Really using Oracle analytic SQL functions

Really Using
Analytic Functions
Kim Berg Hansen
T. Hansen Gruppen A/S

Who is this Kim?
• a Danish SQL and PL/SQL Developer:
http://dspsd.blogspot.com
• Professional geek since 1996
• Oracle programmer since 2000
• Single SQL Statement mantra (©Tom Kyte)
• Danish Beer Enthusiast (http://ale.dk)
• Likes to cook
• Reads sci-fi
2 2012-12-05 #ukoug2012 Really Using Analytic Functions

What’s up?
• Why analytics?
• Case 1: Top selling items
• Case 2: Picking by FIFO
• Case 3: Efficient picking route
• Case 4: Picking efficiency
• Case 5: Forecasting sales
• Case 6: Forecast zero firework stock
• Case 7: Multi-order FIFO picking (time permitting)
• Any questions?


Why analytics?
• Normal SQL functions operate on one row
• Aggregates can do more rows but loose detail
• When you need details together with subtotals, ranks,
ratios, comparisons, you could do:
 Client operations (tool or code with variables/arrays)
 Scalar subqueries (multiple access of same data)
 Analytic functions (often much more efficient )
• Analytics allow you to operate across the entire resultset,
not just a single row


Top Selling Items
Case 1


Top selling items
• Classic task for a programmer:
• Show top three by product group
• Also show how big percentage they
sold of the total
– Both of the total by product group
– And of the grand total


Tables
create table items(
item varchar2(10) primary key,
Items with groups
grp varchar2(10),
name varchar2(20)
)
/

create table sales ( Sales per month
item varchar2(10) references items (item),
mth date,
qty number
)
/


Data - items
insert into items values ('101010','AUTO','Brake disc');
insert into items values ('102020','AUTO','Snow chain');
5 autoparts
insert into items values ('103030','AUTO','Sparc plug');
insert into items values ('104040','AUTO','Oil filter');
insert into items values ('105050','AUTO','Light bulb');

insert into items values ('201010','MOBILE','Handsfree');
insert into items values ('202020','MOBILE','Charger'); 5 mobile
insert into items values ('203030','MOBILE','iGloves');
insert into items values ('204040','MOBILE','Headset');
accessories
insert into items values ('205050','MOBILE','Cover');


Data – sales AUTO
insert into sales values ('101010',date '2011-04-01',10);
Sales for various
insert into sales values ('101010',date '2011-06-01',12); months of 2011 for
insert into sales values ('102020',date '2011-03-01', 7); the autoparts
insert into sales values ('102020',date '2011-07-01', 8);


Data – sales MOBILE
Sales for various
insert into sales values ('201010',date '2011-06-01', 7); months of 2011 for
insert into sales values ('202020',date '2011-03-01',21); the mobile
accessories


Base select
select i.grp
, i.item
Join items and sales
, max(i.name) name
, sum(s.qty) qty
from items i Sales for 2011
join sales s
on s.item = i.item
where s.mth between date '2011-01-01' and date '2011-12-01' Group by to get
group by i.grp, i.item
order by i.grp, sum(s.qty) desc, i.item
total sales for 2011
per item


Base select
GRP ITEM NAME QTY
---------- ---------- -------------------- -----
Couple of items in
AUTO 101010 Brake disc 33 each group have
AUTO 103030 Sparc plug 33 identical sales
AUTO 105050 Light bulb 28
AUTO 104040 Oil filter 22
AUTO 102020 Snow chain 15
MOBILE 202020 Charger 44
MOBILE 204040 Headset 35
MOBILE 203030 iGloves 28
MOBILE 205050 Cover 28
MOBILE 201010 Handsfree 18


Which TOP?
select g.grp, g.item, g.name, g.qty
, dense_rank() over (partition by g.grp order by g.qty desc) drnk
, rank() over (partition by g.grp order by g.qty desc) rnk
, row_number() over (partition by g.grp order by g.qty desc, g.item) rnum
from (
select i.grp
, i.item
, max(i.name) name
,
from
sum(s.qty) qty
items i
Base select as
join
on
sales s
s.item = i.item inline view
where s.mth between date '2011-01-01' and date '2011-12-01'

) g
order by g.grp, g.qty desc, g.item


Which TOP?
GRP ITEM NAME QTY DRNK RNK RNUM
---------- ---------- -------------------- ----- ----- ----- -----
The three different
AUTO 101010 Brake disc 33 1 1 1 functions handle
AUTO 103030 Sparc plug 33 1 1 2 ties differently
AUTO 105050 Light bulb 28 2 3 3
AUTO 104040 Oil filter 22 3 4 4
AUTO 102020 Snow chain 15 4 5 5
MOBILE 202020 Charger 44 1 1 1
MOBILE 204040 Headset 35 2 2 2
MOBILE 203030 iGloves 28 3 3 3
MOBILE 205050 Cover 28 3 3 4
MOBILE 201010 Handsfree 18 4 5 5


Without inline view
select i.grp
, i.item
Analytics calculated last, so can include
, max(i.name) name group by expressions as well as aggregates
, sum(s.qty) qty
, dense_rank() over (partition by i.grp order by sum(s.qty) desc) drnk
, rank() over (partition by i.grp order by sum(s.qty) desc) rnk
, row_number() over (partition by i.grp order by sum(s.qty) desc, i.item) rnum
from items i
join sales s
on s.item = i.item
order by i.grp, sum(s.qty) desc, i.item


Without inline view
GRP ITEM NAME QTY DRNK RNK RNUM
---------- ---------- -------------------- ----- ----- ----- -----
Identical results
AUTO 101010 Brake disc 33 1 1 1
AUTO 103030 Sparc plug 33 1 1 2
AUTO 105050 Light bulb 28 2 3 3
AUTO 104040 Oil filter 22 3 4 4
AUTO 102020 Snow chain 15 4 5 5
MOBILE 202020 Charger 44 1 1 1
MOBILE 204040 Headset 35 2 2 2
MOBILE 203030 iGloves 28 3 3 3
MOBILE 205050 Cover 28 3 3 4
MOBILE 201010 Handsfree 18 4 5 5


TOP 3 – rank()
select g.grp, g.item, g.name, g.qty, g.rnk
from (
Analytic function
select i.grp cannot be in
, i.item
, max(i.name) name
where clause
, sum(s.qty) qty
from items i So inline view
join sales s
on s.item = i.item and filter on the
where s.mth between date '2011-01-01' and date '2011-12-01' alias
) g
where g.rnk <= 3
order by g.grp, g.rnk, g.item


TOP 3 – rank()
GRP ITEM NAME QTY RNK
---------- ---------- -------------------- ----- -----
rank() works like the
AUTO 101010 Brake disc 33 1 olympics – two gold
AUTO 103030 Sparc plug 33 1 medals mean no
AUTO 105050 Light bulb 28 3
MOBILE 202020 Charger 44 1
silver medal
MOBILE 204040 Headset 35 2
MOBILE 203030 iGloves 28 3
MOBILE 205050 Cover 28 3


TOP 3 – dense_rank()
from (
select i.grp
, i.item
, max(i.name) name
, sum(s.qty) qty
, dense_rank() over (partition by i.grp order by sum(s.qty) desc) rnk
from items i
join sales s
on s.item = i.item
) g
where g.rnk <= 3


TOP 3 – dense_rank()
---------- ---------- -------------------- ----- -----
dense_rank() also
AUTO 101010 Brake disc 33 1 gives equal rank at
AUTO 103030 Sparc plug 33 1 ties, but does not
AUTO 104040 Oil filter 22 3
skip ranks
MOBILE 205050 Cover 28 3


TOP 3 – row_number()
from (
select i.grp
, i.item
, max(i.name) name
, sum(s.qty) qty
, row_number() over (partition by i.grp order by sum(s.qty) desc, i.item) rnk
from items i
join sales s
on s.item = i.item
) g
where g.rnk <= 3


TOP 3 – row_number()
---------- ---------- -------------------- ----- -----
row_number() just
AUTO 101010 Brake disc 33 1 numbers
AUTO 103030 Sparc plug 33 2 consecutively
If ties, then result is
”random”, so a good
idea always to use
”unique” order by


Percent of total
, round(g.g_pct,1) g_pct ratio_to_report()
, round(g.t_pct,1) t_pct
from ( returns number
select i.grp
, i.item between 0 and 1
, max(i.name) name
, sum(s.qty) qty
, 100 * ratio_to_report(sum(s.qty)) over (partition by i.grp) g_pct
, 100 * ratio_to_report(sum(s.qty)) over () t_pct Multiply with 100 to
from items i
join sales s get percent
on s.item = i.item
) g
where g.rnk <= 3


Percent of total
GRP ITEM NAME QTY RNK G_PCT T_PCT
---------- ---------- -------------------- ----- ----- ------ ------
AUTO 101010 Brake disc 33 1 25.2 11.6
AUTO 103030 Sparc plug 33 1 25.2 11.6
AUTO 105050 Light bulb 28 3 21.4 9.9
MOBILE 202020 Charger 44 1 28.8 15.5
MOBILE 204040 Headset 35 2 22.9 12.3
MOBILE 203030 iGloves 28 3 18.3 9.9
MOBILE 205050 Cover 28 3 18.3 9.9


Top selling items
• What kind of top three do you wish?
– DENSE_RANK()
– RANK()
– ROW_NUMBER()

• PARTITION BY groups of items

• RATIO_TO_REPORT for percentages

Picking by FIFO
Case 2


Picking by FIFO

• Items stored in different locations in warehouse
• Pick an order by First-In First-Out principle

Tables
create table inventory (
item varchar2(10),
Item, location, quan
loc varchar2(10), tity and date of
qty number, purchase
purch date
)
/

create table orderline ( Order number,
ordno number,
item varchar2(10),
item and quantity
qty number ordered
)
/


Data
insert into inventory values('A1', '1-A-20', 18, DATE '2004-11-01');
2 items each 5
insert into inventory values('A1', '1-C-05', 18, DATE '2004-11-03'); locations various
insert into inventory values('A1', '2-D-07', 9, DATE '2004-11-04');
purchase dates
insert into inventory values('B1', '1-A-02', 18, DATE '2004-11-06');
insert into inventory values('B1', '1-B-11', 4, DATE '2004-11-05');
insert into inventory values('B1', '1-C-04', 12, DATE '2004-11-03');
insert into inventory values('B1', '1-B-15', 2, DATE '2004-11-02');
insert into inventory values('B1', '2-D-23', 1, DATE '2004-11-04');

insert into orderline values (1,'A1',24); One order with a
insert into orderline values (1,'B1',18);
quantity of both
items


What to pick
Picking application sets bind variable for which order to pick
(Could be sales order, batch order, shop refill order)
variable pick_order number;

begin
:pick_order := 1;
end;
/


What can we pick
select o.item
, o.qty ord_qty
Join orderline to
, i.loc inventory to see all
, i.purch that potentially can
, i.qty loc_qty
from orderline o
be picked
join inventory i
on i.item = o.item
where o.ordno = :pick_order
order by o.item, i.purch, i.loc


What can we pick
ITEM ORD_QTY LOC PURCH LOC_QTY
---------- ------- ---------- ---------- -------
Visually we can see
A1 24 1-A-20 2004-11-01 18 we need 18 A1 from
A1 24 2-A-02 2004-11-02 24 first location and 6
A1 24 1-C-05 2004-11-03 18
A1 24 2-D-07 2004-11-04 9
from second loc.
A1 24 1-A-31 2004-11-05 12
B1 18 1-B-15 2004-11-02 2
B1 18 1-C-04 2004-11-03 12 Likewise we will
B1 18 2-D-23 2004-11-04 1 empty first 3 locs of
B1 18 1-B-11 2004-11-05 4
B1 18 1-A-02 2004-11-06 18 B1 and pick 3 from
fourth location


Accumulate
select o.item
, o.qty ord_qty
Let’s try to create a
, i.loc rolling sum of the
, i.purch
, i.qty loc_qty
qty for each item
, sum(i.qty) over (
partition by i.item
order by i.purch, i.loc
rows between unbounded preceding and current row
) sum_qty
from orderline o
join inventory i
on i.item = o.item


Accumulate
ITEM ORD_QTY LOC PURCH LOC_QTY SUM_QTY
---------- ------- ---------- ---------- ------- -------
Yup, when our sum
A1 24 1-A-20 2004-11-01 18 18 is greater than the
A1 24 2-A-02 2004-11-02 24 42 ordered qty, it looks
A1 24 1-C-05 2004-11-03 18 60
A1 24 2-D-07 2004-11-04 9 69
like we have enough
A1 24 1-A-31 2004-11-05 12 81
B1 18 1-B-15 2004-11-02 2 2
B1 18 1-C-04 2004-11-03 12 14
B1 18 2-D-23 2004-11-04 1 15
B1 18 1-B-11 2004-11-05 4 19
B1 18 1-A-02 2004-11-06 18 37


Filter accumulated
select s.*
from ( So let’s try to filter
select o.item, o.qty ord_qty
, i.loc, i.purch, i.qty loc_qty
on that
, sum(i.qty) over (
partition by i.item
rows between unbounded preceding and current row
) sum_qty
from orderline o
join inventory i
on i.item = o.item
) s
where s.sum_qty < s.ord_qty
order by s.item, s.purch, s.loc


Filter accumulated
ITEM ORD_QTY LOC PURCH LOC_QTY SUM_QTY
---------- ------- ---------- ---------- ------- -------
FAIL!
A1 24 1-A-20 2004-11-01 18 18
B1 18 1-B-15 2004-11-02 2 2
B1 18 1-C-04 2004-11-03 12 14 Missing the last
B1 18 2-D-23 2004-11-04 1 15 location for each
item


Accumulate previous
select o.item
, o.qty ord_qty
One small change to
, i.loc our rolling sum:
, i.purch
, i.qty loc_qty
, sum(i.qty) over (
partition by i.item Sum of rows up to
order by i.purch, i.loc but not including
rows between unbounded preceding and 1 preceding
) sum_prv_qty the current row
from orderline o
join inventory i
on i.item = o.item


Accumulate previous
ITEM ORD_QTY LOC PURCH LOC_QTY SUM_PRV_QTY
---------- ------- ---------- ---------- ------- -----------
As long as the sum
A1 24 1-A-20 2004-11-01 18 of the previous rows
A1 24 2-A-02 2004-11-02 24 18 are not enough, we
A1 24 1-C-05 2004-11-03 18 42
A1 24 2-D-07 2004-11-04 9 60
continue
A1 24 1-A-31 2004-11-05 12 69
B1 18 1-B-15 2004-11-02 2
B1 18 1-C-04 2004-11-03 12 2 When the previous
B1 18 2-D-23 2004-11-04 1 14 rows are sufficient,
B1 18 1-B-11 2004-11-05 4 15
B1 18 1-A-02 2004-11-06 18 19 we stop


Filter previous
select s.*
, least(s.loc_qty, s.ord_qty - s.sum_prv_qty) pick_qty Now we can filter
from (
select o.item, o.qty ord_qty correctly
, nvl(sum(i.qty) over (
partition by i.item
order by i.purch, i.loc nvl() for first row
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
least() to get qty to
where o.ordno = :pick_order be picked at that
) s
where s.sum_prv_qty < s.ord_qty location
order by s.item, s.purch, s.loc


Filter previous
ITEM ORD_QTY LOC PURCH LOC_QTY SUM_PRV_QTY PICK_QTY
---------- ------- ---------- ---------- ------- ----------- --------
A1 24 1-A-20 2004-11-01 18 0 18
A1 24 2-A-02 2004-11-02 24 18 6
B1 18 1-B-15 2004-11-02 2 0 2
B1 18 1-C-04 2004-11-03 12 2 12
B1 18 2-D-23 2004-11-04 1 14 1
B1 18 1-B-11 2004-11-05 4 15 3


Picking list FIFO
select s.loc
, s.item Now order by
, least(s.loc_qty, s.ord_qty - s.sum_prv_qty) pick_qty
from ( location to make a
pick list
partition by i.item
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
) s
where s.sum_prv_qty < s.ord_qty
order by s.loc


Picking list FIFO
LOC ITEM PICK_QTY
---------- ---------- --------
Ready for operator
1-A-20 A1 18 to go picking
1-B-11 B1 3
1-B-15 B1 2
1-C-04 B1 12
2-A-02 A1 6
2-D-23 B1 1


Picking small qty
select s.loc
, s.item Change pick policy
from ( by changing order:
partition by i.item Here empty small
order by i.qty, i.loc -- << only line changed
rows between unbounded preceding and 1 preceding quantities first to
),0) sum_prv_qty
from orderline o clean out locations
join inventory i
on i.item = o.item
) s
order by s.loc


Picking small qty
LOC ITEM PICK_QTY
---------- ---------- --------
Lots of picks
1-A-20 A1 3
1-A-31 A1 12
1-B-11 B1 4 Will clean locations
1-B-15 B1 2 quickly for new
1-C-04 B1 11
2-D-07 A1 9
incoming goods
2-D-23 B1 1


Picking few picks
select s.loc
, s.item Or policy of picking
from ( as few times as
possible
partition by i.item
order by i.qty desc, i.loc -- << only line changed
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
) s
order by s.loc


Picking few picks
LOC ITEM PICK_QTY
---------- ---------- --------
Only two picks
1-A-02 B1 18
2-A-02 A1 24
But will be at
expense of leaving
small quantities all
over warehouse


Picking short route
select s.loc
, s.item Policy of not driving
from ( to the far
warehouse if
partition by i.item
possible
order by i.loc -- << only line changed
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
) s
order by s.loc


Picking short route
LOC ITEM PICK_QTY
---------- ---------- --------
All picked in the
1-A-02 B1 18 very fist aisle in the
1-A-20 A1 18 warehouse
1-A-31 A1 6


Picking by FIFO
• SUM() by item

• Ordered by purchase date

• Rolling sum to find how much was picked by ”previous
rows”

• Filter away rows where sufficient has already been
picked

Efficient picking route
Case 3


Picking small qty
select s.loc
, s.item Same data as FIFO
from ( picking case
partition by i.item But for this case we
order by i.qty, i.loc -- << only line changed
rows between unbounded preceding and 1 preceding will use the policy of
),0) sum_prv_qty
from orderline o picking small
join inventory i
on i.item = o.item
quantities
) s
order by s.loc


Picking small qty
LOC ITEM PICK_QTY
---------- ---------- --------
Because that gives
1-A-20 A1 3 many picks and
1-A-31 A1 12 shows this case best
1-B-11 B1 4
1-B-15 B1 2
1-C-04 B1 11
2-D-07 A1 9
Notice anything
2-D-23 B1 1 about these data?


Picking route

• Is this a smart route to drive?

Better picking route

• We need to change direction every other aisle


Decipher loc
select to_number(substr(s.loc,1,1)) warehouse
, substr(s.loc,3,1) aisle In this case location
,
,
to_number(substr(s.loc,5,2)) position
s.loc
can be split into
, s.item warehouse, aisle
from ( and position simply
by substr()
partition by i.item
order by i.qty, i.loc
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item

) s
order by s.loc


Decipher loc
WAREHOUSE AISLE POSITION LOC ITEM PICK_QTY
--------- ----- -------- ---------- ---------- --------
Now we can use
1 A 20 1-A-20 A1 3 analytics on the
1 A 31 1-A-31 A1 12 individual parts of
1 B 11 1-B-11 B1 4
1 B 15 1-B-15 B1 2
the location
1 C 4 1-C-04 B1 11
2 D 7 2-D-07 A1 9
2 D 23 2-D-23 B1 1


Rank aisles
, substr(s.loc,3,1) aisle Ordering by
, dense_rank() over (
order by to_number(substr(s.loc,1,1)) -- warehouse warehouse and aisle
, substr(s.loc,3,1) -- aisle
) aisle_no will give same rank
, to_number(substr(s.loc,5,2)) position
, s.loc to positions in same
, s.item
from (
aisle
select o.item, o.qty ord_qty, i.loc, i.purch, i.qty loc_qty
partition by i.item
),0) sum_prv_qty dense_rank()
from orderline o
join inventory i
on i.item = o.item
ensures consecutive
) s
ranks
order by s.loc


Rank aisles
WAREHOUSE AISLE AISLE_NO POSITION LOC ITEM PICK_QTY
--------- ----- -------- -------- ---------- ---------- --------
Now we have
1 A 1 20 1-A-20 A1 3 numbered the aisles
1 A 1 31 1-A-31 A1 12 in the order they
1 B 2 11 1-B-11 B1 4
1 B 2 15 1-B-15 B1 2
are to be visited
1 C 3 4 1-C-04 B1 11
2 D 4 7 2-D-07 A1 9
2 D 4 23 2-D-23 B1 1


Odd up, even down
select
s2.warehouse, s2.aisle, s2.aisle_no, s2.position, s2.loc, s2.item, s2.pick_qty mod() and case
from (
, substr(s.loc,3,1) aisle
order by to_number(substr(s.loc,1,1)) -- warehouse
allows us to order
, substr(s.loc,3,1) -- aisle
) aisle_no
, s.loc, s.item
positive on odd
from (
partition by i.item
aisles and negative
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
on even aisles
) s

) s2
order by s2.warehouse
, s2.aisle_no
, case
when mod(s2.aisle_no,2) = 1 then s2.position
else -s2.position
end


Odd up, even down
--------- ----- -------- -------- ---------- ---------- --------
And so aisle
1 A 1 20 1-A-20 A1 3 1-A is ascending,
1 A 1 31 1-A-31 A1 12 1-B is descending,
1 B 2 15 1-B-15 B1 2
1 B 2 11 1-B-11 B1 4
1-C is ascending,
1 C 3 4 1-C-04 B1 11 2-D is descending
2 D 4 23 2-D-23 B1 1
2 D 4 7 2-D-07 A1 9


Single door

• Direction has to ”restart” per warehouse

Partition warehouse
select s2.warehouse, s2.aisle, s2.aisle_no, s2.position, s2.loc, s2.item, s2.pick_qty
from (
select to_number(substr(s.loc,1,1)) warehouse Move the
, substr(s.loc,3,1) aisle
partition by to_number(substr(s.loc,1,1)) -- warehouse
warehouse part
order by
) aisle_no
substr(s.loc,3,1) -- aisle from the order by to
, s.loc, s.item the partition by
from (
partition by i.item
),0) sum_prv_qty
from orderline o
join inventory i
on i.item = o.item
) s
) s2
order by s2.warehouse
, s2.aisle_no
, case
when mod(s2.aisle_no,2) = 1 then s2.position
else -s2.position
end


Partition warehouse
--------- ----- -------- -------- ---------- ---------- --------
Now the aisle_no is
1 A 1 20 1-A-20 A1 3 restarted for each
1 A 1 31 1-A-31 A1 12 warehouse, so the
1 B 2 15 1-B-15 B1 2
1 B 2 11 1-B-11 B1 4
first visited aisle of a
1 C 3 4 1-C-04 B1 11 warehouse is always
2 D 1 7 2-D-07 A1 9
odd and therefore
2 D 1 23 2-D-23 B1 1
sorted ascending


Efficient picking route
• DENSE_RANK() to number the aisles in order visited

• Order the output
– ”Up” on odd aisles
– ”Down” on even aisles

• Partition by warehouse if door is missing


Picking efficiency
Case 4


Picking efficiency
• How fast can operators
pick items?
• How much do they wait
idle for totes to arrive?


Table
create table missions (
missionid number primary key,
Missions are
loadunit number, everytime a tote
departpos varchar2(10), (loadunit) goes from
departtime date,
arrivepos varchar2(10),
one position to
arrivetime date another position on
)
the conveyor system
/


Mission data
insert into missions values ( 35986751, 10063485, 'STORE', timestamp '2012-04-12 06:38:07', 'PLF4', timestamp '2012-04-12 08:00:03' );
...
2690 inserts snipped out
...
insert into missions values ( 35992214, 10064588, 'PLF4', timestamp '2012-04-12 11:13:20', 'STORE', timestamp '2012-04-12 11:15:12' );


Arrivals
select a.arrivepos pos
, a.arrivetime time
All missions arriving
, a.loadunit at picking stations
, a.missionid PLF4 and PLF5 on
from missions a
where a.arrivepos in ('PLF4','PLF5')
April 12th after
and a.arrivetime >= to_date('2012-04-12 08:00:00', 08:00
'YYYY-MM-DD HH24:MI:SS')
and a.arrivetime <= to_date('2012-04-12 23:59:59',
order by a.arrivepos, a.arrivetime


Arrivals
POS TIME LOADUNIT MISSIONID
---- -------- --------- ---------
PLF4 08:00:03 10063485 35986751
PLF4 08:00:11 10069588 35986762
PLF4 08:01:09 10059580 35986754
...
PLF4 12:47:51 10069370 35990243
PLF4 12:47:58 10026743 35990248
PLF4 12:49:06 10013439 35990250
PLF5 08:00:00 10040198 35987250
PLF5 08:00:07 10008351 35987251
PLF5 08:00:14 10068629 35987225
...
PLF5 11:28:47 10078376 35990936
PLF5 11:28:56 10035491 35990918
PLF5 11:29:07 10010287 35991015

1453 rows selected.


Departures
select d.departpos pos
, d.departtime time
All missions
, d.loadunit departing from
, d.missionid picking stations
from missions d
where d.departpos in ('PLF4','PLF5')
PLF4 and PLF5 on
and d.departtime >= to_date('2012-04-12 08:00:00', April 12th after
and d.departtime <= to_date('2012-04-12 23:59:59',
08:00
order by d.departpos, d.departtime


Departures
POS TIME LOADUNIT MISSIONID
---- -------- --------- ---------
PLF4 08:00:00 10067235 35988299
PLF4 08:00:08 10063485 35988300
PLF4 08:01:07 10069588 35988307
...
PLF4 11:13:43 10082114 35992219
PLF4 11:13:52 10033235 35992220
PLF4 11:14:59 10056459 35992223
PLF5 08:00:06 10040198 35988296
PLF5 08:00:13 10008351 35988302
PLF5 08:00:35 10068629 35988303
...
PLF5 11:08:36 10018796 35992157
PLF5 11:08:45 10058221 35992158
PLF5 11:09:00 10030575 35992159

1247 rows selected.


Combined events
select pos, time, ad, loadunit, missionid
from (
select a.arrivepos pos, a.arrivetime time, 'A' ad, a.loadunit, a.missionid
from missions a
and a.arrivetime >= to_date('2012-04-12 08:00:00','YYYY-MM-DD HH24:MI:SS')
and a.arrivetime <= to_date('2012-04-12 23:59:59','YYYY-MM-DD HH24:MI:SS')
union all
select d.departpos pos, d.departtime time, 'D' ad, d.loadunit, d.missionid
from missions d
and d.departtime >= to_date('2012-04-12 08:00:00','YYYY-MM-DD HH24:MI:SS')
and d.departtime <= to_date('2012-04-12 23:59:59','YYYY-MM-DD HH24:MI:SS')
) s1
order by pos, time


Combined events
POS TIME AD LOADUNIT MISSIONID
---- -------- -- --------- --------- Arrivals and
PLF4
PLF4
08:00:00
08:00:03
D
A
10067235 35988299
10063485 35986751
departures joined
PLF4 08:00:08 D 10063485 35988300 allows us to see the
PLF4 08:00:11 A 10069588 35986762
PLF4 08:01:07 D 10069588 35988307 loadunit arriving
PLF4 08:01:09 A 10059580 35986754 and a little bit later
PLF4 08:01:14 D 10059580 35988308
PLF4 08:01:16 A 10056277 35986755 departing
PLF4 08:01:24 D 10056277 35988309
PLF4 08:01:26 A 10081310 35986764
PLF4 08:01:39 D 10081310 35988310
PLF4 08:01:41 A 10016906 35986752
...
2700 rows selected.


Lead the next event
with s1 as (
select a.arrivepos pos, a.arrivetime time,
from missions a
'A' ad, a.loadunit, a.missionid The analytic
and a.arrivetime >= to_date('2012-04-12
and a.arrivetime <= to_date('2012-04-12
08:00:00','YYYY-MM-DD HH24:MI:SS')
function lead() gives
union all
select d.departpos pos, d.departtime time,
from missions d
'D' ad, d.loadunit, d.missionid for each row the
and d.departtime >= to_date('2012-04-12
and d.departtime <= to_date('2012-04-12
time of the next row
)
select pos
, time
, lead(time) over (
partition by pos
order by time, missionid
) nexttime
, ad
, loadunit
from s1
order by pos, time


Lead the next event
POS TIME NEXTTIME AD LOADUNIT
---- -------- -------- -- --------- So on each ’D’ row
PLF4
PLF4
08:00:00
08:00:03
08:00:03
08:00:08
D
A
10067235
10063485
NEXTTIME is the
PLF4 08:00:08 08:00:11 D 10063485 time of the
PLF4 08:00:11 08:01:07 A 10069588
PLF4 08:01:07 08:01:09 D 10069588 following ’A’ row
PLF4 08:01:09 08:01:14 A 10059580
PLF4 08:01:14 08:01:16 D 10059580
PLF4 08:01:16 08:01:24 A 10056277
PLF4 08:01:24 08:01:26 D 10056277
And on each ’A’ row
PLF4 08:01:26 08:01:39 A 10081310 NEXTTIME is the
PLF4 08:01:39 08:01:41 D 10081310
PLF4 08:01:41 08:01:57 A 10016906 time of the
...
2700 rows selected.
following ’D’ row


Lead on
with s1 as (
select a.arrivepos pos, a.arrivetime time,
from missions a
'A' ad, a.loadunit, a.missionid
lead() accepts a
second parameter
and a.arrivetime >= to_date('2012-04-12 08:00:00','YYYY-MM-DD HH24:MI:SS')
and a.arrivetime <= to_date('2012-04-12 23:59:59','YYYY-MM-DD HH24:MI:SS')
union all
select d.departpos pos, d.departtime time, 'D' ad, d.loadunit, d.missionid
from missions d
and d.departtime >= to_date('2012-04-12
and d.departtime <= to_date('2012-04-12
23:59:59','YYYY-MM-DD HH24:MI:SS') telling how many
)
select pos, time rows forward the
, lead(time) over (
partition by pos function should
) nexttime
”look”
, lead(time,2) over (
partition by pos
) next2time
, ad, loadunit
from s1
order by pos, time


Lead on
POS TIME NEXTTIME NEXT2TIM AD LOADUNIT
---- -------- -------- -------- -- --------- The NEXT2TIME
PLF4
PLF4
08:00:00
08:00:03
08:00:03
08:00:08
08:00:08
08:00:11
D
A
10067235
10063485
column ”looks” 2
PLF4 08:00:08 08:00:11 08:01:07 D 10063485 rows forward
PLF4 08:00:11 08:01:07 08:01:09 A 10069588
PLF4 08:01:07 08:01:09 08:01:14 D 10069588
PLF4 08:01:09 08:01:14 08:01:16 A 10059580
PLF4 08:01:14 08:01:16 08:01:24 D 10059580
PLF4 08:01:16 08:01:24 08:01:26 A 10056277
PLF4 08:01:24 08:01:26 08:01:39 D 10056277
PLF4 08:01:26 08:01:39 08:01:41 A 10081310
PLF4 08:01:39 08:01:41 08:01:57 D 10081310
PLF4 08:01:41 08:01:57 08:01:59 A 10016906
...
2700 rows selected.


Filter double lead
with s1 as (
select a.arrivepos pos, a.arrivetime time, 'A' ad, a.loadunit, a.missionid
from missions a
and a.arrivetime >= to_date('2012-04-12
and a.arrivetime <= to_date('2012-04-12
union all
select d.departpos pos, d.departtime time,

'D' ad, d.loadunit, d.missionid
Since we use the
double lead we now
from missions d
and d.departtime >= to_date('2012-04-12 08:00:00','YYYY-MM-DD HH24:MI:SS')
and d.departtime <= to_date('2012-04-12 23:59:59','YYYY-MM-DD HH24:MI:SS')
)

select pos, time arrive, nexttime depart, next2time nextarrive, loadunit
from ( have all the data
select pos, time
, lead(time) over ( necessary on the ’A’
partition by pos
order by time, missionid rows and do not
) nexttime
, lead(time,2) over ( need the ’D’ rows
partition by pos
order by time, missionid anymore
) next2time
, ad, loadunit
from s1
) s2
where ad = 'A'
order by pos, arrive


Filter double lead
POS ARRIVE DEPART NEXTARRI LOADUNIT
---- -------- -------- -------- ---------
We can now see a tote
PLF4 08:00:03 08:00:08 08:00:11 10063485 arrives 08:00:03, leaves
PLF4 08:00:11 08:01:07 08:01:09 10069588
PLF4 08:01:09 08:01:14 08:01:16 10059580
again at 08:00:08, and a
PLF4 08:01:16 08:01:24 08:01:26 10056277 new tote arrives at
PLF4 08:01:26 08:01:39 08:01:41 10081310
PLF4 08:01:41 08:01:57 08:01:59 10016906 08:00:11
...
PLF4 10:59:47 10:59:54 10:59:56 10076144
PLF4 10:59:56 11:00:11 11:00:12 10012882 Note the tote that
PLF4 11:00:12 11:00:28 11:00:29 10035898
PLF4 11:00:29 11:00:42 11:00:44 10076793 arrived 10:59:56 leaves
... after 11:00:00
1453 rows selected.


Pick and wait
with s1 as ( ... )
select pos, arrive, depart, nextarrive Calculate pick
, (depart - arrive) * 24 * 60 * 60 pickseconds
, (nextarrive - depart) * 24 * 60 * 60 waitseconds seconds and wait
from (
from (
seconds
select pos, time
, lead(time) over (
partition by pos order by time, missionid
) nexttime
) next2time
, ad, loadunit
from s1
) s2
where ad = 'A'
) s3 Filter on desired 3
where arrive >= to_date('2012-04-12 08:00:00','YYYY-MM-DD HH24:MI:SS')
and arrive <= to_date('2012-04-12 10:59:59','YYYY-MM-DD HH24:MI:SS') hour interval
order by pos, arrive


Pick and wait
POS ARRIVE DEPART NEXTARRI PICKSECONDS WAITSECONDS
---- -------- -------- -------- ----------- ----------- How fast did the
PLF4 08:00:03 08:00:08 08:00:11 5 3
PLF4 08:00:11 08:01:07 08:01:09 56 2 operator pick and
PLF4 08:01:09 08:01:14 08:01:16 5 2
PLF4 08:01:16 08:01:24 08:01:26 8 2 how long time did
...
PLF4 08:58:08 08:58:08 09:11:36 0 808 he wait for a new
PLF4 09:11:36 09:12:55 09:12:56 79 1
... tote to arrive
PLF4 10:59:47 10:59:54 10:59:56 7 2
PLF4 10:59:56 11:00:11 11:00:12 15 1
PLF5 08:00:00 08:00:06 08:00:07 6 1
PLF5 08:00:07 08:00:13 08:00:14 6 1
...
PLF5 10:57:54 10:59:58 10:59:59 124 1
PLF5 10:59:59 11:00:09 11:00:10 10 1

1155 rows selected.


Hourly stats
with s1 as ( ... )
select
,
pos
trunc(arrive,'HH24') hour
Now we can use the
,
,
count(*) picks
avg(pickseconds) secondsprpick
previous select as
,
,
sum(pickseconds)/60 minutespicked
100*sum(pickseconds)/sum(pickseconds+waitseconds) pickpct basis for some plain
, avg(waitseconds) secondsprwait
, sum(waitseconds)/60 minuteswaited statistics by the
, 100*sum(waitseconds)/sum(pickseconds+waitseconds) waitpct
,
,
avg(pickseconds+waitseconds) secondsprcycle
sum(pickseconds+waitseconds)/60 minutestotal
hour
, 60 * count(*) / sum(pickseconds+waitseconds) cyclesprmin
from (
select pos, arrive, depart, nextarrive
, (depart - arrive) * 24 * 60 * 60 pickseconds
, (nextarrive - depart) * 24 * 60 * 60 waitseconds
from (
from (
select pos, time
, lead(time) over (
) nexttime
) next2time
, ad, loadunit
from s1
) s2
where ad = 'A'
) s3
where arrive >= to_date('2012-04-12 08:00:00','YYYY-MM-DD HH24:MI:SS')
and arrive <= to_date('2012-04-12 10:59:59','YYYY-MM-DD HH24:MI:SS')

) s4
group by pos, trunc(arrive,'HH24')
order by pos, trunc(arrive,'HH24')


Hourly stats
sec sec sec cycle
pr min pick pr min wait pr min pr
POS HOUR PICKS pick pickd pct wait waitd pct cycle total min
----- -------- ------ ----- ------ ----- ----- ------ ----- ----- ------ -----
PLF4 08:00:00 156 20.3 52.9 73.9 7.2 18.7 26.1 27.5 71.6 2.2
PLF4 09:00:00 159 13.2 35.0 71.9 5.1 13.6 28.1 18.3 48.6 3.3
PLF4 10:00:00 165 19.8 54.5 90.8 2.0 5.5 9.2 21.8 60.0 2.8
PLF5 08:00:00 247 12.9 53.2 85.3 2.2 9.2 14.7 15.2 62.4 4.0
PLF5 09:00:00 179 15.9 47.4 82.3 3.4 10.2 17.7 19.3 57.6 3.1
PLF5 10:00:00 249 10.9 45.4 75.3 3.6 14.9 24.7 14.5 60.4 4.1

6 rows selected.


Picking efficiency
• Log over tote missions arriving and departing the
picking stations

• LEAD() on mission log to find the departure following
an arrival => picking time

• LEAD(,2) on mission log to find the arrival following a
departure => waiting time


Forecasting sales
Case 5


Forecasting sales
• Forecast the sales
of next year
• But follow the
trend of the item


Table
create table sales (
item varchar2(10),
Simple table of
mth date, monthly sales by
qty number item
)
/


Data
insert into sales values ('Snowchain', date '2008-01-01', 79);
Item Snowchain
insert into sales values ('Snowchain', date '2008-03-01', 24); sells good in winter
... and trends up
insert into sales values ('Sunshade' , date '2008-01-01', 4);
insert into sales values ('Sunshade' , date '2008-02-01', 6); Item Sunshade sells
...
good in summer
insert into sales values ('Sunshade' , date '2010-10-01', 11); and trends down


Slope
select sales.item Graph slope: y-axis is qty
, sales.mth
, sales.qty
x-axis is a number with the scale of 1=a month
, regr_slope( Range between gives sliding 2-year window
sales.qty
, extract(year from sales.mth) * 12 + extract(month from sales.mth)
) over (
partition by sales.item
order by sales.mth
range between interval '23' month preceding and current row
) slope
from sales
order by sales.item, sales.mth


Slope
ITEM MTH QTY SLOPE
---------- ---------- ----- -------- Slope value most
Snowchain 2008-01-01 79
Snowchain 2008-02-01 133 54.000 accurate for 2010
Snowchain
...
2008-03-01 24 -27.500
data where 2 year
Snowchain
Snowchain
2010-10-01
2010-11-01
1
73
-2.274
-2.363
sliding window
Snowchain 2010-12-01 160 -.991 contains full set of
Sunshade 2008-01-01 4
Sunshade 2008-02-01 6 2.000 data
Sunshade 2008-03-01 32 14.000
...
Sunshade 2010-10-01 11 .217
Sunshade 2010-11-01 3 -.200
Sunshade 2010-12-01 5 -.574

72 rows selected.


Transpose using slope
select item, mth, qty
, qty + 12 * slope qty_next_year
As x-axis had scale
from ( of 1=a month and y-
select sales.item, sales.mth, sales.qty
, regr_slope( axis was qty,
sales.qty
, extract(year from sales.mth) * 12 + extract(month from sales.mth) multiplying slope
) over (
with 12 gives how
order by sales.mth
much qty goes up or
) slope down in a year
from sales
)
where mth >= date '2010-01-01'
order by item, mth


Transpose using slope
ITEM MTH QTY QTY_NEXT_YEAR
---------- ---------- ----- -------------
Snowchain 2010-01-01 167 188,313043 Sunshade 2010-01-01 2 -11,617391
Snowchain 2010-03-01 42 96,3913043 Sunshade 2010-03-01 28 9,11304348
Snowchain 2010-08-01 1 -3,4295652 Sunshade 2010-08-01 25 31,7147826
Snowchain 2010-11-01 73 44,6434783 Sunshade 2010-11-01 3 ,594782609


Forecast
select item
, add_months(mth, 12) mth
Rather than column
, greatest(round(qty + 12 * slope), 0) forecast QTY_NEXT_YEAR we
from (
select sales.item, sales.mth, sales.qty add a year to the
, regr_slope(
sales.qty
month and call it a
) over (
forecast
order by sales.mth
) slope We round the
from sales
)
numbers and skip
where mth >= date '2010-01-01' any negatives
order by item, mth


Forecast
ITEM MTH FORECAST
---------- ---------- ---------
Snowchain 2011-01-01 188 Sunshade 2011-01-01 0


Actual + forecast
select item, mth, qty, type
from (
select sales.item, sales.mth, sales.qty, 'Actual' type
UNION ALL of the
from sales
union all
actual data and the
select item
, add_months(mth, 12) mth forecast data for a
, greatest(round(qty + 12 * slope), 0) qty
, 'Forecast' type complete set of
from (
select sales.item, sales.mth, sales.qty
, regr_slope(
sales data that can
sales.qty
) over (
be shown in a graph
order by sales.mth
) slope
from sales
)
where mth >= date '2010-01-01'
)
order by item, mth


Actual + forecast
ITEM MTH QTY TYPE
---------- ---------- ----- ----------
Snowchain 2008-01-01 79 Actual Sunshade 2008-01-01 4 Actual
... ...
Snowchain 2011-01-01 188 Forecast Sunshade 2011-01-01 0 Forecast
... ...


Actual + forecast
Data from previous
slide is the graph
• ”Actual” is
normal lines
• ”Forecast” is
stapled lines

Forecasting sales
• REGR_SLOPE() to calculate trend

• RANGE window for sliding trend calculation over
three years

• ”Transpose” last years sales by the slope to get
next years forecast


Forecast zero stock
Case 6


Forecast zero stock
• Fireworks sell like crazy
last week of December
• What hour will a store
run out of stock?


Tables
create table fw_store (
shopid varchar2(10) primary key,
Stores are defined
containers integer by how many
) storage containers
/

create table fw_sales (
shopid varchar2(10) references fw_store (shopid),
Sales are hourly
saleshour date, data per shop in
salesnem number Net Explosive Mass
)
/


Tables
create table fw_daybudget (
shopid varchar2(10) references fw_store (shopid),
Daily budget of
budgetdate date, Net Explosive Mass
budgetnem number per shop
)
/

create table fw_hourbudget (
Percentage of a
hour integer, days budget
percent number expected to be in
)
/ each hour


Data - store
insert into fw_store values ('AALBORG' , 4);
insert into fw_store values ('GLOSTRUP' , 4);
insert into fw_store values ('HADERSLEV', 3);


Data - sales
insert into fw_sales
select shopid, day + numtodsinterval(hour,'hour') saleshour, salesnem
from (
select 'AALBORG' shopid, date '2011-12-27' day,
4 h9, 6 h10, 5 h11, 20 h12, 19 h13, 22 h14, 27 h15, 11 h16, 16 h17, 4 h18 from dual union all
select 'AALBORG' , date '2011-12-28', 7, 17, 18, 13, 27, 28, 20, 14, 10, 19 from dual union all
select 'AALBORG' , date '2011-12-29', 10, 14, 20, null, null, null, null, null, null, null from dual union all
select 'GLOSTRUP' , date '2011-12-27', 1, 6, 6, 14, 17, 17, 13, 15, 7, 7 from dual union all
select 'GLOSTRUP' , date '2011-12-28', 4, 14, 30, 35, 22, 21, 35, 34, 15, 25 from dual union all
select 'GLOSTRUP' , date '2011-12-29', 6, 13, 50, null, null, null, null, null, null, null from dual union all
select 'HADERSLEV', date '2011-12-27', 4, 7, 13, 15, 17, 13, 18, 19, 10, 3 from dual union all
select 'HADERSLEV', date '2011-12-28', 8, 5, 14, 18, 20, 18, 15, 24, 12, 1 from dual union all
select 'HADERSLEV', date '2011-12-29', 1, 19, 33, null, null, null, null, null, null, null from dual
) s1
unpivot exclude nulls (
salesnem for hour in (
h9 as 9, h10 as 10, h11 as 11, h12 as 12, h13 as 13, h14 as 14, h15 as 15, h16 as 16, h17 as 17, h18 as 18
)
)


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