I am the Chief Operations Officer and Head of Corporate Development for ThroughPut Inc., a Supply Chain Management AI software company.
Ongoing supply chain issues can often be traced back to one thing: outdated models. For years now, the industry has worked with antiquated processes, procedures and technologies. As a result, communication gaps have become almost the norm, putting a company’s ability for capacity planning into question. One false signal sent upstream can easily result in incorrect material or resource allocations throughout the entirety of manufacturing and distribution.
Mismatched capacity during surges makes it near impossible to meet demand. Miscalculated slumps, however, can be just as problematic. Stockpiles build up, and products are left to sit until they expire, become irrelevant or must be discounted to move them out the door. In such cases, working capital is either locked up in wrong-sized inventories or obliterated, leading to lower profitability and earnings per share.
Working in the supply chain management software space at ThroughPut, I’ve observed that many companies can trace problems back to their data systems. These companies operate in silos, often basing estimates and capacity planning on incomplete data. Consequently, stock-outs and pileups develop across the entire supply chain network, with the weakest link driving what can be produced or moved from one place to another.
But this quagmire isn’t the result of silos alone. Complicating matters further is the communication breakdown between teams. Materials movement and resource forecasts drive capacity planning, while capacity planning drives sales forecasts, which drives demand planning, and so on. Even if these teams were working off correct information (which isn’t usually the case), mistrust in each team’s estimates leads to overcompensation or under-compensation, further exacerbating false signals and leading to greater inefficiencies.
By leveraging more data sources and modern technologies, demand and capacity planners can overcome communication barriers and speak a common language. Everyone can work off the same information to make accurate recommendations based on real-time data that all teams can trust. Think of it as a holistic approach to capacity planning, where data from disparate sources come together to provide an accurate picture of demand. Not only can this correct issues with capacity planning, but it also allows for alignment in key performance indicators (KPIs) and goals across the end-to-end supply chain.
Correcting Communication Gaps
Communication gaps from poor data and technology use lead to real-world financial and operational consequences, impacting both customer and employee satisfaction and frustrating everyone from suppliers to partners to investors. To correct issues in capacity planning, fill communication gulfs with the following strategies:
1. Encourage organizational transparency.
Siloed information can quickly lead to an “us versus them” mentality in organizations. Practice open and transparent communication as a leadership team to keep the flow of information continuous between groups. Share what’s working, what’s not working and what would help all teams meet their KPIs and goals moving forward.
More important, recognize the different KPIs and goals within departments to help identify potential communication rifts. Then, look for internal or external solutions to remedy information bottlenecks.
2. Integrate the right data sources.
Data has infinite value — as long as it’s accessible. Inaccessible data can create bottlenecks that cause even larger physical bottlenecks in real-world supply chain networks. As different local groups unknowingly contribute to systemic inefficiencies, they ultimately plague everyone along the supply chain. To make data more accessible, consider how it could improve outcomes and ensure each team can leverage the right data to maintain their processes, make informed decisions and meet different KPIs and goals.
For best results, consider implementing cutting-edge technologies to manage your data. Supply chain artificial intelligence (AI), for example, leverages disparate data sources to collect, aggregate and analyze information to better predict optimal materials movement for demand and capacity planning.
3. Conduct pilot projects to test new tools and processes.
Data bottlenecks can easily lead to miscommunication between departments, and a lack of clarity between sales and planning departments can be particularly problematic for the supply chain. New processes and technologies can often correct the issue, but be sure to avoid large-scale system overhauls. Significant rebuilds can be expensive and time-consuming, and they might not provide the return on investment (ROI) you anticipate.
Instead, consider conducting small pilot programs to test new tools, innovations or processes. Pilot programs are a quick way to learn what you don’t know and quantify the potential return without draining your budget or resources.
4. Scale successful projects.
With a better understanding of the resources you need to implement a new tool or process, it becomes much easier to scale up almost any solution companywide. And properly scaled solutions can reduce data, communication and even physical bottlenecks — or make them much more manageable. In turn, you can drive more accurate demand forecasts and optimize capacity planning to make the right amount of product and move goods at the right time.
If you leave communication gaps to linger, inefficiencies will persist and continue to grow across the end-to-end supply chain. But when you tie data and communications together across the entire enterprise and solve systemically for better operational and financial results, everyone wins — customers included.
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