Supply chains are the lifeline of most companies. Whether they are manufacturing pharmaceuticals, drilling for oil or selling burgers, data is the essential fuel to make a product or service and get it to their customers.
The internet of things and machine learning are expanding the number of potential data sources exponentially as technology advances, producing a ripple effect that greatly impacts supply chains. Using this data to improve the efficiency of supply chains offers tremendous opportunity to return value to a company. Many companies, however, have failed to use this to their advantage.
Supply chain management was once a simpler process, with limited inputs and limited outputs: raw material – manufacturing – shipping – returns. If a company produced 100 units, they would sell 100 units and then manage the return rate for them. But global access and modernization have changed every aspect of the supply chain, and every step now includes multiple sub-steps, each with thousands of potentially useful data elements.
Companies need to leverage this data to create efficiencies, reduce costs and please customers. Historically, many companies that faced a hiccup along their supply chain provisioned a new piece of technology to, hopefully, solve that specific problem. That technology upgrade would take months or years to fully implement, and in the end, it may not even have fully addressed the issue, or the environment might have shifted to the degree that the problem was no longer relevant. This method also often involved significant upfront investment, placing a financial anchor on the project from the start.
Rather than rushing to apply technology quickly, companies need to visualize operational benefits by embracing a culture of innovation. Today’s cloud tools bring with them opportunities for complete digital transformation while minimizing software and data platform deployment cycles. Using these modern tools to produce business value enables ongoing innovation as companies evolve business processes and respond to changing conditions.
The supply chain is the perfect opportunity to fully embrace digital transformation. Technology is woven into the fabric of all business processes and is pushing companies to become technology companies in their own right. This fact, along with the shift toward the consumption of software as a service, allows companies to focus on innovation and make better decisions driven by real-time data that ultimately increases customer value.
To establish a supply chain that maximizes the value of their data, companies must establish baseline performance metrics, remove silos and focus on automation.
Establish baseline performance metrics
When working with a partner to optimize supply chains, the first step should be establishing a baseline performance view for the chain and all associated sub-systems as well as key performance indicator (KPI) metrics to evaluate ongoing modifications.
Process improvement can’t be measured if the starting point is unknown. By establishing a baseline first – how long something takes to happen, the percentage of misentered information, the production costs of 100 units – the company and the technology partner will know if changes being made are providing the expected impact.
Just because you own a Ferrari doesn’t mean you can race it. It’s the same for a given technology. Just because a company has it, doesn’t mean their organizational maturity matches their new capabilities, or that they are utilizing it to its full advantage. People, processes and technology must move in unison.
The stages of a supply chain don’t operate in a vacuum. Evaluation must be conducted holistically so adaptable data strategies can be developed that ensure data quality, speed of access and a positive cost/benefit ratio. Accomplishing this means not only gaining insights into the current state of a company’s supply chain but enabling predictive insights.
Say a company’s supply chain has four main steps: A, B, C and D. Typically each of those steps is optimized individually rather than comprehensively. Companies need their supply chain systems to work and adapt as one. If step B is shortened by four days, that improvement means nothing if the rest of the supply chain doesn’t adapt to the new timeline for step B.
Organizations don’t have to build large data science departments to understand or use data – working with the right partner, a company can leverage their disparate data without duplicating it and without impacting governance. Enhanced visibility drives innovative thinking, and with self-service dashboards and reports, employees throughout the company are empowered to leverage data, accelerating the speed to insights and opportunity – because often people don’t see an opportunity until they see a problem.
Focus on automation
Supply chains inevitably contain numerous manual, mundane tasks that should be automated, and with that automation comes an increase in data quality that benefits the entire supply chain. By freeing staff from those repeatable tasks, employees gain more opportunities to observe and seek additional innovation. With automation, business rules provide consistency so employees can manage exceptions and focus on holistic views and predictive planning.
Modern technologies are consumable and delivered in an agile method from the cloud as services rather than products, shortening implementation cycles. Fully realizing those benefits, though, requires companies to think and operate like technology companies to remain competitive, fostering collaboration and innovation as a culture. Starting with the end in mind will keep innovation and daily objectives clear. And to do that, companies need a partner that can facilitate the evolution of their people, processes and technology.
Supply chain management that utilizes data to drive action and continuous improvement sets successful businesses apart. Let’s start a conversation about how Blueprint can develop and implement the technology solutions to use data to optimize your supply chain.