Satisfaction fell among customers and field workers alike, while both customer churn and employee attrition increased. Hampered by old technology, the business couldn’t overcome its problems in workforce management and personnel scheduling. A North American telco illustrates the challenge: amid skyrocketing demand for internet capacity, the organization struggled to reassign its technicians (who were long used to providing on-site installation and repair) to resolve problems remotely. First (and least expected) was the impact of the COVID-19 pandemic on operations globally as abrupt swings in demand stretched spreadsheet-based workforce-scheduling models past their limits. Three recent factors have forced it up the strategic corporate agenda. Optimizing workforce management matters now more than ever. As a result of these challenges, businesses can lose the opportunity to streamline their offerings and provide better service to customers-and thus lose income too. And to be truly valuable, scheduling models must be integrated with other models, such as demand forecasting. What’s more, existing scheduling tools are not always user friendly and may require a team of data scientists to maintain and update. New schedules must be calculated using fresh inputs very quickly, yet most all-in-one optimization models take hours to deliver updated schedules. Factor in unforeseen changes-such as employees not showing up for work at a moment’s notice or spikes in demand-and the pressures on optimization models become even greater. The constantly changing picture and high number of decision variables generate complex traditional computer models that often take a long time to run. To operate with the greatest efficiency, businesses must deploy the right number of workers to meet demand and minimize employee downtime on any given day. Extreme variability-in workforce types and operations, as well as across sectors and businesses-makes these solutions hard to standardize.Įven within individual businesses, the complexity of workforce planning and the demand for dynamic action make agile decision making difficult. Optimizing schedules is one of the most challenging of all optimization problems. This article explores how it can drive a long-needed transformation by bringing greater speed, flexibility, and intelligence to bear on the problem of optimizing schedules, so companies can deploy the people they need when they need them and unlock new levels of efficiency. Recent advances in technology and declining costs have made end-to-end, AI-driven schedule optimization a real possibility-and an opportunity. Yet until now, workforce planning hasn’t enjoyed the same level of digital transformation. In recent years, advanced data applications and AI have optimized many business processes. The challenges of a constrained labor supply and higher wages remain at the fore. The past two years have brought the inefficiencies of traditional processes to the surface more keenly than ever before as the COVID-19 pandemic placed an unforeseen strain on day-to-day operations across many sectors. Traditional workforce management processes, which rely heavily on time-consuming and inconsistent manual steps, can no longer provide the dynamic workforce scheduling needed in the face of ongoing labor market disruptions. Today, workforce planning has reached a turning point.
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