Crash Course on:
Inventory Management
Inventories today: a curse, a blessing, a must..?
Stanisław Krzyżaniak
1
Course programme:
1.
Why do we keep inventories?
2.
Typical demand classifications and analyses helpful in inventory
management.
3.
Why high quality forecasting is so important for inventory
management?
4.
Cost aspects of inventory management.
5.
Material Decoupling Point - dependent and independent demands,
deterministic and stochastic approaches to inventory management.
Stanisław Krzyżaniak
2
Course programme:
6.
Calculation of safety and cycle stock.
7.
Classical stock replenishment systems (Reorder Point and Cycle
Review) and their typical combinations.
8.
Safety stock in case of dispersed inventories (square-root law).
9.
Information Decoupling Point concept and its role in reduction of
stock levels.
10. Review of selected logistics concepts and solutions oriented on stock
reduction/rationalisation .
Stanisław Krzyżaniak
3
Course programme:
1.
Why do we keep inventories?
2.
Typical demand classifications and analyses helpful in inventory
management.
3.
Why high quality forecasting is so important for inventory
management?
4.
Cost aspects of inventory management.
5.
Material Decoupling Point - dependent and independent demands,
deterministic and stochastic approaches to inventory management.
Stanisław Krzyżaniak
4
Crash Course on Inventories
Why do we keep
inventories?
Stanisław Krzyżaniak
5
Why do we keep inventories?
Inventory (stock) - materials in a supply chain or in
a segment of a supply chain, expressed in
quantities, locations and/or values, not used at
present, but kept for the future use (consumption/
/sale).
Based on ELA Terminology
Stanisław Krzyżaniak
6
THROUGHPUT TIME IN A SUPPLY CHAIN
Why do we keep inventories?
5%
Utility value
95%
Stanisław Krzyżaniak
Among
others,
keeping
inventories
is also here.
?
7
Why do we keep inventories?
Income
-
Costs
Customer
service
=
Profit
Working
capital
Return on
Investment
Inventories
Stanisław Krzyżaniak
8
Why do we keep inventories?
Income
-
Costs
Customer
service
=
Profit
Working
Assets
capital
Return on
Investment
Assests
Inventories
Stanisław Krzyżaniak
9
Why do we keep inventories?
Stock classification
By type and position in a pipeline
 Materials stock (raw materials, components)
 Work-in-progress stock
 Finished products stock
 Spare parts and auxiliary materials stock
Stanisław Krzyżaniak
10
Why do we keep inventories?
Deliveries
Raw materials
and components
Production
Raw materials
and components
Work-in-progress
Distribution
Finished
products
Consumer
goods
Work-in-progress
Auxillary materials, spare parts
Stanisław Krzyżaniak
11
Why do we keep inventories?
Do you keep a stock of bread in your household?
Stanisław Krzyżaniak
12
Why do we keep inventories?
Do you keep a stock of bread in your household?
Stanisław Krzyżaniak
13
Why do we keep inventories?
Stock classification
By the reasons for keeping
 Cycle stock
 Safety stock
 Seasonal stock
 „Speculation” stock
 Strategic stock
Stanisław Krzyżaniak
14
Why do we keep inventories?
Stock classification
By rotation
 Fast moving (rotating) stock
 Slow moving (rotating) stock
 Not moving (rotating) stock
• Obsolete stock
• Emergency stock
Stanisław Krzyżaniak
15
Why do we keep inventories?
Stock structure
Q
Cycle stock
SC
SS
Safety stock
Surplus stock
Ssp
Stanisław Krzyżaniak
16
Why do we keep inventories?
Stock maintaining, replenishment and its quantity result from:





uncertainty of real demand,
uncertainty of real quantity, quality and timing of deliveries,
seasonal access to some materials and goods,
service level required by a customer,
expected difficulties with an access to some goods (expected rise of
prices),
 discounts offered for purchases of larger quantities,
 some technical and/or organisational conditions of deliveries.
Stanisław Krzyżaniak
17
Why do we keep inventories?
Cycle stock:
 Lack of possibilities to fully synchronise
supplies with consumption,
 Technical and/or organisational conditions (limitations),
 Economic incentives (discounts).
Safety stock:
 Random fluctuations of demand,
 Forecast errors,
 Long replenishment lead times,
 Unpredictable delays,
 Required service level.
Surplus stock:
 Miscalculation of factors influencing safety stock,
 Wrong estimation of the required service level,
 Excessive safety measures taken to avoid stock-outs.
Stanisław Krzyżaniak
18
Why do we keep inventories?
So, is this good or bad to have inventories?
Good, because they:
Bad, because they:
 Guarantee a continuous access to all
 Take space (warehouses),
kinds of goods when supplies are
discontinues,
 Guarantee access to goods in periods
when they are not available,
 Ensure required service level
compensating random variations of
demand,
 Ensure required service level
compensating delays of deliveries
random variations of replenishment
lead time.
 Cost money (space, losses, capital);
Stanisław Krzyżaniak
carrying stock may cost annually up to
30% of its value.
19
Course programme:
1.
Why do we keep inventories?
2.
Typical demand classifications and analyses helpful in inventory
management.
3.
Why high quality forecasting is so important for inventory
management?
4.
Cost aspects of inventory management.
5.
Material Decoupling Point - dependant and independent demands,
deterministic and stochastic approaches to inventory management.
Stanisław Krzyżaniak
20
Crash Course on Inventories
Demand analyses
Stanisław Krzyżaniak
21
Demand analyses
• ABC analysis,
• XYZ analysis,
• Customer and supplier related analyses,
• Trends & Seasonality,
• Random deviations - demand profile,
• Life-cycle analysis,
• Identification of „wild” demand .
Stanisław Krzyżaniak
22
Demand analyses - ABC classification
No
Item code
1
10-01
2
3
No of used/sold
items
Unit price
Value of used/sold
items
Cummulated value
of used/sold items
15344
3,5
53 704,00
53 704,00
7,0%
10-02
23
76,1
1 750,30
55 454,30
7,2%
10-03
557
11
6 127,00
61 581,30
8,0%
4
10-04
1270
5,43
6 896,10
68 477,40
8,9%
5
10-05
7088
2,05
14 530,40
83 007,80
10,8%
6
10-06
278
20,66
5 743,48
88 751,28
11,6%
7
10-07
1513
2,53
3 827,89
92 579,17
12,1%
8
10-08
13
1178
15 314,00
107 893,17
14,1%
9
10-09
997
6,53
6 510,41
114 403,58
14,9%
10
10-10
8724
0,4
3 489,60
117 893,18
15,4%
11
20-01
1245
2,46
3 062,70
120 955,88
15,8%
12
20-02
7688
20,9
160 679,20
281 635,08
36,8%
13
20-03
679
6,2
4 209,80
285 844,88
37,3%
14
20-04
1190
5,14
6 116,60
291 961,48
38,1%
15
20-05
25799
0,89
22 961,11
314 922,59
41,1%
16
20-06
1409
32,6
45 933,40
360 855,99
47,1%
17
20-07
133
74,86
9 956,38
370 812,37
48,4%
18
20-08
799
15,34
12 256,66
383 069,03
50,0%
19
20-09
9887
9,3
91 949,10
475 018,13
62,0%
20
20-10
2234
2,40
5 361,60
480 379,73
62,7%
21
30-01
22
208,9
4 595,80
484 975,53
63,3%
22
30-02
12778
20,4
260 671,20
745 646,73
97,3%
23
30-03
79
63
4 977,00
750 623,73
98,0%
24
30-04
1313
9,92
13 024,96
763 648,69
99,7%
25
30-05
557
4,81
2 679,17
766 327,86
100,0%
Stanisław Krzyżaniak
23
Demand analyses - ABC classification
Rules of the ABC analysis:
o establish a criterion,
o rank and sort assortment according to the established
criterion,
o calculate a total sum,
o calculate cumulative sums,
o calculate percentage share of cumulative sums in the
total sum,
o assign to group A items responsible for 80% of the
criterion value, to group B – items responsible for further
15%, and the remaining items – to group C.
Stanisław Krzyżaniak
24
Demand analyses - ABC classification
80:20
20%
A
30-50%
B
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C
25
Demand analyses - ABC classification
Stanisław Krzyżaniak
26
Demand analyses – ABC/XYZ classification
5
20-06
1409
32,6
45 933,40
612 936,90
6
20-05
25799
0,89
22 961,11
635 898,01
7
10-08
13
1178
15 314,00
651 212,01
8
10-05
7088
2,05
14 530,40
665 742,41
9
30-04
1313
9,92
13 024,96
678 767,37
10
20-08
799
15,34
12 256,66
691 024,03
Price
80,0%
83,0%
85,0%
86,9%
88,6%
90,2%
Z
Y
X
10-08
30-04
A
20-05
Quantity
B
Stanisław Krzyżaniak
C
27
Demand analyses - XYZ classification
Price
Z
Y
X
Quantity
Stanisław Krzyżaniak
28
Demand analyses - XYZ classification
Price
Z
Y
X
Quantity
Stanisław Krzyżaniak
29
Demand analyses - XYZ classification
Unit price
Value of used/sold
items
15344
3,5
53 704,00
53 704,00
7,0%
10-02
23
76,1
1 750,30
55 454,30
7,2%
10-03
557
11
6 127,00
61 581,30
8,0%
4
10-04
1270
5,43
6 896,10
68 477,40
8,9%
5
10-05
7088
2,05
14 530,40
83 007,80
10,8%
6
10-06
278
20,66
5 743,48
88 751,28
11,6%
7
10-07
1513
2,53
3 827,89
92 579,17
12,1%
8
10-08
13
1178
15 314,00
107 893,17
14,1%
No
Item code
1
10-01
2
3
No of used/sold
items
Cummulated value Cumm.
of used/sold items
[%]
9
10-09
997
6,53
6 510,41
114 403,58
14,9%
10
10-10
8724
0,4
3 489,60
117 893,18
15,4%
11
20-01
1245
2,46
3 062,70
120 955,88
15,8%
12
20-02
7688
20,9
160 679,20
281 635,08
36,8%
13
20-03
679
6,2
4 209,80
285 844,88
37,3%
14
20-04
1190
5,14
6 116,60
291 961,48
38,1%
15
20-05
25799
0,89
22 961,11
314 922,59
41,1%
16
20-06
1409
32,6
45 933,40
360 855,99
47,1%
17
20-07
133
74,86
9 956,38
370 812,37
48,4%
18
20-08
799
15,34
12 256,66
383 069,03
50,0%
19
20-09
9887
9,3
91 949,10
475 018,13
62,0%
20
20-10
2234
2,40
5 361,60
480 379,73
62,7%
21
30-01
22
208,9
4 595,80
484 975,53
63,3%
22
30-02
12778
20,4
260 671,20
745 646,73
97,3%
23
30-03
79
63
4 977,00
750 623,73
98,0%
24
30-04
1313
9,92
13 024,96
763 648,69
99,7%
25
30-05
557
4,81
2 679,17
766 327,86
100,0%
Stanisław Krzyżaniak
30
Demand analyses - XYZ classification
No
Item code
1
20-05
10-01
10-01
10-02
30-02
10-03
20-09
10-04
10-10
10-05
20-02
10-06
10-05
10-07
20-10
10-08
10-07
10-09
20-06
10-10
30-04
20-01
10-04
20-02
20-01
20-03
20-04
20-04
10-09
20-05
20-08
20-06
20-03
20-07
10-03
20-08
30-05
20-09
10-06
20-10
20-07
30-01
30-03
30-02
10-02
30-03
30-01
30-04
10-08
30-05
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
No of used/sold
items
25799
15344
15344
23
12778
557
9887
1270
8724
7088
7688
278
7088
1513
2234
13
1513
997
1409
8724
1313
1245
1270
7688
1245
679
1190
1190
997
25799
799
1409
679
133
557
799
557
9887
278
1554
133
22
79
12778
23
79
22
1313
13
557
Stanisław Krzyżaniak
Unit price
Value of used/sold
items
0,89
3,5
3,5
76,1
20,4
11
9,3
5,43
0,4
2,05
20,9
20,66
2,05
2,53
2,40
1178
2,53
6,53
32,6
0,4
9,92
2,46
5,43
20,9
2,46
6,2
5,14
5,14
6,53
0,89
15,34
32,6
6,2
74,86
11
15,34
4,81
9,3
20,66
3,45
74,86
208,9
63
20,4
76,1
63
208,9
9,92
1178
4,81
22
53961,11
704,00
531704,00
750,30
2606671,20
127,00
916949,10
896,10
3 489,60
14
530,40
1605679,20
743,48
143530,40
827,89
5 361,60
15
314,00
36827,89
510,41
453933,40
489,60
133024,96
062,70
6 896,10
160
679,20
34062,70
209,80
66116,60
116,60
6 510,41
22
961,11
12
45256,66
933,40
49209,80
956,38
6 127,00
12
256,66
2 679,17
91
949,10
55743,48
361,30
94956,38
595,80
4 977,00
260
671,20
14750,30
977,00
4 595,80
13
024,96
152314,00
679,17
Cummulated value Cumm.
of used/sold items
[%]
53 704,00
7,0%
55 454,30
7,2%
61 581,30
8,0%
68 477,40
8,9%
83 007,80
10,8%
88 751,28
11,6%
92 579,17
12,1%
107 893,17
14,1%
114 403,58
14,9%
117 893,18
15,4%
120 955,88
15,8%
281 635,08
36,8%
285 844,88
37,3%
291 961,48
38,1%
314 922,59
41,1%
360 855,99
47,1%
370 812,37
48,4%
383 069,03
50,0%
475 018,13
62,0%
480 379,43
62,7%
484 975,23
63,3%
745 646,43
97,3%
750 623,43
98,0%
763 648,39
99,7%
766 327,56 100,0%
31
Demand analyses - XYZ classification
Basic parameters describing demand variability
Mean (average) demand
D
D1 D2 ......... Dn
n
Standard deviation
(D D1 )2
D
(D D2 )2 ...... (D Dn )2
n 1
Coefficient of variation
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D
D
D
32
Demand analyses - XYZ classification
D = 82,7
D = 4,5
D=0,055
Stanisław Krzyżaniak
33
Demand analyses - XYZ classification
D = 40,9
D = 9,4
D=0,23
Stanisław Krzyżaniak
34
D = 4,21
D = 2,07
D=0,49
Demand analyses - XYZ classification
Stanisław Krzyżaniak
35
Demand analyses - XYZ classification
D = 0,0417
D = 0,201
D=4,82
Stanisław Krzyżaniak
36
Demand analyses - XYZ classification
Coefficient of variation as a criterion for the XYZ classification
X
Y
Z
D
D
D
0,2
0,2
Stanisław Krzyżaniak
D
D
0,6
D
0,6
37
Demand analyses – ABC/XYZ classification
A
B
C
X
AX Group
High turnover in terms of value,
high and even periodical
consumption (daily, weekly
demand). Highly reliable
forecasts.
BX Group
Medium turnover in terms of
value, high and even periodical
consumption (daily, weekly
demand). Highly reliable
forecasts.
CX Group
Low turnover in terms of value,
high and even periodical
consumption (daily, weekly
demand). Highly reliable
forecasts.
Y
AY Group
High turnover in terms of value,
high and even periodical
consumption (daily, weekly
demand). Less reliable forecasts
(significant forecast errors).
BY Group
Medium turnover in terms of
value, high and even periodical
consumption (daily, weekly
demand). Less reliable forecasts
(significant forecast errors).
CY Group
Low turnover in terms of value,
high and even periodical
consumption (daily, weekly
demand). Less reliable forecasts
(significant forecast errors).
Z
AZ Group
High turnover in terms of value,
high and even periodical
consumption (daily, weekly
demand). Very poor reliability
of forecasts.
BZ Group
Medium turnover in terms of
value, high and even periodical
consumption (daily, weekly
demand). Very poor reliability
of forecasts.
CZ Group
Low turnover in terms of value,
high and even periodical
consumption (daily, weekly
demand). Very poor reliability
of forecasts.
Stanisław Krzyżaniak
38
Demand analyses – ABC/XYZ classification
AX
BX
CX
AY
BY
CY
AZ
BZ
CZ
Stanisław Krzyżaniak
39
Demand analyses – trends and seasonality
Demand
General pattern of a time series – seasonality with a trend
Seasonality
Trend
Year I
Year II
Stanisław Krzyżaniak
Year III
Year IV
Time
40
Demand analyses
Analysis of a demand variability and profiles
Stanisław Krzyżaniak
41
Demand analyses – variability and profiles
D = 82,1
D = 4,5
D=0,055
Stanisław Krzyżaniak
42
Demand analyses – variability and profiles
50
40
30
20
10
0
Stanisław Krzyżaniak
43
Demand analyses – variability and profiles
50
40
30
20
10
0
Stanisław Krzyżaniak
44
Demand analyses – variability and profiles
Frequency
0,40
0,30
0,20
0,10
0
10
20
Stanisław Krzyżaniak
30
40
50 Demand
45
D = 82,1
D = 4,5
D=0,055
Demand analyses – variability and profiles
Stanisław Krzyżaniak
46
Demand analyses - ABC classification
D = 40,9
D = 9,4
D=0,23
Stanisław Krzyżaniak
47
D = 40,9
D = 9,4
D=0,23
Demand analyses – variability and profiles
Normal distribution can be applied in the case of fast moving goods (groups X and Y)
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48
Demand analyses - ABC classification
D = 0,0385
D = 0,197
D=5,03
Stanisław Krzyżaniak
49
D = 0,0385
D = 0,197
D=5,03
Demand analyses – variability and profiles
Poisson distribution can be applied in the
case of slow moving goods, where
calculated average demand (for a chosen
time unit) D is equal to the square of the
2
standard deviation D
D .
Stanisław Krzyżaniak
50
Demand analyses – variability and profiles
Poisson distribution
D = 0,0385
D = 0,5
D = 1,0
D = 2,0
D = 3,0
D = 4,21
Stanisław Krzyżaniak
51
Demand analyses – variability and profiles
Inventory management v. life cycle
DEMAND
Phases:
Introduction
Saturation
Development
Withdrawal
Decline
TIME
Stanisław Krzyżaniak
52
Demand analyses – variability and profiles
D = 38,4;
D = 5,84
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D = 42,5;
D = 15,0
53
Course programme:
1.
Why do we keep inventories?
2.
Typical demand classifications and analyses helpful in inventory
management.
3.
Why high quality forecasting is so important for inventory
management?
4.
Cost aspects of inventory management.
5.
Material Decoupling Point - dependant and independent demands,
deterministic and stochastic approaches to inventory management.
Stanisław Krzyżaniak
54
Crash Course on Inventories
Why high quality forecasting
is so important
for inventory management?
Stanisław Krzyżaniak
55
Basic forecasting models and techniques
Inventory planning requires foreseeing future events,
influencing processes which determine stock level:
• demand and its variability (trends, seasonality, random
nature),
• changes of replenishment lead time and its variability,
• cost relationships.
Foreseeing future events means:
• forecasting (based on formal procedures, formulas
and algorithms),
• prediction (expert analysis).
Stanisław Krzyżaniak
56
Basic forecasting models and techniques
Forecasting can be based on:
•„internal data” (e.g. historical data about
sales of nutrients for infants),
•„external data” (e.g. demographical data
regarding increase or decrease of births).
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57
Basic forecasting models and techniques
Demand – D
Random factor
Seasonality
Trend
Year I
Year II
Stanisław Krzyżaniak
Year III
Year IV
time – t
58
Basic forecasting models and techniques
Selected forecasting metods
Exponential smoothing – Winter’s model
Econometric models
Seasonal coefficients model
Heuristic models
Analytical models
Analog models
Exponential smoothing – Holt’s model
Scenarios
Exponential smoothing – Brown’ model
Simulations
Weighted moving average
Moving average
Arithmetical average
Shortterm forecasts
Longterm forcests
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59
Basic forecasting models and techniques
Exponential smoothing
– all data considered in a forecast
– selection of the smoothing constant –
– calculation of a forecast:
(0 <
Dk 1
Dk
Dk
Dk
Dk 1
Dk
Dk (1
)
< 1)
= 0 – constant forecast model
= 1 – naive forecast
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60
Basic forecasting models and techniques
0,0
Exponential smoothing
12
10
7,00
8
6
4
2
0
64
65
66
67
68
69
70
Dk 1
Stanisław Krzyżaniak
71
72
Dk
73
74
75
76
Dk
77
78
79
Dk
61
Basic forecasting models and techniques
0,1
Exponential smoothing
12
10
7,11
8
6
4
2
0
64
65
66
67
68
69
70
Dk 1
Stanisław Krzyżaniak
71
72
Dk
73
74
75
76
Dk
77
78
79
Dk
62
Basic forecasting models and techniques
0,3
Exponential smoothing
12
10
6,83
8
6
4
2
0
64
65
66
67
68
69
70
Dk 1
Stanisław Krzyżaniak
71
72
Dk
73
74
75
76
Dk
77
78
79
Dk
63
Basic forecasting models and techniques
0,5
Exponential smoothing
12
10
6,68
8
6
4
2
0
64
65
66
67
68
69
70
Dk 1
Stanisław Krzyżaniak
71
72
Dk
73
74
75
76
Dk
77
78
79
Dk
64
Basic forecasting models and techniques
0,7
Exponential smoothing
12
10
6,68
8
6
4
2
0
64
65
66
67
68
69
70
Dk 1
Stanisław Krzyżaniak
71
72
Dk
73
74
75
76
Dk
77
78
79
Dk
65
Basic forecasting models and techniques
0,9
Exponential smoothing
12
10
6,84
8
6
4
2
0
64
65
66
67
68
69
70
Dk 1
Stanisław Krzyżaniak
71
72
Dk
73
74
75
76
Dk
77
78
79
Dk
66
Basic forecasting models and techniques
1,0
Exponential smoothing
12
10
7,00
8
6
4
2
0
64
65
66
67
68
69
70
Dk 1
Stanisław Krzyżaniak
71
72
Dk
73
74
75
76
Dk
77
78
79
Dk
67
Basic forecasting models and techniques
How to assess forecast quality?
Three areas of assessment:
 Quality of a forecast model
 Forecast accuracy
 Forecast acceptance
Stanisław
68Krzyżaniak
68
Basic forecasting models and techniques
How to assess forecast quality?
Three areas of assessment:
 Quality of a forecast model
 Forecast accuracy
 Forecast acceptance
Stanisław
69Krzyżaniak
69
Basic forecasting models and techniques
Quality of a forecast model
t
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Demand 120 132 141 150 159 166 173 181 184 190 195 199 206 210 215 220 223 230 233 236
Stanisław Krzyżaniak
70
Basic forecasting models and techniques
Determination coefficient
n
ŷ t
R2
y
2
y
2
t 1
n
yt
R
2
0,1
t 1
y t - real, historical value of the considered variable for the period „t”,
ŷ t - theoretical (model) value of the considered variable for the period „t”,
y - mean value of the considered variable for n periods.
Stanisław Krzyżaniak
71
Basic forecasting models and techniques
Quality of a forecast model
t
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Demand 120 132 141 150 159 166 173 181 184 190 195 199 206 210 215 220 223 230 233 236
Stanisław Krzyżaniak
72
Basic forecasting models and techniques
How to assess forecast quality?
Three areas of assessment:
 Quality of a forecast model
 Forecast accuracy
 Forecast acceptance
Stanisław
73Krzyżaniak
73
Basic forecasting models and techniques
Forecast accuracy
t
Forecast
Demand
1
50
51
2
49
51
3
48
56
Today
4
50
52
5
52
53
6
53
48
7
52
63
8
54
56
9
54
49
10
54
52
11
54
49
12
54
45
13
50
36
14
46
57
15
48
48
16
47
36
17
44
38
18
43
49
19
46
62
20
47
57
Forecast
Real demand
Stanisław Krzyżaniak
74
Basic forecasting models and techniques
Forecast accuracy ex post measures
1
Absolute forecast error ex post
Formula
q t y t y*t
n
2
Mean absloute forecast error ex post
(y t
e 0
e
t 1
n
y *t
yt
3
4
y *t )
100
Relative forecast error ex post
t
Mean relative forecast error ex post
1 n y t y*t
100
n t 1 yt
yt
y t - real, historical value of the considered variable for the period „t”,
y t - forcast value of the considered variable for the period „t”,
Stanisław Krzyżaniak
75
Basic forecasting models and techniques
Forecast accuracy ex post measures
5
6
Mean square error
Formula
s
1 n
(y t
nt1
*
n
Mean absolute error (deviation)
d
i 1
y *t ) 2
y *t
yt
n
y t - real, historical value of the considered variable for the period „t”,
y t - forecast value of the considered variable for the period „t”,
s*, d
Stanisław Krzyżaniak
min
76
Basic forecasting models and techniques
How to assess forecast quality?
Three areas of assessment:
 Quality of the forecast model
 Forecast accuracy
 Forecast acceptance
Stanisław Krzyżaniak
77
Basic forecasting models and techniques
Today
Forecast acceptance
t
Forecast/model
Demand
1
37
37
2
3
4
5
38,8 40,6 42,4 44,2
38
42
39
46
Absolute forecast error ex ante
T
s*
(T t ) 2
n
(t
t)
2
1
1
n
6
46
49
7
8
9
10
47,8 49,6 51,4 53,2
49
44
55
52
11
55
?
12
13
56,8 58,6
?
?
Relative forecast error ex ante
t
y
t
*
t
100
t 1
Stanisław Krzyżaniak
78
Basic forecasting models and techniques
Today
Forecast acceptance
t
Forecast/model
Demand
1
37
37
2
3
4
5
38,8 40,6 42,4 44,2
38
42
39
46
Absolute forecast error ex ante
T
s*
(T t ) 2
n
(t
t)
2
1
1
n
6
46
49
7
8
9
10
47,8 49,6 51,4 53,2
49
44
55
52
11
55
?
12
13
56,8 58,6
?
?
Relative forecast error ex ante
t
y
t
*
t
100
t 1
Stanisław Krzyżaniak
79
Basic forecasting models and techniques
It has to be distinguished between
a wrong forecast and a forecast error.
What are the consequences of wrong
forecasts (wrong forecast models)?

Wrong safety stock levels: too high or too low,
What are the consequences of high forecast
errors?

High safety stock levels.
Stanisław Krzyżaniak
80
Course programme:
1.
Why do we keep inventory?
2.
Typical demand classifications and analyses helpful in inventory
management.
3.
Why high quality forecasting is so important for inventory
management?
4.
Cost aspects of inventory management.
5.
Material Decoupling Point - dependant and independent demands,
deterministic and stochastic approaches to inventory management.
Stanisław Krzyżaniak
81
Crash Course on Inventories
Cost aspects
of inventory management
Stanisław Krzyżaniak
82
Cost aspects
 Replenishment costs (Cr)
 Carrying costs (Cc)
 Stock-out costs (Co)
We can distinguish
 Fixed (independent) costs (Crf, Ccf, Cof)
 Variable (dependant) costs (Crv, Ccv, Cov)
Stanisław Krzyżaniak
83
Cost aspects
• Replenishment costs (Cr)
– fixed (independent) costs (Crf)
• fixed costs of a purchasing department
and other departments responsible for
receipts (rooms, media, salaries,
overheads)
• fixed transport costs (fleet depreciation,
salaries, overheads)
Stanisław Krzyżaniak
84
Cost aspects
• Replenishment costs (Cr)
– variable costs (dependent on a number
of orders) (Crv)
• Order preparation costs,
• Quality control costs,
• Transport costs (fuel, maintenance),
• Custom clearance fees.
Stanisław Krzyżaniak
85
Cost aspects
• Replenishment costs (Cr)
– variable costs (dependent on a number of
orders) over a given period (Crv) can be
calculated by multiplying a unit cost related
to a single order/delivery (cr) and a number
of deliveries (nd) realised in the period.
Crv
Stanisław Krzyżaniak
c r nd
86
Cost aspects
• Carrying costs (Cc)
– fixed (independent) costs (Ccf)
• Depreciation and exploitation costs of a
warehouse (building)
• Depreciation costs of a warehouse
equipment
• Salaries plus overheads
Stanisław Krzyżaniak
87
Cost aspects
• Carrying costs (Cc)
– variable costs (dependent on stock
quantity) – Ccv
• Cost of maintaining special storing conditions,
• Stock insurance costs,
• Cost of losses and stock depreciation,
• Cost of a tied-up capital (cost of credit or lost
incomes from a deposit).
Stanisław Krzyżaniak
88
Cost aspects
• Carrying costs (Cc)
– variable costs (dependent on stock quantity)
– (Ccv ) over a given period depends on a
storing period (time) and stock value (eg.
purchase price).
A unit inventory carrying cost can be calculated as:
Cc v
c c pu
cc – coefficient of periodical inventory carrying cost (cc = 0.05–0.20 per year)
pu – unit price
Stanisław Krzyżaniak
89
Cost aspects
• Stock-out costs (Co)
– „fixed” (independent costs (Cof):
• Additional transport cost for „emergency” purchase,
• Fixed penalty fee paid to the customer,
• Permanent loss of a customer (loss of future incomes),
• Loss of the market reputation (temporary or permanent
loss of a group of customers),
• Cost of stopping a production line.
Stanisław Krzyżaniak
90
Cost aspects
• Stock-out costs (Co)
– „Variable” costs (dependant on the shortage
quantity) – Cov
• Higher price for „emergency” purchase,
• Penalty fee based on number of missing items,
• Lost margin income due to particular transaction,
• Less margin income due to selling a substitute product,
• Cost of the postponement (transaction and payment
postponed),
• Cost of lost production.
Stanisław Krzyżaniak
91
Course programme:
1.
Why do we keep inventories?
2.
Typical demand classifications and analyses helpful in inventory
management.
3.
Why high quality forecasting is so important for inventory management?
4.
Cost aspects of inventory management..
5.
Material Decoupling Point - dependant and independent demands,
deterministic and stochastic approaches to inventory management.
Stanisław Krzyżaniak
92
Crash Course on Inventories
Material Decoupling Point –
dependent and independent demand
Stanisław Krzyżaniak
93
Material Decoupling Point
Delivery
Production
Distribution
Reaction time
Lead time gap
Lead time gap
Customer order lead time
Customer order lead time
Customer order lead time
Lead time gap
Source: M. Christopher. Logistics and Supply Chain Management. Startegies for
Reducing Costs and Improving Services. 1st edition. Financial Times. Prentice Hall
Stanisław Krzyżaniak
94
Material Decoupling Point
Information (orders)
Delivery
Inventory
Stanisław Krzyżaniak
95
Material Decoupling Point
Information (orders)
Delivery
Inventory
Stanisław Krzyżaniak
96
Material Decoupling Point
Delivery
Production
Stanisław Krzyżaniak
Distribution
97
Material Decoupling Point
Delivery
Production
Dependent
demand
4
5
3
Distribution
2
1
Independent
demand
Stanisław Krzyżaniak
98
Material Decoupling Point
Delivery
Production
Residual stock
Cycle and safety stock
of finished goods
Dependent
demand
Calculations
Material Requirements Planning
Stanisław Krzyżaniak
Distribution
2
Independent
demand
Forecasts
99
Course programme:
6.
Service level and safety stock.
7.
Classical optimisation of the cycle stock.
8.
Classical stock replenishment systems (Reorder Point and Cycle
Review) and their typical combinations.
9.
Safety stock in case of dispersed inventories (square-root law),
10. Information Decoupling Point concept and its role in reduction of
stock levels.
11. Review of selected logistics concepts and solutions oriented on stock
reduction/rationalisation.
Stanisław Krzyżaniak
100
Crash Course on Inventories
Service level
and safety stock
Stanisław Krzyżaniak
101
Service level and safety stock
D = 40,9
D = 9,4
D=0,23
Stanisław Krzyżaniak
102
Service level and safety stock
Stanisław Krzyżaniak
103
Service level and safety stock
Knowledge of a demand distribution over a selected
time unit (e.g. a day, a week) is very important. But this
is not enough to manage inventories in a proper way.
Stock replenishment is realised over a defined time
period, called a replenishment lead time.
It is very important to know this time.
The key issue is the demand distribution over the
replenishment lead time
Stanisław Krzyżaniak
104
Service level and safety stock
Demand distribution within
replenishment cycle
Stanisław Krzyżaniak
105
Service level and safety stock
Replenishment lead time
Replenishment lead time – the period of time between
the moment the decision is made that a product is to
be replenished and the moment the product is
available for use.
Delivery lead time is the time between the receipt of the
customer order and the delivery of the product to the customer
and is always shorter then the replenishment lead time.
Stanisław Krzyżaniak
106
Service level and safety stock
Replenishment lead time
Replenishment
cycle
Delivery
cycle
Decision on replenishment
Placing an order
Receipt of the order
Starting production or picking the order
Preparation for loading
Loading
Arrival to the customer (receiver)
Approval of the delivery (quality control)
Unloading
Availability for use
Stanisław Krzyżaniak
107
Service level and safety stock
Replenishment lead time
The need for replenishment occurs
The need is recognized
Decision on replenishment
Placing an order
Receipt of the order
Starting production or picking the order
Preparation for loading
Loading
LT
Arrival to the customer (receiver)
Approval of the delivery (quality control)
Unloading
Availability for use
Stanisław Krzyżaniak
108
D = 40,9
D = 9,4
D=0,23
Service level and safety stock
LT=4
D = 163,6
D = 18,8
D=0,121
Stanisław Krzyżaniak
109
Service level and safety stock
D;
0
20
40
D
60
Probability density functions
80
100
DLT
D LT;
120
160
Stanisław Krzyżaniak
140
180
200
DLT
220
LT
D
240
260
280
110
Service level and safety stock
Distribution function
Probability that demand over the replenishment cycle
will not exceed the given level
Probability density function
0
20
40
60
80
100
Stanisław Krzyżaniak
120
140
160
180
200
220
240
111
Service level and safety stock
p(DLT≤195) 0,953
185
0
20
190
40
60
195
200
80
100
Stanisław Krzyżaniak
205
120
140
160
180
200
220
240
112
f(D)
f(LT)
f(T)
5050
150
100
100
150
P
D
LT
f(DLT)
Normal distribution
340
Stanisław Krzyżaniak
500
660
DLT
113
Service level and safety stock
Service level
Stanisław Krzyżaniak
114
Service level and safety stock
Suppliers’ Sevice Level
indicators
• Time to inform about
emergency situation,
• Relaibilty of deliveris
(quality, quanitity,
time),
Customer oriented
Sevice Level
indicators
Row materials
and
components
Finished goods
• Flexiblity of deliveries
(time, quantity)
• Avalibility of ordered
raw materials and
components
• Avalibility of goods
• Responsiveness to
customer needs
(flexibility).
Stanisław Krzyżaniak
115
Service level and safety stock
Customer service level
SL1
Probability of not getting out of stock
(Probability that a stock-out situation will not
occur over a replenishment lead time)
Stanisław Krzyżaniak
116
Service level and safety stock
220
How often?
200
180
160
DLT
140
120
100
80
60
40
20
0
Stanisław Krzyżaniak
117
Service level and safety stock
0,045
0,04
0,035
Service level
Probability of serving demand
within replenishment cycle
0,03
Stock-out probability
within a replenishment cycle
0,025
0,02
0,015
0,01
0,005
0
1
4
7
10 13
16 19 22 25 28
31 34 37 40 43
46 49 52 55
DLT
58 61 64 67 70
73 76 79 82 85
88 91 94 97 100
Stock level at the beginning of a replenishment cycle
Stanisław Krzyżaniak
118
Service level and safety stock
0,045
0,04
0,035
Service level
0,03
Probability of serving demand
within replenishment cycle
Stock-out probability
within a replenishment cycle
0,025
0,02
0,015
0,01
50%
0,005
0
1
4
7
10 13
16 19 22 25 28
31 34 37 40 43
Stock level at the
beginning of a
replenishment cycle
Stanisław Krzyżaniak
46 49 52 55
58 61 64 67 70
73 76 79 82 85
88 91 94 97 100
DLT
119
Service level and safety stock
0,045
0,04
0,035
Service level
0,03
Stock-out probability
within a replenishment cycle
Probability of serving demand
within replenishment cycle
0,025
0,02
nd ndstock out
SL1
nd
0,015
0,01
0,005
0
1
4
7
10 13
16 19 22 25 28
31 34 37 40 43
Stock level at the
beginning of the
replenishment cycle
Stanisław Krzyżaniak
46 49 52 55
58 61 64 67 70
SS
DLT
73 76 79 82 85
88 91 94 97 100
Safety stock
Ss
DLT
120
Service level and safety stock
f (SL)
SL
Stanisław Krzyżaniak
121
Service level and safety stock
Variable demand
Stock level
[t]
SS
LT
LT
LT
Constant
replenishment cycle lead time
DLT
Stanisław Krzyżaniak
D
LT
122
Service level and safety stock
Stock level
Constant demand
LT
LT
SS
[t]
Variable
replenishment cycle lead time
LT1
DLT
Stanisław Krzyżaniak
LT
D
123
Service level and safety stock
Stock level
LT
Variable demand
LT
LT
SS
[t]
Variable
replenishment cycle lead time
LT1
Stanisław Krzyżaniak
DLT
2
D
LT
2
LT
D2
124
Service level and safety stock
Customer service level
SL2
Fill rate for customer orders
(Probability of meeting the expected demand –
in terms of quantity)
Stanisław Krzyżaniak
125
Service level and safety stock
0,045
0,04
FR
0,035
Q
nsh
Q
How many units can be
missing? What is the
expected number of shorteges
nsh?
0,03
0,025
0,02
0,015
0,01
0,005
0
1
4
7
10 13
16 19 22 25 28
31 34 37 40 43
46 49 52 55
58 61 64 67 70
73 76 79 82 85
88 91 94 97 100
DLT
Stock level at the beginning of the replenishment cycle
Stanisław Krzyżaniak
126
f(D)
f(LT)
f(T)
50
100
150
LT
D
Real service level resulting
from the assumtion that the
demand distribution is
compatible with the normal
distribution.
Expected
service
level
Normalndistribution
340
Stanisław Krzyżaniak
500
660
DLT
127
Service level and safety stock
Safety stock (SS)
Average demand – D
Demand variability
–
D
Replenishment lead time – T
Lead time variability
–
LT
(forecast std. deviation)
Cost based optimisation:
• safety stock carrying cost
• stock-out cost
Stanisław Krzyżaniak
Service level:
• probability of a no stock-out situation
over a replenishment cycle (probability
measure),
• demand (order) fulfilment (quantity
measure).
•Strategic (tactical) decisions
• Intuitive,
• Casual.
128
Service level and safety stock
Ss
Q
Cso
Cc(Ss)v
SL opt
Crv
Sr
Cc(Sr)v
Q opt
Ss opt
Sr opt
TC = Crf + Ccf + Crv + Cc(Sr)v + Cc(Ss)v + Cso
Cost relationships in optimisation of safety stock
Stanisław Krzyżaniak
129
Service level and safety stock
1400,0
Cost
A sum of stock carrying and stock-out costs
1200,0
1000,0
800,0
Stock carrying cost
600,0
400,0
Stock-out cost
200,0
0,0
90
91
92
93
94
95
96
97
98
99
SL [%]
Optimal serice level
Opitmal safety stock
Stanisław Krzyżaniak
130
Course programme:
6.
Service level and safety stock.
7.
Classical optimisation of the cycle stock.
8.
Classical stock replenishment systems (Reorder Point and Cycle
Review) and their typical combinations.
9.
Safety stock in case of dispersed inventories (square-root law),
10. Information Decoupling Point concept and its role in reduction of
stock levels.
11. Review of selected logistics concepts and solutions oriented on stock
reduction/rationalisation.
Stanisław Krzyżaniak
131
Crash Course on Inventories
Cycle stock optimisation
Stanisław Krzyżaniak
132
Cycle stock optimisation
Q
Q
CS
CS
SS
Stanisław Krzyżaniak
SS
133
Cycle stock optimisation
Ss
Q
Cso
Cc(Ss)v
SL opt
Crv
Sr
Cc(Sr)v
Q opt
Ss opt
Sr opt
TC = Crf + Ccf + Crv + Cc(Sr)v + Cc(Ss)v + Cso
Cost relationships in optimisation of cycle stock
Stanisław Krzyżaniak
134
Cycle stock optimisation
A principle of calculating the Economic Order Quantity
Variable cost of replenishment and carrying the cycle stock:
Crv
Cc v
Dp
Q
cr
0,5 Q pu c c p
Dp – forecast (planned) demand over a period p (eg. a year – annual demand,
Q – order quantity
cr - replenishment cost related to a single order/delivery
pu – unit price (or production cost per unit),
ccp – inventory cost carrying coefficient for a period p.
Stanisław Krzyżaniak
135
Cycle stock optimisation
C
O
S
T
Ccv
Crv
EOQ
Q
Economic Order Quantity
Stanisław Krzyżaniak
136
Cycle stock optimisation
Economic Order Quantity
EOQ
Stanisław Krzyżaniak
2 Dp c r
pu c c p
137
Course programme:
6.
Service level and safety stock.
7.
Classical optimisation of the cycle stock.
8.
Classical stock replenishment systems (Reorder Point and
Cycle Review) and their typical combinations.
9.
Safety stock in case of dispersed inventories (square-root law).
10. Information Decoupling Point concept and its role in reduction of
stock levels.
11. Review of selected logistics concepts and solutions oriented on stock
reduction/rationalisation.
Stanisław Krzyżaniak
138
Crash Course on Inventories
Basic replenishment
systems
Stanisław Krzyżaniak
139
Basic replenishment systems
Orders
S
U
P
P
L
I
E
R
Demand fluctuations
Decisions
Stock
Lead time distribution
Stanisław Krzyżaniak
C
U
S
T
O
M
E
R
Demand distribution
140
Basic replenishment systems
There are two basic replenishment (ordering)
systems:
• Replenishment system based on a reorder level (BQ)
• variable replenishment periods
• safety stock: when to order?
• fixed order quantity
• Replenishment system based on a periodic review (ST)
• variable order quantity
• safety stock: how many (how much) to order?
• fixed replenishment periods
Stanisław Krzyżaniak
141
Basic replenishment systems
Replenishment system
based on a reorder level
(when to order?)
Stanisław Krzyżaniak
142
Basic replenishment systems
Replenishment system based on a reorder level
Basic principles and rules:
• fixed quantity of orders and deliveries (Q = const)
• variable replenishment period (t)
• current knowledge of the economic stock (Se)
• calculated reorder level (order point – B)
B = D LT + SS (SS =
D(LT))
• an order is placed whenever the economic stock falls below
the reorder level: Se≤B
Stanisław Krzyżaniak
143
Basic replenishment systems
Replenishment system based on a reorder level
Economic stock =
= Physical stock
+ quantity of that product ordered but
not yet received
– quantity of that product already
reserved
Stanisław Krzyżaniak
144
Basic replenishment systems
Replenishment system based on a reorder level
Ss
Safety stock:
(SL)
DLT
I. If
D
0 and
LT
0 then :
DLT
D
LT
II. If
LT
0 and
D
0 then :
DLT
LT
D
III. If
D
0 and
LT
0 then :
LT
2
LT
DLT
Stanisław Krzyżaniak
2
D
D
2
145
Basic replenishment systems
Replenishment system based on a reorder level
Variable demand
S
T
O
C
K
B
LT
Ss
LT
Variable replenishment
lead time
Stanisław Krzyżaniak
LT
[t]
LT1
146
Basic replenishment systems
Replenishment system
based on periodic review
(how much to order?)
Stanisław Krzyżaniak
147
Basic replenishment systems
Replenishment system based on a periodic review
Basic principles and rules:
• fixed review and replenishment period (To=const)
• variable quantity of orders and deliveries (Q)
• current knowledge of the economic stock (Se)
• calculated maximum level S
S = D LT+To) + SS (SS =
D(LT))
• an order quantity is calculated as: Q= S-Se
Stanisław Krzyżaniak
148
Basic replenishment systems
Replenishment system based on a periodic review
Ss
Safety stock:
0 and
(SL)
I. If
D
0 then :
II. If
LT
0 and
D
0 then :
III. If
D
0 and
LT
0 then :
DLT
2
D
LT
Stanisław Krzyżaniak
DLT
DLT
D
DLT
(LT T0 )
LT T0
LT
D
2
LT
D
2
149
Basic replenishment systems
Replenishment system based on a periodic review
S
T
O
C
K
S
Variable demand
[t]
LT
T0
LT
T0
LT
T0
Ss
Stanisław Krzyżaniak
150
Basic replenishment systems
Alternative (hybrid)
replenishment systems
Stanisław Krzyżaniak
151
Basic replenishment systems
Review
period
(Tr)
Order
period (T0)
(A) – BQ
(B) – ST
fixed
Order
quantity
(Q)
Reorder
level
(B/s)
fixed
fixed
fixed
fixed
(C) – BS
fixed
(D) – sQ
fixed
(E) – sS
fixed
Stanisław Krzyżaniak
Maximum
level
(S)
fixed
fixed
fixed
fixed
fixed
152
BQ
Basic replenishment systems
Q
LT
Q
Q
LT
LT
B
Reorder level based system (a lot of small orders)
Stanisław Krzyżaniak
153
ST
Basic replenishment systems
S
LT
LT
LT
LT
LT
T0
T0
T0
T0
T0
Periodic review
Stanisław Krzyżaniak
154
BS
Basic replenishment systems
S
LT
LT
LT
B
Recommended in the case of relatively small number of big orders (MIN-MAX)
Stanisław Krzyżaniak
155
sQ
Basic replenishment systems
Q
Q
Q
s
LT
LT
T0
T0
LT
T0
T0
T0
Fixed cycle of possible orders of fixed quantity
Stanisław Krzyżaniak
156
sS
Basic replenishment systems
S
LT
LT
T0
T0
LT
s
T0
T0
T0
Small number of big orders – fixed review cycle
Stanisław Krzyżaniak
157
Course programme:
6.
Service level and safety stock.
7.
Classical optimisation of the cycle stock.
8.
Classical stock replenishment systems (Reorder Point and Cycle
Review) and their typical combinations.
9.
Safety stock in case of dispersed inventories (square-root law).
10. Information Decoupling Point concept and its role in reduction of
stock levels.
11. Review of selected logistics concepts and solutions oriented on stock
reduction/rationalisation.
Stanisław Krzyżaniak
158
Crash Course on Inventories
Safety stock in case of dispersed
inventories (square-root law)
Stanisław Krzyżaniak
159
D1
D2
D3
D4
100 Dispersed
units; D1
20 units
Dinventories
1
D1
D1
1,40
1,60
1,20
1,40
1,20
1,00
1,00
0,80
0,80
0,60
0,60
0,40
0,40
1,60
1,40
0,20
1,40
1,20
1,20
1,00
1,00
0,80
0,80
0,60
0,60
0,40
0,40
0,20
0,20
0,00
0,00
Regional Warehouse 1
0,20
1,40
Regional Warehouse 2
1,20
1,00
0,80
0,60
0,00
0,00
1,60
1,60
1,40
1,40
1,20
1,20
1,00
1,00
0,80
0,80
0,60
0,60
0,40
0,40
0,40
1,60
1,40
0,20
1,20
1,00
0,00
0,80
0,60
0,40
0,20
0,00
1,60
1,40
1,20
1,00
0,80
0,60
0,40
0,20
0,00
0,20
Regional Warehouse 3
0,00
0,20
Regional Warehouse 4
0,00
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160
Dispersed inventories
1,40
1,20
1,00
0,80
1,60
1,40
1,40
1,20
1,20
1,00
1,00
0,80
0,80
0,60
0,60
0,40
0,40
0,20
0,20
0,60
0,00
0,00
1,40
Central warehouse
1,20
1,00
0,80
0,60
0,40
1,60
1,40
0,40
0,20
1,20
1,00
0,00
0,80
0,60
0,40
0,20
0,00
1,60
1,40
1,20
1,00
0,80
0,60
0,40
0,20
0,00
0,20
0,00
Stanisław Krzyżaniak
161
Dispersed inventories
1,60
1,40
1,40
1,20
1,20
1,00
1,00
0,80
0,80
0,60
0,60
0,40
0,40
0,20
0,20
0,00
0,00
1,40
1,20
1,00
0,80
0,60
0,40
1,60
1,40
0,20
1,20
1,00
0,00
0,80
0,60
0,40
0,20
0,00
1,60
1,40
1,20
1,00
0,80
0,60
0,40
0,20
0,00
DMC DRW1 DRW2
2
DMC
2
DRW1
2
DRW2
Stanisław Krzyżaniak
DRW3
2
DRW3
......... DRWN
.....
2
DRWN
162
Dispersed inventories
If:
DRW1
DRW2
DRW3
......... DRWN
2
DMC
2
DRW1
2
DRW2
2
DRW3
then:
DCW
N DRW
2
DCW
2
DRW
N
.....
DMC
Stanisław Krzyżaniak
DRW
2
DRWN
N
2
DRW
DRW
163
Dispersed inventories
DCW
2
DRW1
2
DRW1
?
SS CW
S
2
SRW 1
S
2
SRW 2
Stanisław Krzyżaniak
2
DRW1
Ss
S
2
SRW 3
....
2
DRW1
DLT
D
...... S
2
SRWN
LT
164
Course programme:
6.
Service level and safety stock.
7.
Classical optimisation of the cycle stock.
8.
Classical stock replenishment systems (Reorder Point and Cycle
Review) and their typical combinations.
9.
Safety stock in case of dispersed inventories (square-root law).
10. Information Decoupling Point concept and its role in reduction
of stock levels.
11. Review of selected logistics concepts and solutions oriented on stock
reduction/rationalisation .
Stanisław Krzyżaniak
165
Crash Course on Inventories
Information Decoupling Point
Stanisław Krzyżaniak
166
Information Decoupling Point
Information (orders)
Delivery
Inventory
Stanisław Krzyżaniak
167
Information Decoupling Point
D = 38,4;
D = 5,84
Stanisław Krzyżaniak
D = 42,5;
D = 15,0
168
Information Decoupling Point
Information (orders)
Delivery
Inventory
Stanisław Krzyżaniak
169
Information Decoupling Point
Information (orders)
Delivery
Inventory
Stanisław Krzyżaniak
170
Solutions oriented on stock reduction
6.
Service level and safety stock.
7.
Classical optimisation of the cycle stock.
8.
Classical stock replenishment systems (Reorder Point and Cycle
Review) and their typical combinations.
9.
Safety stock in case of dispersed inventories (square-root law).
10. Information Decoupling Point concept and its role in reduction of
stock levels.
11. Review of selected logistics concepts and solutions oriented on
stock reduction/rationalisation.
Stanisław Krzyżaniak
171
Solutions oriented on stock reduction
Stock structure
Q
Cycle stock
SC
SS
Safety stock
Surplus stock
Ssp
Stanisław Krzyżaniak
172
Safety stock
Solutions oriented on stock reduction
Shorter replenishment
cycles
Revision of service levels
ABC/XYZ/CAV analysis
SS
2
D
LT
2
LT
D
2
Fewer delays
Forecats
Centralised stock
Stanisław Krzyżaniak
173
Solutions oriented on stock reduction
Forecats
CS
1
EOQ
2
Reduction of
replenishment costs.
Improvements of
ordering process.
1
2
2 Do c r
pu c c o
Reduction of inventory
carrying costs.
Cycle stock
Stanisław Krzyżaniak
174
Solutions oriented on stock reduction
EOQ
SC
SS
Stanisław Krzyżaniak
CS
SS
EOQ
2
1
2
2
D
LT
2 Do c r
pu c c o
2
LT
D
2
175
Solutions oriented on stock reduction
• Just-in Time – JiT
• Quick Response – QR
• Efficient Consumer Response – ECR
• Vendor Managed Inventory – VMI
• Co-managed Inventory – CMI
• Collaborative Planning, Forecasting and Replenishment – CPFR
Stanisław Krzyżaniak
176
Solutions oriented on stock reduction
Just-in-Time
CS
SS
Stanisław Krzyżaniak
EOQ
2
1
2
2
D
LT
2 Do c r
pu c c o
2
LT
D
2
177
Solutions oriented on stock reduction
Qiuck Response
CS
SS
Stanisław Krzyżaniak
EOQ
2
1
2
2
D
LT
2 Do c r
pu c c o
2
LT
D
2
178
Solutions oriented on stock reduction
Collaborative
Planning,
Forecasting and
Replenishment
CS
SS
Stanisław Krzyżaniak
EOQ
2
1
2
2
D
LT
2 Do c r
pu c c o
2
LT
D
2
179
Solutions oriented on stock reduction
Continous
replenishemnt
CS
SS
Stanisław Krzyżaniak
EOQ
2
1
2
2
D
LT
2 Do c r
pu c c o
2
LT
D
2
180
Solutions oriented on stock reduction
Vendor Managed
Inventory
Co-Managed
Inventory
CS
SS
Stanisław Krzyżaniak
EOQ
2
1
2
2
D
LT
2 Do c r
pu c c o
2
LT
D
2
181
Solutions oriented on stock reduction
Performance measures – stock controll
Stock cover:
SCO
Stock turnover:
D
S
Stanisław Krzyżaniak
STO
S
D
182
Solutions oriented on stock reduction
Sales (demand) - D
Average stock level - S
D
475
52
9,13
D
560
52
10,77
SCO
SCO
S
D
S
D
Stanisław Krzyżaniak
Last year
This year
475
31
560
35
31
9,13
35
10,77
3,39
3,25
STO
D
S
475
31
15,3
STO
D
S
560
35
16
183
Crash Course on inventorises.
Thank
you for yourToday
attention!
Inventories
Inventories today: a curse, a blessing, a must..?
In a way – INEVITIBILITY, but …..
…. don’t let them get out of control!
Stanisław Krzyżaniak
184
Summary of the questionnarie
1.
Do you keep in your household a stock of:
Stanisław Krzyżaniak
185
Summary of the questionnarie
1.
In case of permanent stock is it high or low level stock?
Stanisław Krzyżaniak
186
Summary of the questionnarie
2. Please indicate three most important reasons for keeping inventories
in your company:
Stanisław Krzyżaniak
187
Summary of the questionnarie
3. Which, in your opinion, would be the most effective ways to reduce
stock levels in your company?
Stanisław Krzyżaniak
188
Summary of the questionnarie
4. What are the most critical barriers for implementation of solutions
leading to stock reduction in your company?
Stanisław Krzyżaniak
189
Contact:
stanislaw.krzyzaniak@ilim.poznan.pl
mateusz.boruta@ilim.poznan.pl
Instytut Logistyki i Magazynowania
(Institute of Logistics and Warehouisng)
Poznań - POLAND
www.ilim.poznan.pl
Tel. +48-61-852-98-98
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190