Today’s consumers, expecting unlimited choice in shopping, purchasing, and consumption, have sparked a competitive explosion in product variation, points of sale, delivery options, integrated sales, and delivery policies. Meanwhile, global sourcing, regulatory variation, and supply risk have complicated the upstream picture (such as through tariffs, for example), making it harder than ever in some cases to meet consumer demand at a profit.
Collaborative Planning, Forecasting and Replenishment (CPFR), a trademark procured from GS1 US, is a concept that aims to enhance supply chain integration by supporting and assisting joint practices. It is a process by which a company integrates planning, forecasting and other data points from a variety of sources, such as internal stakeholders, suppliers, and even customers. This shared information helps retailers better plan for and profitably satisfy customer demand -- an enticing advantage for any retailer.
Given the huge amount of data from numerous sources CPFR requires, its very nature calls for an advanced analytics solution. The right solution, like prescriptive analytics, can save retail and CPG companies alike significant time by synchronizing and analyzing data from all sources and using findings to better inform supply-chain decisions. Here are two ways prescriptive analytics can improve the CPFR process and help retailers make faster, smarter decisions:
It Reduces Complexity
Any process that draws on massive amounts of data from multiple sources is bound to be complicated, and CPFR is no exception. The CPFR process engages multiple interfaces between two separate entities with different paces of doing business and multiple touchpoints throughout the organization (supply chain, sales, account management, etc.). That complexity is amplified by the antiquated analytics methodologies that many retailers use to source and distribute the needed data -- specifically, report-based systems.
Unfortunately, the CPFR process is often bottlenecked and/or over-complicated by the reports that stakeholders use to distribute their respective information. These reports, many of which turn out to be hundreds of pages long, are loaded with data points that may or may not be relevant to the CPFR process. One or more data scientists must comb through the reports, find the key insights, and interpret them through the lens of CPFR, a highly inefficient process.
Due to the increasingly complex nature of supply chains with the need to optimize data, prescriptive analytics has become a necessity for business planning processes. Prescriptive analytics offers CPFR users a significant advantage over report-based systems. With this robust analytics tool, raw data becomes “smart” tasks, distributed to the appropriate stakeholder with specific action steps to resolve. Under a report-based system, identifying who should perform what task could take a data scientist days, by which time the insight may no longer be actionable. It’s important to catch and weed out supply-chain inefficiencies and sources of waste in near-real time. Prescriptive analytics enables fast actionability.
It Removes Bias
As previously mentioned, the key to successful CPFR is a collaborative exchange of data between companies -- most often in the form of multiple reports. In addition to being confusing and inefficient, reports are also prone to bias. There are two main types of bias in retail: personal and political. Personal biases are based on an individual’s perspectives and/or mindset; for example, someone with a mathematical mindset may interpret a report strictly by its numbers. On the other hand, someone with a planning background may look at the same report and have a completely different interpretation. Political biases are based on an individual’s business interests. For example, a retail planner might look at a CPFR analyst’s demand projection, consider it too high, and accuse them of inflating the projection just to get the retailer to buy more.
So how do we eliminate bias? If I may quote Peter Brand from the movie Moneyball, “Mathematics cut[s] straight through that.”
Enter prescriptive analytics. A good prescriptive analytics solution mathematically calculates demand based on the latest, near-real-time (and historical when necessary) statistics. It issues findings to the relevant stakeholder as a simple, plain-text opportunity (not a report), as well as root cause information and required corrective actions. Because this is a fact based on mathematical calculations, not a bias-prone report requiring interpretation, it cannot be disputed and is immune from bias. Operations can continue smoothly, without the debates and finger-pointing so common with reports.
The role of prescriptive analytics in CPFR is clear. It helps retail employees at the edge make smarter business decisions. This in turn results in higher-quality products and services, better customer experience, increased productivity, and reduced costs - all clear competitive advantages in today’s retail environment.
Written by Guy Yehiav
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