Client: A multinational consumer goods company
Business need addressed:
- The consumer goods company wanted to realize additional benefits from its already best-in-class supply chain. Due to the constraints of its standard operating procedures, it struggled to find innovations that could reimagine its order management function
- Key resources were under significant pressure and unable to make effective decisions due to the lack of visibility on its end-to-end data and/or the impact of decisions on others
- A Lean DigitalSM design thinking approach to look at the problem from the user's perspective
- Designing a System of EngagementTM around the user to analyze data from multiple disparate Systems of Record enabling a single view of an order and, critically, how that order impacts other orders
- Utilizing robotic process automation, natural language processing, sentiment analysis and cognitive computing to equip the user with customer insight and ease of collaboration
- Potential of $100 million of bottom-line impact per year, direct reduction in order management costs due to a cut in transactional activities by up to 90%
- Enhancing end-to-end supply chain performance through, for instance, truck load optimization
- Facilitating the reduction of working capital tied up in the supply chain e.g. buffer stock
- The potential to make data a more strategic asset for the company
- Embedding speed and agility in the process to enable the execution of numerous and less thoroughly planned, promotions
- Full end-to-end visibility of an order and its impact has vastly improved retailer (customer) experience
- Giving order management analysts more time to spend proactively addressing more complex challenges to add greater value
A global leader in consumer products manufacturing, with a sophisticated supply chain, is struggling to reimagine its order management function, which is diluting the transformative impact of its supply chain innovations. The key people involved in these operations feel the burden in a number of ways. This was the starting point for a design-thinking led effort that combined the observation of human experiences and emotions, with an understanding of enterprise strategy and processes.
On a typical day, the company's order management analysts have to manage a high volume of messages. These can be frustrated buyers tracking shipments, an irate sales person at a new product presentation with a buyer only to have the meeting shortened because a promotional item had been cut from an order, or a third-party logistics company that missed the requested delivery date.
Many emails with attachments require action, lists of items that haven't been shipped, requests for order details, or alerts with information on potential delays in product availability. After turning on the order management system, the analysts receive a flood of orders. With multiple tools, and no way to prioritize these messages and orders efficiently, the quality of decision making depends on the analyst's personal competence and judgment.
Collaboration within the business is rudimentary and relies on the order management analysts to navigate a maze of people, processes and technology. Speed to resolution is rewarded over business impact, and there is a significant variance in performance between the best and the least skilled (and energized) staff members. A retailer buyer's day is equally unpredictable—and mistakes often result in a loss of revenue or potential stock outs at stores. Error-prone situations (e.g. promotional orders) often result in volume leakage due to a mismatch in supply and demand (such as shelf availability, or buffer stock).They also create conflict between the players—from retail buyers to the manufacturer's order management, sales, and supply chain groups.
After working with the organization to observe and understand these challenges, Genpact decided to solve them primarily as a “data problem," and surround the analytics solution with digital technologies. In this way the company could reimagine the end-to-end process from shelf data all the way to product supply in keeping with Genpact's Lean DigitalSM approach. This reimagination project was based on design thinking, which is a problem-solving approach that focuses on people and their emotional responses. It helps identify what matters to end users, both outside and inside the organization.
Figure 1: Persona-focused observation key to uncovering opportunities
For the consumer goods manufacturer, the behavior of its order management analysts scarcely resembled that of the sometimes emotional and impulsive consumers the process is meant to serve. Indeed, those involved in managing orders can be thought of as rational beings making logical and metric-based decisions. They apply this sensible mindset daily to a set of processes where their actions, and the resulting impact, affect the entire supply chain down to the consumers at the store shelf, and upstream into planning and production. Design thinking was crucial to discovering latent sources of value for key process stakeholders and enabling fast, iterative testing of intermediate solutions that gave way to a digital solution specifically built to transform the company's order management function.
Figure 3 illustrates the order management intelligent operation Genpact created to sense, act, and learn from the continuous interactions with the retail client. The Data-to-Insight-to-Action loop is facilitated by an array of advanced digital technologies and analytics that consolidate data across the value chain, anticipate future issues, alert the parties, prioritize actions for the staff involved, help seamless reporting, and facilitate collaboration between parties. The solution design leverages digital technologies that have become mainstream and reliable in recent years. These include:
- Robotic process automation (RPA) – for the retrieval of data from older retail and ERP systems and marts
- Natural language processing (NLP) – for the parsing of email inbox data and preparation of alerts and prioritization
- Natural language generation (NLG) – to automate the creation of communications for quick interaction and reporting
- Cognitive computing – to analyze responses to common queries and suggestions to users
- Predictive analytics – for detecting changes in order trends to spot promo items, or shifts in sentiment of collaboration between supply chain and individual customers
- Dynamic workflow – to enable the dispatch of cases to the most appropriate agent
Figure 2: Order Management Virtual Assistant SM facilitates the order management analyst's 'last mile' match of supply/demand, orchestrates information flows, prioritizes workflow, and delivers recommendations and implications
These solutions, when combined, create a powerful System of EngagementTM that complements the company's pre-existing Systems of Record. It fully leverages the power of data analytics, including POS data often available to the manufacturer but rarely used to facilitate order-level transactions.
Figure 3: Intelligent order management operations for the consumer goods manufacturer
At the process level, the impact of this Lean Digital solution is expected to be transformative. Order management analysts are now free to spend more time going deeper, proactively solving future challenges in a more insightful manner to add greater value. And the retailer's buyer has improved transparency and can become fully involved in the business in a non-intrusive fashion because they have knowledge and insights into the impact of the decisions being made. The solution provides a new way of working together—one that is smarter, faster, more efficient, and more effective. In addition to a potential direct reduction in order management costs, due to a cut in transactional activities by up to 90%, and an estimated overall reduction in time by a third, the order management team can now focus on value added activities, assisted by the intelligent system.
Out of stocks will be reduced and revenue opportunities maximized. Inventory on hand will be reduced as the product will arrive as and when required. Changes in behavior, such as flexibility in the timelines or quantity delivered for certain orders (now backed up by data that provides a view of the implications), at both the manufacturer and retailer, will have the ability to reduce the cost to serve. Speed and agility will be added back into the system as promotion and new introductions can be brought to market faster with an increased degree of flexibility. Data-driven decision making led by the manufacturer may very well result in a new way of doing business. This is a Lean Digital order management function, an intelligent operation, that amplifies the power of a modern supply chain to generate competitiveness.