ENABLING SALES GROWTH WITH BREAKTHROUGH CAPACITY INCREASE

By Patrick Smith

 

Situation

Amidst unprecedented market growth a food processing company was expecting 20% year-on-year revenue increases, and was reaching the limit of their capacity to meet demand. During the previous three years production lines had been pushed to nameplate rates, downtime had been reduced to world-class levels, and production schedules had been optimized to keep up with incoming orders. With years of improvement already realized, many believed that there was little opportunity to improve capacity without a major capital expansion. Leaders were excited by the prospect of growth, but knew that a capital solution would be expensive, time-consuming, and sacrifice short-term market share expansion. Leaders engaged Stroud to find and realize further non-capital capacity increase across their asset base to meet the growing sales demand in-year.

 

Finding More Opportunity

With the success it had achieved, this organization understandably believed that it had looked at everything and picked up every piece of low hanging fruit.  They felt the only way forward was to expend enormous effort on complex opportunities that would fall short of enabling them to hit the projected sales demand.  In addition, they had successfully implemented conventional improvement methods: benchmarking performance to competitors, eliminating visible losses, and increasing equipment to nameplate speeds. There was nothing obvious left to do.

Stroud engagements start with the assumption that every operation has significant untapped, often unseen, potential. This potential is masked by a lack of the advanced tools necessary to find and capture new opportunities, the belief that everything has already been tried, or a combination of the two as in this case.

To help organizations see the untapped potential in their operations, Stroud takes a different approach. Zero-Based Analysis is used to compare an operation to its theoretical best possible performance, based on scientific first principles. This approach often demonstrates 2-3 times more opportunity than organizations have previously seen using traditional methods because it abandons the false constraints, narratives, and boundaries imposed by historic performance and beliefs. For this facility, the first step was to understand the maximum total potential by comparing current output rates to what the output could be in a perfect world: the opportunity to run machines at the theoretical maximum speeds, limited only by physical constraints (not the nameplate), with no downtime, instantaneous maintenance, and perfect yield. This constraint-free approach helped the organization identify over 200% more opportunity to increase line output than was previously believed to exist.

 

How Zero Based Analysis Helps

Previous Lean-based initiatives had identified the opportunity to increase speed by up to 5% by allowing the unit to operate consistently at its nameplate.  This was being achieved only during a small percentage of production runs. Using Zero-Based Analysis the team first quantified all physical properties that impact the rate the roaster could process product. The team showed that there were actually four physical properties that controlled how much the unit could process, not just the belt speed as previously believed. By understanding and quantifying the perfect-world scenario for all four of these factors, the team showed that total unit throughput could actually be increased by up to 70%.

Not only could realizing this magnitude of opportunity be a game-changer for the organization, it helped to challenge the conventional belief that little opportunity remained to improve within the broader organization. Leaders were both excited and daunted: this throughput  opportunity had the potential to help them exceed their capacity improvement targets but would mean shifting focus from fixing known downtime problems to tackling the unknown problems associated with pushing the perceived limits of their assets’ capacity.

 

Unleashing Potential

Finding opportunity is a critical first step to improvement, but actually attaining new levels of performance requires overcoming limitations with the right combination of tools, capabilities, and motivation. To tackle game-changing opportunities, Stroud implements a Rapid Results Team (RRT) approach. An RRT is a dedicated cross-functional group with knowledge of the process, a clearly scoped step-change objective, and the authority and support required to realize and sustain solution(s). A Stroud facilitator helps coach the team through the RRT process and result realization, including training and guidance on problem solving, challenging organizational constraints around what’s possible, and helping steer the initiative strategically with site leadership.

The first Rapid Results Team here was focused on a high-throughput roasting line, where demand for a new product had created an immediate need for 26% more production. On this specific line the operations team had already optimized their shift schedule, pulled on maintenance to firefight mechanical downtime issues, and recommended a new line be built to handle the increased demand. Although the Zero-Based Analysis determined that speed was the highest priority opportunity to increase throughput on the line, the operations group had the long-held belief that changing any aspect of the roasting process would immediately cause product quality to deteriorate. It was thought that the team had already optimized machine speed to maximize throughput while meeting quality specifications.

To increase line capacity quickly, the team first needed to identify and prioritize which specific problems to solve. The team constructed a profile of line output by unit to identify which process step was limiting the production: the line bottleneck. By understanding both the current operating speed of each unit and the zero-based speed if all constraints were eliminated, the team could identify what units needed to improve to meet targets and how much potential existed.

Within days the team identified that the flow through the roaster as the only limiting factor preventing the line from meeting capacity demands.

 

Variable Analysis

With focus now honed on the roaster, the team sought to understand and prioritize the factors could be improved to impact the overall throughput. Since its purpose was to cook product to a specified moisture and colour, the unit’s speed was defined as how many pounds of product could be dried and coloured to spec per hour of operation.

To solve this output-limiting problem the team used a tool called Variable Analysis: a structured, first-principles problem solving approach that uses a deductive and data-driven progression to identify a problem’s root cause. Using this approach the problem was broken down into a simple yet exhaustive set of variables that could potentially impact roaster throughput.

By conducting a rapid sensitivity analysis of each capacity lever, the team found that although all 4 factors had opportunity to improve, the key limiting factor was the amount of time required for product to pass through the roaster to achieve its final quality, or dwell time. If dwell time could be reduced by a significant amount, while still meeting specifications, then throughput could be increased. Although adjusting this setting was a simple change in belt speed, from an organizational standpoint the dwell time was already optimized for output and quality, so altering any elements of the roasting process would cause quality (and therefore overall throughput) to decline.

When confronted with any constraint or problem, the Rapid Result Team’s role is to systematically challenge and and seek to overcome the constraint in a rigorous and efficient manner. To get the facts behind the impact of dwell time on product quality, the team expanded their Variable Analysis tree to define what factors directly impact the necessary dwell time. The team could then systematically test how both speed and quality were impacted as these different factors changed.

To understand how sensitive product moisture and color would be to changes in dwell time the team designed a new test to study how these things actually changed throughout their time through the roasting process, rather than just at the end of the roaster. With help from line operators and quality technicians, the team collected data on quality factors across the roasting bed, and generated new insight into the actual progression of roasting through the unit.

With a consistently acceptable input feed, the team could track throughout the roaster bed when the desired moisture and color was obtained. Data from this study showed that product became dried to its required moisture spec after half of the dwelling time, meaning it was being over-dried beyond that point. Similar opportunity existed when color was analyzed. However, the team was still unable to increase belt speed, because the organization remained concerned that increasing roaster belt speed would change the way quality factors behaved through the drier. The belief was that, on a faster moving belt, moisture wouldn’t be removed at the same rate and the study’s data wasn’t enough to overcome their concerns.  So the team, undeterred, investigated further.

Thermodynamically we understood that the rate that moisture left the product would be primarily impacted by two factors: the temperature difference between the product and the air, and the rate that water diffused from the product (the mass transfer coefficient).

The team tested temperature first because adjusting it was easy to trial and measure. The group quickly learned that adjusting the temperature played almost no role in changing the moisture removal rate, so diffusion must be the limiting factor. Since diffusion was a physical property, governed only by the product’s unalterable physical characteristics, changing the dwell time would have a direct correlation with the moisture removed from the product. This insight guaranteed that dwell time in the roaster would be the only controlling factor to meet moisture specifications, and therefore belt speed could be increased without deteriorating quality.

Bringing facts and data to bare enabled the team to confidently increase the speed of the process with the existing equipment and put to rest the concerns of compromised product quality. This simple solution led to an increase in roaster capacity without any capital spending. Once the solution was known, sustainability features were installed, operations personnel trained on the new settings, and within the first two months line throughput had increased by 40%.

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Plant leadership was impressed by the significant gains made and the amount of learning that happened throughout their organization. The plant’s manufacturing manager stated that in eight weeks the team had learned more about the line than the company had ever known. By utilizing a facts-based, deductive problem solving approach the team was able to understand the detail behind perceived constraints, overcome them, and instill a new belief that better performance was possible.

 

Delivering Results

This progress on the roasting line represented not only an improved line capacity, but also increased excitement and capability to challenge long-standing constraints within the organization. By immersing themselves in the Rapid Results Team experience the team members developed their comfort and ability to challenge perceived constraints, and six months after the initial roasting RRT was completed, an operator used his learnings from the team to improve line throughput by a further 10%.

By leading with head-turning results, where many believe little is possible, this approach not only delivers significant financial value, it inspires and arms organizations to realize further step-changes in performance.Over the course of the next year, RRTs worked on nine high-priority lines, and increased throughputs by 20% to 50% on each one. With the additional capacity created, growing demand targets were met, millions in capital spending was deferred, and the leadership team was more confident they could meet growing demand for years to come.

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