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Production Management I (Prof. Schuh) Lecture 8

Materials Management and Tools and Methods for Material Management 1

page 1© WZL / FIR

Production Management I

Vorlesungbetreuer:Dipl.-Ing. Alexander [email protected] Raum 209Tel.: 47705-436

- Lecture 8 -Materials Management andTools & Methods for Material Management

Learning target of this lecture:• Overview of tasks and objectives of operations control• Understanding of the challenges in real life• Understanding of the principles and processes of operations control• Knowledge of applicable methods



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Focus of the Lecture 8

Materials procurement planning

Static lot sizes calculationStatic lot sizes calculation

Dynamic lot sizes calculation


Materials requirement planning

Stochastic requirement planning


Deterministic requirement planning


Materials stock planning


Reasons for emergency stock levels


Objective and challenges of materials planningObjective and challenges of materials planning

Item Structuring as planning tool – ABC, XYZ Analysis


Functions and place within company of materials planningFunctions and place within company of materials planning

Order Initiation – Time based, Quantity basedOrder Initiation – Time based, Quantity based

Types of Material Requirement


Heuristic requirement Planning


Impact of emergency stock levels


Techniques to determine emergency stock levels


Picture notes:



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Materials Management Objectives & Challenges

Functions and objectives of Materials Management

Correct quantity of material with the quality

required, supplied to production on schedule

Low level of capital bound in storages/

warehouses

Utilization of optimal purchasing concepts in

terms of price, quantity, quality and timing

Supply of products and spare parts on schedule

to sales market

Master Data Management- Order related, Non

order related

Order Initiation -Time based, Quantity Based

Uncertain material requirement

Time

Req

uire

men

t

Part 4711

Part 4712

techn. modification

Old New

Problem of quantity

- Number of parts

- Number of procurement operations

- Number of suppliers

Challenges of Materials Management

Target conflicts

Lot size StockLow capital commitment Low requirement for spaceRational manufactureHigh availability

... ... ...

Requirements Effects

Picture notes:Materials Management deals with all processes that concern the supply and the disposal of material as well as the improvement of the creation of value within the company. Primary technical main task: Supply and Disposal of material in necessary quantity and quality on schedule at the right place thus it assists in improvement of the creation of value within the company. Among other associated functions material management also has to ensure low level of capital bound in storages and warehouse. It must ensures on time delivery while utilizing optimal purchasing concepts in terms of price, quantity, quality and timing.These targets form the real challenge for material management because on one side it should ensure desired service quality by keeping stocks to ensure high availability and on the contrary it should to be cost effective through reduction of capital tie up caused by stocks, inventory and in process material staging, so challenge is to find the golden center between these conflicts.



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The position of material planning within the company

ProductsRaw materials,purchased parts,

assemblies

Constructionjob preparation

Information flow for customer productionKey: Material flow Information flow

Incoming goods

Incoming goods

warehouse

Distribution

Finishedgoods

warehouse

Production / Assembly

- Internal transportation

- Temporary storage of parts/assemblies

SalesDetermination of the order volume

Supplier PurchasingSupplierselection

SCHEDULING

- Requirements planning - Order initiation- Order quantity calculation - Stock control

Ord

ers

Rel

ease

orde

rO

rder

s Customer

Rel

ease

orde

rPicture notes:The term “material planning” incorporates the functions of •Requirement planning•Order quantity calculationIt is also important to define the criteria that triggering material planning operation, and any material planning process must also take account of existing stock levels, outstanding orders, reserved materials and safety stocks, so the function of•Order initiation•Stock controlAre further essential part of a complete material planning system. Finally, the individual elements must be linked together by an information system that incorporates every function. In computer-aided material planning system, this is known as management of master data. Material planning requires information from a number of company departments in order to carry out its tasks. It also acts as a source of information for many other departments. When the scope of an order is determined in sales, for example, the replacement times have to be taken into account in order to determine the delivery date. Production material planning needs information about the ordering costs and times of materials in order to determine the most suitable raw material for parts produced in-house. If outline agreements have been concluded with customers or suppliers, call-forward notices are processed directly via material planning. In this case, the flow of information is not routed via sales or purchasing.



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Material requirement types (REFA*)

For planning and determining material requirements(by origin and product level)

For determining material requirements

(in consideration of warehouse stocks)

Requirement forsaleable products(market demand)

Primary requirement

Requirement forsupplies

Tertiary requirement

Period-related primary,

secondary ortertiary

requirement

Gross requirementminus

availablewarehouse stock

Net requirementGross requirement

Types of material requirement

Requirement forraw materials,

parts and groups for producing the

primary requirement

Secondary requirement

Example –Trading goods,

Finished product

Components, Semi-finished

goods, Subassembly

Example –Raw materials and supplies

* REFA - Association for work design, work structure, industrial organization and corporate development

Picture notes:According to REFA (Reichsausschuss für Arbeitszeitermittlung), material requirement means the type and quantity of the material needed to manufacture products to supply the sales market over a given period. The more precisely a requirement can be determined, then the more accurate material planning will be. The material requirement can be subdivided into five classes.However it is to be mentioned, that certain materials can not be clearly assigned to one of the above mentioned types of materials. Such classifications depend on concrete products and the processes that are necessary for their production. Additionally the classification above applies for producing companies. In retail-companies the Materials Management deals only with trading goods and finished goods.



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According to: Wiendahl

Gross requirement

Additional requirement+

Total requirement=

Stock level-

-

No. earmarked (Reservations)

+

Emergency level+

Net requirement=

- Scrap- Spare parts- Test purposes- etc.

Order inventory(When products are externally manufactured)

- Workshop stock(When products are manufactured in-company)

Available stock

Net Requirement

Picture Notes:

According to REFA, the gross requirement means the requirement per planning period, excluding any warehouse and working stocks. Thus, the net requirement means the gross requirement minus any already available warehouse and working stocks. Material requirement planning is based on the net requirement. One of the task of stock control is to gain an accurate picture of various stocks (warehouse, workshop, on order or reserved) that make up the available stock, which is required to calculate net requirement.



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ABC-Analysis for classification of material

ABC- Analysis

Focus on the value based importance of the goods

Create graph, broken down intoA, B and C ranges

Sort the itemsin descending

orderimportance

Calculate total(specify relative or

absolute)

Define the itemsand scale ofimportance

20% 50% 100%Percentage of items

100%

A

BC

Per

cent

age

of s

ales

• Focus on the value based importance of goods

• Elementary considerations:a small amount of A-goods is a big value based share of the material requirements of a period

• A-goods demand an intensive management

20% of items (A) represent80% of sales30% of items (B) represent15 % of sales50% of items (C) represent5% of sales

(numerical example)

Picture notes:ABC analysis is primarily used to determine quantity/value ratios for item (e.g. proportion of total sales attribute to given item). The task of ABC analysis is to determine the economic significance of the items by ranking them in order of precedence and assigning them to the different value groups (A, B and C). This serves to define differentiated material planning methods and procedures for individual class item. Different requirements in terms of storage space and conveyors can also be quantified by classifying the parts to be planned and stored e.g. by volume and/or weight. This in turn, cuts the cost of storage, space, transportation and picking.With the ABC-Analysis three groups of goods are defined in which, according to several practical applications, the quantitative and value based shares are described as followed :

• A-goods, approx. a share of 20% in the quantity of the goods and approx. a share of 80% in the value of these goods

• B-goods, approx. a share of 30% in the quantity of the goods and approx. a share of 15% in the value of these goods

• C-goods, approx. a share of 50% in the quantity of the goods and approx. a share of 5% in the value of these goods

The interrelations suggest that the small amount of important A-goods are planned carefully. A items are certainly worthwhile to accurately calculate the optimal order quantity, to monitor changes in stock levels and to obtain market and customer information. On the contrary for the unimportant and numerous C-goods no special efforts are made.



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Classification of the goods into three groups:

X - goods: high predictability (goods with small prediction uncertainty,e.g. up to 10% of the average value)

Y - goods: medium predictability (goods with medium prediction uncertainty, e.g. up to 10-50% of the average value)

Z - goods: low predictability (goods with high prediction uncertainty and in particular time series with sporadic and unsteady demand)

Source: Thommen (2000)

Time

X - goodY - goodZ - good

Demand

XYZ-Analysis for classification after demand predictions

• Focus on the predictability of demand

• Elementary considerations:the more unsteady the predictability is, the bigger the safety stock level should be chosen

XYZ- Analysis

Picture notes:The XYZ-Analysis focuses on the predictability of the demand. It is easier for a company to fulfill the targets of Materials Management if the demanded amount of goods shows small deviations and thus are well predictable. With big deviations in demand difficulties and additional costs in storage and material supply are to be expected.The planning of material sourcing should aim at the future demand, which is estimated with prediction methods. To achieve an efficient use of the available resources the goods are classified in three groups by the XYZ-Analysis:

•X-goods: Items with high predictability, e.g. up to 10% of the average value•Y-goods: Items with medium predictability, e.g. 10-50% of the average value•Z-goods: Items with low predictability, in particular items with sporadic and unsteady demand.



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A

X

Y

Z

Highconsumption value

Highprediction value

B C

Medium consumption value


Lowconsumption value



Mediumprediction value






Lowprediction value


Lowprediction value


Lowprediction value

Combination of ABC- and XYZ-AnalysisConsumption

High

Low

Safety Stock

HighLow

Safety Stock

Forecast-accuracy

High

Picture notes:A combined use of ABC- and XYZ-Analysis is illustrated in the picture above. Combining the results of the ABC and XYZ analyses gives a matrix containing nine different item classes. The characteristics of different item classes range from classes with high forecast accuracy and high consumption (known as “AX” items) to item classes with low forecast accuracy and low consumption (known as “CZ” items).Now the separated goods from classes AX, BX, CX, AY, BY, CY, AZ, BZ and CZ can be supplied by different principles. E.g. AX-goods could be purchased Just-in-time, because a continuous sourcing by a nearby supplier is easier to manage according to small fluctuations. Vice versa it can be thought about an intern production of Z-goods , because the regarded orders are even for extern suppliers not interesting and thus expensive.



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Three supply-principles are distinguished

•High need of coordination

•Danger of supply instabilitiesand stagnant production => high costs for the company

•High costs of storage•High dependency on supplier

Material-supply-guidelines

To stock supply(consumption-oriented)

Single-sourcing in case of demand (dependent on

requirements)• Low level of capital and storage costs

• Warranty of continuous production process

• Protection against sourcing risks

• Benefit from price advantages

Just-in-time(JIT)

• Low stock levels • Low level of capital and storage costs

• Warranty of continuous production process

• Low need of coordination

Source: Grochla (1985)

Picture notes:Three principles of material supply are distinguished:• The Just-in-time principle tries to connect the advantages of both of the two other material-

supply-principles and to exclude their disadvantages. With the aid of contracts it is arranged with the suppliers to deliver the required amount of material on schedule, according to dates determined by the production process of the sourcing enterprise

• The to stock supply, where the material is kept in storage until its use. This is the most common supply principle in practice.

• Single-sourcing, demand specific: the sourcing of a material just takes place when it is required, according to a certain production order.



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Methods of requirements planning

Requirements planningbased on subjectiveestimates made by

the planner

?

Exact requirements planningby quantity and time

based on specific ordersor production schedules

Requirement-controlled material planning Consumption-driven material planning

Stochasticrequirements planning

Deterministicrequirements planning

Requirements planning using demand forecasts

based on statistical evaluations

Consumption

TimeToday

Heuristicrequirements planning

Picture Notes:Three different methods may be used to analyse primary, secondary and tertiary requirements. Deterministic requirements planning determines requirements exactly by quantity and time on the basis of specific orders or the production schedule. Typical applications for this type of requirements planning are situations in which the processing time needed to manufacture the products is shorter than the delivery time demanded by the market. Stochastic requirements planning involves forecasting the expected requirements using statistical forecasting methods based on past consumption values. Finally, with heuristic requirements planning, the determined requirements are based only on the planner’s subjective estimates. This is the method preferred when the other two methods prove too expensive due to the low value of the goods under consideration or if there is insufficient data available to allow these methods to be applied.



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Basic data for the three requirements planning methods

Deterministic

Requirements planning methods

Stochastic Heuristic

- Primary requirements

- Product structure

- Lead or process times

- Customer orders

- Production schedule

- Secondary requirements

- Work schedule data

- Technologicalindicators

- Consumption data of theproduct componentsin the past periods

- Sales data of the finalproduct in the past periods

- Consumption data of thesupplies in the past periods

- Characteristicproperties of thefinal product

- Characteristic properties of the final product

- Characteristicproperties of thefinal product

Prim

ary

Seco

ndar

yTe

rtia

ry

Type

of r

equi

rem

ent

Picture Notes:The type of requirement is an essential influencing variable in the requirements planning: primary, secondary or tertiary requirement. Figure provides an overview of the basic data for each of the three methods according to the type of requirement.



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Deterministic requirements planning methods

Simple methodbut takes some time

to get used to it

Complex productswith multiple

uses

Determination of usage and

requirements for individual parts, very impractical

Complicated withimpracticalbehaviour

Analytical requirements planningWhat does the product consist of?

Synthetic requirements planningWhat is the part contained in?

E1

G1 T1 G2

G2 T1 T2 T3

T2 T3

E1

G1

G2 G2T1 T1

T2 T2T3 T3

2 5 1

2 3 2 3

2 3

2 5 1

2 3

2 3 2 3

E1

T1 G1 G2

T2 T3

52

2

1

3

2 3

E1 E2

G1 T1 G2

G2 T1 T2 T3

T2 T3

2 5 1

2 3 2 3

2 3

E1 G1 E2

T1

Simple products(possibly withoutmultiple names)

Productionlevel method

Disposition stepstechnique

Gozintomethod

Renettingmethod

Where-used billof material

Deterministic calculation of requirements entails precise determination of material requirements in terms of quantity and date required.Deterministic calculation of requirements entails precise determination of material requirements in terms of quantity and date required.

Picture notes:

The different deterministic requirements planning methods are illustrated in Fig. above. The analytical procedure starts from the product (parts list), and breaks it down gradually “From top to bottom”, in contrast to the synthetic method which starts from the part (usage statement) and looks at the usage at the individual levels. The analytical breakdown of requirements is particularly relevant in practice, and there are four different methods that can be applied

Production Level - Adv: Exact determination of requirement dates

Disadv: Decomposition of the components on several structural levels

Used For: Straightforward products without repeated use of components

Disposition Step - Adv: Decomposition of requirement on only one structural level each

Disadv: More difficult to determine exact requirement dates

Used For: Complex products

Gozinto Method - Adv: Straightforward mathematical requirement

Disadv: Unclear presentation, takes time to get used to

Used For: Complex products

Renetting method - Adv: Only method that can take account of multiple requirements in various products and production levels.

Disadv: Time-consuming



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Procedure for stochastic requirements planning

Moving averageExponential smoothing

Regression analysis

Selectthe method

Recordtime series

Determine theconsumption model

Draw up the demand forecast Evaluate the

forecast quality

Constant SeasonalTrend

Time

Consumption

TimeToday

Consumption

Time

Consumption Forecast error=?

Picture notes:The individual steps of the stochastic requirements planning are summarised in Fig.

With this type of requirements planning, the expected requirement is derived from the past consumption values, i.e. it creates a forecast of future consumption. It starts with recording the consumption pattern for particular time period then next task is to determine the consumption model (constant, seasonal or trend model). Selecting proper methods depends upon number of criteria's and each method has different characteristics like Moving average method exclude accidental irregularities in the course of a time series also if a current consumption value is available, the oldest consumption value of the moving interval is substituted with the current interval.With the exponential method the weight of the value decreases as the "age" of the values increases. The new forecast value is corrected by a weighted forecast error of the last forecast value.In regression analysis approximated with a mathematical function and extrapolated intothe future.Once proper method is selected next task is to draw up the demand forecast and evaluation of the forecast quality which essentially gives the inputs for the next forecast and future planning.



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Consumption Models as Basis for Stochastic Determination of RequirementC

onsu

mpt

ion

Time

Con

sum

ptio

n

Time

Con

sum

ptio

n

Time

Consumption model

Without trend With trend

Con

sum

ptio

n

Time

Pure constant model

Seasonal constant model

Pure trend model

Seasonal trend model

Without seasonal fluctuation

With seasonal fluctuation

Without seasonal fluctuation

With seasonal fluctuation

Source: REFA

Picture notes:

Stochastic Calculation of Requirements

The stochastic calculation of requirements differs from the deterministic mode in that it is based on historical data, which is extrapolated for the future using stochastic methods and which therefore carries a degree of statistical uncertainty. The consumption pattern must first be interpreted before a suitable technique can be selected.

All forecasting methods require all past consumption values to be recorded in full and assigned to the correct periods in order to obtain realistic consumption statistics. One conventional form of representation is the “time series”. Analysis of the time series gives an indication of the pattern of consumption for the part under consideration, and highlights individual factors that determine consumption, such as trends, seasonal influences, irregularities or random fluctuations. Time series analysis can thus be used to determine the consumption model as shown in figure.



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Common Techniques of Stochastic Determination of Requirement

According to: REFA/ Zeigermann/Tempelmeier

Con

sum

ptio

n V

Exponential smoothing of first

order Timet t+1

PtVt-Pt

Pt+1 = Exp. smoothed value 1st order

α = Smoothing factor

( )PVPP tttt −+=+

α1

Exponential smoothing with trend correction

at1 = corrected estimate value of the

trends‘ axis interceptbt

1 = exponential smoothed value of the trends‘ gradient

baP ttt

11

1 +=+

Timea

)1(*1 ++=+

tbaP t

Pt+1 = Forecast valuea = Axis interceptb = Gradient

Regression analysis

Floating mean valueVi = Requirement in period i

Time

n ∑−+=

+=

t

ntiit VP n 1

1

1

Verb

rauc

h

Perioden05

1015202530354045

7 8 9 10

V

V1

Pt+1

VV1

Pt+1Con

sum

ptio

n V

Con

sum

ptio

n V

Con

sum

ptio

n V

Time

Picture notes:

Regression Analysis method - This method approximates the trend in consumption using a mathematical function that is extrapolated into the future. The regression analysis method can be meaningfully used if past consumption exhibits a regular trend or seasonal pattern that can be expressed by a mathematical function. The consumption curve is approximated using a correcting line with the following general shape:

y = a + btThe coefficients a and b are then determined using the consumption values so as to minimize the sum of all deviations from the correcting line. The quality of the prediction essentially depends on the range of scatter of the values. If the values are within a narrow range, the extrapolated consumption value will only be subject to a small deviation error.

Floating mean Value method – The sliding interval consists of a constant number of periods. The smoothing of the forecast value depends on the number of periods included. The method is based on the principle of substituting the oldest consumption value within the sliding interval with the current value when calculating the mean.

Exponential smoothing method With this method, the data for the individual periods is weighted with a “smoothing factor” α, which can assume a value between 0 and 1. The smoothing of theforecast values is dependent on this weighting. For the influence of α we see that the higher the value of α, the higher the weighting of the more recent periods, which allows the method to adapt much faster to actual consumption. The disadvantage is that it increases the sensitivity to random fluctuations. In practice, typical α values are between 0.1 and 0.3. They practically never exceed 0.5. ForExponential smoothing method with trend correction provision is made to include the trend of

the consumption.



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Suitability of Forecasting Methods for Various Consumption Models

Regression analysis

Exponentional floating of the first orderExponentional floating with trend correctionExponential smoothing given seasonal demand *

Multiple regression *

Floating mean value

Pur

e co

nsta

nt

mod

el

Pur

e tre

nd

mod

el

Sea

sona

l cons

tant

m

odel

Sea

sona

l tre

nd

mod

el

Method

Consumption model

Suitable Suitable in some cases Suitable in principle, but not always useful

Source: Dangelmaier * Not further explained in this lecture

Picture notes:The methods used in order to take explicit account of seasonal influences are based on the values recorded in the same period of the previous year. When seasonal trend models are used, a further component which reflects the trend can be added to this technique.



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Comparison of Forecasting Methods

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10Period t

Req

uire

men

t

Actual RequirementFloating mean Value (n = 5)Exp. smoothing of f irst orderExp. smoothing w ith trend correctionRegression analysis for period 7

Picture notes:This graphic shows the diferent forecast methods in comparison to the demand.

The floating mean value is very suitable for pure constant models, since its reaction is sluggish, particularly when the number of periods taken into account “n” is high. The sensitivity of the technique must be increased when trend models are required. However, when this is done, the operation reacts very strongly to any random peaks in requirement. Similar considerations apply to the exponential smoothing method in relation to the definition of the α-factor. When the α-factor is high, the operation reacts very swiftly to any changes in requirement, causing the current requirement values to be heavily weighted when they are included in the forecast. In contrast, when the α-factor is low, the predicted value reacts slowly.



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Conventional Techniques of Determining Lot Size

Source: Wiendahl

Techniques of determining lot size

Techniques of determining lot size

Static(constant lot size)

Static(constant lot size)

Dynamic(variable lot size)

Dynamic(variable lot size)

Basic model (according to Andler)

Extended basic models

Floating economic lot size

Part period technique

WAGNER-WITHIN technique

SILVER-MEAL technique

Picture notes:The dynamic methods are based on the planned variable demand while the static methods is based on a constant store departure speed.

Determining Lot SizesThe deterministic methods of determining lot sizes strive to reduce lot size-dependent costs to an arithmetic minimum. The static methods assume a constant removal rate from the stores whilst the dynamic methods are based on the planned variable requirement values.



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Additional Factors which Impact on the Determination of Lot Size

Source: REFA

General Outsourcing• Available storage space• Liquidity• Storage life of the

products

In-company manufacture• Capacity use of

manufacturing facilities• Flexibility required of

manufacturing facilities• Manufacturing sequence

(throughput times, set-up times)

• Supply capability of supplier

• Pricing• Transport facilities/

packaging units

Uni

t cos

ts

xopt½ xopt 2 xopt

Advisable spread

• Better utilisation of capacity

• Lower prices

• Capacity use of transport facilities

• Better utilisation of capacity

• Lower prices

• Capacity use of transport facilities

• Higher flexibility

• Less requirement for space

• Shorter throughput time

• Higher flexibility

• Less requirement for space

• Shorter throughput time

Lot size

Additional relevant criteria in determining lot size

Picture notes:In addition to the methods previously discussed, in practice, there are diverse additional criteria which are used to determine lot size. When, for example, the machining time for a lot is long in relation to the available capacity, bottlenecks can occur, which lead to delays in processing subsequent orders. Since the overall cost curve is generally very flat in the area around the minimum, permissible tolerance limits can be given for the optimum lot size, without causing the unit costs to rise too steeply.

The three left points arise normally arise with lower and the three right points arise with higher lot sizes.



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In economical terms a model describes a simplified projection of reality:

The model of the optimal order lot size is based on certain assumptions:

The total requirement of the period T is given and matches the sourcing-quantity. The sourcing-quantity can be divided in equal shares x.

The outbound stock movement takes place in continuous and equal rates.

There are neither sourcing shortages nor storage and financial restrictions.

There is no security stock, due to the assumption, that between the extraction of the last unit and the replenishment of the storage no ‘time lag‘ exists.

The acquisition prices are considered as constant.

Source: Jehle (1999)

Assumptions of this model

Static Technique of Determining Lot Size – Andler’s Method



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Planning of material sourcing: optimal order lot size

M = Total demand for Period (1 year)w0 = Acquisition price per pieceF = Fixed costs for order-quantity p = Rate of interest charges (percentage per year)l = Rate of storage costs (percentage per year)

xopt = 200 * M * Fw0 * (l + p)

Total costs per piecekg

Order lot sizex

Storage and inventory costs

kl + kp

Fixed costs per order kf

xopt

kgklkpkf

Costs per piece

Calculation of the optimal order lot size

Picture notes:The picture above shows the graphical derivation of the optimal order-quantity. The geometrical addition of the graph “fixed costs per order” kf (x), and the graph “storage and inventory costs” kl (x) + kp (x) leads to the graph “total cost per piece” kg (x). The optimal order lot size is given by the minimum of the total costs per piece. The graph “total costs per piece” converges for x towards 0 asymptotically towards the graph kf . For x towards unlimited it converges towards the graph kl + kp.A mathematical derivation of the formula for the optimal order-quantity xopt stays here unattended.The deficiencies of the model of the optimal order-quantity result mainly from the assumptions made before. Here was assumed for example, that the demand in the planning period is known und the withdrawal from the stock is constant. Additionally in the model it is not regarded that within the planning period only integer solutions for the optimal order frequency are realizable. Loss and deterioration of the stock, volume discount and part-deliveries as well as the security stock stay also unattended. Furthermore restrictions, like narrow storage capacities and narrow financial resources, are not included in the model.



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Dynamic Methods of determining order quantity with optimized costs

In contrast to the static models, the dynamic lot size models alsotake account of fluctuating requirements that occur at discrete times.

Dynamic Methods:

1. Sliding economic order quantity2. Cost Balancing or Part Period Method3. Dynamic Programming Method

Min Kopt. = Sum (Ordering costs+ Fixed ordering costs+ Stock Holding costs)

Picture notes:Sliding economic order quantity:With this method, the order volume is increased by the order quantity for the next period if this will reduce the unit costs. This is calculated by the ratio of ordering costs plus stock holding costs at an appropriate interest rate for the period to the order quantity. If the unit costs are not reduced by including a further period, then the quantity with the lowest unit costs is the first economic order quantity. The same procedure is applied to the subsequent periods.

Cost Balancing or Part Period Method:It follows from the conventional Andler Order formula that the minimum total cost occurs when the order and stock holding costs are the same. Building on this knowledge, the order quantity is increased by including the requirement for the next period, provided that the associated stock holding costs do not exceed the fixed ordering costs.

Dynamic Programming Methods:The aim of the dynamic programming method is to minimize the total costs calculated by the sum of the ordering costs, fixed ordering costs and stock holding costs incurred during each period. The target function is :

Min Kopt. = (Ordering costs+ Fixed ordering costs+ Stock Holding costs)

Theoretically, the method of determining a sliding economic order quantity and the cost balancing and part period methods (which can only be distinguished by computer generated transformations) are merely approximations methods. This approach contains no impractical premises, however, if we ignore the uncertainty concerning future requirements. The cost curve in the vicinity of the optimum order quantity is very flat. So these heuristic (approximation) methods give usable results in practice with relatively little computation required. The solutions thus are even better if the periods are short and the required quantities do not fluctuate very much.



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Calculating Floating Economically Efficient Lot Size – Dynamic Method

Period1 2 3

Kges, n = Unit cost (for n periods)A = Fixed lot costKL = Storage costs per unit and periodBmi = mean stock level in period “i”xges,n = cumulated lot size (n periods)

Formula

• Single product manufacturing

• Production speed infinitely high

• Stock removal rate variable (actual requirement for period)

Boundary conditions

Summary of the requirement per period up to minimisation of costs per unit.


Stock level170

n=2

n=1

n=3

1

2

3

4

60

30

80

50

n=2

n=1

n=3

Lot size 90Lot size 60

Requirement[units]

Period

4,17

3,38

3,41

Unit cost [€]

nges

n

imiL

nges x

BKAK

,

1,

∑=

⋅+=

Period1 2 3

Kges, n = Unit cost (for n periods)A = Fixed lot costKL = Storage costs per unit and periodBmi = mean stock level in period “i”xges,n = cumulated lot size (n periods)

Formula

• Single product manufacturing

• Production speed infinitely high

• Stock removal rate variable (actual requirement for period)

Boundary conditions



Stock level170

3060

n=2

n=1

n=3

1

2

3

4

60

30

80

50

n=2

n=1

n=3Lot size 170

Requirement[units]

Period

4,17

3,38

3,41

Unit cost [€]

nges

n

imiL

nges x

BKAK

,

1,

∑=

⋅+=

Lot size 220

n=4

Picture notes:In this method, the planned requirements for future periods are condensed until the sum of the lot and storage costs per unit are reduced to a minimum. When the requirement fluctuates substantially, this method has considerable advantages over the Andler formula.



page 25© WZL / FIR

Amountx

Timet

xM

tL tL

xB

tB

A

B

C

D0

xB = Sourcing quantity

xM = Order release quantity

tB = Sourcing time

tL = Storage duration (coverage)

Saw tooth-function of stock movement

Planning of material sourcing: stock movement

Picture notes:According to the fact that in most cases it is not possible, respectively not functional to precisely adjust Materials Management and consume, a partly to stock supply is in general inevitable.Orders are released when a certain stock-level – the order release quantity - is reached. This order release quantity xM must at least match the expected withdrawal during the sourcing-time. For this reason the sourcing-time is declared as the period of time between the re-order notification of the warehouse and the point of time when the materials are available for the suggested use in the manufacturing process.The line [AB] shows the progression of a continuous withdrawal in the time period tL. In the period tL (time of storage) the stock decreases from the quantity of xB units to 0 units. The sourcing time for the material is at tB time units. The order release quantity is xM. Due to the uncertainty of the planning of the material store quantity it is necessary to include a security stock. Security stock is defined as the necessary stock inventory for the compensation of excessive withdrawal or extended sourcing times.



page 26© WZL / FIR

Demand – based order initiation

Order initiation

Time

Order

Order initiation across the requirements planningcycle (e.g. on receipt of an order)

Stock curve

Picture Notes:The order quantity together with the calculated order quantity need to be initiated at exactly the right time. The order initiation method distinguishes between

• Demand-based, time-based and quantity-based order initiation. Demand-based order initiation is the simplest form of order initiation (see Fig. 6-30). In this case, the current requirement for the materials concerned is ordered for an existing job, once the requirement has been analysed. This type of order initiation orders materials for existing jobs only, so no storage is generally required. The order quantity Q and order intervals t are not constant. One disadvantage of this method is that the full replacement time elapses from the moment the order is initiated until the material becomes available. Demand-based order initiation is primarily used for expensive (A) parts and parts with highly fluctuating requirements (Z parts). The main features are:

• A fixed quantity is ordered (and the requirement is generally calculated deterministically) • The order quantity q and order intervals are not constant • It is uncertain whether the requirement will arise again • High risk of change / storage



page 27© WZL / FIR

Time – based order initiation (order cycle method)

Order initiation

Stock

t tt

S = Maximum stock

t tt

Q

Q

Time

Stock

S S

Order initiation

(T,S) policy (T,Q) policy

(T,Q) policy: Stock ischecked periodically and isrestocked by the order quantity Q.

.

(T,S) policy: Each inspection initiates an order amountingto the difference betweenthe current stock and thetarget stock level S..

T = Order cycle Q = Order quantityKey:

Picture notes:Another order initiation method is the order cycle method refer Figure, in which the stock level is checked at fixed intervals and order is initiated when the level falls below a certain reorder point. The order cycle is defined with reference to the production, manufacturing or workshop schedule. The advantage of this method (which is applied to materials with a relatively constant requirement and low value (X/C parts)) is that the stock control is quick. It is characterised by the following features and requirements:

• Less checking is required than with the order point method • Excessive quantities are not removed from stock between two intervals • Realistic, item-related replacement time• It allows joint orders to a supplier to be coordinated

• Suitable for C parts (standard parts, supplies).



page 28© WZL / FIR

Quantity – based order initiation (order point method)

Order initiation

Time

Stock Stock

Time

s

S

Q

Q

Q

Order initiation

(s,S) policy (s,Q) policy

(s,S) policy: If value falls below s, then there is a restock to the target stock level S

(s,Q) policy: If value falls below s, then there isa restock by the order quantity Q

Key: S = Maximum stock s = Reorder point Q = Order quantity

s

Picture notes:The order point method is more time-consuming since it requires checking after each stock movement whether the stock level has reached or even fallen below the reorder point. This method is applied to more expensive materials with fluctuating requirements (primarily B/Y parts) that are stored in the company.

Features and requirements: • Ordering of stochastically determined requirements • Check for reorder point required after each withdrawal from stock • Open orders are taken into account • Realistic, item-related replacement time • A dynamic reorder point must be defined (in relation to consumption)



page 29© WZL / FIR

Reasons for Storing Emergency Stocks

Source: FIR

Time

Sto

ck le

vel

Stock emergency level

Actual requirement

Requirement forecast

Time


Target delivery dateActual delivery date

Time


Stock level assumed

Actual stock level

Sto

ck le

vel

Sto

ck le

vel

Uncertainties in procurement

Uncertainties in determining stock level

Uncertainties in determining

requirements

Picture notes:

Defining Emergency Stock LevelThe purpose of emergency stocks is to avoid bottlenecks in the material supply from occurring when the requirements and stock levels differ from those planned. However, there are conflicting aims even in defining the emergency stock levels. Any increase in the emergency stock level, in an attempt to improve the capacity to supply, is at the expense of increasing storage and stock costs.



page 30© WZL / FIR

Impact of Emergency Stock on Cost

Source: Wiendahl

minimale Kapitalbindung

FMK = Cost incurred as a result of incorrect quantities (German: Fehlmengenkosten)

SBK = Cost of emergency stock(German: Sicherheitsbestands-kosten)

Level of service = x 100%No. of requests satisfied immediately

Total no. of requests

FMK + SBK

SBK

80% 100%

FMK

Level of service

Minimum storagecosts

Maximum readinessto supply

Objectives

Picture notes:The progressive course of emergency stock costs is the result of the additional costs (e.g. for renting or building additional warehouses) when the available storage capacity is exceeded and from the steep increase in handling, transport, and administrative expenditure incurred when a warehouse is filled to capacity. The excess quantity costs result, for example, from loss of production caused by incorrect parts.



page 31© WZL / FIR

Techniques of Determining Emergency Stock Levels

According to: Riggs

Ultra-conservative method

Ultra-conservative method

Percentage methodPercentage method

Heuristic functionHeuristic function

Emergency stock

Maximum daily con-sumptionto date

Maximum poss.

procurement time in days

Emergency level

∅removal

from stock

∅procure-

menttime

Factor(25-

40%)

Emergency level

- ∅ removal from stock- Standard deviation- Replacement procurement time- etc.

f

=

=

x

x x

=

Picture notes:The “ultra-conservative method” of defining the emergency stock level estimates the “worst case”scenario. This normally results in unnecessarily high stock levels. The percentage method, which uses average values for stock removal and for procurement time as well as a factor which can be set individually, is usually more suitable for determining useful emergency stock levels. The heuristic method explained in Fig. is outstanding in that it provides the option of specifying a required service level.



page 32© WZL / FIR

Heuristic Method of Determining Emergency Stock Level

According to: REFA

Emergency stock level: BS = b x s = 4,3 x 10,29 = 44,25 units

(Given 99% level of service)

Emergency stock level: BS = b x s = 4,3 x 10,29 = 44,25 units

(Given 99% level of service)

Quantity removed from stock Determining factor “b”87654321n

10294105127104116100109Removal [units]

Replacement procurement time

∅ removal from stock:

Standard deviation:

unitsxn i 1,1071x =∑=

( )units

xn

xn

s ii

29,10

11

1 22

=

⎥⎦⎤

⎢⎣⎡ −

−= ∑ ∑

(Probability of correctness 95%)

Factor „b“

Number of removals n

1

2

3

4

5

6

7

5 10 20 50 100 200 400

4,3

8

Level of service:99%95%90%

Picture notes:



page 33© WZL / FIR

Focus of the Lecture 8

Materials procurement planning

Static lot sizes calculationStatic lot sizes calculation



Materials requirement planning









Objective and challenges of materials planningObjective and challenges of materials planning



Functions and place within company of materials planningFunctions and place within company of materials planning

Order Initiation – Time based, Quantity basedOrder Initiation – Time based, Quantity based









Picture notes:

production management i - wzl.rwth-aachen.de · production management i (prof. schuh) lecture 8...

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