The document summarizes improvements made to operations at a manufacturing plant in Reynosa, Mexico from February 2006 to April 2007. Initial improvements were made in phases and focused on "quick fixes" while more permanent solutions were developed. Key areas of focus included inventory control, cycle counting, accuracy tracking, production control, purchasing, planning, and value stream mapping. Changes such as implementing kanban systems, eliminating excess purchase orders, and analyzing demand data helped reduce inventory levels from $8.5 million to $5.8 million while increasing inventory turns.

1. Accomplishments
Materials Department
Pentair – Water
Reynosa, Mexico
February 2006 to April 2007

2. Reynosa:February2006toApril2007
Initially, the situation was so desperate that improvements were implemented in phases; introducing “Quick Fixes” in
each area allowed time for more permanent solutions to be developed.
Phased Improvements

3. Inventory Control
 Before
 Pull systems in use at the plant were
triggering production of the wrong parts
 No connection between what was being
produced and what was needed
 After
 Introduced reference locations so that
we could find things
 Reorganized racks and put parts
needed by each Assembly Line next to
each of the lines

4. Cycle Counting
 Before
 Counting 30-40 parts per week,
but only making adjustments on
3-4 parts per week
 Fear of the financial impact of
adjustments was the cause of the
disconnect
 No tracking of parts counted
 Known problems were not being
fixed
 Before production was started, all
parts were counted to make sure
they were really there. (essentially
0% accurate).
 After
 New philosophy
 “Make adjustments fast”
 “Make big adjustments faster”
 Location accuracy monitoring
 Root Cause investigations
 Focus on fixing process
 Focus on training
 Confidence in the system
 98.8% net accuracy, 96.6% absolute
We changed the rules and turned a blind-eye to the financial
impact, focusing instead, on same-day correction of
inventory discrepancies, fixing the causes of inventory
problems, and regaining confidence in the system. Ignoring
the financial impact of adjustments initially caused great
concern, but as the inventory adjustments stabilized the fear
proved to be groundless.

5. Accuracy Tracking
The net adjustments during this time period (2006) were all positive. There had been a large inventory correction during a physical inventory in
August as we re-set all the inventory levels. For months afterward we were in constant communication with the Engineering group in California
fixing incorrect labels on boxes of component parts. An October 2007 physical inventory resulted in a 99.12% net adjustment (91.3% absolute).

6. Location Accuracy Control
The table above shows data collected early in the implementation of location counts (2006). During the following months, the table
turned to nearly all green.

7. Emphasis on Training
My rule was that, if need be, we’ll repeat the training with any individual up to 3 times before finding them a new position. It was fairly typical that
we needed to repeat the training at least once with each worker – often twice. It was rare that someone failed to get the message after the second
or third retraining that we were serious about making change.

8. Production Control
 Before
 3 skids per week of duplicated
production
 The same sales order was often
made twice
 After
 April 2006: Introduced a combination
work order/sales order document
 Since the document was printed only
once, we eliminated the duplicate
production problem
One week’s worth of
duplicated production
Creation and implementation of an Access database to control production.
January 2008 note: This program is still be used and has operated
continuous since my departure without maintenance to the program.
The highlights indicated that the production was complete. All
that was necessary to duplicate the production was one of the
shift supervisors failing to make the highlight. Less obvious was
the problems associated with partial production of an order.
[Message from Sandra, January 2008]
Hi Tim! =)
Yes, your database still is in use.
*~Thanks~*

9. Pull Systems
 Before
 Pull system not maintained
 Cardboard wasted
 After
 Reorganized pull system using
cards as signals
 Purchased Reusable bins
 2 sigma 95% service level
philosophy
Boxes were discarded after one use
The philosophy in place on my arrival was a “Lean Application” based on
dedicating molding machines to assembly work centers. This idea has
worked in other locations, but in Reynosa this resulted in poor machine
utilization. The introduction of 5 new molding machines was thought to be
an absolute requirement. When I changed the mindset to shared
utilization of molding machines, we found that this $1.6 million of
proposed capital spending was unnecessary.

10. Queue – Load Analysis
Created visibility into the amount of work at the plant. Prior to this, there was no way to see and understand trends in order to
make decisions about staffing levels in Operations.

11. Rough Cut Capacity Overview
 Molding Center RCCP
 Created the ability to see load vs. capacity
 Created the ability to “Build Ahead” molded parts to
avoid deterioration in past due during the busy
season
 Created the ability to see demands on particular
molds
The chart shown above could be updated at the push of a button. By referencing the information
above we were able to redirect molds to machines and better balance the machine utilization.
Having real time data allowed us to continually evaluate mold and machine load and make
adjustments to match our capabilities to projected sales.

12. Created dispatch lists giving Production Supervisors and Planners detailed information about
queue levels in front of molding and assembly work centers. Prior to this, there was no way to
see the load in front of each machine, no way to prioritize work, no way to provide feedback
on completion dates to Customer Service.
•A non-blank entry here
indicates that an order is late to
the Spa/Bath Customer
•This information is tied to the
“Spa/Bath” Late-to-Customer
Data
•The new direction to the
planners is to stop printing
travelers if the material is not
available.
•“Molding” in this field indicates
that components still need to be
molded
•We plan to introduce a new
code “At Machine” which will
indicate that the part is soon to
be completed. In this case, it
will be ok to give the traveler to
Assembly
•Note that there are generally 2
persons working in each
Assembly Cell
•Traveler has been printed and
is at the Assembly Cell
•The Planner should start to
select orders to print based on
material availability and urgency
instead of blind adherence to
the due dates
•We will modify our practice of
blindly printing all travelers of a
particular part to be run back-to-
back
•We need to start watching work
centers and correcting routings
when they are incorrect.
Dispatch List Creation

13. Created Rough-Cut capacity reports which could be automatically updated, creating visibility to what was happening at each work center both
current and in the coming 4 months.
Pool Items cross-
referenced to
Reynosa work
centers
Rough Cut Capacity
information by Cell.
Updated daily
All demands
integrated
Rough-Cut Capacity Planning Detail

14. Purchasing
 Before
 MRP driving false demands
 No clear ordering processes
 No way to analyze demands for
purchased parts
 No way to tell who buys the part
 $150,000 of Excess inventory added
monthly
 $4.0 million dollars of open POs!
 E-mail ordering of parts
 Panic buying
 After
 Implementation of buyer codes
 Implementation of web-based kanban
system
 Designation between high volume parts
replenished by pull and low volume
purchased using MRP
 $180,000 of open POs
 Eliminated blanket purchase orders
 Plan for Every Part
E-kanban System
The web-based tool shown had been implemented by the IT group at the
headquarters in North Carolina, but none of the sites within the Division were
making full use of it’s functionality. I invited the IT group to come to the plant
and help us install this tool. The results were nothing short of amazing.
See the following inventory reduction on the next slide.

15. Inventory Turns & Inventory Levels
Inventory Turns - Reynosa
COGS/[(BI+EI)/2]
3.11
3.51
1.99
1.57
2.36
2.56
3.10
2.74
3.10
2.82
4.31
4.46
$8,580,586
$7,729,356
$6,972,000
$6,616,134
$6,434,136
$6,087,067
$5,754,472
$5,958,990$5,815,694
$7,431,911
$8,714,958
$8,399,112
-
1.00
2.00
3.00
4.00
5.00
6.00
May 06 June 06 July 06 Aug 06 Sep 06 Oct 06 Nov 06 Dec 06 Jan 07 Feb 07 Mar 07 Apr 07
Tunrs
$0
$1,000,000
$2,000,000
$3,000,000
$4,000,000
$5,000,000
$6,000,000
$7,000,000
$8,000,000
$9,000,000
$10,000,000
Inventory/COGS
Turns Inventory COGS std orders COGS DRK
P:Sandra DataInventory Turn CalculationsTurn Calculations
The introduction of the web-based Kanban system and the elimination of blanket orders and uncontrolled e-mail ordering had an immediate and
sustained impact on inventory levels. Inventory levels at the plant dropped by $3 million. The decrease was sustainable even as sales volume
increased.

16. Materials’ Department
 Before
 Buyers were operating without
supervision.
 Planning was not performed.
 After
 Planners control production schedule
 Communication of expected ship dates
to Customer Service
 Excel Training
 Data collection and analysis by staff
members
 Very strong planners & buyers
Once a department was formed, synergies emerged between Production,
Purchasing and Planning processes

17. Value Stream Mapping –
Distribution
 Distribution  Assembly
Partnering with the PIMS group and division’s Distribution Centers, we created Value Stream Maps to target our efforts both within the plant and
within the overall supply chain.

18. Inter-site ERP Problems
 Before
 Forecasted demands were being
doubled and tripled
 Inventory rose by $3 million from bad
MRP data
 Forecasts did not “net-out” inventory at
the distribution centers
 After
 Reorganized demand flow to transfer
demand from the DCs to the Plant,
netting out inventory at the DCs
In researching the Supply Chain Process, I discovered that forecasts were being loaded directly onto the plant, driving component inventory even when
sufficient quantities of finished goods were sitting at the DCs. QAD already had tools for correctly routing demand through distribution centers but it wasn’t
until we team-up with IT that we were able to correctly diagnose the error and reorganize the demand flow.

19. Data!
 Before
 No item master
 Leadtimes not loaded
 Safety stocks and order
parameters in chaos
 We didn’t know who the
suppliers were
 After
 Routings overhauled
 Work Centers Reorganized
 Item Master exists
 We know which items need to be
stocked
 Supplier codes added
I will never forget asking to get the item master for the Spa Bath Division and being told that such a thing did not exist.

20. Cycle Times – time between receipt of order and ship date
SDC: Stocked at the DC’s
ATO: Assembled when we receive a customer order
CSO: Customer Special Order
Customer Service levels increased and cycle times decreased at the same time that inventory levels fell both at the plant and at the distribution
centers.

21. The Materials Team Reynosa 2006
Andre Fleury, Plant Manager; Sandra Esparza, Planner; Claudia Garza, Buyer; Ricardo Gongora, Planner; Tim Bina, Materials Manager; Enrique Villarreal, Shipping
Receiving Supervisor; Elizabeth Varela, Inventory Control Supervisor.
Jan 2008 note: Except for Elizabeth (who found a job outside Pentair as a Materials Manager) and myself, this team is still in-tact in Reynosa despite high turnover
levels in the city. The skill level among this group of employees is as strong as anywhere in Pentair. Jeff Henson, who I interviewed and recommended as my
replacement, is now the Materials Manager and is continuing to lead improvement efforts in both Reynosa and North Carolina.