Reducing quality & waste losses by 70% with automated root cause analysis

Case study: International baked goods manufacturer

Company size: 4,200 employees over 38 sites worldwide
Annual Turnover: €1.2 billion
Industry: Food
Production line: Bread

Download the case study

(5 min reading)

Reducing quality & waste losses by 70% with automated root cause analysis

Download the case study

(5 min reading)

Case study: International baked goods manufacturer

Company size: 4,200 employees over 38 sites worldwide
Annual Turnover: €1.2 billion
Industry: Food
Production line: Bread

The Challenge:

Persistent quality & waste production losses

The company had significant process-driven losses in quality and waste at their toasted bread line.

Problems contributing to these losses included:

Slide Rejects due
to weight
Net weight
overweight
Size variability Quality variables
(i.e. moisture)
Reduce raw
material variance
Color
inconsistencies

Process experts used advanced self-serve analytics platforms to investigate – but failed to identify the causes of their losses. In most cases, no clear process inefficiencies were apparent, and the data tags they investigated were operating within the permitted ranges!


The Challenge:

Persistent quality & waste production losses

The company had significant process-driven losses in quality and waste at their toasted bread line.

Problems contributing to these losses included:

Slide Rejects due
to weight
Net weight
overweight
Size variability Quality variables
(i.e. moisture)
Reduce raw
material variance
Color
inconsistencies

Process experts used advanced self-serve analytics platforms to investigate – but failed to identify the causes of their losses. In most cases, no clear process inefficiencies were apparent, and the data tags they investigated were operating within the permitted ranges!


The Solution:

Identifying & preventing the hidden causes of process-driven losses

Using the Seebo solution, their process experts identified the hidden causes of their production losses and received clear recommendations on how to prevent them. Production teams also received real-time alerts as soon as those inefficiencies were detected – enabling them to prevent losses before they occurred.

The process inefficiencies Seebo identified were invisible to the human eye, as they were caused by a complex combination of behaviors among the hundreds of interrelated data tags on the line.

For example, in the case of rejects due to underweight, The Seebo solution found that when the baking temperature was above 204 degrees, and the baking conveyor speed was less than 5 M/S, instances of underweight increased several-fold. 

(Read the full case study for more interesting findings).

This issue wasn’t spotted by process experts because both the temperature and conveyor speed was within their permitted ranges. With hundreds of data tags and even more interrelationships between the different tags, it was impossible for their process experts to ever pick up on this unique behavior.


The Solution:

Identifying & preventing the hidden causes of process-driven losses

Using the Seebo solution, their process experts identified the hidden causes of their production losses and received clear recommendations on how to prevent them. Production teams also received real-time alerts as soon as those inefficiencies were detected – enabling them to prevent losses before they occurred.

The process inefficiencies Seebo identified were invisible to the human eye, as they were caused by a complex combination of behaviors among the hundreds of interrelated data tags on the line.

For example, in the case of rejects due to underweight, The Seebo solution found that when the baking temperature was above 204 degrees, and the baking conveyor speed was less than 5 M/S, instances of underweight increased several-fold. 

(Read the full case study for more interesting findings).

This issue wasn’t spotted by process experts because both the temperature and conveyor speed was within their permitted ranges. With hundreds of data tags and even more interrelationships between the different tags, it was impossible for their process experts to ever pick up on this unique behavior.


The Result:

70%

Reduction in quality & waste losses

138

New insights learned about their process

~€1M

Savings on a single production line

The Result:

70%

Reduction 
in quality & waste losses

138

New insights learned about their process

~€1M

Savings 
on a single production line

Get the full case study >

Download the case study

(5 min reading)

Get the full case study >

Download the case study

(5 min reading)

Leading baked goods manufacturers predict & prevent losses with Seebo