Embracing Cloud for Business Optimization
1. What are the benefits of cloud computing for the manufacturing and supply chain industry, and how has your company embraced it?
In this era of Industry 4.0, the need for higher efficiencies both on the shop floor & in the enterprise, along with the emergence of highly competitive, globally distributed, multi-partner ecosystems has driven manufacturing & supply chains to adopt cloud computing as the primary driver for this transformation. The manufacturing side requires analytics at scale as well as Industrial IOT, which are natural candidates for cloud computing. In fact, without cloud computing, it would be difficult to envisage manufacturers being able use many forms of new production systems, from 3D printing and high-performance computing (HPC) to the Internet of Things (IoT) and industrial robots. Similarly, supply chains that are global in nature benefit from the ubiquitous connectivity of cloud. A global supply chain also ensures a highly agile and responsive ecosystem; the intelligence of cloud systems enable adjustability to market volatility, and to scalability needs. In summation, cloud computing is helping manufacturers innovate, reduce costs, and increase their competitiveness. In an interesting way, cloud computing democratizes access to and use of the latest technologies which has a direct outcome of reducing entry barriers for newer players, thereby increasing competition!
Having the ability to sense demand enables the TMS to be more nimble
As a technology enabler for the next-gen manufacturing businesses, we have been part of several cloud transformation journeys. A Global Auto Manufacturer who is a customer of ours was looking for an effective solution to manage around 25 TB of digital assets stored across 15 disparate applications, for three global brands. The solution was to build aCloud-based integrated platform for search, browse, review, download, reporting and information management. The benefits included 99.5 percent application availability, 50 percent cost optimization, and adoption across multiple countries, all of which directly contribute to the aforementioned benefits.
2. Companies are moving fast towards cloud-based TMS. Please share how this is affecting end-to-end supply chain.
Today, customers have come to expect their orders to be in stock and ready to be delivered on demand, and “connected commerce” has affectedeverything from building materials to automobiles to specialty chemicals. This has been made possible by cloud based TMSs.While cloud-based TMS started with the intent of minimizing upfront infrastructure costs, coupled with analytics and IoT today, these are fast evolving to support a connected global ecosystem of suppliers and customers. Such aTMS can now feed in information alerting the supply chain of delays, losses, or early arrivals, which enables the supply chain to be more agile and effective. Likewise, having the ability to sense demand enables the TMS to be more nimble. Cloud TMSs have moved to web-based platforms combining cloud, mobile and social technologies helping create complex, yet seamless supply chain networks comprised of connected customers, suppliers, competitors, carriers and third-party providers. In this set-up,all pertinent information is available via the web on a 24/7/365 basis in real-time leading to significant increase in efficiencies and speed for the supply chain.
3. What does the internet of things mean for the manufacturing or supply chain industry? How can technology be used to mitigate rising supply chain costs?
For manufacturing organizations, the internet of things (IoT) offers game-changing opportunities to gain valuable insights that can enable greater control over operations, innovative approaches to market, transformation of products to meet changing customer demands and reduced supply chain costs. Manufacturing organizations can leverage IoT to digitize every process in the value chain. Below are a few examples of the benefits of this digital transformation for organizations, partners and customers.
Visibility to improve manufacturing and planning
• Monitor production operations: Production operations can be monitored in near real-time. Organizations can use insights into energy and material consumption to dramatically improve resource planning, understand causes for inefficiencies, and enable predictive maintenance that reduces operation costs and downtime.
• Safety: Sensors installed in the shop floor can track environmental conditions, equipment operations and potential hazards to improve overall safety.
Optimize supply chain and distribution
• Supply chain optimization: Advances in logistics through digital technologies such as supply chain integration, GPS-enabled tracking and RFID-based inventory management allows for synchronization of supply and production planning.
• Route optimization: Telematics and connected vehicles enable intelligent route determination and delivery optimization, leading to cost savings and improved distribution planning.
4. Most organizations have high hopes for using big data analytics in their supply chain but many have had challenges in deploying it. What are your thoughts on this?
Big data analytics – or data analytics in general – is driven by the envisaged outcomes. The clearer the outcomes are, the more likely a big data analytics initiative will be successful. Lack of clarity is the core challenge faced by most organisations, Organizations need to consider the following key points while planning a big data analytics program:
a. Why is it needed and how can the benefits be derived? Are the benefits quantitative or qualitative?
b. Is data available across the sources and if yes, what is the quality of this data?
c. Data democratization shouldbe the larger goal to ensure that the right data is made available to the right audience so that the adoption rate is higher resulting in the success of this program?
With reasonable direction, organizations can start collecting data that’s relevant, and then build out initial models and subsequently refine them. A key point to keep in mind is that big data analytics is an ongoing journey, not a single stop
The top three use case for big data in supply chain are traceability, relationship management and forecasting. A good example of the right usage of big data analytics is the work we Mindtree did on demand forecasting for a global consumer goods manufacturer. The need was to identify and automate the process of APO demand disaggregation with control checks and defined logics at various cuts of categories and customers or clusters. Our solution considered identifying data challenges involved in demand disaggregation. We analyzed demand disaggregation at various levels of hierarchy in order to identify best level of hierarchy for demand disaggregation. The benefits included better Mean Absolute Percent Errors meaning more accurate forecasting compared to ‘As-Is’ method in Quarterly growth plan Cycle 1 (120 days) and better value compliance in Quarterly growth plan Cycle 2 (90 days). We automated the entire forecasting technique and with lesser rework and more accurate results, reducing the time required in disaggregation and review.
With your rich experience of managing IT organization and steering technology for your enterprise, can you please share some of the unique lessons learned and your advice for fellow tech decision makers.
Mindtree has been servicing customers on IT services for the past 18 years and have been part of many exciting journeys. We are a company that was born when the “Digital Era” was initiated and have engaged with global customers to help them define their digital transformation programs and change the way their businesses run. Given this rich experience, some of our key learnings have been:
• Governance is key: Thenew trends in technology like IoT, Cloud, AI/ML, Big data analytics not only mean a big change from a technology implementation standpoint but also from an organizational change management perspective. Hence, the planning for such an implementation needs strong governance to come into play
• Start with understanding what’s already in play: In a lot of instances, the existing systems & processes are not technically ready to integrate seamlessly or provide information in the as-is state which clearly implies that sufficient time & energy needs to be spent in understanding the existing technology landscape and the implications of the future roadmap
• Think big, Start smart, Scale fast: This has worked best for most of our customers when they are in the path of adopting new technologies. For example, for data analytics, while the need for data analytics and big data has been well-articulated, the key to the success is user adoption and effective usage. Considering this, what has worked well is identifying a key pilot/BU/department where the right data is available, and the ROI is well defined. This is then used as a use case to spread the platform across the organization.
An example of the learnings depicted above is our work with a leading consumer goods manufacturing company thatwanted to build an assortment planning mechanism for its salesmen to automate the outlet sales execution that allows more lines per call to be achieved. We engaged with the customer to ideate and build “Decision Support Group” (DSG) to serve as the analytics backbone. To that we added “Advanced Predictive Analytics” for generating cross-sell & out-of-stock recommendations at an outlet level. This was rolled out as a pilot to limited markets and then scaled across 25+ countries. DSG currently publishes 20M+ width pack recommendations that translates to approximately $40 Million additional sales revenue per year.