Ops leaders struggle to assess customer demand related risks in real time. This increases revenue risk. This is a bigger issue amid rising uncertainties like tariffs and inflation (circa 2025). Learn one of the many ways how Zyom helps customers such as Cambium Networks tackle Revenue risks.
Most Operations leaders can’t answer in real time, or even near real time:
Which portion of my planned customer demand is at risk this quarter?
Which orders are at risk?
“At risk” here implies a significant likelihood that customer orders would not materialize, or even if it does, cannot be shipped on time.
Changing signals make it impossible to get demand and supply plans aligned, even using well-crafted spreadsheets and sophisticated, but siloed systems — planning cycles drag, errors creep in, critical Revenue and cross-functional meetings and forums (such as Sales & Ops Planning or S&OP) result in more unanswered questions, and by the time the risks are visible, revenue is already slipping away, or worse (orders lost to competitors).
In times of “garden variety” uncertainty these questions to uncover risk are hard to answer. In an environment of high uncertainty such as now (starting circa Q4 , 2024, to late Q3, 2025 in the US), identifying demand risk has become increasingly frustrating. Largely due to tariffs and pressured trade relations which directly impacts significant trade volumes from partners across the board (larger partners such as the EU, Japan, South Korea, and relatively smaller ones like Vietnam), these risks have increasingly grown for US based product companies. And this without taking into account the slowing employment picture and persistent inflationary trends which, in many industries, is already dampening if not damaging demand.
Branded product manufacturers like Cambium Networks cut planning cycles by 60%-80% and gave key executives and operating team members direct line-of-sight into supply risk with Zyom, starting in 2017. Their SVP of Ops called it “a system that brought our vision to fruition.” Cambium continues to partner with Zyom as it navigates demand corrosive uncertainties.
If you can’t see revenue at risk until the quarter’s target is already slipping, and key operating team members are scrambling to get supply inbound, you’re too late.
Key Questions for COO and Operations team
What is one key system capability from a supply Operations standpoint that companies such as Cambium need to ensure they spot revenue risk as early as possible? How should this capability evolve?
What is one valuable decision prompt that the system can automatically provide to speed up decisions and actions from Supply Operations? This was not technically feasible prior to the advent of the newer generative AI technologies (late 2022 ChatGPT launch). i.e., traditional Machine Learning approaches till the public advent of GenAI (LLM) technologies.
To find out more, drop a comment here, or email us at: products@zyom.com
Sources & Acknowledgement: Zyom’s Cambium Networks Case Study.
John Duvenage provided key inputs to structure and composition.
Disclaimer: Generative AI was not used for composing any of the writeups on this site (including this one). GenAI was used to generate the pictures in this article and for content summarization. At this point of time, there is no plan to use GenAI to generate new content on this site. Readers will be informed in advance if this changes.
Lead time metrics seldom gets senior leadership level attention outside of Supply Operations, until something blows up badly, such as the 2011 tsunami overwhelming Japan’s economy and its swift, cascading impact on automotive and electronics supply chains world-wide.
More recently, in the midst of the world-wide pandemic, there has been a spate of headline-grabbing bad news from large auto makers and other industries, all traced back to growing lead-time of parts/ component and products[i].
Auto makers, after seeing an unexpected surge in demand starting Q3, 2020, are now stuck in neutral, exposed to painful revenue and profit shortfalls due to semiconductor chip shortages over the near-term (calendar Q1 through Q2/early Q3, 2021), possibly longer – forced to idle factories and people, for months. Unexpectedly large (and growing) lead-time of critical parts are squeezing both top and bottom lines. All this at a time when auto, and other industries, are trying to get back to some semblance of ‘normal operations’ after intermittent and prolonged shutdowns earlier in the pandemic.
The current lead time debacle need not have been this bad, the pandemic and subsequent sharp surge in demand (across some segments) notwithstanding.
Lead time of products, key components and raw materials are critical variables which require timely and regular attention of (yes) CEO/ COO of any product company serving multiple geographies and relying on global supply networks. Now, with long and uncertain lead times in the form of persistent shortages, it has the CEO’s attention again.
How do we break out of this endless cycle of using lead time as a ‘reactive’ metric, and use it to gain an operating advantage?
What’s your Lead time? A Measurement Gap
Wildly swinging lead-times are usually the tip of the iceberg. Below the surface are many causal forces –
inadequate manufacturing capacity, new industries competing for scarce capacity and supply (e.g., auto industry vying for the same fab capacity used by electronics makers), or
gaps in planning and collaboration processes (with supply chain partners), missing system capabilities, or simply not knowing whatinnovations are available to tackle lead-time unreliability. This is the purview of this write-up.
One of the primary needs is the ability to measure the lead-time of products – quickly and accurately. To date, planners, buyers and analysts, even in larger, well-run companies find themselves leaning on spreadsheets and “notes” (from their latest calls with supplies) when asked –
“What’s the lead time of XYZ product?” – their own product, which is getting supply constrained.
Most often, the product’s lead time data in their ERP systems is dated. Makes sense – most of the lead-time info in their ERP system is supplied by the buyer/planner’s spreadsheet.
For component parts and critical sub-assemblies that are procured from suppliers, product companies are often totally dependent on the lead-time data they get from their manufacturing partners – CM[1] in hi-tech electronics product makers or Tier 1 suppliers in automotive and other manufacturing-intensive supply chains. With an arms-length relationship with the eventual parts’ suppliers (either Tier 2, or sometimes upstream), it’s not surprising that these numbers fed to the product companies can be dangerously stale.
Astute operations and supporting IT teams understand these gaps– that ERP is a system for ‘recording’ (storing) lead-time data, and not designed to measure lead-time. They need a different approach, different processes to capture this data quickly and accurately, and often, a new enabling system.
Astute operations and supporting IT teams understand these gaps – that ERP is a system for ‘recording’ (storing) lead-time data, and not designed to measure lead-time. They need a different approach..
Tackling unreliable Lead times – Focus on right Process & System
However, before embarking on a project to plug the gap – ‘fix lead-time’ data and systems, it’s important to identify any bottlenecks in the end-to-end processes from demand through supply planning and all the steps that lead to the subsequent shipments from suppliers. For supply chains that are impacted by long lead-times on components that are further upstream of their Tier 1 supplier (or CM/ODM[2]), analyzing this end-to-end process is just a start, and may not close the gap due to variability in component lead-times.
If you have not done this, it is best to wrap your arms around product lead times looking at processes and interactions with the immediate upstream tier of supply, at the get go – i.e., between the product company and its Tier 1 supplier (CM/ODM).
Once the process bottlenecks and disconnects are removed, the company is in a position to systematically measure the lead-time of their products from this vantage point (with Tier 1 supplier).
As soon as companies gain visibility and some control over product lead time, they can plan the more demanding and potentially uncharted territory of expanding these processes to include critical Tier 2 supply.
Design for Implementation and usage
Once process related constraints are identified and resolved (via suitable agreements with supply chain partners to share data), companies can proceed to the next step, namely – providing a system enabler that works in simple manner to capture lead-times.
Specialized solutions built on the cloud are ideal, since most processes are executed collaboratively. Ensure that the system is fast to implement and quickly gains traction with all users, including the supplier users. A “large, ERP mindset” (‘small army’ of people, ‘large’ implementation centered) and ‘hit-and-miss’ post implementation stabilization and usage, is a sure shot to an expensive failure.
Take the lead with your Lead time
A recent article outlines our findings of new approaches and innovations in process and system from younger, dynamic growing product companies that are successfully scaling operations while facing larger competitors, as well as larger technology companies with leading supply chain operations practices, both of which have navigated supply chain disruptions – large and small.
Use the information from this article to brainstorm with your senior leaders (CEO/ COO) specific areas that need to be re-thought through and acted upon, both at a macro and micro-process level
For example, in the case of macro-process, answer key questions such as –
How can product Lead times be measured systematicallywhich is closer to reality (if not real-time)?
What is the end-to-end process and supporting system needed that canmeasure lead-times accurately?
What is the end-to-end process and supporting system needed that canmeasure lead-times accurately?
For micro-process dive into specific processes and system changes that are economically implementable, such as –
A Lead-time review process to identify lead time outliers and take corrective actions rapidly.
Ideas from the above referenced article can help you define the extent of your lead time challenges and opportunities, providing you an outline of a few key process and system areas that need to be rethought, redesigned (as needed) and retooled. Use these to bring your lead-time picture into much sharper focus, gaining an operating advantage in the process.
Lead time requires focused leadership on process and system. Falling behind is not an option.
*Please email contactus@zyom.com with questions or additional information needs.
[2] ODM = original design manufacturer (using in hi-tech electronics supply chains)
[1] CM = contract manufacturer (in hi-tech electronics supply chain)
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