wiesbaden, 2003, pp.35.

[11] Stadtler, H., Kilger, C., Meyr, H.: Supply Chain Manangement and Advanced Planning – Concepts, Models, Software and Case Studies, Fifth Edition, Heidelberg Berlin, 2015, pp. 177.

[12] Cl. Werner, H.: Supply Chain Management – Grundlagen, Strategien, Instrumente und Controlling, Second Edition, Wiesbaden, 2002, pp. 227, Ferber, S.: Strategische Kapazitäts- und Investitionsplanung in der globalen Supply Chain eines Automobilherstellers, Diss., Augsburg, 2005, pp. 59, Konrad, G.: Theorie, Anwendbarkeit und strategische Potentiale des Supply Chain Management, Wiesbaden, 2005, pp. 116.

[13] Stadtler, H., Kilger, C., Meyr, H.: Supply Chain Manangement and Advanced Planning – Concepts, Models, Software and Case Studies, Fifth Edition, Heidelberg Berlin, 2015, pp. 177.

[14] Cl. Bretzke, WR. „Available to Promise“: Der schwierige Weg zu einem berechenbaren Lieferservice, published in: Logistik Management, Edition 2, 2007, pp. 9.

[15] Cl. Pibernik, R.: Advanced available-to-promise: Classification, selected methods and requirements for operations and inventory management, published in: International Journal of Production and Economics, Edition 93, Frankfurt, 2005, pp. 240.

[16] Cl. Fleischmann, B., Meyr, H.: Customer orientation in advanced planning systems, published in: Dyckhoff, H., Lackes, R., Reese, J.: Supply chain management and reverse logistics, Berlin, 2003, pp. 506-508.

[17] Cl. Fleischmann, B., Meyr, H.: Customer orientation in advanced planning systems, published in: Dyckhoff, H., Lackes, R., Reese, J.: Supply chain management and reverse logistics, Berlin, 2003, pp. 507f, Fleischmann, B., Geier, S.: Global available-to-promise (global ATP), published in: Stadtler, H., Fleischmann, b., Grunow, M., Meyr, H., Sürie, c.: Advanced planning in supply chains – Illustration the concepts using SAP APO case study, Chapter 7, Berlin, 2012, pp. 197.

[18] Cl. Fleischmann, B., Meyr, H.: Customer orientation in advanced planning systems, published in: Dyckhoff, H., Lackes, R., Reese, J.: Supply chain management and reverse logistics, Berlin, 2003, pp. 51.

[19] Cl. Fleischmann, B., Meyr, H.: Customer orientation in advanced planning systems, published in: Dyckhoff, H., Lackes, R., Reese, J.: Supply chain management and reverse logistics, Berlin, 2003, pp. 507f, Fleischmann, B., Geier, S.: Global available-to-promise (global ATP), published in: Stadtler, H., Fleischmann, b., Grunow, M., Meyr, H., Sürie, c.: Advanced planning in supply chains – Illustration the concepts using SAP APO case study, Chapter 7, Berlin, 2012, pp. 197.

[20] Cl. Kilger, C, Meyr, H.: Demand Fulfillment and ATP, published in Stadtler, H., Kilger, C., Meyr, H.: Supply Chain Manangement and Advanced Planning – Concepts, Models, Software and Case Studies, Fifth Edition, Chapter 9, Heidelberg Berlin, 2015, pp. 116.

[21] Cl. Kilger, C, Meyr, H.: Demand Fulfillment and ATP, published in Stadtler, H., Kilger, C., Meyr, H.: Supply Chain Manangement and Advanced Planning – Concepts, Models, Software and Case Studies, Fifth Edition, Chapter 9, Heidelberg Berlin, 2015, pp. 179ff.

[22] Cl. Kilger, C, Meyr, H.: Demand Fulfillment and ATP, published in Stadtler, H., Kilger, C., Meyr, H.: Supply Chain Manangement and Advanced Planning – Concepts, Models, Software and Case Studies, Fifth Edition, Chapter 9, Heidelberg Berlin, 2015, pp. 184-194.

Details

Seiten
31
Jahr
2017
ISBN (eBook)
9783668722231
ISBN (Buch)
9783668722248
Dateigröße
741 KB
Sprache
Deutsch
Katalognummer
v428231
Institution / Hochschule
Ernst-Abbe-Hochschule Jena, ehem. Fachhochschule Jena
Note
1,0
Schlagworte
Supply Chain Management Logistics Bullwhip effect Demand Fulfillment Available to Promise Assembly-to-Order Master Planning Order Promising Bottleneck Planning Demand Supply Matching Supply Chain Simulation Module Information and Funds Flow

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Titel: Optimize visibility of final demand in the supply chain