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How to Use Industry Benchmark Data for Optimizing Agile Manufacturing Strategies

How companies can bring greater context to their benchmark data.

Mfg Data

Benchmarking is an essential tool for any manufacturer. By measuring your firm’s performance relative to what’s happening in the wider contexts of the business landscape, you’re enabled to spot opportunities to improve processes. 

In the context of lean and agile manufacturing practices, benchmark data can be even more useful. To truly achieve the advantage of “just in time” production, your demand projections need to be as accurate as possible, and first-party data alone can’t give you a big enough picture. Especially given the extent to which geopolitical events have been influencing market forces and consumer demand in recent years, it’s all the more critical that the benchmark data you reference be truly representative of your niche. 

Indeed, it's all too easy to measure yourself against the wrong data by ignoring context. Many reports that are used for benchmarking encompass diverse swaths of a given industry, which compromises the relevance of the metrics in those reports to any given manufacturer. For example, looking at your cost of materials per unit will be wildly different if your plant creates jigsaw puzzles or portable gaming consoles, even though both are in the children’s toys category 

Therefore, comparing manufacturing performance with your competitors while ignoring key differences in manufacturing techniques and customer demands might place your metrics at a disadvantage. You might incorrectly view your performance as substandard when the opposite is true. Truly focusing on your benchmarks by industry, then, requires using alternative data that speaks to your company’s niche as you define it.  

Here's how you can use the right benchmarking data to boost your agile manufacturing workflows. 

Production Benchmarking

There's no single way of manufacturing a product, and for many manufacturers, production processes represent a type of intellectual property, with few details made available to the general public. Production benchmarking is thus a challenging exercise that is often reduced to surface-level metrics that incorrectly categorize performance. 

This is why it’s so important to engage in both internal and external benchmarking. 

With internal benchmarking, you will analyze first-party data, which is why it’s easier to create processes around. Comparing product variants and families will help you understand the nuances between manufacturing processes and procurement. Make sure to bring context by examining the need for SKU-specific production performance improvement. 

For instance, two product variants that have the same procurement needs might require dramatically different manufacture times. This metric does not offer enough context. Diving deeper and exploring the reasons for longer manufacturing times will help you unearth efficiency gains. Are lengthier times justified in this case? Is there any performance gain to be achieved, and do these gains offer you higher ROI? 

Some companies make the mistake of driving efficiency without correlating these initiatives to ROI. All they end up doing is increasing production strain and recording insignificant business benefits. Benchmarking across your competition is also helpful in identifying centers of excellence in your organization. 

Given that small variations in product variables can skew manufacturing times, it’s important to consider metrics such as customer perception and satisfaction and correlate them to production times when benchmarking performance. For example, are your customers demanding faster deliveries? Removing a time-consuming production feature might be justified if this is the case. 

Lean Procurement

Benchmarking procurement is especially tricky because so much of the data you'll be using is internal. In essence, you'll be benchmarking against your past self, and there's a danger of missing the bigger competitive picture.

For example, you might have had a number of downturn years, whereby Q4 demand for your product lagged. Using alternative data sources to look at web traffic pattern benchmarks by industry – segmenting by key retail partner websites, a custom index of competitor brand websites, or to audience engagement across your category – can reveal patterns that your first-party data won’t necessarily show you. 

This allows you to forecast order demand, and therefore your own firm’s need to procure raw materials, with greater precision. Moreover, it’s advisable to enforce context by linking procurement times to manufacturing benchmarks. If lengthy procurement times lead to slower go-to-market times compared to your competitors, there's something you need to fix in your procurement processes. 

Comparing vendor lead times is a good place to begin. Which vendors can react to product changes the fastest the best, and which ones are best placed to absorb market shocks? 

In an ideal world, you'll find vendors that can do both. However, this isn't the case, and you must build agility into your procurement to offset these inefficiencies. For instance, you can draft plans to rely on the agile vendor when demand shocks arise and lean on other vendors more when your product needs change over time. 

When comparing performance, evaluate procurement times per product group or family. These datasets will lack context if the products you're comparing are wildly different. However, you can compare some common aspects, such as logistics lead times and delivery efficiency. 

Consider the impact technology has on your procurement process as well. Automation is helping more manufacturers than ever before to free up their employees’ time to conduct value-added analysis. Measure cycle times before and after tech installation and process efficiency to understand the impact. 

Here, it might be a good idea to compare your procurement metrics against global industry benchmarks, such as the ones published by APQC. Note that you must view data within the right context, such as sector and product-level data. 

Enhancing Performance

Functional benchmarking is a tricky exercise, but it often unlocks disruptive techniques. Agile manufacturing is a product of functional benchmarking, originating from the total quality management practices in the Japanese auto industry. 

Bringing context to these metrics is critical. Begin by looking at shared functions that you can port to your industry. For instance, the rise of digital twins in the aerospace industry lends itself well to multiple domains. 

To properly evaluate context, you must dive into the reference industry and draw the right parallels. Functional benchmarking is not a quick process, but the time you invest will pay itself off many times over. For instance, comparing human error rates in your manufacturing process to that in another industry calls for you to understand the latter's processes deeply.

 Reliability and quality metrics such as rework requests or damaged products also lend themselves well to functional benchmarks. The trick is to take the time to analyze other industries and not rush to measure metrics just for the sake of it. 

Agile Demands Intelligent Benchmarking

Benchmarking is more than comparing metrics against each other. You must understand the context behind the numbers, since benchmark averages can skew data significantly. By taking the time to analyze your benchmarking assumptions, you'll easily create the right performance standards that enhance production quality.

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