Supply Chain Digital Transformation: Strategic supply chain technology findings
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Supply Chain
- Competitive Differentiation
Publication | Update: Oct 2020
Most companies’ supply chains are nowhere near achieving the unified state that Gartner describes. Most are valiantly trying to coax improvements out of legacy systems and B2B cloud platforms that have major drawbacks, and are incapable of grounding a successful and sustained transformation.
To address this problem at its root, we need to recognize the supply chain is a business network problem, not a company problem. As such, we need to start with a multi-party digital network, one that enables all trading partners to connect once and then manage connections to trading partners digitally rather than through physical, hardwired connections.
Multi-party networks can take in data from all trading partners and systems, and through a permissions framework, share that data appropriately with the relevant parties. This eliminates virtually all the latency and greatly reduces the integration efforts of all parties. Companies no longer need to repetitively integrate to each trading partner or to multiple portals or platforms to reap advantages:
- Rapid implementation and time to value
- Increased agility with adaptable virtual communities
- Higher revenues due to increased services levels and fewer stockouts
- Global visibility into orders and shipments with increased transparency
- Reduced inventories across all sites
- Lower cost of goods sold
- Better traceability and product safety
Here are the key trends from Gartner’s study findings as compiled by Technology Media Telecom analyst, David Deans:
Hyper-automation: hyper-automation can encourage broader collaboration across domains and act as an integrator for disparate and siloed functions.
Digital Supply Chain Twin: it's a digital representation of the physical supply chain derived from data across the supply chain and its operating environment. That makes the DSCT the basis for all local and end-to-end decision making.
"DSCTs are part of the digital theme that describes an ever-increasing merger of the digital and physical world," Christian Titze, vice president analyst at Gartner added. "Linking both worlds enhances situational awareness and supports decision-making."
Continuous Intelligence: leverages a computer’s ability to process data at a much faster pace than people can.
Supply chain leaders can look at the processed data in near real-time, understand what is happening and take action immediately.
"There are already several use cases for CI in decision support and decision automation. For example, retailers utilize CI to automatically react to customer behaviors when they shop online. This enables better customer service, more customer satisfaction and tailored offers that lead to higher sales revenue," Christian Titze explained.
Supply Chain Governance and Security: an increasingly important macro trend, as global risk events are on the rise and security breaches impact companies on both the digital and physical levels.
"Gartner anticipates a wave of new solutions to emerge for supply chain security and governance, especially in the fields of privacy as well as cyber and data security," said Christian Titze, vice president analyst at Gartner. "Think advanced track-and-trace solutions, smart packaging and next-gen RFID and NFC capabilities."
Edge Computing and Analytics: it's the rise of edge computing -- where data is processed and analyzed close to its collection point -- coincides with the proliferation of Internet of Things (IoT) devices. Edge computing is making its way into the manufacturing industry. For example, some organizations have adopted driverless forklifts for their warehouses. Heavy equipment sellers can use edge computing to analyze when a part needs maintenance or replacement.
Artificial Intelligence: deploying AI in the supply chain consists of a toolbox of technology options that help companies understand complex content, engage in a natural dialogue with people, enhance human performance and take over routine tasks.
"AI technology is present in a lot of already existing solutions, but its capabilities evolve on a constant basis," Titze added. "Currently, the technology primarily helps supply chain leaders solve long-standing challenges around data silos and governance. Its capabilities allow for more visibility and integration across networks of stakeholders that were previously remote or disparate."
5G Wireless Networks: when compared to its predecessors, 5G is a massive step forward with regards to data speed and processing capabilities. The ubiquitous nature of 5G boosts its potential for supply chains. For example, running a 5G network in a factory can minimize latency and enhance real-time visibility and IoT capabilities.
Immersive Experience: technology that improves or enhances the user experience -- such as virtual, augmented and mixed reality -- has the potential to radically influence the trajectory of supply chain management. Those new interaction models amplify human capabilities.
The Digital Universe
Digital twin technology is an emerging technology that promises to deliver significant value. “Digital twins” are simply digital representations of physical objects or systems, virtual copies of things like parts, engines, vehicles, containers and even companies.
Gartner has extended this concept to supply chains. One of Gartner’s top supply chain technology trends for 2019 is the “digital supply chain twin,” which Gartner defines as, “a digital representation of the relationships between all the relevant entities of an end-to-end supply chain — such as products, customers, markets, distribution centers and warehouses, plants, finance, attributes and weather.”
Whatever you want to call it, we call it an Enterprise Social Graph, that underlies One Network’s multi-party business network. It is a precise map of each company, its sites, products and relationships to other business partners. It enables companies to create and manage agile, virtual business communities of trading partners, to share data and manage multi-party processes.
In these virtual communities of supply chain business partners, traditional paper-based, phone and fax processes are replaced with real-time digital processes. The difference is, these processes are more accurate, faster and can be continuous and even autonomous.
Further, supply chain planners and managers are replicated digitally, autonomous virtual agents monitor the network, identify anomalies or potentially missed milestones, and recommend solutions or resolve the problems autonomously.
For example, with One Network Enterprises’ Chain of Custody Solution, digital agents can monitor the network and identify when pre-configured parameters are violated. If digital agents detect that IoT sensors on a container with a shipment of drugs goes out of temperature range, they can alert managers and recommend alternate sources of supply, or trigger a new order.
Digital supply chain twins present a huge opportunity for taking costs and delays out of the supply chain, and making a more efficient, responsive and safer supply chain. Multi-party digital networks enable the sensing and managing of the physical supply chain to a degree never before possible. They enable technologies such as IoT and AI to work together to enable precise measurement, optimization, and automation of multi-party business processes — that can scale to optimize even the most complex supply chains.
Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.