The manufacturing data gap is a critical risk to supply chain intelligence
It’s a regular weekly meeting of the supply chain team, and the contrast in data quality between manufacturing and everyone else is glaring: The production planning team shows detailed data on current inventory and cross references demand-planning numbers to identify any risks to the company’s ability to meet sales forecasts. The logistics team references its own software to show the location and contents of every container, pallet and box of the company’s finished products. The head of manufacturing, meanwhile, rolls in a whiteboard, grabs a handful of markers and jots down production estimates for the next week off a yellow notepad.
That’s a snapshot of the ability of many corporations to manage their supply chains from raw material sourcing through delivery to the end customer. The planning process is awash in information covering all aspects of the business except for one critical area: manufacturing.
Without real-time manufacturing production and product quality information, supply chain risk management is too dependent on guesswork. Understanding yield and available production capacity in real time allows the manufacturer to move beyond forecasts and instead make fact-based decisions on lead times, capacity and strategic risk management. Unfortunately, for most supply chain executives, this data remains something of a black box.
Forecasted output is based off capacity numbers that most plants set based on past performance. Even though these may not be the most reliable, we at least know a capacity forecast number to plan around. But predicting how much finished product we’ll actually have at the end of the month involves a good deal of guesswork. Too often, we also have little visibility into the bottlenecks that are throttling output and struggle to quickly determine the cause of downtime.
The lack of clarity isn’t necessarily due to a lack of data. Numerous analyst reports have found that manufacturers produce more data than any other industry (see, for example, “Engineering the 21st Century Digital Factory“). The widespread incorporation of sensors onto manufacturing equipment has flooded many companies with machine data. The problem is that relatively few have been able to turn that data into real-time visibility of their production process.
Supply chain problems: Three scenarios
Consider these scenarios:
Scenario 1: Capacity planning, capacity utilization
The company’s latest gadget, the XPS, is a hit with reviewers and customers. The sales team is swinging for the fences, and online merchants and big-box chains have put in big orders. Unfortunately, the only idea the company has about its ability to deliver on this demand is based on a static plant capacity number stored in the production planning modules of its ERP systems.
The company knows from past experience that actual production can vary dramatically from expectations, but it still can’t get the right data from production to improve forecasts and doesn’t know how to analyze what data it does have. If it’s wrong, it won’t be able to replace lost sales on its previous gadget model, earnings will take a hit and things will get ugly at the quarterly board meeting.
The questions: How many units can we commit to producing next quarter? How much will we produce under current conditions? How much capacity do we have to increase production? What resources would it take to further increase production? What would be the cost of increasing production?
The answers to these questions — and the keys to unlocking hidden capacity — lie in a thorough analysis of machine data. A manufacturer can compare production across similar lines, identify those with lagging output, and find and fix the root cause. Analysis can find production bottlenecks and unused resources and identify the most cost-effective way to increase capacity (e.g., add a second shift vs. replace a costly piece of equipment that is creating a production bottleneck). With these answers in hand, manufacturing leaders have better decision-making ability.
Scenario 2: Production variability
The company had projected that each of its five new XPS production lines would make 15,000 units a day, but they’ve been averaging only 12,000 units, with three of the lines unable to exceed 11,000 units.
The questions: Why are these three seemingly identical lines performing so far below expectations? How soon can we diagnose the problems and bring them up to their full targets? Can we squeeze even more out of our top two lines?
The fundamental question here is: What is causing the variation? — and the answer lies in the data. Is it because one line is experiencing more downtime? If so, which variables are associated with the higher downtime? Or is scrap rate higher? If so, a root cause analysis can identify where the defects are being introduced, and what variables correlate with higher defect rates.
Scenario 3: Track and trace, root cause analysis
The company has had to recall 25,000 defective units. The company knows which part has been failing and knows they were all made at the same plant during a two-week timespan. What is causing the defect?
With the right data available, the manufacturer can drill down until the cause is found. A crucial part of the data modeling is being able to associate specific products or batches with the conditions of each machine at the time the batch passed through that machine or process. Did all the defective products pass through a specific line or machine? How were the settings and conditions on that machine different from the other machines not associated with the defect? Or with the conditions on the same machine before or after the two-week period when the defective products were made?
Having the right data model in place enables the manufacturer to not only diagnose the root cause of the problem, but also to narrow the scope of the recall to only products directly impacted by the processes in question.
While manufacturers can separately optimize demand planning, sourcing, manufacturing or distribution, they won’t achieve transformational benefits unless all parts of the organization can see, understand and use the data from all parts of the process. By taking an integrated view of customer demand, sourcing, production and distribution, manufacturers can achieve savings that really matter.
Having real-time visibility into production data is the first step into creating a more agile supply chain organization. By better understanding production dependencies from real-time accurate production data coming directly from the machines, supply chain teams can move beyond production planning based on static inaccurate capacity numbers and reactive problem solving, and instead make fact-based decisions that transform the way their organizations operate.
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