Professionals in the supply chain industry need uncanny reflexes. The moment they get a handle on raw materials, labor expenses, international legislation, and shipping conditions, the ground shifts beneath them and all the effort they put into pushing their boulder up the hill comes undone. With the global nature of today’s supply chain environment, the factors governing your bottom line are exceptionally unpredictable. Fortunately, there’s a solution for this problem: predictive operational analytics.
This particular branch of analytics offers an opportunity for organizations to anticipate challenges before they happen. Sounds like an indisputable advantage, yet as of 2019, only 30% of supply chain professionals are using their data to forecast their future.
Though most of the stragglers plan to implement predictive analytics in the next ten years, they are missing incredible opportunities in the meantime. Here are some of the competitive advantages that companies are missing when they choose to ignore predictive operational analytics.
Enhanced Demand Forecasting
How do you routinely hit a moving goalpost? As part of an increasingly complex global system, supply chain leaders are faced with an increasing array of expected and unexpected sales drivers from which they are pressured to determine accurate predictions about future demand. Though traditional demand forecasting yields some insight from a single variable or small dataset, real-world supply chain forecasting requires tools that are capable of anticipating demand based on a messy, multifaceted assembly of key motivators. Otherwise, they risk regular profit losses as a result of the bullwhip effect, buying far more products or raw materials than are necessary.
For instance, one of our clients, an international manufacturer, struggled to make accurate predictions about future demand using traditional forecasting models. Their dependence on the historical sales data of individual SKUs, longer order lead times, and lack of seasonal trends hindered their ability to derive useful insight and resulted in lost profits. By implementing machine learning models and statistical packages within their organization, we were able to help them evaluate the impact of various influencers on the demand of each product. As a result, our client was able to achieve an 8% increase in weekly demand forecast accuracy and 12% increase in monthly demand forecast accuracy.
This practice can be carried across the supply chain in any organization, whether your demand is relatively predictable with minor spikes or inordinately complex. The right predictive analytics platform can clarify the patterns and motivations behind complex systems to help you to create a steady supply of products without expensive surpluses.
Smarter Risk Management
The modern supply chain is a precise yet delicate machine. The procurement of raw materials and components from a decentralized and global network has the potential to cut costs and increase efficiencies – as long as the entire process is operating perfectly. Any type of disruption or bottleneck in the supply chain can create a massive liability, threatening both customer satisfaction and the bottom line. When organizations leave their fate up to reactive risk management practices, these disruptions are especially steep.
Predictive risk management allows organizations to audit each component or process within their supply chain for its potential to destabilize operations. For example, if your organization currently imports raw materials such as copper from Chile, predictive risk management would account for the threat of common Chilean natural disasters such as flooding or earthquakes. That same logic applies to any country or point of origin for your raw materials.
You can evaluate the cost and processes of normal operations and how new potentialities would impact your business. Though you can’t prepare for every possible one of these black swan events, you can have contingencies in place to mitigate losses and maintain your supply chain flow.
Formalize Process Improvement
As with any industry facing internal and external pressures to pioneer new efficiencies, the supply chain industry cannot rely on happenstance to evolve. There needs to be a twofold solution in place. One, there needs to be a culture of continuous organizational improvement across the business. Two, there needs to be apparatuses and tools in place to identify opportunities and take meaningful action.
For the second part, predictive analytics is one of the most effective tools. Machine learning algorithms are exceptional at unearthing inefficiencies or bottlenecks, giving stakeholders the fodder to make informed decisions. Since predictive analytics removes most of the grunt work and exploration associated with process improvement, it’s easier to create a standardized system of seeking out greater efficiencies. Finding new improvements is almost automatic.
Ordering is an area that offers plenty of opportunities for improvement. If there is an established relationship with an individual customer (be it retailer, wholesaler, distributor, or the direct consumer), your organization has stockpiles of information on individual and demographic customer behavior. This data can in turn be leveraged alongside other internal and third-party data sources to anticipate product orders before they’re made. This type of ordering can accelerate revenue generation, increase customer satisfaction, and streamline shipping and marketing costs.
Are you ready to compete with data-driven organizations?
It’s time you embrace predictive analytics in your supply chain. Schedule a whiteboard session to evaluate your current state and determine how to implement quick predictive analytics wins in your organization.
Dale Hanson is the Data Science Practice Lead at Aptitive with hands on project experience across the entire data science practice. Dale works with clients to help drive strategic insight by implementing, productionalizing, and maximizing ROI for data science projects.