Modern manufacturing is a complex endeavor for any company. Equipment capability, labor and productivity must be balanced against demand, sales forecasts, supply chain issues and many other factors to produce the right volume at the right time. Too much and the inventory excess will impact cash flow. Too little and customers will seek products elsewhere as production stalls.

To keep manufacturing balanced between all these inputs, companies use capacity planning to keep production optimized. Simply put, with capacity planning, a company gauges production capacity in terms of equipment, staff and maintenance that is required to meet demand. This allows them to make critical decisions that impact the profitability of the company over time and decisions that can reduce costs and improve competitiveness.

Capacity Planning Strategy

Depending on the products manufactured, companies have a variety of strategies available to them for capacity planning. For companies with heavy seasonal demand, capacity planning may utilize a Lead Strategy. Here, capacity is added prior to demand to keep them ahead of the demand curve.

If a manufacturing company has an agile production system, say one with few process steps or rapid completion of units, then they may seek to use Lag Strategy. In Lag Strategy, capacity is only planned after demand has occurred. This reserves finances until they are needed and depends on the ability to add capacity quickly.

Many small and medium sized companies (SMBs) use Match Strategy for capacity planning. In a Match Strategy, capacity is added incrementally, increasing proportionally as demand increases. This “pay as you go” strategy is useful in balancing the cost of capacity increases.

5 Production Capacity Planning Challenges

Regardless of the strategy used, there are five production capacity planning challenges to be aware of that can impact production no matter the scale and complexity of the organization.

1. Data Collection – Traditional manufacturing has long relied upon siloed data in its attempt to plan capacity. Disparate, unconnected systems meant manual reconciliation of data before consumption. This added time and caused data to be outdated before it could be used. As capacity planning relies on inputs from demand forecasts, supply chain, warehouse management and many other areas, a disconnected system meant reliance upon Excel spreadsheets and human capability to identify and manage trends.
2. Data Quality – In most operations, capacity data comes in the form of records and reports and must be manually aggregated before information can be consumed. Once this is done, planners add in supply and demand data and develop a formula that shows available capacity. If at any step the multiple data inputs are wrong or outdated or if they exist in different formats (ex. different units of measure), they again must be standardized before they can be utilized for planning purposes. And because the data sources are not linked, any new iterations must go through the same process all over again.
3. Formulas and Calculations – Planners use many formulas and calculations in arriving at a capacity plan. This includes things such as material availability, load by work center, alternate sourcing, attribute-based planning rules and more. Spreadsheets have long been the “go-to” for planners to calculate these elements and accurate calculations are crucial. If data entry errors or bad data are present, the plan could be wrong. And in addition to requiring a lag based on the time needed to assemble the data, new information and changes must be input into several sources, again lengthening the time to produce a plan and creating the risk of errors.
4. Planning Level – In addition to the above challenges, capacity planning is often done at different levels. Rough cut planning is usually done at the master schedule level and is used for short-term planning of a week to two months. Aggregate planning uses a 2-18-month planning window to provide a longer view that allows a company to ensure that demand can be met long-term. It also helps smoothen supply chain issues and allows them time to look at production cost reductions. Each level requires larger data sets and longer time periods as they are used for different decision-making tasks. Because of this, the challenges of data collection, data quality and formulas and calculations are multiplied in complexity, opening the door for errors associated with those issues.
5. Communication – With so many “moving parts”, and with few of them interconnected, any breakdown or gap in communication is risky to the integrity of the capacity plan. This is true for supplier communication, which often happens through email or fax. It is also true internally where siloed systems for purchasing may not communicate with those in production or scheduling. This reduces collaboration and leaves planners open to being blind-sided by new data, missing data or errors in existing data.

How Software Can Eliminate the Challenges in Capacity Planning

Planning software mitigates and eliminates the challenges to production capacity planning. Because collected data is unsiloed and kept in a unified system, it is standardized and available for all, delivering a single version of truth for the enterprise.

Data quality is also improved with software. It eliminates the need for manual aggregation and reconciliation of different data types. This reduces error and frees up time and resources needed to understand the results and make decisions based on real-time information. Because all functional areas are linked through automation, changes in data in one location is advanced throughout the entire platform and changed dynamically.

Today’s software comes with advanced analytics, machine learning, and a wide range of analytical tools that can plan capacity to the unit, cost, margin or revenue. Because dynamic real-time calculations can be performed by the system, it eliminates the need for multiple spreadsheets and manual data inputs. The accuracy of the formulas and calculations saves time and reduces errors.

Planning software also allows for multiple views and iterations at different levels. From advanced analytics and machine learning algorithms to multi echelon “what-if” scenarios that help identify capacity constraints, plans can be produced for short-term operational needs as well as medium and long-term strategic planning.

Finally, capacity planning software improves communication and collaboration. With intuitive dashboards, alerts and other tools, data from the ERP system can be leveraged to optimize plans based on current material and capacity constraints. The system can be integrated with ERP and MRP systems to provide BOM-level finished goods forecasts at the component level. These functions can trigger alerts, schedule replenishments, identify constraints and drive cross-functional collaboration in real-time.

While capacity planning is complex, today’s cloud-based software from DemandCaster can deliver solutions to overcome the challenges inherent in the old version of the process. By deploying this software, the system can deliver value in the form of reduced errors, lower costs and greater precision to better meet service levels and to drive process improvements.

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