Automotive supply chain networks are becoming incredibly complex as pricing pressures and cost of doing more with less place stress on operational leaders. Across large, mid-sized and small organizations, aligning supply and accurate demand forecasting is a common challenge. Industry estimates suggest that manufacturing organizations typically overproduce by 20% to manage market volatility and demand spikes. However, this is done on an ad-hoc basis. Precise forecasting, quality management, reduced inventory holding costs, and responsive fulfillment to customer needs are some of the key emerging requirements demanded from agile enterprises of the future. Therefore, it is important to understand what organizations must do to better prepare for the future.
The Benefits of Accurate Demand Forecasting
- Reduced inventory costs
- Responsive customer service and demand fulfillment
- Improvement in leveraging working capital
- Shorter time to market for customized new products
- Improved accuracy between demand forecasting and fulfillment
- Shift from reactive to proactive and prescriptive planning operations
- Ability to sense demand and generate a supply plan that assures profitability
- Create a single version of the truth
- Optimize the performance of supply-chain networks
But, accurate demand forecasting is complex due to significant challenges associated with its process inconsistencies and the lack of one common data source. Let’s examine the top four critical issues faced by planning functions within organizations.
1. Volume of Data
30% of an organization’s data volume is generated from the enterprise layer. The remaining 70% is generated from its plant and value networks. However, most of this data resides in siloes and is not often displayed together nor seen through a single pane of glass. Traditionally, companies have used complex Excel spreadsheets to manage demand forecasting, supplier quality, demand fulfillment, production execution and other functions. These were viable for small-scale operations, but in today’s complex scenario, there is an inability to scale with this tool. The industry needs to move away from “status-quo” and embrace technology to drive effective decision making. Managing today’s business with obsolete and unscalable tools further undermines business performance.
Streamlining the process from data collection and data orchestration to analytics and executing actions to adapt based on the data collected is an important loop for organizations to close and subsequently achieve the desired efficiency levels.
2. New Products and Faster Fulfillment
Today’s manufacturing operations were not designed with customization in mind. As customer-driven economies become mainstream, the entire forecasting process and infrastructure need to change to better suit the new requirements. With technology refresh cycles happening every four years, customers are expecting newer products in rapid fashion. This is driving organizations to customize new products and shrink time to market for new launches. Further, the rapid customization puts acute pressure on planning cycles, as the traditional mode allows for mass production but not mass customization. Manufacturers are also engineering products-as-a-service business models to drive customer engagement, interactivity and longevity in customer lifecycle management.
A case in point: A large European automobile manufacturer customizes the trims within the vehicle and customers are also able to make last minute changes to their vehicular selection of choice. On earlier occasions, customized vehicles had longer lead times, but the manufacturing flexibility available today allows many car manufacturers to deliver customized vehicles in the same time frame as mass produced vehicles. Another automotive trend is to bundle the car leasing costs, insurance, and concierge services all into one payment. The ability to customize the experience for the customer, by providing a user-centric product experience, is where organizations are differentiating.
3. Siloed Planning Infrastructure
There are a number of organization siloes that already exist due to historical operations. Converging these islands of information into one single data view is an important initiative organizations need to take in order to view their businesses holistically and better balance the risks and trade-offs. Further, multiple LOBs bring in data that is verified and measured (via KPIs) in different ways. These factors severely undermine planning function efficiency.
To overcome these challenges, there needs to be coordination between LOBs, organizational functions, a single database and a single view of the plan across the organization. This is where solutions like integrated business planning and execution come into play. It brings in consistency in forecasting processes and drives integration and alignment across the business.
4. Increase Efficiency
The traditional manufacturing value chain is a linear process that lacks flexibility and has been a primary cause of lower business efficiencies. The lack of standardization in processes across the various manufacturing functions (demand forecasting, materials planning, supply-chain planning, expense planning, resource planning, quality management, etc.) has led to delays in time to market for many organizations.
To achieve zero latency, organizations need to inculcate the ability to detect events before they occur, understand the potential impact of the event and subsequently make changes to the processes so that the foreseen event can be avoided. This requires careful orchestration and “what-if” analysis to be done in real time. We are not there yet, but the market offers innovative advanced analytics solutions to help customers reach this future state of operations.
The Need for Accurate Demand Forecasting
Silos in organizations today restrain enterprises from achieving a single version of the truth. Using advanced analytics, AI, and ML, customers may also be able to drive process optimization and bring the processes back to desired efficiency levels. The ability to leverage AI to generate insights and recommendations from the volume of enterprise data gathered will set organizations apart in the future. The combination of big data, cloud and advanced analytics will help reduce deployment costs and improve operational flexibility. Also, cloud is one of the fastest ways to scale with standardized processes.
The planning portfolio of solutions used today is complex and many organizations prefer not to take risks due to the efforts required to overhaul the portfolio. However, they are not realizing the disadvantages caused by this complex collection of solutions – forecasting inefficiencies that are being experienced across the business. Customers need to consistently invest in cloud-based software to stay relevant in the industry and have an agile infrastructure. While the market is awash with several solution providers offering point solutions, it makes better economic sense to work with a solution provider that has deep industry knowledge, a comprehensive integrated solution, and a strong track record.