Study informants and secondary data
| S&OP informants and AI experts | ||||||
|---|---|---|---|---|---|---|
| ID | Title | Experience, interview duration | AI experience | Company profile | ||
| Industry | Product portfolio | Plant location | ||||
| S&OP informants | ||||||
| I1 | S&OP manager | >10 years, 120 min | Considering AI | Heavy machinery | Renewable energy solutions | Sweden |
| I2 | S&OP manager | >10 years, 90 min | Implementing AI | Heavy machinery | Power generation equipment | Sweden |
| I3 | S&OP manager | >10 years, 60 min | Testing AI | Electrical components | Wiring devices, IT infrastructure | France, USA |
| I4 | S&OP manager | >10 years, 120 min | Testing AI | Heavy machinery | Hydraulic systems | Canada |
| I5 | Planning specialist | >10 years, 90 min | Implementing AI | Industrial machinery | Pneumatic systems | Germany |
| I6 | Demand planner | >10 years, 90 min | Considering AI | Industrial machinery | Industrial machinery | UK |
| I7 | Project planner | >10 years, 90 min | Testing AI | Industrial machinery | Aerospace components | Germany |
| I8 | Supply and demand planner | >8 years, 45 min | Considering AI | Industrial machinery | Machining solutions | Sweden |
| AI experts | ||||||
| E1 | AI solutions architect | >10 years, 90 min | >8 years | End-to-end IT consultation and implementation solutions | IT transformation for high-tech manufacturing | Netherlands (HQ), Germany USA |
| E2 | Lead manufacturing and managing consultant | >25 years, 60 min | >6 years | |||
| E3 | AI adoption consultant | >4 years, 75 min | >4 years | |||
| S&OP informants and AI experts | ||||||
|---|---|---|---|---|---|---|
| ID | Title | Experience, interview duration | AI experience | Company profile | ||
| Industry | Product portfolio | Plant location | ||||
| I1 | S&OP manager | >10 years, 120 min | Considering AI | Heavy machinery | Renewable energy solutions | Sweden |
| I2 | S&OP manager | >10 years, 90 min | Implementing AI | Heavy machinery | Power generation equipment | Sweden |
| I3 | S&OP manager | >10 years, 60 min | Testing AI | Electrical components | Wiring devices, IT infrastructure | France, USA |
| I4 | S&OP manager | >10 years, 120 min | Testing AI | Heavy machinery | Hydraulic systems | Canada |
| I5 | Planning specialist | >10 years, 90 min | Implementing AI | Industrial machinery | Pneumatic systems | Germany |
| I6 | Demand planner | >10 years, 90 min | Considering AI | Industrial machinery | Industrial machinery | UK |
| I7 | Project planner | >10 years, 90 min | Testing AI | Industrial machinery | Aerospace components | Germany |
| I8 | Supply and demand planner | >8 years, 45 min | Considering AI | Industrial machinery | Machining solutions | Sweden |
| E1 | AI solutions architect | >10 years, 90 min | >8 years | End-to-end IT consultation and implementation solutions | IT transformation for high-tech manufacturing | Netherlands (HQ), Germany |
| E2 | Lead manufacturing and managing consultant | >25 years, 60 min | >6 years | |||
| E3 | AI adoption consultant | >4 years, 75 min | >4 years | |||
| Secondary materials | |||
|---|---|---|---|
| ID | Data type | Description | Use in this study |
| D1 | Planning tools | Excel, ERP, SIOP/MPS tool | To infer the potential for AI integration in current companywide systems |
| D2 | Process documentation | S&OP Playbook | To provide insight into current S&OP practices |
| D3 | Forecasting | Current forecasting methods and outputs | To infer forecasting specifics concerning the potential for AI integration |
| D4 | Demand planning | Current demand planning processes | To infer the potential for AI integration in demand planning processes |
| D5 | Accuracy metrics | Demand plan accuracy metric | To demonstrate potential for controlling AI improvement in forecasting |
| D6 | Decision making | Decision log | To show current reliance on human judgment |
| D7 | Manufacturing planning | CMMS SAP R/3 PM module | To infer the potential for AI integration in manufacturing scheduling systems |
| D8 | Issue management | Escalations and actions under SIOP | To infer the potential for AI integration in future collaboration systems |
| D9 | Continuous improvement | CI need reports | To infer the potential for AI integration through current continuous improvement processes |
| D10 | Workforce planning | Strategic Workforce Planning tool (SWP) TM1 | To infer the potential for AI integration in workforce planning systems |
| D11 | Strategic planning | Strategic plan | To infer the potential for AI integration for informing strategies across S&OP practices |
| D12 | Process flow | S&OP flow swim lane diagram | To show current data flow and potential for AI integration |
| D13 | Simulation | Plant simulation for discrete event simulations | To infer the potential for AI integration in current plant simulation systems |
| D14 | Change management | Change board meeting outputs | To reveal organizational approach to potential AI adoption |
| D15 | Market projections | Customer projections/R10 | To illustrate the need for AI in market forecasting |
| D16 | Training | S&OP Playbook and training material | To reveal current platforms for AI learning and areas where AI could enhance training |
| D17 | Sales materials | Request for Quotation (RFQ) | To infer AI integration potential in forecasting and demand planning |
| Secondary materials | |||
|---|---|---|---|
| ID | Data type | Description | Use in this study |
| D1 | Planning tools | Excel, ERP, SIOP/MPS tool | To infer the potential for AI integration in current companywide systems |
| D2 | Process documentation | S&OP Playbook | To provide insight into current S&OP practices |
| D3 | Forecasting | Current forecasting methods and outputs | To infer forecasting specifics concerning the potential for AI integration |
| D4 | Demand planning | Current demand planning processes | To infer the potential for AI integration in demand planning processes |
| D5 | Accuracy metrics | Demand plan accuracy metric | To demonstrate potential for controlling AI improvement in forecasting |
| D6 | Decision making | Decision log | To show current reliance on human judgment |
| D7 | Manufacturing planning | CMMS SAP R/3 PM module | To infer the potential for AI integration in manufacturing scheduling systems |
| D8 | Issue management | Escalations and actions under SIOP | To infer the potential for AI integration in future collaboration systems |
| D9 | Continuous improvement | CI need reports | To infer the potential for AI integration through current continuous improvement processes |
| D10 | Workforce planning | Strategic Workforce Planning tool (SWP) TM1 | To infer the potential for AI integration in workforce planning systems |
| D11 | Strategic planning | Strategic plan | To infer the potential for AI integration for informing strategies across S&OP practices |
| D12 | Process flow | S&OP flow swim lane diagram | To show current data flow and potential for AI integration |
| D13 | Simulation | Plant simulation for discrete event simulations | To infer the potential for AI integration in current plant simulation systems |
| D14 | Change management | Change board meeting outputs | To reveal organizational approach to potential AI adoption |
| D15 | Market projections | Customer projections/R10 | To illustrate the need for AI in market forecasting |
| D16 | Training | S&OP Playbook and training material | To reveal current platforms for AI learning and areas where AI could enhance training |
| D17 | Sales materials | Request for Quotation (RFQ) | To infer AI integration potential in forecasting and demand planning |
Source(s): Created by authors
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