Tag: forecasts

Guest Post: Merging OM Tradition with Digital Innovation

Dr J. Prince Vijai is Assistant Professor of Operations Management at IBS Hyderabad, in India.

The transition from traditional OM to digital operations is not a replacement but an evolution. Digital tools enhance the classical OM framework by adding intelligence, speed and adaptability.

1. Process Optimization and Automation In classical OM, process optimization involved detailed mapping and iterative improvements. With digital operations, AI can now identify inefficiencies, simulate improvements and automate decision-making without human intervention. Siemens has integrated sensors, cloud platforms and AI to create a digital thread across product design, manufacturing and logistics resulting in a 20% reduction in production time and a 30% reduction in energy consumption.

2. Inventory and Supply Chain Management Traditional inventory models rely on forecasts and safety stock assumptions. Digital operations use real-time data from IoT sensors and machine learning to predict demand, monitor inventory levels and automate replenishment. For instance, Walmart uses AI and IoT to streamline its vast supply chain, reducing stockouts and improving shelf availability.

3. Forecasting and Scheduling Operations managers have long used statistical tools for forecasting. Digital operations use advanced analytics and machine learning to provide more accurate, dynamic forecasts. Real-time analytics enables organizations to quickly adapt to market changes, weather disruptions or supply chain breakdowns.

4. Quality Management Traditional quality management emphasizes inspection and control charts. Digital quality management integrates data from machines, sensors and customer feedback for continuous, real-time quality assurance. Predictive maintenance, enabled by digital twins and IoT, reduces downtime and improves asset reliability. For example, GE developed digital twins to monitor the performance of jet engines in real time, enabling predictive maintenance and reducing unexpected failures.

The shift to digital operations is not without challenges. Employees accustomed to traditional processes may resist adopting new technologies. Data from different departments or legacy systems can be siloed, limiting visibility and coordination. Implementing AI, IoT and automation involves significant expenses. And digital operations increase exposure to cyber risks.  

Future trends include:

  • Hyperautomation that combines  AI and machine learning to automate increasingly complex tasks.
  • Cognitive operations that use AI not just to automate but to learn and adapt continuously.
  • Edge computing that enables data processing closer to the source (e.g., in factories or stores) for faster insights.
  • Green operations that leverage digital tools to track carbon footprints and support sustainable practices.

Embracing the synergy between OM and digital operations is a strategic imperative for long-term success.

OM in the News: Merck’s Move to Prevent Drug Shortages

Merck, the Germany-based pharmaceutical, needs to stockpile medications to make sure it has enough on hand because some expire before they can be used. Its supply-and-demand forecasts are about 85% accurate. To sharpen its predictions, Merck plans to use analytics and machine learning to predict and prevent drug shortages, a move that could also save it money. Its new platform, from TraceLink Inc., can analyze data in real time from organizations within Merck’s supply chain, including pharmacies, hospitals and wholesalers.

The U.S. had 600-1,200 drug shortages every year from 2014 and 2019, reports The Wall Street Journal (Oct. 15, 2019). Shortages can happen due to issues with manufacturing, supply-and-demand forecasts, and natural disasters. Drugs in short supply have included antibiotics, chemotherapy and cardiovascular treatments. More precise supply-and-demand forecasts mean pharmaceuticals could save hundreds of millions of dollars annually, a benefit of not having excess drugs on hand and avoiding expedited shipment costs.

On average, pharmaceutical companies carry 156 days of inventory. For retailers selling consumer products, it is 78 days. For IT equipment, it is 57 days. Pharmaceuticals traditionally have predicted demand for drugs based on historical data and input from sales teams. But as many as 10 entities handle a drug before it gets to a patient, including manufacturers, pharmacies and wholesale distributors. “It’s a highly complex supply chain,” said TraceLink’s CEO. The TraceLink network includes data from more than 275,000 organizations world-wide, including hospitals, retail pharmacies, wholesale distributors and drugmakers.

TraceLink’s algorithms give Merck signals about the days of inventory for a specific drug and how long it will take for a drug to get to a particular phase in the supply chain. A better supply-and-demand forecast also makes it easier for Merck to expand into locations without a reliable supply-chain infrastructure, such as parts of Africa and Southeast Asia.

Classroom discussion questions:

  1. Why do pharm firms carry such a large inventory?
  2.  How might data analytics improve forecasting at Merck?

OM in the News: Apple Suppliers Suffer With Uncertainty

Apple CEO Tim Cook at an Apple store in Italy,

Lower-than-expected demand for Apple’s new iPhones and the company’s decision to offer more models have created turmoil along its supply chain and made it harder for Apple to predict the number of components and phones it needs, writes The Wall Street Journal (Nov. 20, 2018).

Recently, Apple slashed production orders for all 3 of the iPhone models it unveiled in September, frustrating Apple suppliers and workers who assemble the phones and their components. The slowdown has ripped throughout Apple’s supply chain.

Big suppliers of iPhone components, including Qorvo, Lumentum, and Japan Display, cut their quarterly profit estimates, implying a reduction in previously placed orders from Apple, which accounts for 1/3-1/2 of their revenue. At Foxconn , Apple’s largest iPhone assembler, thousands of workers have voluntarily left its Chinese plants earlier than they intended after Foxconn cut overtime hours that typically are available. (Many workers rely on overtime as a major source of income).

Hundreds of suppliers built their businesses on the back of smartphones, and none benefited more than those providing components for Apple. But the iPhone production cuts have reignited frustration among suppliers and raised worries about Apple’s ability to forecast demand since it started releasing 3 flagship models instead of 2 last year. Apple also continues to sell some older models in its stores, further complicating forecasting.

The company’s suppliers have been rattled before. The iPhone 6, introduced in 2014, sold better than Apple’s expectations, and suppliers scrambled to meet increased orders. The following year, demand for the iPhone 6s fell short of forecasts, leaving suppliers to grapple with excess inventories and underused production capacity. Last year, many suppliers were hurt by Apple’s excessively optimistic production forecast for the iPhone X, which it later slashed by some 20 million units for the 2018 first quarter.

While making components for 200 million-plus iPhones remains a tremendous business for suppliers, most relied on the growth in iPhones sold to boost their profits and pay for huge capital expenditures. “The freeway of Apple suppliers is littered with roadkill,” said one industry analyst.

Classroom discussion questions:

  1. What forecasting techniques can be used by Apple to predict demand for a new phone?
  2. What are the advantages and disadvantages of being an iPhone supplier?