Faster. Better. Smarter. These are some of the attributes that manufacturers and distributors are expected to embody in their business practices and unequivocally delivery on. While matter of fact in theory, especially in today’s technology-driven enterprise world, the unexpected changes projected outcomes.
For the transportation and pharmaceutical sectors, anything less than timeliness can equate to failure in epic proportions. Langham Logistics president Cathy Langham knows this all too well. What worked in the past is just not enough to navigate through instability, the pandemic taught us that, she says.
Before Business Intelligence
“Although transportation capacity has right-sized back to 2019 levels, capacity planning has become more focused on building supply chain resilience and ensuring a robust and flexible infrastructure to handle disruptions effectively,” Langham says.
Warehouse capacity planning has evolved from the just-in-case (JIC) model common during the coronavirus pandemic, she adds. The business landscape has shifted for a lot of companies — especially pharmaceuticals — which are reshoring manufacturing and bringing supply chains closer to home.
Recent learnings contorted and reshaped today’s supply chains. The integration of business intelligence (BI) across the profession is a foundational backdrop to cross-functional and collaborative end-to-end success, though not without its challenges.
A Bigger Picture
Before understanding how best to apply business intelligence, Sasha Risner, a business intelligence solution architect at Flexware Innovation LLC, a Hitachi Group company, says to work the process step by step. “First, identify specific goals that you’d like business intelligence to help facilitate and list the potential issues that could stand in the way of success,” she says.
When in earnest to see goals to completion, Risner says it can be tempting to consider specific technologies too quickly without allowing for due diligence and thoughtful consideration. She suggests holding stakeholder interviews as the means toward deeper understanding of underlying business processes, existing technology, and the people who interact with both. By achieving multi-disciplinary alignment on desired goals and problems before recommending technology solutions, collective support is garnered further solidifying the larger strategic path.
For companies immersed in warehouse relocation efforts, says Langham, key considerations should include: (1) manufacturing facility locations, (2) whether the facilities are wholly owned or contract manufacturing, (3) port locations for inbound raw materials and (4) location of customers and end markets.
BI Thrives with Attention
Intelligence is only as good as its consistent use, increasing levels of improvement across a company. As such, business intelligence must be assessed to ensure that its programming remains on point or requiring modification. Devoting time and attention to BI helps maximize its effectiveness. Just as supply chains exercise agility and evolve, so too follows business intelligence.
Risner says assessments will have similarities and differences. “Pieces of the business intelligence platform should be closely monitored,” she says. “Systems that allow proactive measures to be taken expedite responsiveness. If any process fails, notifications can be sent and alerts received, often before the business even knows there is a problem. Issues can be identified and fixed early.” Business intelligence teams can remain ahead of the curve, reduce downtime while reviewing root cause analysis results, and then notify other staffers, she says, instead of the other way around.
BI assessments should be completed in conjunction with the monitoring of source system platform changes, Risner says, and those changes should be factored into regular strategic planning development roadmap sessions. “In working with a variety of external platforms and vendors, they constantly evolve and push out updates,” she says. “This is even truer now that many companies have moved to cloud systems.”
She adds that business intelligence platforms accessing data may engage changes in external systems. To avoid disrupting the flow of data into analytics systems, carefully integrate the desired changes in your development timeline.
For both internal and external systems, John Huybers, director information technology for Langham Logistics, says company feedback indicates that BI platforms are constantly being assessed. Moreover, he encourages the use of a “customer feedback loop to make sure the information being provided is meeting needs and expectations.”
Huybers and other Langham executives realized the benefits of upgrading their business intelligence platforms, after bringing in Flexware Innovation to design, integrate and launch a more effective system. The process enabled the company to work smarter and generate faster, more impactful data analytics and performance.
As more companies decide to embrace artificial intelligence (AI) in their data systems, more questions and risks arise. Without a trusted two-way relationship in the process, Risner says full understanding of the available options and risks may not be adequately explained.
“As businesses recognize the value in data driven decision making, AI acceptance and use will grow.” However, the choice for AI integration should be carefully evaluated. AI can give companies a competitive advantage, she says, by enhancing aspects of logistics operations, including by: (1) predicting demand and inventory levels, (2) predicting machine maintenance and anomalies, (3) analyzing real-time traffic and weather to optimize shipping routes, (4) identifying potential supply chain risks in real time (geopolitical events, labor strikes) and (5) providing quick and personalized customer service.
“The key to data driven decisions is data and businesses will need to collect and store as many data points as possible, increasing risk. Data systems become more complex further increasing security risks,” Risner says. There are also ethical and social considerations regarding biases and misuse, she adds. As with any system, an AI model will need to ensure that it continues to balance business needs and risks. “But to mitigate these risks, it is imperative that companies exercise continuous improvement and monitoring of data systems, never stop learning and stay aware of available options.”
Power and Risk in Visibility
Achieving visibility through business intelligence, with or without AI, can present a conundrum of security challenges. Is it a lofty goal to truly mitigate risk while allowing an open source of information?
Communication and collaboration are essential across internal and external networks. “Identifying the sensitive information helps build a security strategy balancing transparency and data protection. Then, with the right technology that best suits specific needs, whether that is report level access, row level security, data masking, data classification and cataloging, and/or other technology safeguards, you implement,” Risner says.
However, getting leaders of an organization to agree on which data and to what degree that data should be shared is often the hard part, she says. And each company will have a different data sharing policy and will likely carry guidelines on data related to specific job roles, making collaboration across internal stakeholders and external partners essential.
Being the Source of the Source
On some level, we are all in search of that single source of truth, whatever that truth may be and the way it is defined. Risner states that truth is found when people, process, and technology exist in alignment. Often, conflicting ideologies and practices get in the way.
Receiving information from multiple sources is a caveat to inefficient and outdated reporting. “It is common for multiple reports to show different results, even when the information should be the same,” she says. The likely culprit is a lack of oversight over a reporting catalog or when building new reports before identifying the report already exists. “Providing a single source of truth won’t avert problems rooted in bad processes,” Risner says, making the case for business intelligence and its ongoing adoption.