Traditionally, almost all business intelligence and analytics solutions were designed as general-purpose platforms or desktop tools suitable for any industry, department, or use case. That does mean, of course, that the burden of developing data models, data pipelines, dashboards, and reports falls to the customer. They effectively start with a 'blank sheet of paper,' which, in theory, can be developed to fit the organization's exact needs. However, the reality is that the organization may not have either the skills, resources, or time to develop an analytics solution that quickly meets business requirements.

The answer to avoiding this overhead of entirely homegrown analytics is to use a modern analytic application that can deliver ready-to-use analytics, right "out-of-the-box." Deployment and adoption of these analytic applications are accelerated because of built-in content developed by industry domain experts that understand the industries, roles, data, business applications, and specific analytics required by typical business user personas. A significant amount of development time can be saved because this domain-expertise-driven content can replace typical internal development processes.

Automotive manufacturers and their suppliers continue to increase their collaboration in various ways, whether this be through robotic process automation (RPA), data collection and exchange, or other methods that provide insights for scalability. With true, multi-enterprise collaboration, there are cross-training opportunities to convey customer needs and preferences for product innovation, giving manufacturers the ability to make rapid adjustments in real time. Collaborative behavior makes this happen, and it all starts with transparency.

However, building real trust, collaboration, and transparency goes beyond using a specific technology or workflow. At its core, this is about changing the culture of automotive OEMs and throughout their suppliers at every tier. Automotive companies who are true leaders in the industry consider the health of the entire supply network, not just their immediate interests. Making this change—as with almost all substantive transformations—requires change be from the top executive level down.

To keep up with lofty yet mandatory customer expectations, your supply chain can’t afford to stop investing in technology that improves processes, connects business partners and IoT data, and uses machine learning algorithms to improve business outcomes. If the people tasked with running your supply chain are relying on poorly built or outdated systems filled with data quality issues, then they need new technology solutions and strategies to overcome these shortcomings to create true value.

As you strive to balance cost while simultaneously improve your customer service, you are no doubt confronting issues that require better collaboration amongst your group as well as the numerous companies you interact with daily. The days of each department or company working on an island while hoping groups further downstream can correct any mistakes are long gone.

Nowadays, those of us in the supply chain space realize the need for ongoing collaboration with everyone who touches our supply chain. This can take the form of sharing data around forecasts, inventory positions, capacity plans, order status (both at rest and in-transit), as well as visibility into shipments.

Competitive supply chains must enable a new level of coordinated performance that creates a high-fidelity picture of in-process flows across your extended network. Building the continuous supply chain supports contextual deviations, conveys alerts and drives continuous planning via sense & respond capabilities.

Optimizing multi-party business processes

Supply chains are complex networks where over 80% of the data and processes sits within partner systems. To see and act on the latest picture of your supply chain, your company needs that data from each of your partners, but the problem is most companies rely solely on an enterprise-centric approach to solve a multi-enterprise problem.

The only way to overcome those limitations is to adopt a "network approach." Connecting all partners to shared processes, data and metrics managed within a single platform creates a single version of truth for all parties. This allows supply chains to eliminate the data silos and inherent latency in order to reduce the root causes of friction, variability and costs in today’s supply chains, both internally and externally.

The evolution of supply chain business models has been accelerated by unprecedented disruptions. As a result, there has been a clear shift from analog supply chains models of the past into a trajectory towards a digitalized ecosystem. These new business models recognize and necessitate the move to a digital and integrated supply chain that provides visibility and ease of use for consumers.

This journey is about moving towards a world where all parties participating across your network are connected to a single version of the truth. Supply chain thinking is shifting from ‘linear supply chains’ to ‘supply chain eco-systems’ propelled and supported by digital technologies.

Gartner has established a five-stage model of the journey to supply chain maturity, ranging from manual, analog systems to a fully digitalized ecosystem. Key differentiators along this evolutionary chain include consideration of data and collaboration, with the ultimate goal of integrating data across multiple systems to achieve a fully connected ecosystem that provides transparency and visibility for your company both internally and externally.

I recently posted on LinkedIn to ask my colleagues for advice on how to manage my exploding workload. According to the latest Mental Health Index: U.S. Worker Edition from Total Brain, between November and December there was a 48% increase in the risk of depression—a risk level not seen since this past spring. Further, employees’ focus dropped 62%—a record low since the inception of the research in February 2020. While remote working has its benefits, most of us would argue that we need to make some significant changes for this style of working to be sustainable. Below are a few of the suggestions and ideas from professionals that commented on the post.

The urgency of data security and privacy can’t be overstated. In 2021, the global average cost of a data breach was $4.24 million, representing a 9.8% increase over 2020. Yet the immediate costs are a trifle when compared to the incalculable, lasting brand, reputation, and business damage that a data security incident causes.

Understanding the present threat

Risks associated with information security threats increase minute by minute. Bad actors with malicious intent are continuously evolving their strategies and attempting increasingly creative and sophisticated security breaches. The constant potential threat of cyberattacks and security breaches can be taxing on IT teams and organizations that need vigilance, resources, talent, and educational resources just to stay ahead.

The number of potential attackers is also expanding to include not only independent attackers and small groups, but also state-sponsored hacking organizations that are much better organized and funded. These larger groups can afford to devote multiple resources to breaching the defenses of small and large organizations over a long period of time—a level of commitment attackers once reserved only for the most strategic targets.

Rising costs, increasing complexity, growing customer demands, and expanding global supply chains are all intrinsically linked challenges that manufacturers and distributors face on a daily basis. Meanwhile, expanding omni-channel markets are forcing manufacturers and distributors to not only change how they sell to customers, but also redefine who their customers are. And customers are exerting even further
pressure with demands for customization and personalization of products.

Global expansion adds to the challenge of maintaining visibility into inventory, shipping, and tracking—often across borders, continents, and oceans. In addition to making sophisticated warehouse operations even more complex, this all makes it difficult for manufacturers and distributors to remain competitive, keep costs down, and maintain profitability. Ineffective order management, excessive labor costs, and inefficient asset use just exacerbate the problem. To tackle these challenges, manufacturers and distributors must reconsider how their warehouse management practices, processes, and systems need to change in order to improve warehouse productivity, visibility, and costs.

In a data-driven world, navigating the challenges of today’s supply chains requires an unprecedented level of agility. This calls for supply chain tools that deliver network-wide trading partner connectivity to enable real-time visibility, demand and supply planning, and production scheduling insights.

Fortunately, modern tools deployed in an organization’s technology stack can digitally transform the supply chain, revealing new opportunities for innovation across the entire network.

At the same time, automation and advanced analytics delivered in a digital environment can synchronize production and distribution activities to match demand.