Sound practices: Reinvigorating the supply chain for an audio equipment giant

A multinational manufacturer of high-tech audio equipment, and a Genpact client since 2018, needed a faster and more precise way to calculate market response to its NPIs. This was the trigger for revamping its entire supply chain process.

There was no time to lose. Revenue and reputation were at stake. The usual six-month wait for NPI demand forecasts using sell-through intelligence was a recipe for disaster, and a 50% accuracy rate was no longer acceptable. Ever-expanding variables, such as COVID-induced supply chain disruptions, market volatility, consumer ratings, expert reviews, competitor promotions, and product performance reports, were making traditional forecasting techniques worse than obsolete. They were misdirecting resources and hampering decision-making.

To evolve and thrive, the firm needed full control of its manufacturing supply chain and to work more effectively with outsourcing partners. It also had to exploit signals in the marketplace from online sources and elsewhere, using the data it gleaned to achieve the best possible outcomes. It needed to slash operating costs by integrating all its delivery channels with cross-functional sales and operations planning processes. And predictions based on old-fashioned spreadsheets or other unnecessary manual effort from staff had to stop.

So when the enterprise recently planned the release of a set of premium headphones, it took the opportunity to launch the supply chain overhaul — a natural test case and an ambitious goal. It wanted to achieve an NPI demand forecast just four weeks after going to market – a forecast that was measurably more accurate than 50%.

Genpact came in with a mandate to create a proof-of-concept project that investigated all the known demand variables potentially impacting the response to the new headphones. Pursuing that goal was the starting point of an end-to-end transformation of the firm's supply chain.

We dug deep into the workings of the firm's supply chain to get to the root of the systemic issues that hindered its operations. We began by taking a current-state assessment of the function using deep-learning artificial intelligence (AI). We then created a baseline that simulated historical performance and explored fluctuating factors such as inventory, network, transportation, and cost to serve. Applying that information:

• We conducted workshops to prepare the company for the future-state operating model we envisioned
• We refreshed processes for demand planning, supply planning, supplier collaboration, and inventory management. For example, we digitized sales and marketing functions and integrated them with the supply chain planning software we introduced
• We redesigned the company's organization model to accommodate the newly reconfigured processes and to align them to market best practices
• We helped create a center of excellence (COE) that maintains the supply chain planning function
• We took over the demand planning process to run it as a managed service, using the supply chain planning software for statistical forecasting
• We ensured that the new forecasting approach would be applicable to other products and time frames
• We identified external influencing factors – online product reviews, consumer chatter, and so on – to help reduce errors in forecasting from 60% to 20%

The new digital supply chain is more agile, accurate, and accessible across the board. Forecasts today are 65% more accurate than before, and they're produced in just a couple of weeks.

The company now enjoys enhanced visibility into contract manufacturer (CM) operations. It can now view and use CMs' manufacturing capacity and component constraints to create a more balanced supply plan. In addition, the firm has achieved better control over – and a clearer picture of – inventory levels.

The result: it can plan more efficiently and deploy its people more effectively. With the new system in place, the company has cut its headcount by 20%.

The right combination of people, process, and technologies was key to the project's success. Our methodology and delivery approach, alongside supply-chain-specific innovations in AI and machine learning, all worked together to surpass expectations.

The client is so happy that it is looking to expand the success of this project to other product segments and regions in the next phase. And we're looking across the organization together to identify more areas of improvement that can be automated, digitized, and integrated in the future.