(Editor’s Note: This is an online-only article attributed to the June/July 2017 issue.)
Manufacturers, especially in the food and beverage industry, count on customer loyalty to drive recurring revenue from repeat purchases. But in a time when consumers have more brand options than ever and just as many social channels to voice their complaints, it’s nearly impossible to retain their business. Convenient packaging, quick preparation, new flavors, and celebrity endorsements only go so far. The product must be great, rather than simply good, to edge out the competition. And what makes any product great? Quality.
To establish and maintain a level of quality that meets consumer (and federal) demands, manufacturers have to pay close attention to every detail of the products’ lifecycle. This commitment requires extensive time and resources to conduct incoming, spot, and final inspections; export, compile, and analyze data; and make adjustments to inventory levels, process parameters, and packing strategies.
Stop Suffering from Detached Quality Initiatives
Most manufacturers consider quality a plant-level function that’s managed at each individual location. Amazingly, data is rarely standardized from one location to the next, using variations for labels like “weight,” “oz.,” or “ounces.” While this makes sense to the human eye, there’s no way to aggregate data that is not standardized and there’s too much data to manually compile. The vice president of quality reviews reports from the individual plants, but it’s not a true view of overall operations.
Further, data collection strategies are pre-third industrial revolution, putting many manufacturers at least one industrial revolution behind technological capabilities. In a recent survey by InfinityQS of 260 manufacturers, including some of the world’s largest manufacturing organizations, 75 percent of respondents noted that they are still manually collecting data. Astoundingly, 47 percent of those rely on pencil and paper.
This means that once a quality check is complete, the data gets lost in siloed databases and overflowing filing cabinets. When the auditor comes to check on compliance, quality professionals have to scramble to find the right file or piece of paper to satisfy requirements.
Compiling reports for auditors or management takes hours to splice information together from disparate sources. And the resulting information can only summarize what already happened. That means quality professionals can only make recommendations based on intuition and best guesses of what to change so it doesn’t happen again.
According to a joint report by ASQ and APQC, this strategy has led to “approximately 60 percent of organizations [saying that] they don’t know or don’t measure the financial impact of quality. This lack of measurement may be attributed to not having a common method for capturing the financial impact.”
Quality professionals aren’t fortunetellers, but in a business world that lives and dies by numbers, they must prove their value like any other department. What if you could predict how processes would react to incremental changes in specifications? Would it be possible to streamline these processes, while maintaining quality and decreasing costs? You’d have to completely re-imagine quality.
Embrace the Excellence Loop
The first step in the pursuit of manufacturing excellence is automation. With the sheer volume of data that manufactures collect, it is imperative to implement automated data collection strategies that gather and standardize data from all sources, including devices, databases, OPC servers, text files, and enterprise systems.
Automating data collection removes the potential for human error and improves the accuracy of your data, promoting better decision making. Adding automated alarms can increase response times to out-of-specification products and reduce the risk of recalls by stopping production faster.
As data is collected across products, lines, facilities, and suppliers, it must be unified in a centralized database. Through the power of cloud computing, plant floor data can easily be uploaded to a single repository, but make sure all plants have standardized naming conventions so the data can be aggregated.
Once the data is unified, it’s possible to visualize more than the quality of a single product, line, or facility. You achieve real-time visibility of the entire enterprise, from end to end—including suppliers, incoming inspection, raw materials, in-process checks from shop floor operators and the quality lab, process data, packaging, and finished products.
With this visibility, a quality intelligence solution can reveal insights that were previously blocked by silos or locked in filing cabinets. The actionable information, generated about the enterprise’s processes, suppliers, and manufacturing operations, can identify opportunities for improvement.
According to a study by Honeywell, “respondents said they believe data can enable well-informed decisions in real time (63 percent), limit waste (57 percent), and predict the risk of downtime (56 percent).” But unfortunately, in the more than 30 percent of respondents that don’t have predictive/data analytics in place, and don’t plan to do so in the next year, many believe their companies can grow without it, that they already have what they need, or the benefits are overrated.
Is $1 million in sustainable annual savings overrated?
Applying the operational insights consistently across the enterprise streamlines, optimizes, and transforms processes and operations. For one manufacturer, eliminating overfill on 12 production lines in a single facility saved more than $250,000 in two years. When applied to multiple facilities, the sustainable annual cost savings exceed $1 million.
Re-evaluate Strategies for Improvement to Multiply Savings
Specified weight requirements in the food industry are a key performance indicator and regulatory requirement. If packages are filled too much, the cost of ingredients increases from overfill and negatively impacts profitability. Alternately, under-filling incurs the wrath of regulatory organizations and consumers alike, destroying your brand’s reputation for “cheating” the customers.
For one food manufacturer with more than 200 products and a three-shift, 24-hour operation, in-spec weight variances didn’t present a problem. But within the quality data they collected, there was a massive opportunity to improve profitability. After a thorough analysis of quality data and operational processes for each of the company’s top 15 products at one plant, summaries of net weight data over a 12-month period were compiled to compare performance across all production lines.
Based on the data in the summaries, different “what-if” scenarios were proposed to determine the best opportunity for cost savings. In the first scenario, the company considered what would happen if they took a traditional approach of reducing fill weights by 5 percent and maintaining current processes. This would lead to a $160,000 annual cost savings in raw materials. However, this would likely put some of the packages underweight and is therefore not feasible.
Instead, what if the variability in weights was reduced across all production lines? Based on the visibility and insights attained from the collected data, it was clear that each product ran differently on each production line (see chart). This variation was wide-spread, from line-to-line, shift-to-shift, week-to-week, and month-to-month. While few products were actually out-of-spec, the negative impact of variability on the overall plant performance was distinct and typical for an organization that had never before visualized or summarized data across the enterprise.
The team quickly evaluated how to reduce the variances by standardizing manufacturing methods, procedures, setups, and processes. Conservatively, the company could see more than $3 million in raw material cost savings based on an analysis of one variable for only 15 of more than 200 products in a single plant. The potential, exponential, enterprise-wide cost savings is staggering.
Manufacturers Reaping Benefits
While many manufacturers are hesitant to embrace revolutionary technologies, some are already reaping the benefits of re-imagining quality.
- Ben & Jerry’s uses quality intelligence to identify instances to improve run capability and raw material usage. Because high-quality ingredients can cost up to $800 for a single barrel, fine tuning processes with more precise specification limits results in less raw material variation, increased cost savings, and a higher quality product for the consumer.
- Michael Foods’ operations and quality executives easily identify variation trends and can use control reports to help determine root causes. Desktop dashboards display exactly what is happening on the plant floor—or across multiple plants—in real time to see whether a particular line or plant is running efficiently and consistently.
- Nestlé Waters has real-time visibility over production processes—both within the individual sites and from the corporate level across 26 factories. By tracking trends in quality data, the company’s quality professionals are able to make more accurate and timely decisions about process improvements.
Instead of perceiving quality issues as factory floor problems, manufacturers must understand that the quality data that identifies these problems is also an untapped source of insight to drive strategic transformation across their entire enterprise. When you shift your thinking about quality from “how can I fix this” to “how can I use this to my advantage,” you are already ahead of your competition. It’s not about what you reacted to yesterday, but instead what you can proactively do tomorrow, or next year to meet the demands of an ever-changing industry. The result can be elevated product quality, improved efficiency, exponential cost savings, and impressed customers with a renewed loyalty to your company.
It is this realization that quality data is a manufacturer’s greatest competitive advantage that will enable you to achieve manufacturing excellence, catapult profitability, and maintain the level of quality that your customers know and expect from the manufacturer of their favorite food.
Lyle is the founder and CEO of InfinityQS International Inc.