The fast pace of technological change can make it difficult for companies to decide which new ideas they should implement. A Graduate Certificate in Logistics and Supply Chain Management (GCLOG) capstone research project* identified technologies that could benefit three companies in Brazil and evaluated the pros and cons of adopting each one. The research sheds light on the factors that management needs to consider when assessing the innovative change.
The challenge of managing fast-paced technological innovation is a company-wide phenomenon but is especially evident in the supply chain function. Artificial intelligence, big data, blockchain and robotics are some of the technologies that are fueling unprecedented change in company supply chains.
Paradoxically, while the pace of change is extremely rapid, disruptions caused by innovation usually take place incrementally in supply chains. Research carried out by MIT CTL Deputy Director Jim Rice shows that fast-moving disruptions akin to that caused by the introduction of the smartphone in consumer markets are relatively rare in the supply chain world.
Even so, supply chains that fall behind the technology curve risk losing competitive ground to rivals. The challenge is deciding which innovations will yield the biggest returns, and how to implement them.
Rice defines a supply chain innovation as: “the combining and application of a mix of inventions, existing processes and technologies in a new way that achieves a desirable change in cost, quality, cash and/or service.”
The GCLOG research team used the DHL Trend Radar to identify the most significant trends in supply chain innovation. The Radar captures trends that impact society, business and technology.
Three companies in different industries were chosen as subjects, and the researchers identified one key innovation to evaluate for each enterprise. Experts in each company – an R&D manager, an operations and process improvement coordinator and process intelligence and innovation lead – were briefed on the selected innovation and interviewed about the effort required to implement it as well as the potential returns.
Case One: Pulp and paper company.
Cognitive procurement is the application in the procurement process of self-learning systems that use data mining, pattern recognition and natural language processes to mimic the human brain.**
In this company, numerous parties request goods and services via multiple internal websites. The sheer number of SKUs requested clogs procurement systems and leads to a high number of relatively expensive spot purchases.
A cognitive procurement system could identify ordering patterns, and improve the organization of these activities by, for example, categorizing orders more effectively. Applying the technology in this way would support a more strategic approach to procurement, and hence better negotiation practices and lower costs.
Case Two: Commodity logistics company
Logistics services providers often manage substantial volumes of products and associated information on origins and destinations, weights, volumes and locations. However, they do not always take advantage of these information flows to optimize processes and add value.
This is the situation in the commodity logistics company, which handles large volumes of sugar and grains. Currently, data resides in multiple databases in the company’s ERP system, and the enterprise relies heavily on spreadsheets to make decisions.
The company could use Big Data analytics to derive more value from the mass of data it handles. For example, forecasting demand and adjusting the balance between supply and demand are tasks that could be performed quicker and more accurately with the help of advanced analytics.
Case Three: Consumer goods company
Internet of Things (IoT) technology digitally links products with devices that collect and communicate data.
There are countless applications of IoT technology in the supply chain. One that offers much potential for the consumer goods company in the research study is freight yard management.
The company could use IoT sensors to monitor the movement of truck traffic in and out of its warehouse facilities. Such a system would track truck movements and vehicle parking patterns within freight handling areas in real time, improve yard efficiency by reducing wait times, and lower transportation costs through better truck utilization.
Having identified specific applications, the team created business cases for each one. They confirmed that respective technologies were sufficiently developed in Brazil to support the applications. Also, they invited suppliers to participate in the projects.
The analysis confirmed that in each case, the companies would benefit from adopting the innovations – with some important riders.
An investment of $700,000 in cognitive procurement by the pulp and paper company could yield an estimated saving of 10% in transactional volumes. If the firm focused on strategic sourcing, contract drafting and supplier relationship management, it could save about $ 4 million annually in procurement costs.
The commodities logistics company could capture several benefits if it implements Big Data analytics. For example, the technology has the potential to deliver optimized operations scheduling, better forecasting and improved resource utilization. In this business case, an investment of $ 1 million in hardware solutions and optimization software would bring $ 3.4 million additional profit by enabling the company to win more business through better productivity and port operations cycle time reduction.
For the consumer goods company, an investment of some $ 64,000 in an IoT-based equipment monitoring system could bring annual savings of about $ 35,000. The proposed system employs RFID tags affixed to containers to track units moving through facilities. A sensor network connected to the internet, and scanners positioned at warehouse gates and docks, support a web-based dashboard that the company can use to improve the efficiency of freight handling facilities.
The analysis also flagged some potential problems. The degree to which these innovations are successfully deployed will differ from company to company. Common issues include differences in corporate culture as well as approaches to process change management.
The companies involved in the study plan to take the research to the implementation phase. After approving the cognitive procurement project, the paper and pulp company plans to develop the concept in 2018 and evaluate it for adoption in 2019. The next step for the consumer goods company is creating an SOP (standard operating procedure) and a SLA (service level agreement) within supply chain as well as a bid for IoT suppliers and carriers. And the logistics company plans to invest in hardware solutions and begin configuring and integrating the technology in 2018.
* Applying Tomorrow Supply Chain Innovation Today, by Andre Junqueira, Leandro Camporez, Lucas Bonetto. Capstone project Supervisor: James B Rice Jnr., Deputy Director MIT Center for Transportation & Logistics.
** Mark Perera. (2016), Defining Cognitive Procurement.
For more information on this GCLOG research project contact Christopher Mejia Argueta, Director of the MIT SCALE Network Latin America and Director of the GCLOG program, at: cmejia@mit.edu.